Generating high spatial and temporal soil moisture data by disaggregation of SMAP product and its assessment in different land covers

摘要

Surface soil moisture (SSM) is an important parameter for many applications. Soil Moisture Active Passive (SMAP) satellite mission provides an SSM map at global scale. But its spatial resolution (36 km) is a big restriction for agricultural and hydrological studies at the catchment scale. Therefore, the present study was conducted to disaggregate the passive SMAP soil moisture data using the retrieved Soil Evaporative Efficiency (SEE) at 1-km spatial and daily temporal resolution from Moderate Resolution Imaging Spectroradiometer (MODIS) data and to assess the effectiveness of the method for generating data for different land covers. For this purpose, SMAP data were disaggregated using the SEE retrieved from daily MODIS data located at the southwest part of the United States. The accuracy of spatial and temporal variability of the disaggregated SMAP data was evaluated against the recorded in-situ soil moisture data in 202 stations of the Soil Climate Analysis Network (SCAN) for a period of 1 year. Results indicate that the disaggregated SMAP data have a moderate correlation with in-situ soil moisture data, but it is strongly affected by land cover. The highest accuracy was observed in the pasture/hay land cover class with Correlation Coefficient (R) value of 0.683 and 0.632, Mean Difference (MD) of -0.004 and -0.001, Root-Mean Square Error (RMSE) of 0.049 and 0.056, and unbiased Root-Mean Square Error (ubRMSE) of 0.039 and 0.045 for the disaggregated and original SSM data with the unit of (m(3), m(-3)), respectively. The lowest accuracy was found in the barren land (rock/sand/clay) for the disaggregated and original SSM data with R of 0.0278 and 0.155, MD of -0:081 and -0.052, RMSE of 0.134 and 0.116, and ubRMSE of 0.106 and 0.103, respectively. Results indicate that in overall disaggregation of SMAP data using Disaggregation based on Physical And Theoretical scale Change (DisPATCh) algorithm and MODIS products has a good potential for generating high spatial and temporal resolution of SSM at the catchment scale. But it is strongly affected by the land cover class type, because the calculation of the SEE is based on the Normalized Difference Vegetation Index (NDVI). Therefore, it can be recommended to retrieve the SEE with the attention to land cover class type and employ the other vegetation indices or methods.