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Using satellite-based soil moisture to detect and monitor spatiotemporal traces of agricultural drought over Bundelkhand region of India

机译:使用基于卫星的土壤湿度来检测和监测印度Bundelkhand地区农业干旱的时空痕迹

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Detection and monitoring of seasonal agricultural drought at sub-regional scale is a complex theme due to inefficient spatiotemporal indicators. This study presents a new time-based function of spaceborne soil moisture as an efficient indicator. Bundelkhand of Central India, a frequently agricultural drought affected region, was used as the study area. Rabi agricultural season (October-May) being the dominant agricultural return period, was chosen as the study period. Coarse resolution soil moisture (SMc) obtained from European space agency under climate change initiative program was spatially downscaled (SMd) to meet spatial scale at sub-regional level with overall root-mean-square error under 0.065cm(3)/cm(3). Indirect validation of SMd was done using temporal impact of rainfall/dry spell on SMd and spatiotemporal impact of SMd on vegetation condition. SMd was found to agree with phenomenon as expected in natural processes and hence it was assumed to be validated. The time-based function derived from spatiotemporal SMd (FSMs) was found to be better related with fluctuations in seasonal crop yield (Y-s) at district level as compared to a similar function (FVCIs) derived using vegetation condition index (VCI) from Moderate Resolution Imaging Spectroradiometer. FSMs outperformed FVCIs having better correlation coefficient (R0.8) and Nash-Sutcliffe efficiency coefficient (NSE) than FVCIs for most of the districts. Unlike FVCIs, it also efficiently detected the lowest and highest Y-s for majority of the districts representing better association with agricultural drought. Subsequently, frequent soil moisture deficit areas were mapped by using FSMs to visualize the spatiotemporal severity of agricultural drought in the region during Rabi season.
机译:由于时空指标效率低下,在次区域范围内检测和监测季节性农业干旱是一个复杂的主题。这项研究提出了一种新的基于时间的星载土壤水分函数作为有效指标。研究区域以印度中部的Bundelkhand(一个经常受到农业干旱影响的地区)为研究对象。选择拉比农业季节(10月至5月)作为主要的农业回报期。从欧洲空间局根据气候变化倡议计划获得的粗分辨率土壤水分(SMc)在空间上按比例缩小(SMd),以适应次区域水平的空间规模,且总体均方根误差在0.065cm(3)/ cm(3)以下)。使用降雨/干旱对SMd的时间影响以及SMd对植被状况的时空影响来完成SMd的间接验证。发现SMd与自然过程中预期的现象相符,因此被认为是有效的。与基于中等分辨率的植被状况指数(VCI)得出的类似函数(FVCI)相比,发现基于时空SMd(FSM)的基于时间的函数与地区级季节性作物产量(Ys)的波动关系更好。成像光谱仪。在大多数地区,FSM的相关系数(R0.8)和纳什-萨特克利夫效率系数(NSE)比FVCI更好。与FVCI不同,它还可以有效地检测出大多数地区的最低和最高Y,代表与农业干旱的关系更好。随后,使用FSM绘制了频繁的土壤水分亏缺区域图,以可视化该拉比季节期间该地区农业干旱的时空严重程度。

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