Palagiri Hussain, Pal Manali, Maity Rajib
Department of Civil Engineering, National Institute of Technology Warangal, Warangal, 506004, India.
Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India.
Environ Monit Assess. 2025 Apr 28;197(5):606. doi: 10.1007/s10661-025-14044-z.
Soil moisture (SM) is crucial for identifying agricultural drought, but due to the limited availability of large scale and high-resolution SM data, drought indices based on hydro-meteorological variables are commonly used. Recent advancements in microwave remote sensing, such as the Soil Moisture Active Passive (SMAP) satellite, provide global daily SM at 9-km spatial resolution making it suitable for agricultural drought monitoring. However, while SMAP offers relatively high spatial resolution, it lacks long-term records (available since April 2015), whereas other long-term satellite products have coarser spatial resolution. So, this study evaluates the potential of short-term satellite SM data, specifically SMAP-SM, for agricultural drought characterization using two SM-based indices: Soil Water Deficit Index (SWDI) and Soil Moisture Deficit Index (SMDI). The Godavari basin is selected as study area, which is not so well gauged for SM data, and the agricultural drought in the basin is assessed from 2016 to 2021. The results revealed that both SWDI and SMDI indices effectively captured inter-annual/seasonal variations, demonstrating their robustness in comparison to precipitation-based indices rainfall anomalies (RA) and Standardized Precipitation Index (SPI). Spatial analysis reveals that western basin consistently experiences drought conditions, while the eastern region remains relatively wet. The drought area ratio (DAR) analysis across agro-ecological zones (AEZs) of basin reveals that SWDI is more sensitive to severe and extreme droughts, whereas SPI is more responsive to mild and moderate droughts. Zone-wise DAR showed SWDI and SMDI identified distinct drought conditions across all AEZs, whereas SPI and RA showed evenly distributed drought levels across AEZs, underscoring their broader, less soil-specific focus. These findings emphasize the potential of short-term satellite-based SM, as well as SM-derived indices in advancing agricultural drought characterization, offering valuable insights for policymakers in developing region-specific mitigation strategies and improving drought preparedness in other poorly gauged river basins.
土壤湿度（SM）对于识别农业干旱至关重要，但由于大规模高分辨率土壤湿度数据的可用性有限，基于水文气象变量的干旱指数被广泛使用。微波遥感技术的最新进展，如土壤湿度主动被动（SMAP）卫星，以9公里的空间分辨率提供全球每日土壤湿度数据，使其适用于农业干旱监测。然而，虽然SMAP提供了相对较高的空间分辨率，但它缺乏长期记录（自2015年4月起可用），而其他长期卫星产品的空间分辨率则较粗。因此，本研究使用基于土壤湿度的两个指数：土壤水分亏缺指数（SWDI）和土壤湿度亏缺指数（SMDI），评估短期卫星土壤湿度数据，特别是SMAP-SM，用于农业干旱特征描述的潜力。选择戈达瓦里河流域作为研究区域，该区域的土壤湿度数据测量较少，并对该流域2016年至2021年的农业干旱情况进行了评估。结果表明，SWDI和SMDI指数都有效地捕捉了年际/季节变化，与基于降水的指数降雨异常（RA）和标准化降水指数（SPI）相比，显示出它们的稳健性。空间分析表明，流域西部持续干旱，而东部地区相对湿润。对流域农业生态区（AEZs）的干旱面积比（DAR）分析表明，SWDI对严重和极端干旱更为敏感，而SPI对轻度和中度干旱反应更灵敏。按区域划分的DAR显示，SWDI和SMDI在所有AEZs中识别出不同的干旱状况，而SPI和RA在AEZs中显示出均匀分布的干旱水平，突出了它们更广泛、较少针对土壤的关注点。这些发现强调了基于卫星的短期土壤湿度以及源自土壤湿度的指数在推进农业干旱特征描述方面的潜力，为政策制定者制定区域特定的缓解策略和改善其他数据测量较少的流域的干旱准备工作提供了有价值的见解。
