Abstract
Drought is a complex hazard that has more adverse effects on agricultural production and economic development. Studying drought monitoring techniques and assessment methods can improve our ability to respond to natural disasters. Numerous drought indices deriving from meteorological or remote sensing data are focused mainly on monitoring single drought response factors such as soil or vegetation, and the ability to reflect comprehensive information on drought was poor. This study constructed a comprehensive drought-monitoring model considering the drought factors including precipitation, vegetation growth status, and soil moisture balance during the drought process for the Jing-Jin-Ji region, China. The comprehensive drought index of remote sensing (CDIR), a drought indicator deduced by the model, was composed of the vegetation condition index (VCI), the temperature condition index (TCI), and the precipitation condition index (PCI). The PCI was obtained from the Tropical Rainfall Measuring Mission (TRMM) satellite. The VCI and TCI were obtained from a moderate-resolution imaging spectroradiometer (MODIS). In this study, a heavy drought process was accurately explored using the CDIR in the Jing-Jin-Ji region in 2016. Finally, a three-month scales standardized precipitation index (SPI-3), drought affected crop area, and standardized unit yield of wheat were used as validation to evaluate the accuracy of this model. The results showed that the CDIR is closely related to the SPI-3, as well as variations in the drought-affected crop area and standardized unit yield of crop. The correlation coefficient of the CDIR with SPI-3 was between 0.45 and 0.85. The correlation coefficient between the CDIR and drought affected crop was between −0.81 and −0.86. Moreover, the CDIR was positively correlated with the standardized unit yield of crop. It showed that the CDIR index is a decent indicator that can be used for integrated drought monitoring and that it can synthetically reflect meteorological and agricultural drought information.
Highlights
Drought is one of the most widespread disasters in the world that causes the largest loss of agriculture that supports human society [1,2]
A three-month scales standardized precipitation index (SPI-3), drought affected crop area, and standardized unit yield of wheat were used as validation to evaluate the accuracy of this model
The results showed that the comprehensive drought index of remote sensing (CDIR) is closely related to the SPI-3, as well as variations in the drought-affected crop area and standardized unit yield of crop
Summary
Drought is one of the most widespread disasters in the world that causes the largest loss of agriculture that supports human society [1,2]. Especially in the context of global warming, drought events have clearly increased, causing harm and loss to the development of society. According to the statistics of natural disaster loss in China, the loss from meteorological disasters accounts for. 61% of all the natural disasters in China, and the loss from drought accounts for 55% of the loss from meteorological disasters [3,4]. Researchers across the globe have conducted many studies on combined index for comprehensive drought risk assessment, and some good results have been achieved. The multivariate standardized drought index (MSDI) that probabilistically combined the standardized precipitation index (SPI)
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