The “trapezoid” or “triangle” model constitutes the most popular approach to remote sensing (RS) of surface soil moisture based on coupled thermal (i.e., land surface temperature) and optical RS observations. The model, hereinafter referred to as Thermal-Optical TRAapezoid Model (TOTRAM), is based on interpretation of the pixel distribution within the land surface temperature - vegetation index (LST-VI) space. TOTRAM suffers from two inherent limitations. It is not applicable to satellites that do not provide thermal data (e.g., Sentinel-2) and it requires parameterization for each individual observation date. To overcome these restrictions we propose a novel OPtical TRApezoid Model (OPTRAM), which is based on the linear physical relationship between soil moisture and shortwave infrared transformed reflectance (STR) and is parameterized based on the pixel distribution within the STR-VI space. The OPTRAM-based surface soil moisture estimates derived from Sentinel-2 and Landsat-8 observations for the Walnut Gulch and Little Washita watersheds were compared with ground truth soil moisture data. Results indicate that the prediction accuracies of OPTRAM and TOTRAM are comparable, with OPTRAM only requiring observations in the optical electromagnetic frequency domain. The volumetric moisture content estimation errors of both models were below 0.04 cm3 cm− 3 with local calibration and about 0.04–0.05 cm3 cm− 3 without calibration. We also demonstrate that OPTRAM only requires a single universal parameterization for a given location, which is a significant advancement that opens a new avenue for remote sensing of soil moisture.
|Original language||English (US)|
|Number of pages||17|
|Journal||Remote Sensing of Environment|
|State||Published - Sep 1 2017|
Bibliographical noteFunding Information:
The authors gratefully acknowledge funding from National Science Foundation (NSF) grant no. 1521469. Additional support was provided by the Utah Agricultural Experiment Station, Utah State University, Logan, Utah 84322-4810, approved as UAES journal paper no. 8950.
© 2017 Elsevier Inc.
- Satellite remote sensing
- Soil moisture
- Surface reflectance