Abstract
Drought is the costliest disaster around the world and in China as well. Northeastern China is one of China’s most important major grain producing areas. Frequent droughts have harmed the agriculture of this region and further threatened national food security. Therefore, the timely and effective monitoring of drought is extremely important. In this study, the passive microwave remote sensing soil moisture data, i.e., the SMOS soil moisture (SMOS-SM) product, was compared to several in situ meteorological indices through Pearson correlation analysis to assess the performance of SMOS-SM in monitoring drought in northeastern China. Then, maps based on SMOS-SM and in situ indices were created for July from 2010 to 2015 to identify the spatial pattern of drought distributions. Our results showed that the SMOS-SM product had relatively high correlation with in situ indices, especially SPI and SPEI values of a nine-month scale for the growing season. The drought patterns shown on maps generated from SPI-9, SPEI-9 and sc-PDSI were also successfully captured using the SMOS-SM product. We found that the SMOS-SM product effectively monitored drought patterns in northeastern China, and this capacity would be enhanced when field capacity information became available.
Highlights
Drought is a serious environmental natural hazard affecting the natural environment and a variety of activities of our human society [1,2,3,4]
Losses resulting from droughts are well documented, the proper definition of drought remains a challenge for all researchers [5,6,7]
Agricultural drought is most sensitive to natural drought events and is closely related to soil moisture deficits [9]
Summary
Drought is a serious environmental natural hazard affecting the natural environment and a variety of activities of our human society [1,2,3,4]. Wilhite and Glantz [8] classified drought into the following four categories: meteorological, agricultural, hydrological and social–economic drought. Drought can be effectively monitored using drought indices integrated with weather factors such as rainfall, temperature and evapotranspiration. The most frequently used drought indices include the Palmer Drought Severity Index (PDSI) [10], Percent of Normal Precipitation, deciles [11], SPI (Standardized Precipitation Index) [12] and the Standardized Precipitation Evapotranspiration Index (SPEI) [13]. The SPI only uses precipitation as input and can be obtained for flexible time scales that can be used to monitor meteorological, agricultural or hydrological drought depending on a user’s interests [2,14]. As the SPI is calculated based on a statistical method and is designed to be spatially comparable, it has enhanced drought monitoring capacities and has been employed broadly by researchers and governments [15,16,17]
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