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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 #348757

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Development of soil moisture profiles through coupled microwave-thermal infrared observations in the southeastern United States

item MISHRA, V. - University Of Alabama
item CRUISE, J.F. - University Of Alabama
item HAIN, C. - Goddard Space Flight Center
item MECIKALSKI, J. - University Of Alabama
item Anderson, Martha

Submitted to: Hydrology and Earth System Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/6/2018
Publication Date: 9/25/2018
Citation: Mishra, V., Cruise, J., Hain, C., Mecikalski, J., Anderson, M.C. 2018. Development of soil moisture profiles through coupled microwave-thermal infrared observations in the southeastern United States. Hydrology and Earth System Sciences. 22:4935-4957.

Interpretive Summary: The ability to accurately map root-zone soil moisture on a daily basis over agricultural landscape has benefits to both crop and hydrologic modeling applications. While satellite-based passive microwave sensors provide valuable information about moisture conditions in the soil surface layer (0-5 cm), these observations do not penetrate into the root zone. This paper describes a technique for extrapolating surface moisture observations to deeper soil layers that is well suited for operational applications, requiring minimal ancillary information. The extrapolation algorithm characterizes the diffusion of moisture through the soil column over a period of time. Thermal infrared–based estimates of plant water use provide additional constraints on root-zone moisture content. The method was tested over the southeast United States in comparison with data from the existing soil moisture network and with estimates from an independent land-surface modeling system. Errors in volumetric soil moisture content on the order of 0.06 m3 m-3 were obtained, with lowest errors observed in agriculturally dominant areas. This study demonstrates a simple yet potentially effective means for combining microwave and thermal satellite imagery into a routine soil moisture mapping product.

Technical Abstract: The principle of maximum entropy (POME) can be used to develop vertical soil moisture profiles. The minimal inputs required by the POME model make it an excellent choice for remote sensing applications. Two of the major input requirements of the POME model are the surface boundary condition and profile-mean moisture content. Microwave-based soil moisture estimates from Advanced Microwave Scanning Radiometer (AMSR-E) can supply the surface boundary condition whereas thermal infrared-based moisture estimated from the Atmosphere Land Exchange Inverse (ALEXI) surface energy balance model can provide the mean moisture condition. A disaggregation approach was followed to downscale coarse resolution (~25 km) microwave soil moisture estimates to match the finer resolution (~5 km) thermal data. The study was conducted over multiple years (2006-2010) in the southeastern United States. Disaggregated soil moisture estimates along with the developed profiles were compared with the Noah land surface model (Noah LSM) within the framework of NASA Land Information System (LIS), as well as in-situ measurements from 10 Natural Resource Conservation Services (NRCS) Soil Climate Analysis Network (SCAN) sites spatially distributed within the study region. The overall disaggregation results at the SCAN sites indicated that in most cases disaggregation improved the temporal correlations with unbiased root mean square errors in the range of 0.01-0.09 in volumetric soil moisture. The profile results at SCAN sites showed a mean bias of 0.03 and 0.05; unbiased RMSE of 0.05 and 0.06; and correlation coefficient of 0.44 and 0.48 against SCAN observations and Noah LSM, respectively.