A comprehensive remote sensing-based Agriculture Drought Condition Indicator (CADCI) using machine learning

Abstract

Agriculture drought is a decrease in soil moisture during a growing season. In this study, a comprehensive remote sensing-based Agriculture Drought Condition Indicator (CADCI) was developed to monitor the agriculture drought in semi-arid environments and assess its effectiveness in rainfed agriculture regions in (A) Jordan and (B) Syria. First, remote sensed-based drought-condition spectral indices [i.e., Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Evapotranspiration Condition Index (ETCI), Precipitation Condition Index (PCI), Soil Moisture Condition Index (SMCI), and Vegetation Health Index (VHI)] were calculated using data from Moderate Resolution Imaging Spectroradiometer satellite (MODIS) [Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and evapotranspiration (ET)]; the Global Precipitation Measurement (GPMs); the Soil Moisture Active Passive (SMAPs); and Sentinel-1A. Second, Random Forest (RF) was used to estimate and determine the relative importance of these indices based on Standardized Precipitation Index (SPI) values to select the three spectral indices that have the most monthly short-term relative importance in identifying the agriculture drought for semi-arid environments, which were PCI, TCI, and VCI. Third, these indices were integrated to identify the drought severity based on specific thresholds that compare the pixel-specific value with the study area average value. For instance, a severe drought condition is identified if all three indices indicate a drought condition, a moderate drought or mild drought conditions are identified if any two or any one of the indices indicate drought conditions, respectively. Lastly, a none drought condition is identified if none of the indices indicate a drought condition. Finally, the SPI sets for 1 and 3-months (SPI-1 and SPI-3) were used to evaluate the performance of the CADCI. The results showed the CADCI has a high agreement with SPI-1 classes in the study areas, with overall accuracy and Kappa-values of 85% and 0.80, for study area A and 83% and 0.76 for study area B, respectively. Consequently, the CADCI shows its ability to monitor agricultural drought in semi-arid environments. Perhaps, it could be applicable for larger areas due to the spatial resolution of the input dataset.