ORIGINAL RESEARCH article
Front. Remote Sens.
Sec. Image Analysis and Classification
Volume 6 - 2025 | doi: 10.3389/frsen.2025.1519420
Landcover-Specific Calibration of the Optical Trapezoid Model (OPTRAM) for Soil Moisture Monitoring in the Central Valley, California
Provisionally accepted
Neda Mohamadzadeh1*
Morteza Sadeghi2
Noemi Vergopolan3
Lan Liang2Uditha Bandara2Craig Altare2
Marcellus M Caldas1- 1Kansas State University, Manhattan, United States
- 2California Department of Water Resources, West Sacramento, California, United States
- 3Rice University, Houston, Texas, United States
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The Optical TRApezoid Model (OPTRAM) has been extensively utilized to map high-resolution surface soil moisture (top 0-5 cm) using surface reflectance observations. OPTRAM parameters, the intercept and slope of the dry and wet edges, are typically calibrated by analyzing the data cloud created from the Normalized Difference Vegetation Index (NDVI) and the Shortwave-infrared Transformed Reflectance (STR) in a specified area of interest. One set of parameters is commonly obtained for the entire study area regardless of its soil and landcover types. In this study, we explored to what extent a landcover-specific calibration of OPTRAM can improve its accuracy. In this analysis, we used Sentinel-2 (S2) reflectance and the Cropland Data Layer (CDL) landcover datasets via the Google Earth Engine to generate 20-m resolution soil moisture maps for California’s Central Valley (CV). We evaluated the spatial and temporal accuracy of the CV-wide calibrated OPTRAM (OPTRAM-CV) and landcover-specific calibrated OPTRAM (OPTRAM-LS) against in situ observations and SMAP-HydroBlocks (SMAP-HB), a well-validated 30-m satellite-based soil moisture dataset. Our results indicate that OPTRAM-LS significantly improved the accuracy of soil moisture estimates compared to OPTRAM-CV. The average root mean square error was 0.09 and 0.05 (m3 m-3) for OPTRAM-CV and OPTRAM-LS, respectively. OPTRAM showed less accuracy than SMAP-HB compared to in situ observations but yielded higher resolution than SMAP-HB.
Keywords: soil moisture1, landcover2, Sentinel-23, Central Valley4, Google Earth Engine5
Received: 29 Oct 2024; Accepted: 01 May 2025.
Copyright: © 2025 Mohamadzadeh, Sadeghi, Vergopolan, Liang, Bandara, Altare and Caldas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Neda Mohamadzadeh, Kansas State University, Manhattan, United States
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