Integrating a novel irrigation approximation method with a process-based remote sensing model to estimate multi-years'winter wheat yield over the North China Plain

Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.However,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions.Thus,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing season.This approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and timing.Then,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the NCP.The IPPs were determined using statistical yield data of reference years(2010–2015)over the NCP.Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),respectively.The data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–Wheat.In addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city level.Overall,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.

Spatiotemporal Analysis of Soil Moisture Drought over China during 2008-2016

Drought has been analyzed by various indices based on rainfall, temperature, evapotranspiration or soil moisture data. Remoste-sensing based data has spatial continuity with certain resolution and is useful for drought monitor. The purpose of this study was to investigate quality used of grid relative soil moisture, the spatial and temporal variation of soi moisture, and soil moisture drought based on relative soil moisture over China during 2008-2016. The results show that the relative soil moisture data set can reflect the spatial characteristics of the development of drought in China during 2008-2016. From the spatial distribution analysis, the northwest to northeast, south part of China, and other major arid areas, the performance is particularly evident. The results show that the use of CLDAS V1.0 real-time products, access to time and space continuous soil relative humidity products, can achieve the drought in China real-time