Assessing the accuracy of multi-model approaches for downscaling land surface temperature across diverse agroclimatic zones

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dc.creatorRoy, Debasish
dc.creatorDas, Bappa
dc.creatorSingh, Pooja
dc.creatorSantra, Priyabrata
dc.creatorDeb, Shovik
dc.creatorBhattacharya, Bimal Kumar
dc.creatorGovind, Ajit
dc.creatorJatav, Raghuveer
dc.creatorSethi, Deepak
dc.creatorGhosh, Tridiv
dc.creatorMukherjee, Joydeep
dc.creatorSehgal, Vinay Kumar
dc.creatorPrakash Kumar Jha
dc.creatorGoroshi, Sheshakumar
dc.creatorPrasad, P. V. Vara
dc.creatorChakraborty, Debashis
dc.date2025
dc.date2025-05-30T22:39:52Z
dc.date2025-05-30T22:39:52Z
dc.date.accessioned2026-06-27T14:24:16Z
dc.descriptionLand 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.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/174892
dc.identifier.urihttp://hdl.handle.net/123456789/78694
dc.languageen
dc.publisherNature
dc.rightsOpen Access
dc.sourceRoy, 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
dc.subjectforecasting
dc.subjectfarms
dc.subjectagroclimatic zones
dc.titleAssessing the accuracy of multi-model approaches for downscaling land surface temperature across diverse agroclimatic zones
dc.typeJournal Article

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Centro Internacional para la Investigación Agrícola en las Áreas Secas (ICARDA)