Assessment of agricultural drought based on multi-source remote sensing data in a major grain producing area of Northwest China

Drought is considered to be one of the most serious natural disasters in China, which can result in enormous damage to nature and socio-economy. Compared to traditional ground-based monitoring techniques, remote sensing can effectively compensate for spatial discontinuities at ground stations. The use of remote sensing technology for drought monitoring has irreplaceable advantages. The applicability of the TRMM3B43 dataset for precipitation was firstly verified in the Wei River basin, and the spatiotemporal characteristics of precipitation were analyzed. Based on the TRMM3B43, MODIS NDVI, and MODIS LST datasets, the spatiotemporal variations of drought were secondly investigated by calculating the Precipitation Condition Index (PCI), Vegetation Con-dition Index (VCI), and Temporal Condition Index (TCI). Crop yield was employed as the reference of drought impact for evaluating the applicability of the Scaled Drought Condition Index (SDCI) based on the combination of the PCI, VCI, and VCI by four kinds of weight determination methods, i.e. Analytic Hierarchy Process (AHP), Entropy method, Criteria Importance Through Intercriteria Correlation (CRITIC), and Fuzzy Comprehensive Evaluation (FCE). Finally, the agricultural drought calculated by the SDCI was evaluated against drought area, disaster area, and crop failure area to verify the applicability of the SDCI for agricultural drought disaster assessment in the Wei River basin. The results showed that the SDCI determined by FCE has better correlations with crop yield (R2 =0.45) than the other methods. The SDCI values exhibited a "W" shape from 2003 to 2010 during the growing seasons and agricultural drought showed an increasing trend after 2013. The drought-prone areas shifted from north to south, with the degree of drought firstly decreasing and then increasing. In addition, the SDCI has better correlations with the disaster area (R2 =0.35) than the drought area (R2 = 0.16). At the municipal level, the SDCI could well assess agricultural drought. The results demonstrated that the SDCI can effectively monitor and assess drought impacts on agriculture and may provide helpful information for agri-cultural drought disaster prevention.