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Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil Water Deficit Index

机译:用于农业干旱监测的卫星土壤水分:SMOS得出的土壤水分亏缺指数的评估

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Drought is a major cause of limited agricultural productivity and of crop yield uncertainty throughout the world. For that reason, agricultural drought research and monitoring are of increasing interest. Although soil moisture is the main variable to define and identify agricultural drought, the actual soil water content is rarely taken into account because this type of drought is commonly studied using methodologies based on either climatological data or hydrological modeling. Currently, it is possible to use remote sensing to obtain global and frequent soil moisture data that could be directly used for agricultural drought monitoring everywhere. For example, the SMOS (Soil Moisture and Ocean Salinity) satellite was launched in 2009 and provides global soil moisture maps every 1-2 days. In this work, the Soil Water Deficit Index (SWDI) was calculated using the SMOS 12 soil moisture series in the REMEDHUS (Soil Moisture Measurement Stations Network) area (Spain) during the period 2010-2014. The satellite index was thus calculated using several approaches to obtain the soil water parameters and was compared with the SWDI obtained from in situ data. One approach was based directly on SMOS soil moisture time series (using the 5th percentile as an estimator for wilting point and the 95th percentile and the minimum of the maximum value during the growing season as estimators for field capacity). In this case, the results of the comparison were good, but the temporal distribution and the range of the index data were unrealistic. Other approaches were based on in situ data parameters and pedotransfer functions estimation. In this case, the results were better, and the satellite index was able to adequately identify the drought dynamics. Therefore, the final choice to apply the index in one particular site will depend on the availability of data. Finally, a comparison analysis was made with the SMOS SWDI and two indices (Crop Moisture Index, CMI, and Atmospheric Water Deficit, AWD) commonly used for agricultural drought monitoring and assessment. In both cases, the agreement was very good, and it was proven that SMOS SWDI reproduces well the soil water balance dynamics and is able to appropriately track agricultural drought. (C) 2016 Elsevier Inc. All rights reserved.
机译:干旱是导致全球农业生产力下降和作物单产不确定性的主要原因。因此,对农业干旱的研究和监测越来越引起人们的兴趣。尽管土壤湿度是定义和识别农业干旱的主要变量,但很少考虑实际的土壤含水量,因为通常使用基于气候数据或水文模型的方法研究这种类型的干旱。当前,有可能使用遥感技术获取全球和频繁的土壤水分数据,这些数据可直接用于各地的农业干旱监测。例如,2009年发射了SMOS(土壤水分和海洋盐度)卫星,每1-2天提供一次全球土壤水分图。在这项工作中,使用REMOSHUS(土壤水分测量站网络)区域(西班牙)在2010-2014年期间的SMOS 12土壤湿度序列计算了土壤水分亏缺指数(SWDI)。因此,使用几种方法来计算卫星指数以获得土壤水分参数,并将其与从现场数据获得的SWDI进行比较。一种方法直接基于SMOS土壤水分时间序列(使用第5个百分位数作为枯萎点的估计值,使用第95个百分位数和生长季节最大值的最小值作为田间生产能力的估计值)。在这种情况下,比较的结果很好,但是索引数据的时间分布和范围是不现实的。其他方法基于原位数据参数和pedotransfer函数估计。在这种情况下,结果更好,并且卫星索引能够充分识别干旱动态。因此,将索引应用于一个特定站点的最终选择将取决于数据的可用性。最后,使用SMOS SWDI和两个指标(农作物水分指数,CMI和大气缺水,AWD)进行了比较分析,这两个指标通常用于农业干旱监测和评估。在这两种情况下,协议都非常好,并且证明了SMOS SWDI能够很好地再现土壤水平衡动态并能够适当追踪农业干旱。 (C)2016 Elsevier Inc.保留所有权利。

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