Downscaling And Validating Soil Moisture Products

Soil moisture is an essential environmental and climate variable,which influences energy and water exchanges between the soil and atmosphere.Thus,the estimation of soil moisture is important and yet it is the challenge as well.Satellite remote sensing offers a window of opportunity to provide spatial soil moisture maps.The microwave remote sensing instruments,such as SMAP,SMOS and ASCAT,are widely used to retrieve the soil moisture over a global range.However,most of the microwave products have a relatively coarse resolution?tens of kilometers?,which limits their use in regional hydrologic modelling and hazard prediction.Therefore,in this study,a sub-grid soil moisture variability downscaling method was used to downscale SMAP,SMOS and ASCAT products to 1km resolution,then to do the validation with different in-situ measurement data.The soil moisture spatial variability is mainly affected by the soil texture heterogeneity,which can be used as a proxy for downscaling.A high-resolution soil map from the Soil Grids was used to provide soil texture information for downscaling.A relationship between the soil moisture variability and the mean soil moisture as a function of the mean and standard deviation of Van Genuchten-Mualem?VGM?model hydraulic parameters was then established.This relationship was used in downscaling.The original and downscaled soil moisture products were validated using both point measurements and areal Cosmic-Ray Neutron Probe?CRNP?estimated soil moisture data over Maasai Mara in Kenya.The main result show as below:?1?These three satellite soil moisture products perform differently because of four main factors:sensor,orbit,algorithm and auxiliary data.Moreover,the SMAP and SMOS soil moisture products showed similar spatial patterns whilst the ASCAT soil moisture product is mainly dependent on the porosity data.The triple collocation was applied to assess the random error among three satellite soil moisture products.SMAP showed the least error followed by ASCAT and SMOS.?2?Through the soil moisture variability analysis,a convex relationship was seen between the mean soil moisture and soil moisture variability and the soil moisture variability reach the peak when the mean value around 0.2 cm³ cm^-3.Moreover,this trend is mainly controlled by the pore size distribution factor of the soil after the sensitivity analysis.However,since the study area is relatively homogeneous,the soil moisture variability was noted to be very small.?3?Compared to the original products,the downscaled result can clearly see the sub-grid soil moisture variability.However,due to the homogeneity study area,the soil moisture value difference between original products and downscaled one is quite similar.So,the quality of original products has a decisive effect on the downscaled result.?4?Compared with the point data,the CRNP calculated soil moisture shows wetter trend because its measurement depth is more than 10cm.For original products,the validation result indicates that all three satellites cannot meet the required accuracy of 0.04 cm³ cm^-3.ASCAT shows the best performance?ubRMSE=0.061?followed by SMAP?ubRMSE=0.069?,and the last one is SMOS?ubRMSE=0.103?.In addition,ASCAT performs better over dense vegetation area.While SMAP has less error in the moderate vegetation land cover and bare land.The downscaled result gives better or at least the same performance as original products,but with clearly soil property pattern.Therefore,SMAP and SMOS have better performance than ASCAT,and the satellite-based soil moisture products can be downscaled by predicting the sub-grid soil moisture variability within the coarse resolution pixels,then the downscaling result can be applied to the agriculture and hazard prediction.