Modeling Spatio-temporal Drought Events Based on Multi-temporal,Multi-source Remote Sensing Data Calibrated by Soil Humidity

Inspired by recent significant agricultural yield losses in the eastern China and a missing operational monitoring system, we developed a comprehensive drought monitoring model to better understand the impact of individual key factors contributing to this issue. The resulting model, the ‘Humidity calibrated Drought Condition Index’(HcDCI) was applied for the years 2001 to 2019 in form of a case study to Weihai County, Shandong Province in East China. Design and development are based on a linear combination of the Vegetation Condition Index(VCI), the Temperature Condition Index(TCI), and the Rainfall Condition Index(RCI) using multi-source satellite data to create a basic Drought Condition Index(DCI). VCI and TCI were derived from MODIS(Moderate Resolution Imaging Spectroradiometer) data, while precipitation is taken from CHIRPS(Climate Hazards Group InfraRed Precipitation with Station data)data. For reasons of accuracy, the decisive coefficients were determined by the relative humidity of soils at depth of 10–20 cm of particular areas collected by an agrometeorological ground station. The correlation between DCI and soil humidity was optimized with the factors of 0.53, 0.33, and 0.14 for VCI, TCI, and RCI, respectively. The model revealed, light agricultural droughts from 2003 to 2013 and in 2018, while more severe droughts occurred in 2001 and 2002, 2014–2017, and 2019. The droughts were most severe in January,March, and December, and our findings coincide with historical records. The average temperature during 2012–2019 is 1°C higher than that during the period 2001–2011 and the average precipitation during 2014–2019 is 192.77 mm less than that during 2008–2013. The spatio-temporal accuracy of the HcDCI model was positively validated by correlation with agricultural crop yield quantities. The model thus, demonstrates its capability to reveal drought periods in detail, its transferability to other regions and its usefulness to take future measures.