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Abstract：
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.
摘要：
Prakasam地区不同土壤的水分亏缺条件的检测，安得拉邦,使用标准降水指数（SPI）和归一化植被指数（NDVI）制定的综合指标，在2017-18至2019-20年的连续两个季节中对印度进行了评估。使用R软件分析了56个行政单位研究期间的历史降雨数据，并得出了三个月的SPI。下载了2007年至2020年的MODIS卫星数据，其中前十年的数据用作平均每月NDVI，其余时期的数据用于得出特定月份的异常指数。下载了MODIS卫星数据,使用LST和NDVI，并计算MSI值。NDVI异常是使用MODIS数据得出的，以研究缺水条件的发生和强度。结果表明，SPI值从Kharif季节开始逐渐增加，在8月和9月达到最大值，并逐渐减少，并且在mandals之间变化很大。NDVI异常值在10月和12月最高，在Kharif和Rabi季节，分别。NDVI异常与SPI之间的相关系数表明，在轻质和重质土壤中观察到79％和61％的变异。SPI值为-0.5和-0.75；建立了-1.0和-1.5的NDVI异常值以及0.28和0.26的SMI值，作为轻度和重度质地土壤中水分亏缺条件开始的阈值，分别。总的来说,结果表明，联合使用SMI，SPI,NDVI异常能够为轻质和重质土壤的水分亏缺状况提供近实时指标。轻度土壤的产量下降幅度在6.1％至34.5％之间。这些结果可进一步用于制定有效缓解干旱的策略。
