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

• Main Authors: V. Mishra, J. F. Cruise, C. R. Hain, J. R. Mecikalski, M. C. Anderson
• Format: Article
• Language: English
• Published: Copernicus Publications 2018-09-01
• Series: Hydrology and Earth System Sciences
• Online Access: https://www.hydrol-earth-syst-sci.net/22/4935/2018/hess-22-4935-2018.pdf

<p>The principle of maximum entropy (POME) can be used to develop vertical soil moisture (SM) 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 SM estimates from the Advanced Microwave Scanning Radiometer (AMSR-E) can supply the surface boundary condition whereas thermal infrared-based moisture estimated from the Atmospheric Land EXchange Inverse (ALEXI) surface energy balance model can provide the mean moisture condition. A disaggregation approach was followed to downscale coarse-resolution ( ∼ 25&thinsp;km) microwave SM estimates to match the finer resolution ( ∼ 5&thinsp;km) thermal data. The study was conducted over multiple years (2006–2010) in the southeastern US. Disaggregated soil moisture estimates along with the developed profiles were compared with the Noah land surface model (LSM), 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 differences (ubRMSD) in the range of 0.01–0.09&thinsp;m³&thinsp;m⁻³. The profile results at SCAN sites showed a mean bias of 0.03 and 0.05 (m³&thinsp;m⁻³); ubRMSD of 0.05 and 0.06 (m³&thinsp;m⁻³); and correlation coefficient of 0.44 and 0.48 against SCAN observations and Noah LSM, respectively. Correlations were generally highest in agricultural areas where values in the 0.6–0.7 range were achieved.</p>