ABSTRACT Globally, drought constitutes a serious threat to food and water security. The complexity and multivariate nature of drought challenges its assessment, especially at local scales. The study aimed to assess spatiotemporal patterns of crop condition and drought impact at the spatial scale of field management units with a combined use of time-series from optical (Landsat, MODIS, Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel 1) data. Several indicators were derived such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Land Surface Temperature (LST), Tasseled cap indices and Sentinel-1 based backscattering intensity and relative surface moisture. We used logistic regression to evaluate the drought-induced variability of remotely sensed parameters estimated for different phases of crop growth. The parameters with the highest prediction rate were further used to estimate thresholds for drought/non-drought classification. The models were evaluated using the area under the receiver operating characteristic curve and validated with in-situ data. The results revealed that not all remotely sensed variables respond in the same manner to drought conditions. Growing season maximum NDVI and NDMI (70–75%) and SAR derived metrics (60%) reflect specifically the impact of agricultural drought. These metrics also depict stress affected areas with a larger spatial extent. LST was a useful indicator of crop condition especially for maize and sunflower with prediction rates of 86% and 71%, respectively. The developed approach can be further used to assess crop condition and to support decision-making in areas which are more susceptible and vulnerable to drought.

中文翻译：

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基于卫星多传感器时间序列的局部农业干旱监测

摘要 在全球范围内，干旱对粮食和水安全构成严重威胁。干旱的复杂性和多变量性质对其评估提出了挑战，尤其是在局部范围内。该研究旨在结合使用光学（Landsat、MODIS、Sentinel-2）和合成孔径雷达（SAR）（Sentinel 1）的时间序列，在田间管理单位的空间尺度上评估作物状况的时空模式和干旱影响。 ） 数据。推导出了几个指标，例如归一化差异植被指数 (NDVI)、归一化差异水分指数 (NDMI)、地表温度 (LST)、缨帽指数和基于 Sentinel-1 的反向散射强度和相对表面水分。我们使用逻辑回归来评估干旱引起的为作物生长不同阶段估计的遥感参数的变异性。预测率最高的参数进一步用于估计干旱/非干旱分类的阈值。使用受试者工作特征曲线下的面积对模型进行评估，并使用原位数据进行验证。结果表明，并非所有遥感变量都以相同的方式对干旱条件做出反应。生长季节最大 NDVI 和 NDMI (70–75%) 和 SAR 衍生指标 (60%) 特别反映了农业干旱的影响。这些指标还描绘了具有更大空间范围的应力影响区域。LST 是作物状况的有用指标，特别是对于玉米和向日葵，预测率分别为 86% 和 71%。