A novel soil moisture evaluation framework incorporating brightness temperature and a high-resolution 1 km summer brightness temperature dataset
Accurate estimation of soil moisture (SM) is essential for various hydrological, meteorological, agricultural, and ecological applications. However, evaluating SM on a global scale remains challenging due to the limited availability of in-situ observations and the spatial heterogeneity of SM. Coarse...
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Taylor & Francis Group
2025-12-01
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| Series: | GIScience & Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2025.2491169 |
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| author | Ziyue Zhu Runze Zhang Bin Fang Hyunglok Kim Hoang Hai Nguyen Venkataraman Lakshmi |
| author_facet | Ziyue Zhu Runze Zhang Bin Fang Hyunglok Kim Hoang Hai Nguyen Venkataraman Lakshmi |
| author_sort | Ziyue Zhu |
| collection | DOAJ |
| description | Accurate estimation of soil moisture (SM) is essential for various hydrological, meteorological, agricultural, and ecological applications. However, evaluating SM on a global scale remains challenging due to the limited availability of in-situ observations and the spatial heterogeneity of SM. Coarser resolution SM products, although beneficial for broader area coverage, often struggle to capture fine-scale variations influenced by local hydrological processes, land use, vegetation cover, and microclimates. To address these challenges, this study presents two contributions: a new 1 km brightness temperature (TB) dataset for the summer season and a two-step SM evaluation method. The 1 km TB dataset, developed by integrating SMAP’s 9 km SM product with radiative transfer modeling (RTM) and Mironov model, provides enhanced spatial resolution and is focused on areas where vegetation water content (VWC) is below 3 kg/m2, allowing for a more detailed analysis of SM variations. When validated against SMAP TB data, this dataset showed a solid correlation (R2 = 0.921) and a low root mean square error (RMSE = 4.254 K), making it a useful resource for fine-scale SM monitoring. The two-step evaluation method, which combines physical modeling (RTM and additional models) with machine learning techniques such as non-linear regression and convolutional neural networks (CNN), offers improvements in both temporal and spatial coverage. By transitioning from point-based validation using ISMN to an area-based approach, this method produces SM estimates at the same scale as the evaluated data, addressing the limitations of previous point-scale validations. Comparisons with ISMN data demonstrated the method’s robustness, with key metrics showing improved performance (R2 = 0.749, RMSE = 0.0561 m3/m3) across diverse environmental conditions. Furthermore, the evaluation of the ERA5 dataset using this method revealed a general overestimation of SM, particularly in tropical regions with dense vegetation. These findings are consistent with known ERA5 biases, reinforcing the reliability of the two-step method for global-scale SM evaluations. The results suggest that this approach, which integrates both physical models and machine learning, offers a more comprehensive and reliable framework for SM product evaluation, while the 1 km TB dataset provides valuable support for applications requiring finer spatial resolution. |
| format | Article |
| id | doaj-art-e041f775151f4bfb9e0a59dae9d5decc |
| institution | DOAJ |
| issn | 1548-1603 1943-7226 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj-art-e041f775151f4bfb9e0a59dae9d5decc2025-08-20T03:08:24ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262025-12-0162110.1080/15481603.2025.2491169A novel soil moisture evaluation framework incorporating brightness temperature and a high-resolution 1 km summer brightness temperature datasetZiyue Zhu0Runze Zhang1Bin Fang2Hyunglok Kim3Hoang Hai Nguyen4Venkataraman Lakshmi5Department of Civil and Environmental Engineering, University of Virginia, Charlottesville, VA, USADepartment of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USADepartment of Civil and Environmental Engineering, University of Virginia, Charlottesville, VA, USASchool of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju, Republic of KoreaSchool of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju, Republic of KoreaDepartment of Civil and Environmental Engineering, University of Virginia, Charlottesville, VA, USAAccurate estimation of soil moisture (SM) is essential for various hydrological, meteorological, agricultural, and ecological applications. However, evaluating SM on a global scale remains challenging due to the limited availability of in-situ observations and the spatial heterogeneity of SM. Coarser resolution SM products, although beneficial for broader area coverage, often struggle to capture fine-scale variations influenced by local hydrological processes, land use, vegetation cover, and microclimates. To address these challenges, this study presents two contributions: a new 1 km brightness temperature (TB) dataset for the summer season and a two-step SM evaluation method. The 1 km TB dataset, developed by integrating SMAP’s 9 km SM product with radiative transfer modeling (RTM) and Mironov model, provides enhanced spatial resolution and is focused on areas where vegetation water content (VWC) is below 3 kg/m2, allowing for a more detailed analysis of SM variations. When validated against SMAP TB data, this dataset showed a solid correlation (R2 = 0.921) and a low root mean square error (RMSE = 4.254 K), making it a useful resource for fine-scale SM monitoring. The two-step evaluation method, which combines physical modeling (RTM and additional models) with machine learning techniques such as non-linear regression and convolutional neural networks (CNN), offers improvements in both temporal and spatial coverage. By transitioning from point-based validation using ISMN to an area-based approach, this method produces SM estimates at the same scale as the evaluated data, addressing the limitations of previous point-scale validations. Comparisons with ISMN data demonstrated the method’s robustness, with key metrics showing improved performance (R2 = 0.749, RMSE = 0.0561 m3/m3) across diverse environmental conditions. Furthermore, the evaluation of the ERA5 dataset using this method revealed a general overestimation of SM, particularly in tropical regions with dense vegetation. These findings are consistent with known ERA5 biases, reinforcing the reliability of the two-step method for global-scale SM evaluations. The results suggest that this approach, which integrates both physical models and machine learning, offers a more comprehensive and reliable framework for SM product evaluation, while the 1 km TB dataset provides valuable support for applications requiring finer spatial resolution.https://www.tandfonline.com/doi/10.1080/15481603.2025.2491169Soil moistureevaluationbrightness temperatureremote sensing |
| spellingShingle | Ziyue Zhu Runze Zhang Bin Fang Hyunglok Kim Hoang Hai Nguyen Venkataraman Lakshmi A novel soil moisture evaluation framework incorporating brightness temperature and a high-resolution 1 km summer brightness temperature dataset GIScience & Remote Sensing Soil moisture evaluation brightness temperature remote sensing |
| title | A novel soil moisture evaluation framework incorporating brightness temperature and a high-resolution 1 km summer brightness temperature dataset |
| title_full | A novel soil moisture evaluation framework incorporating brightness temperature and a high-resolution 1 km summer brightness temperature dataset |
| title_fullStr | A novel soil moisture evaluation framework incorporating brightness temperature and a high-resolution 1 km summer brightness temperature dataset |
| title_full_unstemmed | A novel soil moisture evaluation framework incorporating brightness temperature and a high-resolution 1 km summer brightness temperature dataset |
| title_short | A novel soil moisture evaluation framework incorporating brightness temperature and a high-resolution 1 km summer brightness temperature dataset |
| title_sort | novel soil moisture evaluation framework incorporating brightness temperature and a high resolution 1 km summer brightness temperature dataset |
| topic | Soil moisture evaluation brightness temperature remote sensing |
| url | https://www.tandfonline.com/doi/10.1080/15481603.2025.2491169 |
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