 The study aimed to assess spatiotemporal patterns of crop condition and drought impact at the spatial scale of field management units using 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. The parameters with the highest prediction rate were further used to estimate thresholds for drought-non-drought classification. The results revealed that not all remotely sensed variables respond in the same manner to drought conditions, and growing season maximum NDVI and NDMI, 70 to 75 percent, and SAR-derived metrics, 60 percent, reflect specifically the impact of agricultural drought. LST was a useful indicator of crop condition especially for maize and sunflower with prediction rates of 86 percent and 71 percent, 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. This article was authored by Gohar Ghazariyan, Alina Dubovic, Valerie Graw, and others. We are article.tv, links in the description below.