Detection of water deficit conditions in different soils by comparative analysis of standard precipitation index and...

The detection of water deficit conditions in different soils of Prakasam district, Andhra Pradesh, India was assessed in consecutive two seasons of 2017-18 to 2019-20 cropping seasons using combined indicators developed from Standard Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI). Historical rainfall data during the study period of 56 administrative units were analyzed by using R software and derived three-month SPI. The MODIS satellite data from 2007 to 2020 was downloaded out of which the first ten years' data was used as mean monthly NDVI and the remaining period data was used to derive the anomaly index for the specific month. MODIS satellite data was downloaded, using LST and NDVI, and MSI values were calculated. The NDVI anomaly was derived using MODIS data to study the onset and intensity of water deficit conditions. Results indicated that SPI values gradually increased from the start of the Kharif season, reached their maximum during the August and September months, and decreased gradually with high variation among the mandals. The NDVI anomaly values were highest in October and December the for Kharif and Rabi seasons, respectively. The correlation coefficient between NDVI anomaly and SPI reveals that 79% and 61% of the variation were observed in light and heavy textured soils. The SPI values of -0.5 and -0.75; the NDVI anomaly values of -1.0 and -1.5 and SMI values of 0.28 and 0.26 were established as the thresholds for the onset of water deficit conditions in light and heavy textured soils, respectively. Overall, results suggest that the combined use of SMI, SPI, and NDVI anomaly is capable to provide a near-real-time indicator for water deficit conditions in light and heavy texture soils. Yield reduction was higher in light-textured soils ranging from 6.1 to 34.5%. These results can further be used in devising tactics for the effective mitigation of drought.