Passive Microwave Soil Moisture Downscaling Using Vegetation Index and Skin Surface Temperature

Soil moisture satellite estimates are available from a variety of passive microwave satellite sensors, but their spatial resolution is frequently too coarse for use by land managers and other decision makers. In this paper, a soil moisture downscaling algorithm based on a regression relationship between daily temperature changes and daily average soil moisture is developed and presented to produce an enhanced spatial resolution soil moisture product. The algorithm was developed based on the thermal inertial relationship between daily temperature changes and averaged soil moisture under different vegetation conditions, using 1/8° spatial resolution North American Land Data Assimilation System (NLDAS) surface temperature and soil moisture data, as well as 5-km Advanced Very High Resolution Radiometer (AVHRR) (1981–2000) and 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and surface temperature (2002–present) to build the look-up table at 1/8° resolution. This algorithm was applied to the 1-km MODIS land surface temperature to obtain the downscaled soil moisture estimates and then used to correct the soil moisture products from Advanced Microwave Scanning Radiometer–EOS (AMSR-E). The 1-km downscaled soil moisture maps display greater details on the spatial pattern of soil moisture distribution. Two sets of ground-based measurements, the Oklahoma Mesonet and the Little Washita Micronet, were used to validate the algorithm. The overall averaged slope for 1-km downscaled results vs. Mesonet data is 0.219, which is better than AMSR-E and NLDAS, while the spatial standard deviation (0.054 m 3 m −3 ) and unbiased RMSE (0.042 m 3 m −3 ) of 1-km downscaled results are similar to the other two datasets. The overall slope and spatial standard deviation for 1-km downscaled results vs. Micronet data (0.242 and 0.021 m 3 m −3 , respectively) are significantly better than AMSR-E and NLDAS, while the unbiased RMSE (0.026 m 3 m −3 ) is better than NLDAS and further than AMSR-E. In addition, Mesonet comparisons of all three soil moisture datasets demonstrate a stronger statistical significance than Micronet comparisons, and the p value of 1-km downscaled is generally better than the other two soil moisture datasets. The results demonstrate that the AMSR-E soil moisture was successfully disaggregated to 1 km. The enhanced spatial heterogeneity and the accuracy of the soil moisture estimates are superior to the AMSR-E and NLDAS estimates, when compared with in situ observations.

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