The Performance of Spei Integrated Remote Sensing Data for Monitoring Agricultural Drought in the North China Plain

Agricultural drought is significantly affecting sustainable crop production. One of climate-based drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) calculated by the difference between precipitation and potential evapotranspiration (PET), is being more and more widely employed to characterize the spatio-temporal pattern of agricultural drought. However, substantial uncertainties exist in the current PET methods due to unrealistic configurations of the heterogeneity of crop surface characteristics or complicated parameterization scheme. In this study, standard Penman–Monteith (PM) model is modified and derived by meteorological factors and leaf area index (LAI) and albedo data based on remote sensing. Then, the modified SPEI was calculated by the monthly difference between precipitation and PM model integrated remote sensing data (referred as PMRS) over the North China Plain (NCP) between 1982 and 2016. Comparing with the FAO56 reference evapotranspiration (referred as PMRC) and a simplified version of standard PM over open-water surface (referred as PMOW), this study examines how well the PMRS-SPEI captures drought evolution and impacts on crop growth and yield over the NCP in order to evaluate its performance in agricultural drought monitoring. The comparison of SPEIs based on three evapotranspiration equations with soil water moisture in root depth shows that a 6-month lag can be used as the optimum time scale over the NCP. Additionally, the correlation of PMRS-SPEI performs better than PMRC-SPEI and PMOW-SPEI. For typical drought events, PMRS-SPEI6 shows remarkable smaller (larger) severe drought values between April and June (August and October) and a shorter (longer) recovery period in the growing season for wheat (maize). Comparing to PMRC-SPEI6 and PMOW-SPEI6, the PMRS-SPEI6 shows better performance when examining the Vegetation Health Index and Composite Index of Crop Yield Reduction. Additionally, climate-induced crop yield correlates with PMRS-SPEI6 better than PMOW-SPEI6 and PMRC-SPEI6, particularly in the emergence and filling stages for winter wheat, and silking stage for summer maize. These results demonstrate that integrating remotely sensed data can enhance the agricultural drought monitoring accuracy of SPEI.