The European Space Agency (ESA) under the Climate Change Initiative (CCI) has developed a multi-satellite global, daily Soil Moisture (SM) dataset that has paved the ways for agricultural drought studies. To evaluate the performance of this ESACCI SM, two SM-based indices i.e. parametric distribution-based Standardized Soil Moisture Index (SSMI) and non-parametric distribution-based Empirical Standardized Soil Moisture Index (ESSMI) are computed to characterize agricultural drought in the Southern Plateau and Hills (SPH) in India from 1991 to 2020. SSMI and ESSMI are then compared with the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). The yearly temporal analysis revealed a consistent pattern among all the four indices with 2003 and 2020 marked as the driest and wettest years, respectively. On the other hand, monthly temporal analysis indicated SSMI and ESSMI lagged behind SPI and ESSMI suggesting a delayed response of SM to precipitation. Spatial distributions of indices showed that the SM-based indices effectively capture temporal variations of dryness or wetness across seasons. The near normal and mild to moderate droughts predominated (both spatially and temporally) the SPH and SSMI better captured the extreme drought areas compared to ESSMI. Further, Dynamic Threshold Run Theory (DTRT) is introduced to identify and characterize drought events based on their duration, frequency, intensity and peak. The findings revealed a resemblance in spatial distribution between the duration and frequency. The drought peak and intensity revealed a moderate nature of drought conditions. Overall, this study highlights the effectiveness of ESACCI SM product to characterize the agricultural droughts.

中文翻译：

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使用印度南部高原和丘陵 ESACCI 土壤湿度数据进行农业干旱评估的参数和非参数指数


气候变化倡议 (CCI) 下的欧洲航天局 (ESA) 开发了多卫星全球每日土壤湿度 (SM) 数据集，为农业干旱研究铺平了道路。为了评估该 ESACCI SM 的性能，计算了两个基于 SM 的指数，即基于参数分布的标准化土壤水分指数 (SSMI) 和基于非参数分布的经验标准化土壤水分指数 (ESSMI)，以表征南部地区的农业干旱1991 年至 2020 年印度的高原和丘陵 (SPH)。然后将 SSMI 和 ESSMI 与标准化降水指数 (SPI) 和标准化降水蒸散指数 (SPEI) 进行比较。年度时间分析显示，所有四个指数之间存在一致的模式，2003 年和 2020 年分别被标记为最干燥和最潮湿的年份。另一方面，月度时间分析表明 SSMI 和 ESSMI 落后于 SPI 和 ESSMI，表明 SM 对降水的响应延迟。指数的空间分布表明，基于 SM 的指数有效地捕捉了不同季节干燥或湿润的时间变化。接近正常和轻度至中度干旱占主导地位（在空间和时间上），与 ESSMI 相比，SPH 和 SSMI 更好地捕获了极端干旱地区。此外，引入动态阈值运行理论（DTRT）来根据干旱事件的持续时间、频率、强度和峰值来识别和表征干旱事件。研究结果揭示了持续时间和频率之间的空间分布相似。干旱峰值和强度表明干旱状况属于中等程度。总体而言，本研究强调了 ESACCI SM 产品描述农业干旱特征的有效性。