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...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Springer Nature [academic journals on nature.com]
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/175174 |
| _version_ | 1855538326928359424 |
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| author | Roy, Debasish Das, Bappa Singh, Pooja Santra, Priyabrata Deb, Shovik Kumar Bhattacharya, Bimal Govind, Ajit Jatav, Raghuveer Sethi, Deepak Ghosh, Tridiv Mukherjee, Joydeep Kumar Sehgal, Vinay Kumar Jha, Prakash Goroshi, Sheshakumar Vara Prasad, V. P. Chakraborty, Debashis |
| author_browse | Chakraborty, Debashis Das, Bappa Deb, Shovik Ghosh, Tridiv Goroshi, Sheshakumar Govind, Ajit Jatav, Raghuveer Kumar Bhattacharya, Bimal Kumar Jha, Prakash Kumar Sehgal, Vinay Mukherjee, Joydeep Roy, Debasish Santra, Priyabrata Sethi, Deepak Singh, Pooja Vara Prasad, V. P. |
| author_facet | Roy, Debasish Das, Bappa Singh, Pooja Santra, Priyabrata Deb, Shovik Kumar Bhattacharya, Bimal Govind, Ajit Jatav, Raghuveer Sethi, Deepak Ghosh, Tridiv Mukherjee, Joydeep Kumar Sehgal, Vinay Kumar Jha, Prakash Goroshi, Sheshakumar Vara Prasad, V. P. 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 | CGSpace175174 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Springer Nature [academic journals on nature.com] |
| publisherStr | Springer Nature [academic journals on nature.com] |
| record_format | dspace |
| spelling | CGSpace1751742026-01-14T02:15:23Z 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 Kumar Bhattacharya, Bimal Govind, Ajit Jatav, Raghuveer Sethi, Deepak Ghosh, Tridiv Mukherjee, Joydeep Kumar Sehgal, Vinay Kumar Jha, Prakash Goroshi, Sheshakumar Vara Prasad, V. P. Chakraborty, Debashis land surface temperature (lst) ml-based multi-parameter nonlinear regression model (rf) 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. 2025-03-28 2025-06-18T18:30:44Z 2025-06-18T18:30:44Z Journal Article https://hdl.handle.net/10568/175174 en Open Access application/pdf Springer Nature [academic journals on nature.com] Debasish Roy, Bappa Das, Pooja Singh, Priyabrata Santra, Shovik Deb, Bimal Kumar Bhattacharya, Ajit Govind, Raghuveer Jatav, Deepak Sethi, Tridiv Ghosh, Joydeep Mukherjee, Vinay Kumar Sehgal, Prakash Kumar Jha, Sheshakumar Goroshi, V. P. Vara Prasad, Debashis Chakraborty. (28/3/2025). Assessing the accuracy of multi-model approaches for downscaling land surface temperature across diverse agroclimatic zones. Scientific Reports, 15. |
| spellingShingle | land surface temperature (lst) ml-based multi-parameter nonlinear regression model (rf) Roy, Debasish Das, Bappa Singh, Pooja Santra, Priyabrata Deb, Shovik Kumar Bhattacharya, Bimal Govind, Ajit Jatav, Raghuveer Sethi, Deepak Ghosh, Tridiv Mukherjee, Joydeep Kumar Sehgal, Vinay Kumar Jha, Prakash Goroshi, Sheshakumar Vara Prasad, V. P. 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 | land surface temperature (lst) ml-based multi-parameter nonlinear regression model (rf) |
| url | https://hdl.handle.net/10568/175174 |
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