Assessing the accuracy of multi-model approaches for downscaling land surface temperature across diverse agroclimatic zones
Land surface temperature (LST) is a critical parameter for land surface and atmospheric interactions. However, the applicability of current LST estimates for field-level hydrological, agricultural, and ecological operations is challenging due to their coarse spatiotemporal resolution. In the current...
| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Nature Publishing Group
2025
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| Acceso en línea: | https://hdl.handle.net/10568/174892 |
| _version_ | 1855536006336348160 |
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| author | Roy, Debasish Das, Bappa Singh, Pooja Santra, Priyabrata Deb, Shovik Bhattacharya, Bimal Kumar Govind, Ajit Jatav, Raghuveer Sethi, Deepak Ghosh, Tridiv Mukherjee, Joydeep Sehgal, Vinay Kumar Prakash Kumar Jha Goroshi, Sheshakumar Prasad, P. V. Vara Chakraborty, Debashis |
| author_browse | Bhattacharya, Bimal Kumar Chakraborty, Debashis Das, Bappa Deb, Shovik Ghosh, Tridiv Goroshi, Sheshakumar Govind, Ajit Jatav, Raghuveer Mukherjee, Joydeep Prakash Kumar Jha Prasad, P. V. Vara Roy, Debasish Santra, Priyabrata Sehgal, Vinay Kumar Sethi, Deepak Singh, Pooja |
| author_facet | Roy, Debasish Das, Bappa Singh, Pooja Santra, Priyabrata Deb, Shovik Bhattacharya, Bimal Kumar Govind, Ajit Jatav, Raghuveer Sethi, Deepak Ghosh, Tridiv Mukherjee, Joydeep Sehgal, Vinay Kumar Prakash Kumar Jha Goroshi, Sheshakumar Prasad, P. V. Vara Chakraborty, Debashis |
| author_sort | Roy, Debasish |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Land surface temperature (LST) is a critical parameter for land surface and atmospheric interactions. However, the applicability of current LST estimates for field-level hydrological, agricultural, and ecological operations is challenging due to their coarse spatiotemporal resolution. In the current article, we compared three different models, namely 1) Thermal Sharpening (TsHARP), 2) Thin Plate Spline (TPS), and 3) Random Forest (RF) for downscaling LST from 100 to 10 m by using high-resolution Sentinel-1,2 optical-microwave data. TsHARP, TPS, and RF are commonly used methods for improving the spatial resolution of large-scale environmental or climate data to finer scales for field-level applications. The analysis was performed at agricultural farms in the semi-arid, arid, and per-humid regions of India during the winter and summer seasons of 2020-21 and 2021-22. The calibration accuracy of the RF model was in better agreement with the coefficient of determination (R2), root mean square error (RMSE), and normalized RMSE (nRMSE) values ranging between 0.961-0.997, 0.103-0.439 K, and 0.034-0.143%, respectively, and lower values of standard errors for all three locations. Though the validation accuracy of models varied between the regions, RF and TPS consistently outperformed the TsHARP model. Further the impact of individual features on LST downscaling was analyzed using Accumulated Local Effects (ALE) plot. The study concluded that RF is an effective and adaptable strategy that can be used in various agroclimatic zones and land cover types, suggesting its broader applicability in agricultural and ecological operations. Finer resolution LST data with enhanced precision can support tailored field-level decision-making and interventions in agriculture and environmental monitoring. |
| format | Journal Article |
| id | CGSpace174892 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Nature Publishing Group |
| publisherStr | Nature Publishing Group |
| record_format | dspace |
| spelling | CGSpace1748922025-12-08T09:54:28Z Assessing the accuracy of multi-model approaches for downscaling land surface temperature across diverse agroclimatic zones Roy, Debasish Das, Bappa Singh, Pooja Santra, Priyabrata Deb, Shovik Bhattacharya, Bimal Kumar Govind, Ajit Jatav, Raghuveer Sethi, Deepak Ghosh, Tridiv Mukherjee, Joydeep Sehgal, Vinay Kumar Prakash Kumar Jha Goroshi, Sheshakumar Prasad, P. V. Vara Chakraborty, Debashis forecasting farms agroclimatic zones Land surface temperature (LST) is a critical parameter for land surface and atmospheric interactions. However, the applicability of current LST estimates for field-level hydrological, agricultural, and ecological operations is challenging due to their coarse spatiotemporal resolution. In the current article, we compared three different models, namely 1) Thermal Sharpening (TsHARP), 2) Thin Plate Spline (TPS), and 3) Random Forest (RF) for downscaling LST from 100 to 10 m by using high-resolution Sentinel-1,2 optical-microwave data. TsHARP, TPS, and RF are commonly used methods for improving the spatial resolution of large-scale environmental or climate data to finer scales for field-level applications. The analysis was performed at agricultural farms in the semi-arid, arid, and per-humid regions of India during the winter and summer seasons of 2020-21 and 2021-22. The calibration accuracy of the RF model was in better agreement with the coefficient of determination (R2), root mean square error (RMSE), and normalized RMSE (nRMSE) values ranging between 0.961-0.997, 0.103-0.439 K, and 0.034-0.143%, respectively, and lower values of standard errors for all three locations. Though the validation accuracy of models varied between the regions, RF and TPS consistently outperformed the TsHARP model. Further the impact of individual features on LST downscaling was analyzed using Accumulated Local Effects (ALE) plot. The study concluded that RF is an effective and adaptable strategy that can be used in various agroclimatic zones and land cover types, suggesting its broader applicability in agricultural and ecological operations. Finer resolution LST data with enhanced precision can support tailored field-level decision-making and interventions in agriculture and environmental monitoring. 2025 2025-05-30T22:39:52Z 2025-05-30T22:39:52Z Journal Article https://hdl.handle.net/10568/174892 en Open Access application/pdf Nature Publishing Group Roy, D., Das, B., Singh, P., Santra, P., Deb, S., Bhattacharya, B. K., Govind, A., Jatav, R., Sethi, D., Ghosh, T., Mukherjee, J., Sehgal, V. K., Jha, P. K., Goroshi, S., Prasad, P. V. V., & Chakraborty, D. (2025). Assessing the accuracy of multi-model approaches for downscaling land surface temperature across diverse agroclimatic zones. Scientific Reports, 15(1), 10824. https://doi.org/10.1038/s41598-025-92135-0 |
| spellingShingle | forecasting farms agroclimatic zones Roy, Debasish Das, Bappa Singh, Pooja Santra, Priyabrata Deb, Shovik Bhattacharya, Bimal Kumar Govind, Ajit Jatav, Raghuveer Sethi, Deepak Ghosh, Tridiv Mukherjee, Joydeep Sehgal, Vinay Kumar Prakash Kumar Jha Goroshi, Sheshakumar Prasad, P. V. Vara Chakraborty, Debashis Assessing the accuracy of multi-model approaches for downscaling land surface temperature across diverse agroclimatic zones |
| title | Assessing the accuracy of multi-model approaches for downscaling land surface temperature across diverse agroclimatic zones |
| title_full | Assessing the accuracy of multi-model approaches for downscaling land surface temperature across diverse agroclimatic zones |
| title_fullStr | Assessing the accuracy of multi-model approaches for downscaling land surface temperature across diverse agroclimatic zones |
| title_full_unstemmed | Assessing the accuracy of multi-model approaches for downscaling land surface temperature across diverse agroclimatic zones |
| title_short | Assessing the accuracy of multi-model approaches for downscaling land surface temperature across diverse agroclimatic zones |
| title_sort | assessing the accuracy of multi model approaches for downscaling land surface temperature across diverse agroclimatic zones |
| topic | forecasting farms agroclimatic zones |
| url | https://hdl.handle.net/10568/174892 |
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