Drought Risk Assessment and Monitoring of Ilocos Norte Province in the Philippines Using Satellite Remote Sensing and Meteorological Data

Christian Albert Alonzo; Joanna Mae Galabay; Margadrew Nicole Macatangay; Mark Brianne Magpayo; Ryan Ramirez

doi:10.3390/agriengineering5020045

Drought Risk Assessment and Monitoring of Ilocos Norte Province in the Philippines Using Satellite Remote Sensing and Meteorological Data

Alonzo, Christian Albert;Galabay, Joanna Mae;Macatangay, Margadrew Nicole;Magpayo, Mark Brianne;Ramirez, Ryan 2023-04-13 00:00:00 AgriEngineering Article Drought Risk Assessment and Monitoring of Ilocos Norte Province in the Philippines Using Satellite Remote Sensing and Meteorological Data 1 1 1 1 Christian Albert Alonzo , Joanna Mae Galabay , Margadrew Nicole Macatangay , Mark Brianne Magpayo and 1 , 2 , Ryan Ramirez * Department of Civil Engineering, University of Santo Tomas, Manila 1008, Philippines Division of Earth and Space Sciences, National Research Council of the Philippines, Taguig 1631, Philippines * Correspondence: raramirez@ust.edu.ph Abstract: Drought has been known to be a natural hazard reﬂecting geographic and climatic char- acteristics. Satellite technology advancements have beneﬁted drought assessment and monitoring to formulate plans for dealing with this slow-onset disaster. However, combining satellite remote sensing (RS) and meteorological data for drought monitoring is lacking in the literature. This study uses satellite RS and meteorological-based drought indicators to assess drought risk in the Ilocos Norte, Philippines. Data analysis included the retrieval of vegetation conditions using Sentinel-1 and Sentinel-2 data. The standardized precipitation index (SPI) and Keetch–Byram drought index (KBDI) were calculated to account for climatic variabilities. Results revealed that the Sentinel-1 backscatter coefﬁcient decreased by2 dB in the cropland area, indicating crop growth irregularities compared to grassland areas. These irregularities were supported by Sentinel-2 normalized difference vegetation index (NDVI) strong ﬂuctuations during the two-year observation period. A signiﬁcant coefﬁcient of determination (R > 0.60) between the Sentinel-1 backscatter coefﬁcient and Sentinel-2 NDVI was observed for the study area. On the one hand, only KBDI signiﬁcantly correlated (R > 0.60) with the cropland area’s RS data-derived drought indicators. These results revealed RS data variability for Citation: Alonzo, C.A.; Galabay, J.M.; drought risk management but are still valuable for developing an early warning system. Macatangay, M.N.; Magpayo, M.B.; Ramirez, R. Drought Risk Keywords: drought; remote sensing; Sentinel-1 SAR data; Sentinel-2 optical data; Ilocos Norte Assessment and Monitoring of Ilocos Norte Province in the Philippines Using Satellite Remote Sensing and Meteorological Data. AgriEngineering 1. Introduction 2023, 5, 720–739. https://doi.org/ 10.3390/agriengineering5020045 Drought is a natural hazard that reﬂects the region’s exposure to various geographic and climatic characteristics. Due to its widespread impacts over a large area in a certain Academic Editor: Murali period, it is considered more potent than other natural disasters, especially in areas that Krishna Gumma heavily rely on agricultural-based occupations [1]. Mainly attributed to low precipitation for Received: 22 February 2023 an extended period, drought provides negative implications regarding economic and social Revised: 6 April 2023 functions. The consequences of such drought-related outcomes have severely inhibited Accepted: 11 April 2023 the proper functioning of the community regarding water supply, productivity, and food Published: 13 April 2023 security [2]. Hence, the evaluation of probable drought-risk areas must be addressed and mitigated through data-based assessments. Drought indicators represent data to quantify or measure drought severity in a par- ticular area. It uses information on rainfall, streamﬂow, soil moisture, and other hydro- Copyright: © 2023 by the authors. meteorological evidence to monitor vegetation conditions. With this, various indices can be Licensee MDPI, Basel, Switzerland. used to establish the amount of precipitation for a certain period and accordingly compare This article is an open access article its deviation from established standards. Some of the widely used meteorological-based distributed under the terms and indices include the standardized precipitation index (SPI) [3–8], standardized precipitation conditions of the Creative Commons and evapotranspiration index (SPEI) [3,5–7,9,10], multivariate standardized precipitation Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ index (MSPI) [11,12], rainfall anomaly index (RAI) [13,14], and Palmer drought sever- 4.0/). ity index (PDSI) [8,15,16]. However, the scarcity and high spatiotemporal variability of AgriEngineering 2023, 5, 720–739. https://doi.org/10.3390/agriengineering5020045 https://www.mdpi.com/journal/agriengineering AgriEngineering 2023, 5 721 meteorological stations across different regions limit the successful utilization of these meteorological-based indices for drought assessment and monitoring [17]. The application of remote sensing (RS) In drought studies lies in its capability to observe changes in a geographic environment [18]. It can monitor a particular area’s condition before, during, and after a disaster strikes. Similarly, a geographic information system (GIS) creates a centralized mechanism for the RS data through mapping and spatial analysis for a particular disaster scope. Likewise, advancements in satellite technology have been beneﬁcial in drought assessment and monitoring, which help formulate plans to deal with this slow-onset disaster [19,20]. Satellite observations have provided signiﬁcantly contributed to accurately visualizing surface parameters in the possible occurrence and severity of drought for a certain period [21–24]. Speciﬁcally, Sentinel-2 and Landsat optical satellite-derived indices are widely used to evaluate drought on a regional scale [25–33]. The most commonly used optical RS data-derived drought indicators include the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), vegetation condition index (VCI), and enhanced vegetation index (EVI). However, optical satellite data are highly affected by unpredicted atmospheric conditions (i.e., cloud contamination), limiting the collection and utilization of quality optical satellite imageries despite being acquired at shorter time windows with zero costs. On the other hand, synthetic aperture radar (SAR) data complement optical satellite data in regions with considerable cloud cover density in investigating drought. SAR is an active radar imaging technology that works in all weather conditions, day and night. For example, the ﬂeet of twin Sentinel-1 satellites has provided free accessible archived medium-resolution SAR data, which the scientiﬁc community utilizes for various earth observation and geohazard applications such as ﬂoods [34,35], volcano eruptions [36,37], earthquakes [38,39], landslides [40–42], and land subsidence [43–45]. However, limited studies have utilized Sentinel-1 SAR data for drought analysis [46–48]. Speciﬁcally combin- ing freely available optical satellite data (i.e., Sentinel-2 and Landsat-8) and space-borne SAR data (i.e., Sentinel-1) for drought monitoring is rarely reported in the literature [49–51]. Statistical analysis proving the relationship among drought indicators derived across RS and hydrological data is also lacking in the literature. Amid all the innovations and advancements of RS and GIS technologies occurring worldwide, the Philippines is still evidently lacking. Despite the frequent severe weather and climatic events that affect the country each year, mitigation and improvement of conditions are challenging to achieve even up to now because of the scarcity of available and freely accessible data and research in the Philippines. Speciﬁcally, the Philippines’ monitoring drought system is weak, resulting in multiple adverse drought impacts and emergency events. The World Health Organization (WHO) Collaborating Centre for Research on the Epidemiology of Disasters (CRED) recorded eight droughts that occurred in the country affecting 6.5 million people and causing 64.4 million dollars in damage to livelihoods and properties [52]. This record emphasizes the need for the country to improve its technologies concerning the analysis of weather events aside from the numerous typhoons entering the country each year. Mainly, the Ilocos Norte Region in the Philippines has been one of the worst-hit areas as more than 2000 ha of high-value crops, such as rice and corn, were reported to be dried out and destroyed due to water shortage and warmer temperatures, as well as the intensifying El Niño conditions [53]. Damages to livestock and the environment have also been evident as a signiﬁcant population in Ilocos Norte depends on the agricultural industry. This study’s overall objective is to promote the application of RS data in the Philippines’ drought risk management plan. Speciﬁcally, the study assesses Sentinel-1 s backscatter coefﬁcient and Sentinel-2 s NDVI for drought investigation. The study also aims to quan- titatively test the correlations between satellite and meteorological data-derived indices through statistical analysis. Lastly, the study aims to provide a drought early warning system (EWS) using different drought indicators. AgriEngineering 2023, 5, FOR PEER REVIEW 3 backscatt er coeﬃcient and Sentinel-2′s NDVI for drought investigation. The study also aims to quantitatively test the correlations between satellite and meteorological data-de- AgriEngineering 2023, 5 722 rived indices through statistical analysis. Lastly, the study aims to provide a drought early warning system (EWS) using diﬀerent drought indicators. 2. Study Area 2. Study Area Ilocos Norte Province is located in the northwest part of the Philippines and geo- Ilocos Norte Province is located in the northwest part of the Philippines and geo- 0 0 0 0 graphically between N 17°48′ and N 18°29′ latitude and E 120°58′ and E 120°58′ longitude graphically between N 17 48 and N 18 29 latitude and E 120 58 and E 120 58 longitude (Figure 1). The province has a total land area of 362,291 ha, more than 33% of which is (Figure 1). The province has a total land area of 362,291 ha, more than 33% of which is dedicated to agriculture and forestry [54]. Ilocos Norte is a central crop-producing area in dedicated to agriculture and forestry [54]. Ilocos Norte is a central crop-producing area in the Philippines, allowing the region to be a key area for rice production and a signiﬁcant the Philippines, allowing the region to be a key area for rice production and a signiﬁcant rice supplier [55]. rice supplier [55]. Figure 1. The map shows the regional boundary of the Ilocos Norte region in the Philippines and Figure 1. The map shows the regional boundary of the Ilocos Norte region in the Philippines and the the cloud-free Sentinel-2 optical images of the cropland and grassland areas investigated in the cloud-free Sentinel-2 optical images of the cropland and grassland areas investigated in the study. study. For this study, data gathering was focused on Ilocos Norte’s capital, Laoag City. For this study, data gathering was focused on Ilocos Norte’s capital, Laoag City. The The city is located in the west-central part of the province. According to the Philippine cit Atmospheric, y is located in t Geophysical he west-cent and ral p Astr art of onomical the province. Services Accordin Administration g to the Phil (P ippin AGASA), e Atmos- the pheric, Geophysical and Astronomical Services Administration (PAGASA), the tempera- temperature in Laoag can go as high as 41.8 C during the dry season and as low as 11 C t during ure in L the aoa rainy g can g season o as h(P igAGASA, h as 41.8 ° 2018). C duriThe ng the dry majority season of croplands and as low in Ilocos as 11 °C durin Norte arg e t rainfall-dependent he rainy season (PAGA or inSA rained , 2018conditions. ). The majorit The y ofclimate cropland experi s in I enced locos Nort in the e ar pr e r ovi ain nce fall is - classiﬁed as Type I, which is indicated by a distinct dry and wet season. During the wet dependent or in rained conditions. The climate experienced in the province is classiﬁed as season, from May to October, the rice-farming activity highly depends on rainfall. Type I, which is indicated by a distinct dry and wet season. During the wet season, from On the other hand, farmers use groundwater for supplemental irrigation during the May to October, the rice-farming activity highly depends on rainfall. dry season, which happens from November to April [56]. The average annual rainfall of On the other hand, farmers use groundwater for supplemental irrigation during the Ilocos Norte is 2000 mm, and more than 90% of it happens during the wet season. However, dry season, which happens from November to April [56]. The average annual rainfall of the province has been experiencing way below average rainfall, resulting in damaged crops in recent years [57]. AgriEngineering 2023, 5 723 3. Materials and Methods In this study, drought assessment was performed based on remote sensing data and meteorological data. The remote sensing data derived from Sentinel-1 and Sentinel-2 satellite missions were used to identify vegetation conditions. SPI and the Keetch–Byram drought index (KBDI) were calculated from precipitation and temperature to account for climatic variabilities in the region. 3.1. Sentinel-1 SAR Data Sentinel-1 conveys a single C-band wavelength operating at a center frequency of 5.405 GHz. It comprises two satellites, Sentinel-1A and Sentinel-1B, launched in April 2014 and April 2016, respectively. Equipped with SAR, these satellites can operate at wavelengths not impeded by cloud cover or illumination, allowing data acquisition under any time and weather conditions. The 12-day repeat orbit cycle of each Sentinel-1 satellite and ancillary orbital baselines permit SAR data to provide coherent change detection applications useful for assessing land surface conditions. Sentinel-1 SAR data were retrieved from the European Space Agency (ESA) via the Alaska Satellite Facility (ASF) database. The C-band Sentinel-1 analysis was carried out using 24 monthly scenes between 1 January 2019, and 31 December 2020, covering the dry and wet seasons from 2019 to 2020 (Table 1). Each scene contained two layers of the backscattering coefﬁcient, , in vertical–vertical (VV) and vertical–horizontal (VH) polarizations which were converted into decibels units (dB). Table 1. Sentinel-1 synthetic aperture radar (SAR) dataset used in the study. No. Acquisition Date Pass No. Acquisition Date Pass 1 28 January 2019 Ascending 13 23 January 2020 Ascending 2 21 February 2019 Ascending 14 28 February 2020 Ascending 3 17 March 2019 Ascending 15 23 March 2020 Ascending 4 27 April 2019 Ascending 16 28 April 2020 Ascending 5 28 May 2019 Ascending 17 22 May 2020 Ascending 6 21 June 2019 Ascending 18 27 June 2020 Ascending 7 27 July 2019 Ascending 19 21 July 2020 Ascending 8 20 August 2019 Ascending 20 16 August 2020 Ascending 9 25 September 2019 Ascending 21 19 September 2020 Ascending 10 31 October 2019 Ascending 22 25 October 2020 Ascending 11 24 November 2019 Ascending 23 30 November 2020 Ascending 12 30 December 2019 Ascending 24 24 December 2020 Ascending Sentinel-1 SAR data were processed using the Sentinel Application Platform (SNAP). Each extracted satellite image was primarily subjected to a series of standard corrections to reduce error propagation in succeeding processes. Then, a time series analysis of the Sentinel-1 SAR data at VV and VH polarization was generated to analyze the variation of backscatter intensity of surfaces as a function of their vegetation characteristics. 3.2. Sentinel-2 Optical Data Sentinel-2 was launched on 23 June 2015, focusing on providing visual information that can be applied to agriculture, land ecosystems monitoring, forest management, inland and coastal water quality monitoring, disaster mapping, and civil security. The satellite allows a 290 km ﬁeld of view to collect a 100 100 km tile with various spatial resolutions, ranging from 10 m, 20 m, and 60 m. Sentinel-2 optical data were obtained from ESA’s Copernicus Open Access Hub. The data obtained were between 1 January 2019 and 31 December 2020 (Table 2). Once data had been retrieved, the ﬁles were extracted to obtain the spectral bands, clipped to the chosen location for the cropland and grassland areas. These optical data were obtained in Level 1C (L1C) and Level 2A (L2A). L1C is obtained from the top-of-atmosphere reﬂectance, which still includes the atmospheric cover, which requires dark object subtract 1 (DOS1) atmospheric correction from QGIS and other sentinel-based applications. L2A is obtained AgriEngineering 2023, 5 724 from the bottom-of-atmosphere, which automatically removes the atmospheric cover, allowing for more immediate and accurate results without any correction. Table 2. Sentinel-2 optical data used in the study. No. Acquisition Date No. Acquisition Date No. Acquisition Date 1 31 January 2019 9 13 September 2019 17 5 May 2020 2 25 February 2019 10 28 October 2019 18 29 June 2020 3 27 March 2019 11 22 November 2019 19 19 July 2020 4 26 April 2019 12 22 December 2019 20 18 August 2020 5 1 May 2019 13 31 January 2020 21 17 September 2020 6 20 June 2019 14 20 February 2020 22 2 October 2020 7 5 July 2019 15 21 March 2020 23 21 November 2020 8 29 August 2019 16 20 April 2020 24 11 December 2020 Sentinel-2 optical data were processed using the quantum geographic information system (QGIS) software, an open-source, cross-platform application. If the data is obtained in L1C, the whole ﬁle is placed into the DOS1 atmospheric correction procedure before applying it to the raster calculator function, which estimates the NDVI value per pixel, generating an NDVI map. 3.3. SPI Calculation Meteorological data from three automatic weather stations in the Ilocos Norte Region from 2018 to 2020 were obtained. The data included daily precipitation, monthly precipita- tion, and daily average temperature. All meteorological observation data were acquired through PAGASA. The calculation of the SPI was achieved through the use of Microsoft Excel software, where the precipitation data were programmed to the formula developed by [58]. As such, it was computed using Equations (1) and (2): SPI = (P P)/S (1) S = (P P) /n (2) å i where P is the rainfall of the given period, S is the standard deviation, P is the average of the period of the rainfall, and n is the number of data in a single period. This study used a 1-month time scale to present precipitation anomalies and detect dryness or wetness in 2019 and 2020. This time scale reﬂected moisture conditions and seasonal estimation of precipitation. Positive SPI values obtained from monthly rainfall denote more signiﬁcant precipitation than median precipitation, whereas negative SPI values indicate less than medium precipitation. Speciﬁcally, it was further classiﬁed to account for drought-related rainfall deviations (Table 3). Table 3. SPI categories. Anomaly Range of SPI Values Precipitation Regimes 2 < SPI Max Extremely wet Positive 1.5 < SPI 2 Very wet 1 < SPI 1.5 Moderately wet None 1 < SPI 1 Normal precipitation 1.5 < SPI 1 Moderately dry Negative 2 < SPI 1.5 Very dry Min < SPI 2 Extremely dry AgriEngineering 2023, 5 725 3.4. KBDI Calculation The measurements needed for this drought index were the 24-h precipitation, the daily maximum air temperature, and the long-term mean annual precipitation. All these data were obtained from the climatological division of PAGASA. Three years’ worth of data (2018 to 2020) was requested from the PAGASA and was utilized in this study. The measurements from 2018 were only used to initialize the KBDI value to be used on the ﬁrst day of 2019. The previous day’s drought index was needed to calculate the current day’s drought index. This index cannot simply be set to zero since a KBDI equal to zero would mean that the soil is saturated. Thus, this assumption cannot be made at any given time. Therefore, an initialization procedure was required. First, the week of maximum precipitation from the ﬁrst year of data (2018) was determined. The KBDI for that week was set to zero. This week was established as the initial point and served as the basis for the next day’s KBDI. At this point, the calculated KBDI values were considered stabilized, and the initialization was completed. The KBDI on the last day of 2018 was then used to calculate the KBDI on the ﬁrst day of 2019, and so forth until the end of 2020. 3.5. Correlation Analysis Data analysis of various drought-related parameters was conducted using multiple time series graphs, which involved the different indices and data used in this study to observe and present data patterns as months passed from 2019 to 2020. Time graphs were performed monthly to show trends and correlations between different indices. 3.5.1. Relationship between Sentinel-1 Backscatter Coefﬁcient and Sentinel-2 NDVI Pearson’s correlation analysis was used to investigate the relationship between the backscattering coefﬁcients of VV and VH polarization from Sentinel-1 SAR data and NDVI values from Sentinel-2 optical data for the dry and wet seasons. This statistical parameter was applied to measure the strength of the association of SAR data with NDVI to estimate vegetation variations. Equation (3) describes the relationship between the two remote sensing data: y = b + b x (3) 0 1 where y is the backscattering intensity value, b and b are the coefﬁcients of the linear 0 1 regression modeling, and x is the NDVI value. 3.5.2. Relationship of Remotely Sensed Data with Meteorological Data This study plotted a time series analysis using SAR and optical data for the dry and wet seasons. After this, the outcomes of such information for the cropland and grassland areas were correlated to various meteorological-based data (precipitation and temperature) to understand the relationship between different drought indicators. As such, the relationship and correlation between the following were determined—(a) SAR data and SPI, (b) SAR data and KBDI, (c) NDVI and SPI, (d) NDVI and KBDI, and (e) SPI and KBDI. 3.6. Drought Risk Management Plan Six steps were considered in producing a drought risk management plan (Figure 2). Primarily, drought hazard was determined through previous events. These included any evidence of droughts in an area, such as the devastation of crops due to dry soil or lack of precipitation, allowing the most prone to drought risks to be identiﬁed. Next, the drought risks in an area were identiﬁed through different parameters and indices used in this study. Once these were known, drought forecasting was done, and an early warning system was formulated. Lastly, drought risk assessment and monitoring help provide recommendations and action plans to manage drought occurrences. AgriEngineering 2023, 5, FOR PEER REVIEW 7 3.6. Drought Risk Management Plan Six steps were considered in producing a drought risk management plan (Figure 2). Primarily, drought hazard was determined through previous events. These included any evidence of droughts in an area, such as the devastation of crops due to dry soil or lack of precipitation, allowing the most prone to drought risks to be identiﬁed. Next, the drought risks in an area were identiﬁed through diﬀerent parameters and indices used in this study. Once these were known, drought forecasting was done, and an early warning sys- AgriEngineering 2023, 5 tem was formulated. Lastly, drought risk assessment and monito 726 ring help provide rec- ommendations and action plans to manage drought occurrences. Figure 2. Drought risk management plan. Figure 2. Drought risk management plan. 4. Results and Discussion 4. Results and Discussion 4.1. Comparison of VV and VH Channels The study investigated the backscattering intensity values in VV and VH polarizations 4.1. Comparison of VV and VH Channels in the regions of interest of the study area for two years, as shown in Figure 3. As observed The study investigated the backscatt ering intensity values in VV and VH polariza- in Figure 3, the cross-polarized (VH) channel provides a lower backscatter value and more noticeable ﬂuctuations than the co-polarized (VV) channel. Conversely, the signals of tions in the regions of interest of the study area for two years, as shown in Figure 3. As croplands in VV polarization were higher than the VH. These conditions exist due to VH observed in Figure 3, the cross-polarized (VH) channel provides a lower backscatt er value channels having a more pronounced pattern for the vegetation conditions, which is known and more noticeable ﬂuctuations than the co-polarized (VV) channel. Conversely, the sig- to be an effect of volume scattering [59]. Furthermore, a cross-polarized channel provides a lower backscattering value since only a proportion of polarized waves (vertical turning nals of croplands in VV polarization were higher than the VH. These conditions exist due into horizontal) return as a signal to the sensor. A study on estimating winter crop ﬁelds to VH channels having a more pronounced patt ern for the vegetation conditions, which and grasslands in France and Tunisia indicated that the VH radar backscattered signal is is known to be an eﬀect of volume scatt ering [59]. Furthermore, a cross-polarized channel lower than the VV channel as it is more sensitive when characterizing vegetation cover [60]. Therefore, although the cross-polarized channel provides a lower backscatter signal than provides a lower backscatt ering value since only a proportion of polarized waves (vertical the co-polarized channel, the former provides better analysis and interpretation of land turning into horizontal) return as a signal to the sensor. A study on estimating winter crop vegetation conditions. ﬁelds and grasslands in France and Tunisia indicated that the VH radar backscatt ered sig- nal is lower than the VV channel as it is more sensitive when characterizing vegetation cover [60]. Therefore, although the cross-polarized channel provides a lower backscatt er signal than the co-polarized channel, the former provides bett er analysis and interpreta- tion of land vegetation conditions. AgriEngineering 2023, 5, FOR PEER REVIEW 8 AgriEngineering 2023, 5, FOR PEER REVIEW 8 AgriEngineering 2023, 5 727 Figure 3. Time series of Sentinel-1 backscatt er intensity coeﬃcient (σ°) for the vertical–vertical (VV) Figure 3. Figure 3. Tim Time e series series of Sentin of Sentinel-1 el-1 ba backscatter ckscatt er intensity intensity coe coef ﬃcient ( ﬁcientσ(°) for ) for the v theevertical–vertical rtical–vertical (VV) (VV) and vertical–horizontal (VH) polarizations over cropland, mountainside grassland, and low-lying and vertical–horizontal (VH) polarizations over cropland, mountainside grassland, and low-lying and vertical–horizontal (VH) polarizations over cropland, mountainside grassland, and low-lying grassland in Ilocos Norte region, Philippines. grassland in Ilocos Norte region, Philippines. grassland in Ilocos Norte region, Philippines. 4.2. Temporal Analysis of Backscatt er Intensity 4.2. Temporal Analysis of Backscatter Intensity 4.2. Temporal Analysis of Backscatt er Intensity 4.2.1. Cropland Areas 4.2.1. Cropland Areas 4.2.1. Cropland Areas Crop development is classiﬁed into three main phases: vegetative, reproductive, and Crop development is classiﬁed into three main phases: vegetative, reproductive, and Crop development is classiﬁed into three main phases: vegetative, reproductive, and maturation, as shown in Figure 4. First, the vegetation phase characterizes the crop’s ini- maturation, as shown in Figure 4. First, the vegetation phase characterizes the crop’s initial maturation, as shown in Figure 4. First, the vegetation phase characterizes the crop’s ini- tial development of tillers. Since this phase features the gradual increase in plant height, development of tillers. Since this phase features the gradual increase in plant height, the tial development of tillers. Since this phase features the gradual increase in plant height, the backscatt ering signal σ° starts from a low value as the crop ﬁelds are initially dry. backscattering signal starts from a low value as the crop ﬁelds are initially dry. Several the backscatt ering signal σ° starts from a low value as the crop ﬁelds are initially dry. Several irregularities may be found in the vegetative phase in the backscatt er intensity irregularities may be found in the vegetative phase in the backscatter intensity time series. Several irregularities may be found in the vegetative phase in the backscatt er intensity time series. Although there was a decrease in backscatt er (from −16 dB to −17 dB in VH Although there was a decrease in backscatter (from 16 dB to 17 dB in VH and 8 dB time series. Although there was a decrease in backscatt er (from −16 dB to −17 dB in VH and −8 dB to −9 dB in VV) during the wet season, the intensity value at the end of the to 9 dB in VV) during the wet season, the intensity value at the end of the vegetative and −8 dB to −9 dB in VV) during the wet season, the intensity value at the end of the vegetative phase is still higher from its germination stage, indicating that the area is still phase is still higher from its germination stage, indicating that the area is still experiencing vegetative phase is still higher from its germination stage, indicating that the area is still experiencing a typical growth patt ern. On the other hand, during the dry season, it is a typical growth pattern. On the other hand, during the dry season, it is found that there experiencing a typical growth patt ern. On the other hand, during the dry season, it is found that there is a general decrease in backscatt er intensity (from −17 dB to −19 dB in is a general decrease in backscatter intensity (from 17 dB to 19 dB in VH and from found that there is a general decrease in backscatt er intensity (from −17 dB to −19 dB in VH and from −9 dB to −11 dB), which may show that rice crops are aﬀected and do not 9 dB to 11 dB), which may show that rice crops are affected and do not conform with VH and from −9 dB to −11 dB), which may show that rice crops are aﬀected and do not the conform with conventional the conv rice crentional rice op pattern. crop patt ern. conform with the conventional rice crop patt ern. Figure 4. Time series of Sentinel-1 for the vertical–vertical (VV) and vertical–horizontal (VH) Figure 4. Time series of Sentinel−1 σ° for the vertical–vertical (VV) and vertical–horizontal (VH) Figure 4. Time series of Sentinel−1 σ° for the vertical–vertical (VV) and vertical–horizontal (VH) polarizations over cropland areas in Ilocos Norte region, Philippines, indicating the three crop polarizations over cropland areas in Ilocos Norte region, Philippines, indicating the three crop de- polarizations over cropland areas in Ilocos Norte region, Philippines, indicating the three crop de- velopment phases—vegetative, reproductive, and maturation. development phases—vegetative, reproductive, and maturation. velopment phases—vegetative, reproductive, and maturation. Secondly, the reproductive phase indicates that plant growth continues until it reaches Secondly, the reproductive phase indicates that plant growth continues until it Secondly, the reproductive phase indicates that plant growth continues until it its maximum height. From the start of this stage, the backscattering intensity decreases reaches its maximum height. From the start of this stage, the backscatt ering intensity reaches its maximum height. From the start of this stage, the backscatt ering intensity AgriEngineering 2023, 5, FOR PEER REVIEW 9 AgriEngineering 2023, 5 728 decreases as it approaches the end of the phase since the water content for crops is reduced as it approaches the end of the phase since the water content for crops is reduced upon upon completing the growing phase. However, a slight increase in the transmitt ed radar completing the growing phase. However, a slight increase in the transmitted radar signal signal can be observed due to the vertical expansion of vegetation and, ultimately, an in- can be observed due to the vertical expansion of vegetation and, ultimately, an increase crease in the height and volume of rice crops. This patt ern shows that the rise in the vol- in the height and volume of rice crops. This pattern shows that the rise in the volume of ume of vegetation causes an increase in the proportion of emitt ed signals. During the wet vegetation causes an increase in the proportion of emitted signals. During the wet season, season, the backscatt er intensity values for the reproductive phase characterize the typical the backscatter intensity values for the reproductive phase characterize the typical rice crop rice crop growth as there was a decrease in radar signal from the start of this phase. It also growth as there was a decrease in radar signal from the start of this phase. It also revealed revealed that a slight increase in backscatt er intensity during August 2019 might be found, that a slight increase in backscatter intensity during August 2019 might be found, which which indicates that this month is when the height of the rice crop is at its maximum. indicates that this month is when the height of the rice crop is at its maximum. On the other hand, an anomaly of the usual rice crop patt ern is established during On the other hand, an anomaly of the usual rice crop pattern is established during the dry season. A continuously decreasing trend shows that water content in the rice ﬁelds the dry season. A continuously decreasing trend shows that water content in the rice is lowered without the presence of a signiﬁcant volume of vegetation. Furthermore, ﬁelds is lowered without the presence of a signiﬁcant volume of vegetation. Furthermore, backscatt ering values for January to March 2020 are signiﬁcantly lower than the vegetative backscattering values for January to March 2020 are signiﬁcantly lower than the vegetative phase (from −17 dB to −19 dB in VH and −9 dB to −11 dB in VV). phase (from 17 dB to 19 dB in VH and 9 dB to 11 dB in VV). Lastly, the ripening or maturation phase is commonly identiﬁed with a continuous Lastly, the ripening or maturation phase is commonly identiﬁed with a continuous drying of leaves and stems. Unlike the previous crop growth stages, this phase reveals a drying of leaves and stems. Unlike the previous crop growth stages, this phase reveals a constant decrease in σ° for both polarizations. From the study area, both the wet and dry constant decrease in for both polarizations. From the study area, both the wet and dry seasons exhibited a decreasing backscatt ering intensity value. It must be noted that rice seasons exhibited a decreasing backscattering intensity value. It must be noted that rice crops are normally harvested during the wet season, which indicates decreased vegetation crops are normally harvested during the wet season, which indicates decreased vegetation volume and stems moisture content. On the other hand, during the dry season, radar sig- volume and stems moisture content. On the other hand, during the dry season, radar nal in the maturation phase indicates low production during the harvesting period as the signal in the maturation phase indicates low production during the harvesting period as backscatt er intensity at the end of the crop cycle (−18 in VH and −11 in VV) is lower com- the backscatter intensity at the end of the crop cycle (18 in VH and 11 in VV) is lower pared to the end of the wet season (−17 dB in VH and −10 dB in VV). compared to the end of the wet season (17 dB in VH and 10 dB in VV). 4.2.2. Grassland Areas 4.2.2. Grassland Areas Throughout the wet and dry seasons, both grassland areas exhibited a consistent me- Throughout the wet and dry seasons, both grassland areas exhibited a consistent dium to high backscatt er coeﬃcient, as shown in Figure 5. Radar signal for the mountain- medium to high backscatter coefﬁcient, as shown in Figure 5. Radar signal for the moun- side grasslands ranges from −13 dB to −14 dB for VH and −7 dB to −8 dB for VV, whereas tainside grasslands ranges from 13 dB to 14 dB for VH and 7 dB to 8 dB for VV, the low-lying grasslands were found at −12 dB to −13 dB for VH and −7 dB to −8 dB for whereas the low-lying grasslands were found at 12 dB to 13 dB for VH and 7 dB to VV. Since there are no signiﬁcant ﬂuctuations in radar signal throughout the entire time 8 dB for VV. Since there are no signiﬁcant ﬂuctuations in radar signal throughout the series, the backscatt ering signal for the grasslands reveals that the said regions of interest entire time series, the backscattering signal for the grasslands reveals that the said regions provided an almost uniform vegetation condition over time. of interest provided an almost uniform vegetation condition over time. Figure 5. Time series of Sentinel−1 σ° for the vertical–vertical (VV) and vertical–horizontal (VH) Figure 5. Time series of Sentinel-1 for the vertical–vertical (VV) and vertical–horizontal (VH) polarizations over the mountainside grassland and low-lying grassland areas in Ilocos Norte region, polarizations over the mountainside grassland and low-lying grassland areas in Ilocos Norte region, Philippines. Philippines. AgriEngineering 2023, 5, FOR PEER REVIEW 10 AgriEngineering 2023, 5 729 Furthermore, since both grassland areas have low proximity to residential and agri- Furthermore, since both grassland areas have low proximity to residential and agricul- cultural loci, the regions of interest are barely aﬀected, allowing less air pollution and tural loci, the regions of interest are barely affected, allowing less air pollution and creating creating an environment with improved air quality and unhindered soil nutrient content. an environment with improved air quality and unhindered soil nutrient content. Therefore, Therefore, withstanding precipitation variabilities and being away from human interven- withstanding precipitation variabilities and being away from human intervention, the veg- tion, the vegetation in the mountainside and low-lying grasslands does not exhibit signif- etation in the mountainside and low-lying grasslands does not exhibit signiﬁcant drought icant drought risk throughout the two years. risk throughout the two years. 4.3. Temporal Analysis of Sentinel-2 NDVI 4.3. Temporal Analysis of Sentinel-2 NDVI In the analysis of NDVI, cropland areas varied signiﬁcantly compared to both grass- In the analysis of NDVI, cropland areas varied signiﬁcantly compared to both grass- land areas. In the two years of analysis, its rise in value coincides with the start–peak–end land areas. In the two years of analysis, its rise in value coincides with the start–peak–end period of rice planting in the Ilocos Norte region. As the area has also experienced diﬀer- period of rice planting in the Ilocos Norte region. As the area has also experienced dif- ences in rainfall and temperature in the two years, despite the diﬀerence in value, the ferences in rainfall and temperature in the two years, despite the difference in value, the months where the NDVI has increased are still the same in both years, as shown in Figure months where the NDVI has increased are still the same in both years, as shown in Figure 6. 6. Cropland is easily more aﬀected by factors in its surrounding, which may cause land Cropland is easily more affected by factors in its surrounding, which may cause land degradation in an area, causing fewer values in its NDVI in a 10 × 10 m spatial resolution. degradation in an area, causing fewer values in its NDVI in a 10 10 m spatial resolution. In a single pixel, it cannot be avoided that other objects such as the soil, houses, water, and In a single pixel, it cannot be avoided that other objects such as the soil, houses, water, and other objects surrounding the plants are included in a single pixel, which may cause a other objects surrounding the plants are included in a single pixel, which may cause a drop drop in NDVI value, leading to a need to be used with other drought indicators. in NDVI value, leading to a need to be used with other drought indicators. Figure 6. Figure 6. Monthly time series Monthly time series of Sentinel-2 n of Sentinel-2 o normalized rmalized didif ﬀefer rence v ence e vegetation getation index (NDVI) ov index (NDVI) over er the the cropland, mountainside grassland, and low-lying grassland areas in Ilocos Norte region, Philip- cropland, mountainside grassland, and low-lying grassland areas in Ilocos Norte region, Philippines, pines, for two years (2019 and 2020). for two years (2019 and 2020). 4.4. Temporal Analysis of SPI 4.4. Temporal Analysis of SPI Ilocos Norte has relatively average precipitation for the entire year (from 2019 to 2020) Ilocos Norte has relatively average precipitation for the entire year (from 2019 to based on SPI, as shown in Figure 7. However, according to [58], drought is considered to be 2020) based on SPI, as shown in Figure 7. However, according to [58], drought is consid- starting as SPI reaches a value of 0. The month of August peaked in the positive anomaly, ered to be starting as SPI reaches a value of 0. The month of August peaked in the positive followed by July for both years, which means that these were the months the location anomaly, followed by July for both years, which means that these were the months the received the most amount of rainfall. Typhoons signiﬁcantly impact these ﬁndings as, location received the most amount of rainfall. Typhoons signiﬁcantly impact these ﬁnd- according to PAGASA, the peak typhoon season in the Philippines is from July to October. ings as, according to PAGASA, the peak typhoon season in the Philippines is from July to On the other hand, the months from October to June received minimal to no precipitation October. On the other hand, the months from October to June received minimal to no resulting in decreased soil moisture. These ﬁndings coincided with the reported drought precipitation resulting in decreased soil moisture. These ﬁndings coincided with the re- events in the study location. ported drought events in the study location. AgriEngineering 2023, 5, FOR PEER REVIEW 11 AgriEngineering 2023, 5, FOR PEER REVIEW 11 AgriEngineering 2023, 5 730 Figure 7. Standardized Precipitation Index (SPI) of the Ilocos Norte province in the Philippines for Figure 7. Standardized Precipitation Index (SPI) of the Ilocos Norte province in the Philippines for Figure 7. Standardized Precipitation Index (SPI) of the Ilocos Norte province in the Philippines for two years (2019 and 2020). two y twoeyears ars (201 (2019 9 and 2020) and 2020). . 4.5. Temporal Analysis of KBDI 4.5. Temporal Analysis of KBDI 4.5. Temporal Analysis of KBDI Results of the KBDI analysis show that the drought index is signiﬁcantly high from Results of the KBDI analysis show that the drought index is signiﬁcantly high from Results of the KBDI analysis show that the drought index is signiﬁcantly high from December to May, which coincides with the province’s dry season, as shown in Figure 8. December to May, which coincides with the province’s dry season, as shown in Figure 8. December to May, which coincides with the province’s dry season, as shown in Figure 8. However, for June to November, the index trend is inconsistent, especially for the year However, for June to November, the index trend is inconsistent, especially for the year 2020, However, for June to November, the index trend is inconsistent, especially for the year 2020, where there was a noticeable spike in KBDI in June and from late July to early De- where there was a noticeable spike in KBDI in June and from late July to early December. 2020, where there was a noticeable spike in KBDI in June and from late July to early De- cember. Natural phenomena can explain the erratic behavior of the drought index during Natural phenomena can explain the erratic behavior of the drought index during the wet cember. Natural phenomena can explain the erratic behavior of the drought index during the wet season, such as reported droughts and typhoons. Meanwhile, unlike the ﬁgure season, such as reported droughts and typhoons. Meanwhile, unlike the ﬁgure generated the wet season, such as reported droughts and typhoons. Meanwhile, unlike the ﬁgure generated using the daily KBDI values, the trend of the average monthly KBDI is more using the daily KBDI values, the trend of the average monthly KBDI is more consistent and generated using the daily KBDI values, the trend of the average monthly KBDI is more consistent and aligns with the regular wet and dry seasons in Laoag City, which could aligns with the regular wet and dry seasons in Laoag City, which could explain the usual consistent and aligns with the regular wet and dry seasons in Laoag City, which could explain the usual lapses in drought response in the region (Figure 9). lapses in drought response in the region (Figure 9). explain the usual lapses in drought response in the region (Figure 9). Figure 8. Daily Keetch–Byram drought index (KBDI) analysis results in Laoag City, Ilocos Norte Figure 8. Daily Keetch–Byram drought index (KBDI) analysis results in Laoag City, Ilocos Norte Figure 8. Daily Keetch–Byram drought index (KBDI) analysis results in Laoag City, Ilocos Norte province in the Philippines for 2019 and 2020. province in the Philippines for 2019 and 2020. province in the Philippines for 2019 and 2020. AgriEngineering 2023, 5, FOR PEER REVIEW 12 AgriEngineering 2023, 5 731 Figure 9. Average monthly KBDI analysis results in Laoag City, Ilocos Norte province in the Philip- Figure 9. Average monthly KBDI analysis results in Laoag City, Ilocos Norte province in the Philip- pines for two pines for two years (2019 and years (2019 and 2020). 2020). 4.6. Correlation Analysis of Remote Sensing and Meteorological Drought Indicators 4.6. Correlation Analysis of Remote Sensing and Meteorological Drought Indicators The study also investigated the potential of Sentinel-1 SAR data and Sentinel-2 optical The study also investigated the potential of Sentinel-1 SAR data and Sentinel-2 opti- data for drought monitoring, as shown in Figure A1 and Table 4. In investigating the cal data for drought monitoring, as shown in Figure A1 and Table 4. In investigating the potential of using remote sensing data for land monitoring and drought detection, the potential of using remote sensing data for land monitoring and drought detection, the correlation analyses suggest that cross-polarized from backscattering coefﬁcients appeared correlation analyses suggest that cross-polarized from backscatt ering coeﬃcients ap- to be higher than co-polarized with NDVI. Consequently, it can be seen that the correlation peared to be higher than co-polarized with NDVI. Consequently, it can be seen that the results varied from 0.6 to 0.7. This statistical framework is also similar to the study of [61], correlation results varied from 0.6 to 0.7. This statistical framework is also similar to the which statistically quantiﬁed the relation between NDVI and backscatter intensity for study of [61], which statistically quantiﬁed the relation between NDVI and backscatt er sugarcane monitoring. The authors of [61] pointed out that the discrepancies from the intensity for sugarcane monitoring. The authors of [61] pointed out that the discrepancies perfect line ﬁt can be traced to the slight difference in temporal resolution between the from the perfect line ﬁt can be traced to the slight diﬀerence in temporal resolution be- Sentinel-1 SAR data and Sentinel-2 optical data. It is known that the revisit time for tween the Sentinel-1 SAR data and Sentinel-2 optical data. It is known that the revisit time Sentinel-1 and Sentinel-2 satellites is once every 12 and 10 days, respectively. Due to this, a for Sentinel-1 and Sentinel-2 satellites is once every 12 and 10 days, respectively. Due to comparison between the two remote sensing data would allow a slight difference in the this, a comparison between the two remote sensing data would allow a slight diﬀerence observed scenarios and vegetation density for each pair of image acquisitions. in the observed scenarios and vegetation density for each pair of image acquisitions. Table 4. Correlation results for SAR and optical data-derived drought indicators. Table 4. Correlation results for SAR and optical data-derived drought indicators. Area of Interest Area of Interest VV Polarization VH Polarization VV Polarization VH Polarization Croplands 0.6414 0.7170 Croplands 0.6414 0.7170 Mountainside grasslands 0.6123 0.7336 Mountainside grasslands 0.6123 0.7336 Low-lying grasslands 0.6224 0.6806 Low-lying grasslands 0.6224 0.6806 One of the objectives of this study is to determine if there is any signiﬁcant correlation One of the objectives of this study is to determine if there is any signiﬁcant correlation between remote sensing-based and meteorological-based drought indices, as summarized between remote sensing-based and meteorological-based drought indices, as summarized in Table 5. Various correlational analyses in this study reveal a generally low correlation in Table 5. Various correlational analyses in this study reveal a generally low correlation between remote sensing data and meteorological-based data in grassland areas, as seen between remote sensing data and meteorological-based data in grassland areas, as seen in in the analysis between SPI and KBDI with the remote sensing-based indices backscatter the analysis between SPI and KBDI with the remote sensing-based indices backscatt er in- intensity and NDVI. Meanwhile, a signiﬁcant correlation could be observed between tensity and NDVI. Meanwhile, a signiﬁcant correlation could be observed between the the data regarding croplands. The correlation difference is assumed to be caused by the data regarding croplands. The correlation diﬀerence is assumed to be caused by the var- varying slopes, elevations, and soil inﬁltration capacities in these two areas. Because ying slopes, elevations, and soil inﬁltration capacities in these two areas. Because of some AgriEngineering 2023, 5 732 of some parameters, such as the type of crops and the distance between these crops or plants, it is assumed that croplands allow water to inﬁltrate the soil faster than grasslands. Precipitation in mountainside grasslands most likely ends up as runoff as well. Hence, the on-site condition in grasslands reﬂected by the satellite, which is used for remote sensing, cannot be accurately represented by meteorological data such as temperature and precipitation. The distance of ground equipment such as rain gauges and thermometers could be a factor as to why there is a correlation difference. The chosen cropland area for this study is closer to the automatic weather stations of PAGASA compared to the chosen grasslands. Despite this, it can still be concluded that there is an inverse relationship between KBDI and the remote sensing-based indices since an increase in KBDI signiﬁes the occurrence of drought while a decrease in backscatter intensity and NDVI indicates less water on the surface and worsening of vegetation health, respectively. Table 5. Summary of correlation results for remote sensing data and meteorological-based drought indices. Drought Indices Correlation Area of Interest VV VH Croplands 0.5160 0.3162 Backscatter intensity vs. SPI Mountainside grasslands 0.0006 0.0006 Low-lying grasslands 0.0024 0.0004 Croplands 0.6301 0.5600 Backscatter intensity vs. KBDI Mountainside grasslands 0.0012 0.0025 Low-lying grasslands 0.305 0.0105 Croplands 0.4701 Mountainside grasslands 0.0006 SPI vs. NDVI Low-lying grasslands 0.0053 Croplands 0.7066 Mountainside grasslands 0.0105 KBDI vs. NDVI Low-lying grasslands 0.0035 SPI vs. KBDI - 0.5542 4.7. Proposed EWS for Drought Risk Management An EWS for drought occurrence is proposed in this study, seeing the signiﬁcant results obtained from the four drought indices used in this study, as shown in Figure 10. This proactive mechanism allows stakeholders to utilize evidence-based and statistical decisions for drought assessment. Having been able to adapt the categories based on the National Drought Plan of the Philippines, the drought indices used in this study were provided with their corresponding threshold values that can assess monitoring, forecasting, and coping with the impacts of drought based on vegetation conditions and climatic characteristics. Based on the speciﬁed threshold values from the study’s drought indicators, the EWS is divided into four classiﬁcations to detect climate variabilities and identify vegetation and surface conditions. The ﬁrst two decision ruling criteria are based on meteorological deﬁciency classiﬁed from SPI and KBDI. Since meteorological drought is the primary factor that affects crop growth, these parameters have been the ﬁrst to be assessed. In this manner, the lack of rainfall and extreme temperatures may be a factor in drought risk. Consequently, the third and fourth assessments were based on the backscatter intensity and NDVI, which characterized the actual surface and vegetation conditions and provided better drought identiﬁcation concerning crop vegetation health. AgriEngineering 2023, 5 733 AgriEngineering 2023, 5, FOR PEER REVIEW 14 Figure 10. Proposed drought early warning system (EWS). Figure 10. Proposed drought early warning system (EWS). 4.8. 4.8. Im Implications plications Drought is a natural phenomenon in the Philippines, especially in the Ilocos Norte Drought is a natural phenomenon in the Philippines, especially in the Ilocos Norte region, where water shortage, warmer temperatures, and intensifying El Niño conditions region, where water shortage, warmer temperatures, and intensifying El Niño conditions negatively impact crop production. Meanwhile, RS and GIS are emerging data processing negatively impact crop production. Meanwhile, RS and GIS are emerging data processing platforms with minimal applications in Southeast Asian countries. Considering this, the platforms with minimal applications in Southeast Asian countries. Considering this, the main objective of this study was to investigate the potential of RS and meteorological data main objective of this study was to investigate the potential of RS and meteorological data in identifying the drought hazards in the cropland and grassland areas in Ilocos Norte. It in identifying the drought hazards in the cropland and grassland areas in Ilocos Norte. also establishes the relationships between satellite SAR, optical, and meteorological-based It also establishes the relationships between satellite SAR, optical, and meteorological- drought indices, such as SPI and KBDI, for bett er drought risk assessment and crop mon- based drought indices, such as SPI and KBDI, for better drought risk assessment and crop itoring. monitoring. This study analyzed the co-polarized (VV) and cross-polarized (VH) channels for the This study analyzed the co-polarized (VV) and cross-polarized (VH) channels for the croplands, mountainside grasslands, and low-lying grassland areas in the Ilocos Norte croplands, mountainside grasslands, and low-lying grassland areas in the Ilocos Norte region. The Sentinel-1 SAR data backscatt er intensity results reveal the VH channel pro- region. The Sentinel-1 SAR data backscatter intensity results reveal the VH channel pro- duces a lower return signal for all areas of interest than the VV channel. However, the duces a lower return signal for all areas of interest than the VV channel. However, the former provides more observable variations in backscatt er values during the two-year former provides more observable variations in backscatter values during the two-year analysis period. This result was obtained as the VH channel presents a lower proportion analysis period. This result was obtained as the VH channel presents a lower proportion of polarized waves returning to the satellite than the VV channel. Furthermore, due to its of polarized waves returning to the satellite than the VV channel. Furthermore, due to its sensitivity in detecting structural changes in plants brought by volume scatt ering, the sensitivity in detecting structural changes in plants brought by volume scattering, the cross- cross-polarized channel imparts a more excellent interpretation for characterizing vegeta- polarized channel imparts a more excellent interpretation for characterizing vegetation tion cover. Thereby, the results of this investigation revealed the potential of multi-tem- cover. Thereby, the results of this investigation revealed the potential of multi-temporal poral analysis of Sentinel-1 SAR data for monitoring changes in vegetation conditions in analysis of Sentinel-1 SAR data for monitoring changes in vegetation conditions in the the areas of interest. areas of interest. Sentinel-1 backscatt er analysis of the croplands area revealed the phenological rice Sentinel-1 backscatter analysis of the croplands area revealed the phenological rice crop crop phases. For both the dry and wet seasons, rice crop growth irregularities in the veg- phases. For both the dry and wet seasons, rice crop growth irregularities in the vegetative etative and reproductive stages were observed because of lower water content which, in and reproductive stages were observed because of lower water content which, in turn, provided less than the usual amount of healthy vegetation. On the other hand, Sentinel-1 backscatter intensity values for the mountainside and low-lying grassland areas have also AgriEngineering 2023, 5 734 been investigated in this study for drought. It was shown that there are no signiﬁcant ﬂuctuations of the Sentinel-1 backscatter coefﬁcients for both VH and VV polarizations. Radar signals also reﬂect a consistent medium to high backscatter coefﬁcient, indicating an almost uniform vegetation condition over the two-year analysis period. Furthermore, the elevation difference between the two grassland areas did not account for radar signal variability as the regions of interest have almost similar backscattering intensity values. Hence, as human activities do not impede the areas, mountainside and low-lying grasslands did not exhibit profound drought risk during 2019 and 2020. Sentinel-2 NDVI is also analyzed in croplands, mountainside grasslands, and low- lying grasslands. Sentinel-2 NDVI data can immediately reveal all locations of interest, but the satellite optical data requires little to no cloud cover. The analysis of NDVI values reveals that NDVI is greatly affected by activities in nearby locations, despite having good crop health in an area. If the site is near roads and unvegetated lands, NDVI can signiﬁcantly decrease its value. This situation can cause NDVI to need more conﬁdence when dealing with drought and increase its reliance on other drought indicators presented in the study. However, it can still impart important, valuable information for agricultural purposes, such as peak months and vegetation health. When compared to the crop planting periods, the index can show a signiﬁcant relation to the calendar, which is beneﬁcial for identifying crop health per month. As it is known that there was a drought in the early months of 2019, the NDVI values are identical when compared to the values in 2020. This ﬁnding created a pattern that farmers can use to identify when best to prepare for lower crop yields due to various factors related to environmental and climatic variabilities. As NDVI values are greatly affected by human activities, natural occurrences, such as typhoons, increased temperature, and other uncontrollable circumstances, including drought, will also affect its values as NDVI relies on a per-pixel visual representation than a direct analysis of a location. Like Sentinel-1 backscatter intensity values, NDVI can be used at any elevation and location if an available satellite optical image can be obtained from the RS database. These various reasons allow NDVI to be used as an index for different agricultural uses with some limitations due to its capacity to be affected by external factors. The relationship between the two RS data was provided in the study to show the potential of Sentinel-1 and Sentinel-2 data in monitoring vegetation conditions. Correlation between backscattering intensity, in both VH and VV polarization, and NDVI values reveals a good correlation for the croplands, mountainside grasslands, and low-lying grasslands. This statistical data analysis also shows that the correspondence of NDVI with VH polar- ization was higher than the VV polarization, as the former provides more pronounced radar signals for vegetation conditions [46]. Hence, an increase in backscattering value corresponds to a greater NDVI, and at the same time, both agree to monitor vegetation conditions. The results of SPI on a 1-month scale show that drought was present in the study location for 2019 and 2020. There is no standard index set as a threshold indicator for drought. Instead, researchers can ﬁx it if the SPI value is less than 0. With that, even if the calculated index fell under the normal precipitation category, the study’s results indicate that consecutive months of negative SPI value means that drought in terms of deﬁcit in soil moisture is already starting. However, SPI only accounts for precipitation deﬁciency. SPI was correlated to another meteorological index, KBDI, and RS indices derived from Sentinel-1 and Sentinel-2 data to analyze its accuracy in drought indication further. SPI had a fair correlation with other indices with R > 0.50 for cropland areas. However, there was no correlation for the grassland areas. This result suggests that the reliability of SPI in monitoring drought from the study’s location is only limited to cropland areas. Despite that, sites with more ground equipment for collecting precipitation data may lead to different ﬁndings as to the effectiveness of SPI in monitoring drought in grassland areas. Moreover, the KBDI accurately represented the weather conditions in the regions of interest through this study. This observation can be explained by natural phenomena, such AgriEngineering 2023, 5 735 as tropical cyclones and drought occurrences in the province of Ilocos Norte. A signiﬁcant correlation exists between KBDI and other indices, speciﬁcally in croplands. However, the correlation is signiﬁcantly low for mountainside and low-lying grasslands. Therefore, using KBDI to determine the condition of plants in these areas is not recommended. It would be better to rely on NDVI as it represents vegetation health. For better utilization of meteorological indices such as KBDI and SPI, it is recommended to install more ground equipment. Ilocos Norte currently has only three automatic weather stations, which needs to be improved for the whole province. Installing rain gauges and weather thermometers in farmlands is advisable for more accurate monitoring. These installations will increase the reliability of KBDI and SPI, thus creating a simple yet effective system to observe and detect possible drought occurrences in these areas. 5. Outlook Some studies have investigated the potential of seasonal and climatic forecasting for drought assessment. Still, studies have yet to explore the integration of such probabilistic outputs with observational satellite RS and meteorological data. This study analyzed the Sentinel-1 SAR data VV and VH channels for the cropland, mountainside grassland, and low-lying grassland areas in the Ilocos Norte region in the Philippines. The Sentinel-1 backscatter intensity results reveal that the VH channel produces a lower return signal for all regions of interest than the VV channel. However, the Sentinel-1 backscattering coefﬁcient from the VH channel provides a better correlation, with R > 0.60, with the Sentinel-2 NDVI in all regions of interest. Moreover, the Sentinel-1 backscattering coefﬁcient and Sentinel-2 NDVI showed substantial irregularities in cropland areas compared to grassland areas. These results indicate that croplands are more affected by drought than grassland areas. A signiﬁcant correlation exists between RS- and meteorological-based drought indicators, speciﬁcally KBDI, with backscattering coefﬁcient and NDVI. On the contrary, SPI and KBDI show a poor correlation with the backscattering coefﬁcient and NDVI, speciﬁcally in grassland areas of the Ilocos Norte region. Nevertheless, the study contributes to the general understanding of climate-induced variations in crop production by characterizing climatic and vegetation variability for the cropland and grassland areas. Through quantifying vegetation conditions and precipitation deﬁciencies, this study highlights the feasibility of crop condition monitoring on a broader scale of environmental conditions. Thus, the overall outcome of the study presents that drought risk can be assessed appropriately by applying both RS data and meteorological- based drought indicators, allowing for a more outstanding establishment of drought risk management plans and early warning systems. Author Contributions: Conceptualization, C.A.A., J.M.G., M.N.M., M.B.M. and R.R.; methodology, C.A.A., J.M.G., M.N.M. and M.B.M.; formal analysis, C.A.A., J.M.G., M.N.M., M.B.M. and R.R.; data curation, C.A.A., J.M.G., M.N.M. and M.B.M.; writing—original draft preparation, C.A.A., J.M.G., M.N.M. and M.B.M.; writing—review and editing, R.R.; supervision, R.R.; project administration, R.R. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: Not applicable. Acknowledgments: The authors thank the European Space Agency (ESA) for supplying Sentinel-1 and Sentinel-2 data free of charge. The authors also thank the Philippine Atmospheric, Geophysical, and Astronomical Services Administration for providing meteorological data free of charge. Conﬂicts of Interest: The authors declare no conﬂict of interest. 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Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png AgriEngineering Multidisciplinary Digital Publishing Institute http://www.deepdyve.com/lp/multidisciplinary-digital-publishing-institute/drought-risk-assessment-and-monitoring-of-ilocos-norte-province-in-the-esOo7AH4bR

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Abstract

AgriEngineering Article Drought Risk Assessment and Monitoring of Ilocos Norte Province in the Philippines Using Satellite Remote Sensing and Meteorological Data 1 1 1 1 Christian Albert Alonzo , Joanna Mae Galabay , Margadrew Nicole Macatangay , Mark Brianne Magpayo and 1 , 2 , Ryan Ramirez * Department of Civil Engineering, University of Santo Tomas, Manila 1008, Philippines Division of Earth and Space Sciences, National Research Council of the Philippines, Taguig 1631, Philippines * Correspondence: raramirez@ust.edu.ph Abstract: Drought has been known to be a natural hazard reﬂecting geographic and climatic char- acteristics. Satellite technology advancements have beneﬁted drought assessment and monitoring to formulate plans for dealing with this slow-onset disaster. However, combining satellite remote sensing (RS) and meteorological data for drought monitoring is lacking in the literature. This study uses satellite RS and meteorological-based drought indicators to assess drought risk in the Ilocos Norte, Philippines. Data analysis included the retrieval of vegetation conditions using Sentinel-1 and Sentinel-2 data. The standardized precipitation index (SPI) and Keetch–Byram drought index (KBDI) were calculated to account for climatic variabilities. Results revealed that the Sentinel-1 backscatter coefﬁcient decreased by2 dB in the cropland area, indicating crop growth irregularities compared to grassland areas. These irregularities were supported by Sentinel-2 normalized difference vegetation index (NDVI) strong ﬂuctuations during the two-year observation period. A signiﬁcant coefﬁcient of determination (R > 0.60) between the Sentinel-1 backscatter coefﬁcient and Sentinel-2 NDVI was observed for the study area. On the one hand, only KBDI signiﬁcantly correlated (R > 0.60) with the cropland area’s RS data-derived drought indicators. These results revealed RS data variability for Citation: Alonzo, C.A.; Galabay, J.M.; drought risk management but are still valuable for developing an early warning system. Macatangay, M.N.; Magpayo, M.B.; Ramirez, R. Drought Risk Keywords: drought; remote sensing; Sentinel-1 SAR data; Sentinel-2 optical data; Ilocos Norte Assessment and Monitoring of Ilocos Norte Province in the Philippines Using Satellite Remote Sensing and Meteorological Data. AgriEngineering 1. Introduction 2023, 5, 720–739. https://doi.org/ 10.3390/agriengineering5020045 Drought is a natural hazard that reﬂects the region’s exposure to various geographic and climatic characteristics. Due to its widespread impacts over a large area in a certain Academic Editor: Murali period, it is considered more potent than other natural disasters, especially in areas that Krishna Gumma heavily rely on agricultural-based occupations [1]. Mainly attributed to low precipitation for Received: 22 February 2023 an extended period, drought provides negative implications regarding economic and social Revised: 6 April 2023 functions. The consequences of such drought-related outcomes have severely inhibited Accepted: 11 April 2023 the proper functioning of the community regarding water supply, productivity, and food Published: 13 April 2023 security [2]. Hence, the evaluation of probable drought-risk areas must be addressed and mitigated through data-based assessments. Drought indicators represent data to quantify or measure drought severity in a par- ticular area. It uses information on rainfall, streamﬂow, soil moisture, and other hydro- Copyright: © 2023 by the authors. meteorological evidence to monitor vegetation conditions. With this, various indices can be Licensee MDPI, Basel, Switzerland. used to establish the amount of precipitation for a certain period and accordingly compare This article is an open access article its deviation from established standards. Some of the widely used meteorological-based distributed under the terms and indices include the standardized precipitation index (SPI) [3–8], standardized precipitation conditions of the Creative Commons and evapotranspiration index (SPEI) [3,5–7,9,10], multivariate standardized precipitation Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ index (MSPI) [11,12], rainfall anomaly index (RAI) [13,14], and Palmer drought sever- 4.0/). ity index (PDSI) [8,15,16]. However, the scarcity and high spatiotemporal variability of AgriEngineering 2023, 5, 720–739. https://doi.org/10.3390/agriengineering5020045 https://www.mdpi.com/journal/agriengineering AgriEngineering 2023, 5 721 meteorological stations across different regions limit the successful utilization of these meteorological-based indices for drought assessment and monitoring [17]. The application of remote sensing (RS) In drought studies lies in its capability to observe changes in a geographic environment [18]. It can monitor a particular area’s condition before, during, and after a disaster strikes. Similarly, a geographic information system (GIS) creates a centralized mechanism for the RS data through mapping and spatial analysis for a particular disaster scope. Likewise, advancements in satellite technology have been beneﬁcial in drought assessment and monitoring, which help formulate plans to deal with this slow-onset disaster [19,20]. Satellite observations have provided signiﬁcantly contributed to accurately visualizing surface parameters in the possible occurrence and severity of drought for a certain period [21–24]. Speciﬁcally, Sentinel-2 and Landsat optical satellite-derived indices are widely used to evaluate drought on a regional scale [25–33]. The most commonly used optical RS data-derived drought indicators include the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), vegetation condition index (VCI), and enhanced vegetation index (EVI). However, optical satellite data are highly affected by unpredicted atmospheric conditions (i.e., cloud contamination), limiting the collection and utilization of quality optical satellite imageries despite being acquired at shorter time windows with zero costs. On the other hand, synthetic aperture radar (SAR) data complement optical satellite data in regions with considerable cloud cover density in investigating drought. SAR is an active radar imaging technology that works in all weather conditions, day and night. For example, the ﬂeet of twin Sentinel-1 satellites has provided free accessible archived medium-resolution SAR data, which the scientiﬁc community utilizes for various earth observation and geohazard applications such as ﬂoods [34,35], volcano eruptions [36,37], earthquakes [38,39], landslides [40–42], and land subsidence [43–45]. However, limited studies have utilized Sentinel-1 SAR data for drought analysis [46–48]. Speciﬁcally combin- ing freely available optical satellite data (i.e., Sentinel-2 and Landsat-8) and space-borne SAR data (i.e., Sentinel-1) for drought monitoring is rarely reported in the literature [49–51]. Statistical analysis proving the relationship among drought indicators derived across RS and hydrological data is also lacking in the literature. Amid all the innovations and advancements of RS and GIS technologies occurring worldwide, the Philippines is still evidently lacking. Despite the frequent severe weather and climatic events that affect the country each year, mitigation and improvement of conditions are challenging to achieve even up to now because of the scarcity of available and freely accessible data and research in the Philippines. Speciﬁcally, the Philippines’ monitoring drought system is weak, resulting in multiple adverse drought impacts and emergency events. The World Health Organization (WHO) Collaborating Centre for Research on the Epidemiology of Disasters (CRED) recorded eight droughts that occurred in the country affecting 6.5 million people and causing 64.4 million dollars in damage to livelihoods and properties [52]. This record emphasizes the need for the country to improve its technologies concerning the analysis of weather events aside from the numerous typhoons entering the country each year. Mainly, the Ilocos Norte Region in the Philippines has been one of the worst-hit areas as more than 2000 ha of high-value crops, such as rice and corn, were reported to be dried out and destroyed due to water shortage and warmer temperatures, as well as the intensifying El Niño conditions [53]. Damages to livestock and the environment have also been evident as a signiﬁcant population in Ilocos Norte depends on the agricultural industry. This study’s overall objective is to promote the application of RS data in the Philippines’ drought risk management plan. Speciﬁcally, the study assesses Sentinel-1 s backscatter coefﬁcient and Sentinel-2 s NDVI for drought investigation. The study also aims to quan- titatively test the correlations between satellite and meteorological data-derived indices through statistical analysis. Lastly, the study aims to provide a drought early warning system (EWS) using different drought indicators. AgriEngineering 2023, 5, FOR PEER REVIEW 3 backscatt er coeﬃcient and Sentinel-2′s NDVI for drought investigation. The study also aims to quantitatively test the correlations between satellite and meteorological data-de- AgriEngineering 2023, 5 722 rived indices through statistical analysis. Lastly, the study aims to provide a drought early warning system (EWS) using diﬀerent drought indicators. 2. Study Area 2. Study Area Ilocos Norte Province is located in the northwest part of the Philippines and geo- Ilocos Norte Province is located in the northwest part of the Philippines and geo- 0 0 0 0 graphically between N 17°48′ and N 18°29′ latitude and E 120°58′ and E 120°58′ longitude graphically between N 17 48 and N 18 29 latitude and E 120 58 and E 120 58 longitude (Figure 1). The province has a total land area of 362,291 ha, more than 33% of which is (Figure 1). The province has a total land area of 362,291 ha, more than 33% of which is dedicated to agriculture and forestry [54]. Ilocos Norte is a central crop-producing area in dedicated to agriculture and forestry [54]. Ilocos Norte is a central crop-producing area in the Philippines, allowing the region to be a key area for rice production and a signiﬁcant the Philippines, allowing the region to be a key area for rice production and a signiﬁcant rice supplier [55]. rice supplier [55]. Figure 1. The map shows the regional boundary of the Ilocos Norte region in the Philippines and Figure 1. The map shows the regional boundary of the Ilocos Norte region in the Philippines and the the cloud-free Sentinel-2 optical images of the cropland and grassland areas investigated in the cloud-free Sentinel-2 optical images of the cropland and grassland areas investigated in the study. study. For this study, data gathering was focused on Ilocos Norte’s capital, Laoag City. For this study, data gathering was focused on Ilocos Norte’s capital, Laoag City. The The city is located in the west-central part of the province. According to the Philippine cit Atmospheric, y is located in t Geophysical he west-cent and ral p Astr art of onomical the province. Services Accordin Administration g to the Phil (P ippin AGASA), e Atmos- the pheric, Geophysical and Astronomical Services Administration (PAGASA), the tempera- temperature in Laoag can go as high as 41.8 C during the dry season and as low as 11 C t during ure in L the aoa rainy g can g season o as h(P igAGASA, h as 41.8 ° 2018). C duriThe ng the dry majority season of croplands and as low in Ilocos as 11 °C durin Norte arg e t rainfall-dependent he rainy season (PAGA or inSA rained , 2018conditions. ). The majorit The y ofclimate cropland experi s in I enced locos Nort in the e ar pr e r ovi ain nce fall is - classiﬁed as Type I, which is indicated by a distinct dry and wet season. During the wet dependent or in rained conditions. The climate experienced in the province is classiﬁed as season, from May to October, the rice-farming activity highly depends on rainfall. Type I, which is indicated by a distinct dry and wet season. During the wet season, from On the other hand, farmers use groundwater for supplemental irrigation during the May to October, the rice-farming activity highly depends on rainfall. dry season, which happens from November to April [56]. The average annual rainfall of On the other hand, farmers use groundwater for supplemental irrigation during the Ilocos Norte is 2000 mm, and more than 90% of it happens during the wet season. However, dry season, which happens from November to April [56]. The average annual rainfall of the province has been experiencing way below average rainfall, resulting in damaged crops in recent years [57]. AgriEngineering 2023, 5 723 3. Materials and Methods In this study, drought assessment was performed based on remote sensing data and meteorological data. The remote sensing data derived from Sentinel-1 and Sentinel-2 satellite missions were used to identify vegetation conditions. SPI and the Keetch–Byram drought index (KBDI) were calculated from precipitation and temperature to account for climatic variabilities in the region. 3.1. Sentinel-1 SAR Data Sentinel-1 conveys a single C-band wavelength operating at a center frequency of 5.405 GHz. It comprises two satellites, Sentinel-1A and Sentinel-1B, launched in April 2014 and April 2016, respectively. Equipped with SAR, these satellites can operate at wavelengths not impeded by cloud cover or illumination, allowing data acquisition under any time and weather conditions. The 12-day repeat orbit cycle of each Sentinel-1 satellite and ancillary orbital baselines permit SAR data to provide coherent change detection applications useful for assessing land surface conditions. Sentinel-1 SAR data were retrieved from the European Space Agency (ESA) via the Alaska Satellite Facility (ASF) database. The C-band Sentinel-1 analysis was carried out using 24 monthly scenes between 1 January 2019, and 31 December 2020, covering the dry and wet seasons from 2019 to 2020 (Table 1). Each scene contained two layers of the backscattering coefﬁcient, , in vertical–vertical (VV) and vertical–horizontal (VH) polarizations which were converted into decibels units (dB). Table 1. Sentinel-1 synthetic aperture radar (SAR) dataset used in the study. No. Acquisition Date Pass No. Acquisition Date Pass 1 28 January 2019 Ascending 13 23 January 2020 Ascending 2 21 February 2019 Ascending 14 28 February 2020 Ascending 3 17 March 2019 Ascending 15 23 March 2020 Ascending 4 27 April 2019 Ascending 16 28 April 2020 Ascending 5 28 May 2019 Ascending 17 22 May 2020 Ascending 6 21 June 2019 Ascending 18 27 June 2020 Ascending 7 27 July 2019 Ascending 19 21 July 2020 Ascending 8 20 August 2019 Ascending 20 16 August 2020 Ascending 9 25 September 2019 Ascending 21 19 September 2020 Ascending 10 31 October 2019 Ascending 22 25 October 2020 Ascending 11 24 November 2019 Ascending 23 30 November 2020 Ascending 12 30 December 2019 Ascending 24 24 December 2020 Ascending Sentinel-1 SAR data were processed using the Sentinel Application Platform (SNAP). Each extracted satellite image was primarily subjected to a series of standard corrections to reduce error propagation in succeeding processes. Then, a time series analysis of the Sentinel-1 SAR data at VV and VH polarization was generated to analyze the variation of backscatter intensity of surfaces as a function of their vegetation characteristics. 3.2. Sentinel-2 Optical Data Sentinel-2 was launched on 23 June 2015, focusing on providing visual information that can be applied to agriculture, land ecosystems monitoring, forest management, inland and coastal water quality monitoring, disaster mapping, and civil security. The satellite allows a 290 km ﬁeld of view to collect a 100 100 km tile with various spatial resolutions, ranging from 10 m, 20 m, and 60 m. Sentinel-2 optical data were obtained from ESA’s Copernicus Open Access Hub. The data obtained were between 1 January 2019 and 31 December 2020 (Table 2). Once data had been retrieved, the ﬁles were extracted to obtain the spectral bands, clipped to the chosen location for the cropland and grassland areas. These optical data were obtained in Level 1C (L1C) and Level 2A (L2A). L1C is obtained from the top-of-atmosphere reﬂectance, which still includes the atmospheric cover, which requires dark object subtract 1 (DOS1) atmospheric correction from QGIS and other sentinel-based applications. L2A is obtained AgriEngineering 2023, 5 724 from the bottom-of-atmosphere, which automatically removes the atmospheric cover, allowing for more immediate and accurate results without any correction. Table 2. Sentinel-2 optical data used in the study. No. Acquisition Date No. Acquisition Date No. Acquisition Date 1 31 January 2019 9 13 September 2019 17 5 May 2020 2 25 February 2019 10 28 October 2019 18 29 June 2020 3 27 March 2019 11 22 November 2019 19 19 July 2020 4 26 April 2019 12 22 December 2019 20 18 August 2020 5 1 May 2019 13 31 January 2020 21 17 September 2020 6 20 June 2019 14 20 February 2020 22 2 October 2020 7 5 July 2019 15 21 March 2020 23 21 November 2020 8 29 August 2019 16 20 April 2020 24 11 December 2020 Sentinel-2 optical data were processed using the quantum geographic information system (QGIS) software, an open-source, cross-platform application. If the data is obtained in L1C, the whole ﬁle is placed into the DOS1 atmospheric correction procedure before applying it to the raster calculator function, which estimates the NDVI value per pixel, generating an NDVI map. 3.3. SPI Calculation Meteorological data from three automatic weather stations in the Ilocos Norte Region from 2018 to 2020 were obtained. The data included daily precipitation, monthly precipita- tion, and daily average temperature. All meteorological observation data were acquired through PAGASA. The calculation of the SPI was achieved through the use of Microsoft Excel software, where the precipitation data were programmed to the formula developed by [58]. As such, it was computed using Equations (1) and (2): SPI = (P P)/S (1) S = (P P) /n (2) å i where P is the rainfall of the given period, S is the standard deviation, P is the average of the period of the rainfall, and n is the number of data in a single period. This study used a 1-month time scale to present precipitation anomalies and detect dryness or wetness in 2019 and 2020. This time scale reﬂected moisture conditions and seasonal estimation of precipitation. Positive SPI values obtained from monthly rainfall denote more signiﬁcant precipitation than median precipitation, whereas negative SPI values indicate less than medium precipitation. Speciﬁcally, it was further classiﬁed to account for drought-related rainfall deviations (Table 3). Table 3. SPI categories. Anomaly Range of SPI Values Precipitation Regimes 2 < SPI Max Extremely wet Positive 1.5 < SPI 2 Very wet 1 < SPI 1.5 Moderately wet None 1 < SPI 1 Normal precipitation 1.5 < SPI 1 Moderately dry Negative 2 < SPI 1.5 Very dry Min < SPI 2 Extremely dry AgriEngineering 2023, 5 725 3.4. KBDI Calculation The measurements needed for this drought index were the 24-h precipitation, the daily maximum air temperature, and the long-term mean annual precipitation. All these data were obtained from the climatological division of PAGASA. Three years’ worth of data (2018 to 2020) was requested from the PAGASA and was utilized in this study. The measurements from 2018 were only used to initialize the KBDI value to be used on the ﬁrst day of 2019. The previous day’s drought index was needed to calculate the current day’s drought index. This index cannot simply be set to zero since a KBDI equal to zero would mean that the soil is saturated. Thus, this assumption cannot be made at any given time. Therefore, an initialization procedure was required. First, the week of maximum precipitation from the ﬁrst year of data (2018) was determined. The KBDI for that week was set to zero. This week was established as the initial point and served as the basis for the next day’s KBDI. At this point, the calculated KBDI values were considered stabilized, and the initialization was completed. The KBDI on the last day of 2018 was then used to calculate the KBDI on the ﬁrst day of 2019, and so forth until the end of 2020. 3.5. Correlation Analysis Data analysis of various drought-related parameters was conducted using multiple time series graphs, which involved the different indices and data used in this study to observe and present data patterns as months passed from 2019 to 2020. Time graphs were performed monthly to show trends and correlations between different indices. 3.5.1. Relationship between Sentinel-1 Backscatter Coefﬁcient and Sentinel-2 NDVI Pearson’s correlation analysis was used to investigate the relationship between the backscattering coefﬁcients of VV and VH polarization from Sentinel-1 SAR data and NDVI values from Sentinel-2 optical data for the dry and wet seasons. This statistical parameter was applied to measure the strength of the association of SAR data with NDVI to estimate vegetation variations. Equation (3) describes the relationship between the two remote sensing data: y = b + b x (3) 0 1 where y is the backscattering intensity value, b and b are the coefﬁcients of the linear 0 1 regression modeling, and x is the NDVI value. 3.5.2. Relationship of Remotely Sensed Data with Meteorological Data This study plotted a time series analysis using SAR and optical data for the dry and wet seasons. After this, the outcomes of such information for the cropland and grassland areas were correlated to various meteorological-based data (precipitation and temperature) to understand the relationship between different drought indicators. As such, the relationship and correlation between the following were determined—(a) SAR data and SPI, (b) SAR data and KBDI, (c) NDVI and SPI, (d) NDVI and KBDI, and (e) SPI and KBDI. 3.6. Drought Risk Management Plan Six steps were considered in producing a drought risk management plan (Figure 2). Primarily, drought hazard was determined through previous events. These included any evidence of droughts in an area, such as the devastation of crops due to dry soil or lack of precipitation, allowing the most prone to drought risks to be identiﬁed. Next, the drought risks in an area were identiﬁed through different parameters and indices used in this study. Once these were known, drought forecasting was done, and an early warning system was formulated. Lastly, drought risk assessment and monitoring help provide recommendations and action plans to manage drought occurrences. AgriEngineering 2023, 5, FOR PEER REVIEW 7 3.6. Drought Risk Management Plan Six steps were considered in producing a drought risk management plan (Figure 2). Primarily, drought hazard was determined through previous events. These included any evidence of droughts in an area, such as the devastation of crops due to dry soil or lack of precipitation, allowing the most prone to drought risks to be identiﬁed. Next, the drought risks in an area were identiﬁed through diﬀerent parameters and indices used in this study. Once these were known, drought forecasting was done, and an early warning sys- AgriEngineering 2023, 5 tem was formulated. Lastly, drought risk assessment and monito 726 ring help provide rec- ommendations and action plans to manage drought occurrences. Figure 2. Drought risk management plan. Figure 2. Drought risk management plan. 4. Results and Discussion 4. Results and Discussion 4.1. Comparison of VV and VH Channels The study investigated the backscattering intensity values in VV and VH polarizations 4.1. Comparison of VV and VH Channels in the regions of interest of the study area for two years, as shown in Figure 3. As observed The study investigated the backscatt ering intensity values in VV and VH polariza- in Figure 3, the cross-polarized (VH) channel provides a lower backscatter value and more noticeable ﬂuctuations than the co-polarized (VV) channel. Conversely, the signals of tions in the regions of interest of the study area for two years, as shown in Figure 3. As croplands in VV polarization were higher than the VH. These conditions exist due to VH observed in Figure 3, the cross-polarized (VH) channel provides a lower backscatt er value channels having a more pronounced pattern for the vegetation conditions, which is known and more noticeable ﬂuctuations than the co-polarized (VV) channel. Conversely, the sig- to be an effect of volume scattering [59]. Furthermore, a cross-polarized channel provides a lower backscattering value since only a proportion of polarized waves (vertical turning nals of croplands in VV polarization were higher than the VH. These conditions exist due into horizontal) return as a signal to the sensor. A study on estimating winter crop ﬁelds to VH channels having a more pronounced patt ern for the vegetation conditions, which and grasslands in France and Tunisia indicated that the VH radar backscattered signal is is known to be an eﬀect of volume scatt ering [59]. Furthermore, a cross-polarized channel lower than the VV channel as it is more sensitive when characterizing vegetation cover [60]. Therefore, although the cross-polarized channel provides a lower backscatter signal than provides a lower backscatt ering value since only a proportion of polarized waves (vertical the co-polarized channel, the former provides better analysis and interpretation of land turning into horizontal) return as a signal to the sensor. A study on estimating winter crop vegetation conditions. ﬁelds and grasslands in France and Tunisia indicated that the VH radar backscatt ered sig- nal is lower than the VV channel as it is more sensitive when characterizing vegetation cover [60]. Therefore, although the cross-polarized channel provides a lower backscatt er signal than the co-polarized channel, the former provides bett er analysis and interpreta- tion of land vegetation conditions. AgriEngineering 2023, 5, FOR PEER REVIEW 8 AgriEngineering 2023, 5, FOR PEER REVIEW 8 AgriEngineering 2023, 5 727 Figure 3. Time series of Sentinel-1 backscatt er intensity coeﬃcient (σ°) for the vertical–vertical (VV) Figure 3. Figure 3. Tim Time e series series of Sentin of Sentinel-1 el-1 ba backscatter ckscatt er intensity intensity coe coef ﬃcient ( ﬁcientσ(°) for ) for the v theevertical–vertical rtical–vertical (VV) (VV) and vertical–horizontal (VH) polarizations over cropland, mountainside grassland, and low-lying and vertical–horizontal (VH) polarizations over cropland, mountainside grassland, and low-lying and vertical–horizontal (VH) polarizations over cropland, mountainside grassland, and low-lying grassland in Ilocos Norte region, Philippines. grassland in Ilocos Norte region, Philippines. grassland in Ilocos Norte region, Philippines. 4.2. Temporal Analysis of Backscatt er Intensity 4.2. Temporal Analysis of Backscatter Intensity 4.2. Temporal Analysis of Backscatt er Intensity 4.2.1. Cropland Areas 4.2.1. Cropland Areas 4.2.1. Cropland Areas Crop development is classiﬁed into three main phases: vegetative, reproductive, and Crop development is classiﬁed into three main phases: vegetative, reproductive, and Crop development is classiﬁed into three main phases: vegetative, reproductive, and maturation, as shown in Figure 4. First, the vegetation phase characterizes the crop’s ini- maturation, as shown in Figure 4. First, the vegetation phase characterizes the crop’s initial maturation, as shown in Figure 4. First, the vegetation phase characterizes the crop’s ini- tial development of tillers. Since this phase features the gradual increase in plant height, development of tillers. Since this phase features the gradual increase in plant height, the tial development of tillers. Since this phase features the gradual increase in plant height, the backscatt ering signal σ° starts from a low value as the crop ﬁelds are initially dry. backscattering signal starts from a low value as the crop ﬁelds are initially dry. Several the backscatt ering signal σ° starts from a low value as the crop ﬁelds are initially dry. Several irregularities may be found in the vegetative phase in the backscatt er intensity irregularities may be found in the vegetative phase in the backscatter intensity time series. Several irregularities may be found in the vegetative phase in the backscatt er intensity time series. Although there was a decrease in backscatt er (from −16 dB to −17 dB in VH Although there was a decrease in backscatter (from 16 dB to 17 dB in VH and 8 dB time series. Although there was a decrease in backscatt er (from −16 dB to −17 dB in VH and −8 dB to −9 dB in VV) during the wet season, the intensity value at the end of the to 9 dB in VV) during the wet season, the intensity value at the end of the vegetative and −8 dB to −9 dB in VV) during the wet season, the intensity value at the end of the vegetative phase is still higher from its germination stage, indicating that the area is still phase is still higher from its germination stage, indicating that the area is still experiencing vegetative phase is still higher from its germination stage, indicating that the area is still experiencing a typical growth patt ern. On the other hand, during the dry season, it is a typical growth pattern. On the other hand, during the dry season, it is found that there experiencing a typical growth patt ern. On the other hand, during the dry season, it is found that there is a general decrease in backscatt er intensity (from −17 dB to −19 dB in is a general decrease in backscatter intensity (from 17 dB to 19 dB in VH and from found that there is a general decrease in backscatt er intensity (from −17 dB to −19 dB in VH and from −9 dB to −11 dB), which may show that rice crops are aﬀected and do not 9 dB to 11 dB), which may show that rice crops are affected and do not conform with VH and from −9 dB to −11 dB), which may show that rice crops are aﬀected and do not the conform with conventional the conv rice crentional rice op pattern. crop patt ern. conform with the conventional rice crop patt ern. Figure 4. Time series of Sentinel-1 for the vertical–vertical (VV) and vertical–horizontal (VH) Figure 4. Time series of Sentinel−1 σ° for the vertical–vertical (VV) and vertical–horizontal (VH) Figure 4. Time series of Sentinel−1 σ° for the vertical–vertical (VV) and vertical–horizontal (VH) polarizations over cropland areas in Ilocos Norte region, Philippines, indicating the three crop polarizations over cropland areas in Ilocos Norte region, Philippines, indicating the three crop de- polarizations over cropland areas in Ilocos Norte region, Philippines, indicating the three crop de- velopment phases—vegetative, reproductive, and maturation. development phases—vegetative, reproductive, and maturation. velopment phases—vegetative, reproductive, and maturation. Secondly, the reproductive phase indicates that plant growth continues until it reaches Secondly, the reproductive phase indicates that plant growth continues until it Secondly, the reproductive phase indicates that plant growth continues until it its maximum height. From the start of this stage, the backscattering intensity decreases reaches its maximum height. From the start of this stage, the backscatt ering intensity reaches its maximum height. From the start of this stage, the backscatt ering intensity AgriEngineering 2023, 5, FOR PEER REVIEW 9 AgriEngineering 2023, 5 728 decreases as it approaches the end of the phase since the water content for crops is reduced as it approaches the end of the phase since the water content for crops is reduced upon upon completing the growing phase. However, a slight increase in the transmitt ed radar completing the growing phase. However, a slight increase in the transmitted radar signal signal can be observed due to the vertical expansion of vegetation and, ultimately, an in- can be observed due to the vertical expansion of vegetation and, ultimately, an increase crease in the height and volume of rice crops. This patt ern shows that the rise in the vol- in the height and volume of rice crops. This pattern shows that the rise in the volume of ume of vegetation causes an increase in the proportion of emitt ed signals. During the wet vegetation causes an increase in the proportion of emitted signals. During the wet season, season, the backscatt er intensity values for the reproductive phase characterize the typical the backscatter intensity values for the reproductive phase characterize the typical rice crop rice crop growth as there was a decrease in radar signal from the start of this phase. It also growth as there was a decrease in radar signal from the start of this phase. It also revealed revealed that a slight increase in backscatt er intensity during August 2019 might be found, that a slight increase in backscatter intensity during August 2019 might be found, which which indicates that this month is when the height of the rice crop is at its maximum. indicates that this month is when the height of the rice crop is at its maximum. On the other hand, an anomaly of the usual rice crop patt ern is established during On the other hand, an anomaly of the usual rice crop pattern is established during the dry season. A continuously decreasing trend shows that water content in the rice ﬁelds the dry season. A continuously decreasing trend shows that water content in the rice is lowered without the presence of a signiﬁcant volume of vegetation. Furthermore, ﬁelds is lowered without the presence of a signiﬁcant volume of vegetation. Furthermore, backscatt ering values for January to March 2020 are signiﬁcantly lower than the vegetative backscattering values for January to March 2020 are signiﬁcantly lower than the vegetative phase (from −17 dB to −19 dB in VH and −9 dB to −11 dB in VV). phase (from 17 dB to 19 dB in VH and 9 dB to 11 dB in VV). Lastly, the ripening or maturation phase is commonly identiﬁed with a continuous Lastly, the ripening or maturation phase is commonly identiﬁed with a continuous drying of leaves and stems. Unlike the previous crop growth stages, this phase reveals a drying of leaves and stems. Unlike the previous crop growth stages, this phase reveals a constant decrease in σ° for both polarizations. From the study area, both the wet and dry constant decrease in for both polarizations. From the study area, both the wet and dry seasons exhibited a decreasing backscatt ering intensity value. It must be noted that rice seasons exhibited a decreasing backscattering intensity value. It must be noted that rice crops are normally harvested during the wet season, which indicates decreased vegetation crops are normally harvested during the wet season, which indicates decreased vegetation volume and stems moisture content. On the other hand, during the dry season, radar sig- volume and stems moisture content. On the other hand, during the dry season, radar nal in the maturation phase indicates low production during the harvesting period as the signal in the maturation phase indicates low production during the harvesting period as backscatt er intensity at the end of the crop cycle (−18 in VH and −11 in VV) is lower com- the backscatter intensity at the end of the crop cycle (18 in VH and 11 in VV) is lower pared to the end of the wet season (−17 dB in VH and −10 dB in VV). compared to the end of the wet season (17 dB in VH and 10 dB in VV). 4.2.2. Grassland Areas 4.2.2. Grassland Areas Throughout the wet and dry seasons, both grassland areas exhibited a consistent me- Throughout the wet and dry seasons, both grassland areas exhibited a consistent dium to high backscatt er coeﬃcient, as shown in Figure 5. Radar signal for the mountain- medium to high backscatter coefﬁcient, as shown in Figure 5. Radar signal for the moun- side grasslands ranges from −13 dB to −14 dB for VH and −7 dB to −8 dB for VV, whereas tainside grasslands ranges from 13 dB to 14 dB for VH and 7 dB to 8 dB for VV, the low-lying grasslands were found at −12 dB to −13 dB for VH and −7 dB to −8 dB for whereas the low-lying grasslands were found at 12 dB to 13 dB for VH and 7 dB to VV. Since there are no signiﬁcant ﬂuctuations in radar signal throughout the entire time 8 dB for VV. Since there are no signiﬁcant ﬂuctuations in radar signal throughout the series, the backscatt ering signal for the grasslands reveals that the said regions of interest entire time series, the backscattering signal for the grasslands reveals that the said regions provided an almost uniform vegetation condition over time. of interest provided an almost uniform vegetation condition over time. Figure 5. Time series of Sentinel−1 σ° for the vertical–vertical (VV) and vertical–horizontal (VH) Figure 5. Time series of Sentinel-1 for the vertical–vertical (VV) and vertical–horizontal (VH) polarizations over the mountainside grassland and low-lying grassland areas in Ilocos Norte region, polarizations over the mountainside grassland and low-lying grassland areas in Ilocos Norte region, Philippines. Philippines. AgriEngineering 2023, 5, FOR PEER REVIEW 10 AgriEngineering 2023, 5 729 Furthermore, since both grassland areas have low proximity to residential and agri- Furthermore, since both grassland areas have low proximity to residential and agricul- cultural loci, the regions of interest are barely aﬀected, allowing less air pollution and tural loci, the regions of interest are barely affected, allowing less air pollution and creating creating an environment with improved air quality and unhindered soil nutrient content. an environment with improved air quality and unhindered soil nutrient content. Therefore, Therefore, withstanding precipitation variabilities and being away from human interven- withstanding precipitation variabilities and being away from human intervention, the veg- tion, the vegetation in the mountainside and low-lying grasslands does not exhibit signif- etation in the mountainside and low-lying grasslands does not exhibit signiﬁcant drought icant drought risk throughout the two years. risk throughout the two years. 4.3. Temporal Analysis of Sentinel-2 NDVI 4.3. Temporal Analysis of Sentinel-2 NDVI In the analysis of NDVI, cropland areas varied signiﬁcantly compared to both grass- In the analysis of NDVI, cropland areas varied signiﬁcantly compared to both grass- land areas. In the two years of analysis, its rise in value coincides with the start–peak–end land areas. In the two years of analysis, its rise in value coincides with the start–peak–end period of rice planting in the Ilocos Norte region. As the area has also experienced diﬀer- period of rice planting in the Ilocos Norte region. As the area has also experienced dif- ences in rainfall and temperature in the two years, despite the diﬀerence in value, the ferences in rainfall and temperature in the two years, despite the difference in value, the months where the NDVI has increased are still the same in both years, as shown in Figure months where the NDVI has increased are still the same in both years, as shown in Figure 6. 6. Cropland is easily more aﬀected by factors in its surrounding, which may cause land Cropland is easily more affected by factors in its surrounding, which may cause land degradation in an area, causing fewer values in its NDVI in a 10 × 10 m spatial resolution. degradation in an area, causing fewer values in its NDVI in a 10 10 m spatial resolution. In a single pixel, it cannot be avoided that other objects such as the soil, houses, water, and In a single pixel, it cannot be avoided that other objects such as the soil, houses, water, and other objects surrounding the plants are included in a single pixel, which may cause a other objects surrounding the plants are included in a single pixel, which may cause a drop drop in NDVI value, leading to a need to be used with other drought indicators. in NDVI value, leading to a need to be used with other drought indicators. Figure 6. Figure 6. Monthly time series Monthly time series of Sentinel-2 n of Sentinel-2 o normalized rmalized didif ﬀefer rence v ence e vegetation getation index (NDVI) ov index (NDVI) over er the the cropland, mountainside grassland, and low-lying grassland areas in Ilocos Norte region, Philip- cropland, mountainside grassland, and low-lying grassland areas in Ilocos Norte region, Philippines, pines, for two years (2019 and 2020). for two years (2019 and 2020). 4.4. Temporal Analysis of SPI 4.4. Temporal Analysis of SPI Ilocos Norte has relatively average precipitation for the entire year (from 2019 to 2020) Ilocos Norte has relatively average precipitation for the entire year (from 2019 to based on SPI, as shown in Figure 7. However, according to [58], drought is considered to be 2020) based on SPI, as shown in Figure 7. However, according to [58], drought is consid- starting as SPI reaches a value of 0. The month of August peaked in the positive anomaly, ered to be starting as SPI reaches a value of 0. The month of August peaked in the positive followed by July for both years, which means that these were the months the location anomaly, followed by July for both years, which means that these were the months the received the most amount of rainfall. Typhoons signiﬁcantly impact these ﬁndings as, location received the most amount of rainfall. Typhoons signiﬁcantly impact these ﬁnd- according to PAGASA, the peak typhoon season in the Philippines is from July to October. ings as, according to PAGASA, the peak typhoon season in the Philippines is from July to On the other hand, the months from October to June received minimal to no precipitation October. On the other hand, the months from October to June received minimal to no resulting in decreased soil moisture. These ﬁndings coincided with the reported drought precipitation resulting in decreased soil moisture. These ﬁndings coincided with the re- events in the study location. ported drought events in the study location. AgriEngineering 2023, 5, FOR PEER REVIEW 11 AgriEngineering 2023, 5, FOR PEER REVIEW 11 AgriEngineering 2023, 5 730 Figure 7. Standardized Precipitation Index (SPI) of the Ilocos Norte province in the Philippines for Figure 7. Standardized Precipitation Index (SPI) of the Ilocos Norte province in the Philippines for Figure 7. Standardized Precipitation Index (SPI) of the Ilocos Norte province in the Philippines for two years (2019 and 2020). two y twoeyears ars (201 (2019 9 and 2020) and 2020). . 4.5. Temporal Analysis of KBDI 4.5. Temporal Analysis of KBDI 4.5. Temporal Analysis of KBDI Results of the KBDI analysis show that the drought index is signiﬁcantly high from Results of the KBDI analysis show that the drought index is signiﬁcantly high from Results of the KBDI analysis show that the drought index is signiﬁcantly high from December to May, which coincides with the province’s dry season, as shown in Figure 8. December to May, which coincides with the province’s dry season, as shown in Figure 8. December to May, which coincides with the province’s dry season, as shown in Figure 8. However, for June to November, the index trend is inconsistent, especially for the year However, for June to November, the index trend is inconsistent, especially for the year 2020, However, for June to November, the index trend is inconsistent, especially for the year 2020, where there was a noticeable spike in KBDI in June and from late July to early De- where there was a noticeable spike in KBDI in June and from late July to early December. 2020, where there was a noticeable spike in KBDI in June and from late July to early De- cember. Natural phenomena can explain the erratic behavior of the drought index during Natural phenomena can explain the erratic behavior of the drought index during the wet cember. Natural phenomena can explain the erratic behavior of the drought index during the wet season, such as reported droughts and typhoons. Meanwhile, unlike the ﬁgure season, such as reported droughts and typhoons. Meanwhile, unlike the ﬁgure generated the wet season, such as reported droughts and typhoons. Meanwhile, unlike the ﬁgure generated using the daily KBDI values, the trend of the average monthly KBDI is more using the daily KBDI values, the trend of the average monthly KBDI is more consistent and generated using the daily KBDI values, the trend of the average monthly KBDI is more consistent and aligns with the regular wet and dry seasons in Laoag City, which could aligns with the regular wet and dry seasons in Laoag City, which could explain the usual consistent and aligns with the regular wet and dry seasons in Laoag City, which could explain the usual lapses in drought response in the region (Figure 9). lapses in drought response in the region (Figure 9). explain the usual lapses in drought response in the region (Figure 9). Figure 8. Daily Keetch–Byram drought index (KBDI) analysis results in Laoag City, Ilocos Norte Figure 8. Daily Keetch–Byram drought index (KBDI) analysis results in Laoag City, Ilocos Norte Figure 8. Daily Keetch–Byram drought index (KBDI) analysis results in Laoag City, Ilocos Norte province in the Philippines for 2019 and 2020. province in the Philippines for 2019 and 2020. province in the Philippines for 2019 and 2020. AgriEngineering 2023, 5, FOR PEER REVIEW 12 AgriEngineering 2023, 5 731 Figure 9. Average monthly KBDI analysis results in Laoag City, Ilocos Norte province in the Philip- Figure 9. Average monthly KBDI analysis results in Laoag City, Ilocos Norte province in the Philip- pines for two pines for two years (2019 and years (2019 and 2020). 2020). 4.6. Correlation Analysis of Remote Sensing and Meteorological Drought Indicators 4.6. Correlation Analysis of Remote Sensing and Meteorological Drought Indicators The study also investigated the potential of Sentinel-1 SAR data and Sentinel-2 optical The study also investigated the potential of Sentinel-1 SAR data and Sentinel-2 opti- data for drought monitoring, as shown in Figure A1 and Table 4. In investigating the cal data for drought monitoring, as shown in Figure A1 and Table 4. In investigating the potential of using remote sensing data for land monitoring and drought detection, the potential of using remote sensing data for land monitoring and drought detection, the correlation analyses suggest that cross-polarized from backscattering coefﬁcients appeared correlation analyses suggest that cross-polarized from backscatt ering coeﬃcients ap- to be higher than co-polarized with NDVI. Consequently, it can be seen that the correlation peared to be higher than co-polarized with NDVI. Consequently, it can be seen that the results varied from 0.6 to 0.7. This statistical framework is also similar to the study of [61], correlation results varied from 0.6 to 0.7. This statistical framework is also similar to the which statistically quantiﬁed the relation between NDVI and backscatter intensity for study of [61], which statistically quantiﬁed the relation between NDVI and backscatt er sugarcane monitoring. The authors of [61] pointed out that the discrepancies from the intensity for sugarcane monitoring. The authors of [61] pointed out that the discrepancies perfect line ﬁt can be traced to the slight difference in temporal resolution between the from the perfect line ﬁt can be traced to the slight diﬀerence in temporal resolution be- Sentinel-1 SAR data and Sentinel-2 optical data. It is known that the revisit time for tween the Sentinel-1 SAR data and Sentinel-2 optical data. It is known that the revisit time Sentinel-1 and Sentinel-2 satellites is once every 12 and 10 days, respectively. Due to this, a for Sentinel-1 and Sentinel-2 satellites is once every 12 and 10 days, respectively. Due to comparison between the two remote sensing data would allow a slight difference in the this, a comparison between the two remote sensing data would allow a slight diﬀerence observed scenarios and vegetation density for each pair of image acquisitions. in the observed scenarios and vegetation density for each pair of image acquisitions. Table 4. Correlation results for SAR and optical data-derived drought indicators. Table 4. Correlation results for SAR and optical data-derived drought indicators. Area of Interest Area of Interest VV Polarization VH Polarization VV Polarization VH Polarization Croplands 0.6414 0.7170 Croplands 0.6414 0.7170 Mountainside grasslands 0.6123 0.7336 Mountainside grasslands 0.6123 0.7336 Low-lying grasslands 0.6224 0.6806 Low-lying grasslands 0.6224 0.6806 One of the objectives of this study is to determine if there is any signiﬁcant correlation One of the objectives of this study is to determine if there is any signiﬁcant correlation between remote sensing-based and meteorological-based drought indices, as summarized between remote sensing-based and meteorological-based drought indices, as summarized in Table 5. Various correlational analyses in this study reveal a generally low correlation in Table 5. Various correlational analyses in this study reveal a generally low correlation between remote sensing data and meteorological-based data in grassland areas, as seen between remote sensing data and meteorological-based data in grassland areas, as seen in in the analysis between SPI and KBDI with the remote sensing-based indices backscatter the analysis between SPI and KBDI with the remote sensing-based indices backscatt er in- intensity and NDVI. Meanwhile, a signiﬁcant correlation could be observed between tensity and NDVI. Meanwhile, a signiﬁcant correlation could be observed between the the data regarding croplands. The correlation difference is assumed to be caused by the data regarding croplands. The correlation diﬀerence is assumed to be caused by the var- varying slopes, elevations, and soil inﬁltration capacities in these two areas. Because ying slopes, elevations, and soil inﬁltration capacities in these two areas. Because of some AgriEngineering 2023, 5 732 of some parameters, such as the type of crops and the distance between these crops or plants, it is assumed that croplands allow water to inﬁltrate the soil faster than grasslands. Precipitation in mountainside grasslands most likely ends up as runoff as well. Hence, the on-site condition in grasslands reﬂected by the satellite, which is used for remote sensing, cannot be accurately represented by meteorological data such as temperature and precipitation. The distance of ground equipment such as rain gauges and thermometers could be a factor as to why there is a correlation difference. The chosen cropland area for this study is closer to the automatic weather stations of PAGASA compared to the chosen grasslands. Despite this, it can still be concluded that there is an inverse relationship between KBDI and the remote sensing-based indices since an increase in KBDI signiﬁes the occurrence of drought while a decrease in backscatter intensity and NDVI indicates less water on the surface and worsening of vegetation health, respectively. Table 5. Summary of correlation results for remote sensing data and meteorological-based drought indices. Drought Indices Correlation Area of Interest VV VH Croplands 0.5160 0.3162 Backscatter intensity vs. SPI Mountainside grasslands 0.0006 0.0006 Low-lying grasslands 0.0024 0.0004 Croplands 0.6301 0.5600 Backscatter intensity vs. KBDI Mountainside grasslands 0.0012 0.0025 Low-lying grasslands 0.305 0.0105 Croplands 0.4701 Mountainside grasslands 0.0006 SPI vs. NDVI Low-lying grasslands 0.0053 Croplands 0.7066 Mountainside grasslands 0.0105 KBDI vs. NDVI Low-lying grasslands 0.0035 SPI vs. KBDI - 0.5542 4.7. Proposed EWS for Drought Risk Management An EWS for drought occurrence is proposed in this study, seeing the signiﬁcant results obtained from the four drought indices used in this study, as shown in Figure 10. This proactive mechanism allows stakeholders to utilize evidence-based and statistical decisions for drought assessment. Having been able to adapt the categories based on the National Drought Plan of the Philippines, the drought indices used in this study were provided with their corresponding threshold values that can assess monitoring, forecasting, and coping with the impacts of drought based on vegetation conditions and climatic characteristics. Based on the speciﬁed threshold values from the study’s drought indicators, the EWS is divided into four classiﬁcations to detect climate variabilities and identify vegetation and surface conditions. The ﬁrst two decision ruling criteria are based on meteorological deﬁciency classiﬁed from SPI and KBDI. Since meteorological drought is the primary factor that affects crop growth, these parameters have been the ﬁrst to be assessed. In this manner, the lack of rainfall and extreme temperatures may be a factor in drought risk. Consequently, the third and fourth assessments were based on the backscatter intensity and NDVI, which characterized the actual surface and vegetation conditions and provided better drought identiﬁcation concerning crop vegetation health. AgriEngineering 2023, 5 733 AgriEngineering 2023, 5, FOR PEER REVIEW 14 Figure 10. Proposed drought early warning system (EWS). Figure 10. Proposed drought early warning system (EWS). 4.8. 4.8. Im Implications plications Drought is a natural phenomenon in the Philippines, especially in the Ilocos Norte Drought is a natural phenomenon in the Philippines, especially in the Ilocos Norte region, where water shortage, warmer temperatures, and intensifying El Niño conditions region, where water shortage, warmer temperatures, and intensifying El Niño conditions negatively impact crop production. Meanwhile, RS and GIS are emerging data processing negatively impact crop production. Meanwhile, RS and GIS are emerging data processing platforms with minimal applications in Southeast Asian countries. Considering this, the platforms with minimal applications in Southeast Asian countries. Considering this, the main objective of this study was to investigate the potential of RS and meteorological data main objective of this study was to investigate the potential of RS and meteorological data in identifying the drought hazards in the cropland and grassland areas in Ilocos Norte. It in identifying the drought hazards in the cropland and grassland areas in Ilocos Norte. also establishes the relationships between satellite SAR, optical, and meteorological-based It also establishes the relationships between satellite SAR, optical, and meteorological- drought indices, such as SPI and KBDI, for bett er drought risk assessment and crop mon- based drought indices, such as SPI and KBDI, for better drought risk assessment and crop itoring. monitoring. This study analyzed the co-polarized (VV) and cross-polarized (VH) channels for the This study analyzed the co-polarized (VV) and cross-polarized (VH) channels for the croplands, mountainside grasslands, and low-lying grassland areas in the Ilocos Norte croplands, mountainside grasslands, and low-lying grassland areas in the Ilocos Norte region. The Sentinel-1 SAR data backscatt er intensity results reveal the VH channel pro- region. The Sentinel-1 SAR data backscatter intensity results reveal the VH channel pro- duces a lower return signal for all areas of interest than the VV channel. However, the duces a lower return signal for all areas of interest than the VV channel. However, the former provides more observable variations in backscatt er values during the two-year former provides more observable variations in backscatter values during the two-year analysis period. This result was obtained as the VH channel presents a lower proportion analysis period. This result was obtained as the VH channel presents a lower proportion of polarized waves returning to the satellite than the VV channel. Furthermore, due to its of polarized waves returning to the satellite than the VV channel. Furthermore, due to its sensitivity in detecting structural changes in plants brought by volume scatt ering, the sensitivity in detecting structural changes in plants brought by volume scattering, the cross- cross-polarized channel imparts a more excellent interpretation for characterizing vegeta- polarized channel imparts a more excellent interpretation for characterizing vegetation tion cover. Thereby, the results of this investigation revealed the potential of multi-tem- cover. Thereby, the results of this investigation revealed the potential of multi-temporal poral analysis of Sentinel-1 SAR data for monitoring changes in vegetation conditions in analysis of Sentinel-1 SAR data for monitoring changes in vegetation conditions in the the areas of interest. areas of interest. Sentinel-1 backscatt er analysis of the croplands area revealed the phenological rice Sentinel-1 backscatter analysis of the croplands area revealed the phenological rice crop crop phases. For both the dry and wet seasons, rice crop growth irregularities in the veg- phases. For both the dry and wet seasons, rice crop growth irregularities in the vegetative etative and reproductive stages were observed because of lower water content which, in and reproductive stages were observed because of lower water content which, in turn, provided less than the usual amount of healthy vegetation. On the other hand, Sentinel-1 backscatter intensity values for the mountainside and low-lying grassland areas have also AgriEngineering 2023, 5 734 been investigated in this study for drought. It was shown that there are no signiﬁcant ﬂuctuations of the Sentinel-1 backscatter coefﬁcients for both VH and VV polarizations. Radar signals also reﬂect a consistent medium to high backscatter coefﬁcient, indicating an almost uniform vegetation condition over the two-year analysis period. Furthermore, the elevation difference between the two grassland areas did not account for radar signal variability as the regions of interest have almost similar backscattering intensity values. Hence, as human activities do not impede the areas, mountainside and low-lying grasslands did not exhibit profound drought risk during 2019 and 2020. Sentinel-2 NDVI is also analyzed in croplands, mountainside grasslands, and low- lying grasslands. Sentinel-2 NDVI data can immediately reveal all locations of interest, but the satellite optical data requires little to no cloud cover. The analysis of NDVI values reveals that NDVI is greatly affected by activities in nearby locations, despite having good crop health in an area. If the site is near roads and unvegetated lands, NDVI can signiﬁcantly decrease its value. This situation can cause NDVI to need more conﬁdence when dealing with drought and increase its reliance on other drought indicators presented in the study. However, it can still impart important, valuable information for agricultural purposes, such as peak months and vegetation health. When compared to the crop planting periods, the index can show a signiﬁcant relation to the calendar, which is beneﬁcial for identifying crop health per month. As it is known that there was a drought in the early months of 2019, the NDVI values are identical when compared to the values in 2020. This ﬁnding created a pattern that farmers can use to identify when best to prepare for lower crop yields due to various factors related to environmental and climatic variabilities. As NDVI values are greatly affected by human activities, natural occurrences, such as typhoons, increased temperature, and other uncontrollable circumstances, including drought, will also affect its values as NDVI relies on a per-pixel visual representation than a direct analysis of a location. Like Sentinel-1 backscatter intensity values, NDVI can be used at any elevation and location if an available satellite optical image can be obtained from the RS database. These various reasons allow NDVI to be used as an index for different agricultural uses with some limitations due to its capacity to be affected by external factors. The relationship between the two RS data was provided in the study to show the potential of Sentinel-1 and Sentinel-2 data in monitoring vegetation conditions. Correlation between backscattering intensity, in both VH and VV polarization, and NDVI values reveals a good correlation for the croplands, mountainside grasslands, and low-lying grasslands. This statistical data analysis also shows that the correspondence of NDVI with VH polar- ization was higher than the VV polarization, as the former provides more pronounced radar signals for vegetation conditions [46]. Hence, an increase in backscattering value corresponds to a greater NDVI, and at the same time, both agree to monitor vegetation conditions. The results of SPI on a 1-month scale show that drought was present in the study location for 2019 and 2020. There is no standard index set as a threshold indicator for drought. Instead, researchers can ﬁx it if the SPI value is less than 0. With that, even if the calculated index fell under the normal precipitation category, the study’s results indicate that consecutive months of negative SPI value means that drought in terms of deﬁcit in soil moisture is already starting. However, SPI only accounts for precipitation deﬁciency. SPI was correlated to another meteorological index, KBDI, and RS indices derived from Sentinel-1 and Sentinel-2 data to analyze its accuracy in drought indication further. SPI had a fair correlation with other indices with R > 0.50 for cropland areas. However, there was no correlation for the grassland areas. This result suggests that the reliability of SPI in monitoring drought from the study’s location is only limited to cropland areas. Despite that, sites with more ground equipment for collecting precipitation data may lead to different ﬁndings as to the effectiveness of SPI in monitoring drought in grassland areas. Moreover, the KBDI accurately represented the weather conditions in the regions of interest through this study. This observation can be explained by natural phenomena, such AgriEngineering 2023, 5 735 as tropical cyclones and drought occurrences in the province of Ilocos Norte. A signiﬁcant correlation exists between KBDI and other indices, speciﬁcally in croplands. However, the correlation is signiﬁcantly low for mountainside and low-lying grasslands. Therefore, using KBDI to determine the condition of plants in these areas is not recommended. It would be better to rely on NDVI as it represents vegetation health. For better utilization of meteorological indices such as KBDI and SPI, it is recommended to install more ground equipment. Ilocos Norte currently has only three automatic weather stations, which needs to be improved for the whole province. Installing rain gauges and weather thermometers in farmlands is advisable for more accurate monitoring. These installations will increase the reliability of KBDI and SPI, thus creating a simple yet effective system to observe and detect possible drought occurrences in these areas. 5. Outlook Some studies have investigated the potential of seasonal and climatic forecasting for drought assessment. Still, studies have yet to explore the integration of such probabilistic outputs with observational satellite RS and meteorological data. This study analyzed the Sentinel-1 SAR data VV and VH channels for the cropland, mountainside grassland, and low-lying grassland areas in the Ilocos Norte region in the Philippines. The Sentinel-1 backscatter intensity results reveal that the VH channel produces a lower return signal for all regions of interest than the VV channel. However, the Sentinel-1 backscattering coefﬁcient from the VH channel provides a better correlation, with R > 0.60, with the Sentinel-2 NDVI in all regions of interest. Moreover, the Sentinel-1 backscattering coefﬁcient and Sentinel-2 NDVI showed substantial irregularities in cropland areas compared to grassland areas. These results indicate that croplands are more affected by drought than grassland areas. A signiﬁcant correlation exists between RS- and meteorological-based drought indicators, speciﬁcally KBDI, with backscattering coefﬁcient and NDVI. On the contrary, SPI and KBDI show a poor correlation with the backscattering coefﬁcient and NDVI, speciﬁcally in grassland areas of the Ilocos Norte region. Nevertheless, the study contributes to the general understanding of climate-induced variations in crop production by characterizing climatic and vegetation variability for the cropland and grassland areas. Through quantifying vegetation conditions and precipitation deﬁciencies, this study highlights the feasibility of crop condition monitoring on a broader scale of environmental conditions. Thus, the overall outcome of the study presents that drought risk can be assessed appropriately by applying both RS data and meteorological- based drought indicators, allowing for a more outstanding establishment of drought risk management plans and early warning systems. Author Contributions: Conceptualization, C.A.A., J.M.G., M.N.M., M.B.M. and R.R.; methodology, C.A.A., J.M.G., M.N.M. and M.B.M.; formal analysis, C.A.A., J.M.G., M.N.M., M.B.M. and R.R.; data curation, C.A.A., J.M.G., M.N.M. and M.B.M.; writing—original draft preparation, C.A.A., J.M.G., M.N.M. and M.B.M.; writing—review and editing, R.R.; supervision, R.R.; project administration, R.R. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: Not applicable. Acknowledgments: The authors thank the European Space Agency (ESA) for supplying Sentinel-1 and Sentinel-2 data free of charge. The authors also thank the Philippine Atmospheric, Geophysical, and Astronomical Services Administration for providing meteorological data free of charge. Conﬂicts of Interest: The authors declare no conﬂict of interest. 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AgriEngineering – Multidisciplinary Digital Publishing Institute

Published: Apr 13, 2023

Keywords: drought; remote sensing; Sentinel-1 SAR data; Sentinel-2 optical data; Ilocos Norte

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Drought Risk Assessment and Monitoring of Ilocos Norte Province in the Philippines Using Satellite Remote Sensing and Meteorological Data

Drought Risk Assessment and Monitoring of Ilocos Norte Province in the Philippines Using Satellite Remote Sensing and Meteorological Data

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Drought Risk Assessment and Monitoring of Ilocos Norte Province in the Philippines Using Satellite Remote Sensing and Meteorological Data

Drought Risk Assessment and Monitoring of Ilocos Norte Province in the Philippines Using Satellite Remote Sensing and Meteorological Data

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