Landcover-specific calibration of the optical trapezoid model (OPTRAM) for soil moisture monitoring in the Central Valley, California
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...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Remote Sensing |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frsen.2025.1519420/full |
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| author | Neda Mohamadzadeh Morteza Sadeghi Noemi Vergopolan Lan Liang Uditha Bandara Craig Altare Marcellus M. Caldas |
| author_facet | Neda Mohamadzadeh Morteza Sadeghi Noemi Vergopolan Lan Liang Uditha Bandara Craig Altare Marcellus M. Caldas |
| author_sort | Neda Mohamadzadeh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-64c8efb051ce42499f92ec3b98f4ca68 |
| institution | DOAJ |
| issn | 2673-6187 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Remote Sensing |
| spelling | doaj-art-64c8efb051ce42499f92ec3b98f4ca682025-08-20T03:09:44ZengFrontiers Media S.A.Frontiers in Remote Sensing2673-61872025-05-01610.3389/frsen.2025.15194201519420Landcover-specific calibration of the optical trapezoid model (OPTRAM) for soil moisture monitoring in the Central Valley, CaliforniaNeda Mohamadzadeh0Morteza Sadeghi1Noemi Vergopolan2Lan Liang3Uditha Bandara4Craig Altare5Marcellus M. Caldas6Department of Geography and Geospatial Sciences, Kansas State University, Manhattan, KS, United StatesSustainable Groundwater Management Office, California Department of Water Resources, Sacramento, CA, United StatesEarth, Environmental and Planetary Sciences, Rice University, Houston, TX, United StatesSustainable Groundwater Management Office, California Department of Water Resources, Sacramento, CA, United StatesSustainable Groundwater Management Office, California Department of Water Resources, Sacramento, CA, United StatesSustainable Groundwater Management Office, California Department of Water Resources, Sacramento, CA, United StatesDepartment of Geography and Geospatial Sciences, Kansas State University, Manhattan, KS, United StatesThe 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.https://www.frontiersin.org/articles/10.3389/frsen.2025.1519420/fullsoil moisturelandcoverSentinel-2Central Valleygoogle earth engine |
| spellingShingle | Neda Mohamadzadeh Morteza Sadeghi Noemi Vergopolan Lan Liang Uditha Bandara Craig Altare Marcellus M. Caldas Landcover-specific calibration of the optical trapezoid model (OPTRAM) for soil moisture monitoring in the Central Valley, California Frontiers in Remote Sensing soil moisture landcover Sentinel-2 Central Valley google earth engine |
| title | Landcover-specific calibration of the optical trapezoid model (OPTRAM) for soil moisture monitoring in the Central Valley, California |
| title_full | Landcover-specific calibration of the optical trapezoid model (OPTRAM) for soil moisture monitoring in the Central Valley, California |
| title_fullStr | Landcover-specific calibration of the optical trapezoid model (OPTRAM) for soil moisture monitoring in the Central Valley, California |
| title_full_unstemmed | Landcover-specific calibration of the optical trapezoid model (OPTRAM) for soil moisture monitoring in the Central Valley, California |
| title_short | Landcover-specific calibration of the optical trapezoid model (OPTRAM) for soil moisture monitoring in the Central Valley, California |
| title_sort | landcover specific calibration of the optical trapezoid model optram for soil moisture monitoring in the central valley california |
| topic | soil moisture landcover Sentinel-2 Central Valley google earth engine |
| url | https://www.frontiersin.org/articles/10.3389/frsen.2025.1519420/full |
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