Estimation Of All-weather Soil Moisture With Reanalysis Data And Land Surface Temperature

As the link between surface water and groundwater,surface soil moisture(SSM)affects the surface energy budget and water cycle from regional to global scale.In addition,SSM plays a key role in agricultural systems,affecting water and nutrient uptake in plant roots,which further affects crop growth.It is of great significance to monitor the spatial and temporal distribution of SSM for soil moisture and monitoring crop yield prediction.It is difficult for the exciting SSM data to meet the continuity in time and fine space,which limits some researches on fine agricultural management and large-scale monitoring of drought and flood disasters.In addition,China has a vast territory and complex geographical environment,which shows significant regional heterogeneity in climate.As a result,most of the studies on SSM are only aimed at a certain region in China,which can not meet the needs of research in different regions and different scales.Therefore,obtaining SSM with high spatial resolution and spatio-temporal continuity in China has become the focus and difficulty in present study.This study proposes a practical method for estimating SSM based on the synergy of reanalysis data and all-weather land surface temperature to generate spatio-temporal continuum SSM at daily/1-km in a China.In this study,the diurnal temperature difference data were first obtained from the land surface temperature in ERA5-Land with a spatial resolution of 0.1°.Then,the linear relationship between the diurnal temperature difference data and ERA5-Land SSM was established for four major land cover types(forest,farmland,grassland and barren)in China under different vegetation densities(sparse,medium and dense).These linear relationships were then applied to all-weather land surface temperature at 1-km spatial resolution to generate initial daily/1-km SSM for 2019.In addition,it assumes that there is no deviation between the mean SSM of the1-km resolution pixels within a coarse resolution microwave pixel and the microwave SSM value in present study.Using two blended microwaved products to correct the initial dataset.Finally,the estimated daily/1-km SSM were evaluated using ground measured SSM in different climatic zones.The results showed that:(1)the accuracy of SSM corrected by microwave products was higher than that of the initial SSM;(2)in different climatic zones,the SSM corrected by SMOPS(the Soil Moisture Operational Product System)of the National Oceanic and Atmospheric Administration performed better than or equal to the SSM corrected by CCI(the Climate Change Initiative)of the European Space Agency,with the unbiased root mean square error(ub RMSE)was about 0.05 m~3/m~3 which achieved satisfactory accuracy.This is not only has the characteristics of high spatial resolution and all-weather coverage,but also close to the requirement for estimating surface soil moisture in most practical applications(ub RMSE is 0.04 m~3/m~3).