Drought Monitoring in Northern China based on Remote Sensing Data and Land Surface Modeling

In this paper, agricultural and hydrological drought definitions are adopted to estimate the severity of drought in northern China in recent years. Particularly crop transpiration as important parameter is added in the drought index algorithm. Land surface model Noah are used and driven by a combination of meteorological reanalysis dataset (NCEP GDAS) and high resolution precipitation (CMORPH) and surface parameters from satellites (MODIS). The seasonal or yearly surface parameters (such as Albedo and LAI) from climatology are replaced by monthly data derived from MODIS, in order to represent the vegetation dynamics more accurately. Products for Crop transpiration and soil evaporation are derived at passing time of MODIS satellites. Temperature products of MODIS are adopted and are validated by simultaneous observation data of Dongping lake in Shandong province of China. Using vegetation transpiration (LE_v) and latent evaporation (LE₀= R_n-G) with high every 3 hours time resolution and 1km space resolution in north China, plant water stress index (PWSI) can be got. It is feasible that a combination of the land surface models and the two sources ET remote sensing model to monitoring drought using PWSI drought index according to the application of the method in North China.