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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #296239
Title: Passive microwave soil moisture downscaling using vegetation index and skin surface temperature

Author
item FANG, BIN - Collaborator
item LAKSHMI, VENKAT - Collaborator
item BINDLISH, R - Science Systems, Inc
item Jackson, Thomas
item Cosh, Michael
item BASARA, J - University Of Oklahoma

Submitted to: Vadose Zone Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/1/2013
Publication Date: 11/14/2013
Publication URL: http://handle.nal.usda.gov/10113/59876
Citation: Fang, B., Lakshmi, V., Bindlish, R., Jackson, T.J., Cosh, M.H., Basara, J. 2013. Passive microwave soil moisture downscaling using vegetation index and skin surface temperature. Vadose Zone Journal. 12:3. DOI: 10.2136/vzj2013.05.0089.

Interpretive Summary: 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. A soil moisture downscaling algorithm was developed which uses characteristics of the daily temperature changes from satellite and vegetation characteristics from a larger scale model and satellite observations. The higher spatial resolution downscaled soil moisture maps (1 km) developed using this technique displayed greater detail on the spatial pattern of soil moisture. Two sets of ground-based in situ measurements, the Oklahoma Mesonet and the USDA Agricultural Research Service Little Washita Micronet were used to validate the algorithm. The results demonstrate that the original coarser resolution satellite soil moisture was successfully disaggregated to 1 km resolution. The enhanced information on spatial heterogeneity as well as the accuracy of the soil moisture estimates are superior to those provide by either the satellite or the model-based products, based upon comparisons to in situ observations. This approach has the potential to provide a more valuable product for agricultural hydrology for both current applications as well as reanalysis for climate investigations.

Technical Abstract: 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 degree 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 degree 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 versus Mesonet data is 0.219, which is better than AMSR-E and NLDAS, while the spatial standard deviation (0.054) and unbiased RMSE (0.042) of 1 km downscaled results are similar to the other two datasets. The overall slope and spatial standard deviation for 1 km downscaled results versus Micronet data (0.242 m3/m3 and 0.021 m3/m3, respectively) are significantly better than AMSR-E and NLDAS, while the unbiased RMSE (0.026) 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 as well as the accuracy of the soil moisture estimates are superior than the AMSR-E and NLDAS estimates, when compared with in situ observations.