Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Downscaling Land Surface Temperature at Farm-Scale Using Multi-Model Approaches over Diverse Agro Climatic Zones

Debasish Roy
,
Bappa Das
,
Pooja Rathore
,
Priyabrata Santra
ORCID logo ,
Shovik Deb
ORCID logo ,
Bimal K. Bhattacharya
,
Ajit Govind
,
Raguveer Singh Jatav
,
Deepak Sethi
,
Tridiv Ghosh
,
Joydeep Mukherjee
,
Vinay Kumar Sehgal
ORCID logo ,
Sheshakumar Goroshi
,
Debashis Chakraborty
* ORCID logo
Version 1 : Received: 4 January 2024 / Approved: 4 January 2024 / Online: 5 January 2024 (09:28:37 CET)

How to cite: Roy, D.; Das, B.; Rathore, P.; Santra, P.; Deb, S.; Bhattacharya, B.K.; Govind, A.; Jatav, R.S.; Sethi, D.; Ghosh, T.; Mukherjee, J.; Sehgal, V.K.; Goroshi, S.; Chakraborty, D. Downscaling Land Surface Temperature at Farm-Scale Using Multi-Model Approaches over Diverse Agro Climatic Zones. Preprints 2024, 2024010413. https://doi.org/10.20944/preprints202401.0413.v1 Roy, D.; Das, B.; Rathore, P.; Santra, P.; Deb, S.; Bhattacharya, B.K.; Govind, A.; Jatav, R.S.; Sethi, D.; Ghosh, T.; Mukherjee, J.; Sehgal, V.K.; Goroshi, S.; Chakraborty, D. Downscaling Land Surface Temperature at Farm-Scale Using Multi-Model Approaches over Diverse Agro Climatic Zones. Preprints 2024, 2024010413. https://doi.org/10.20944/preprints202401.0413.v1

Abstract

Land surface temperature (LST) is a critical parameter for land surface and atmospheric interactions. However, the applicability of current land surface temperature estimates for field-level hydrological, agricultural, and ecological operations is still challenging due to their coarse spatiotemporal resolution. In the current article, we have downscaled 100 m LST to 10 m by using high-resolution Sentinel-1,2 optical-microwave data and three different models- 1) Thermal Sharpening (TsHARP); 2) Thin Plate Spline (TPS); and 3) Random Forest (RF). The extensive analysis was performed at agricultural farms in the semi-arid (IARI) region of India during the winter and summer seasons of 2020-21 and 2021-22, arid (CAZRI) and per-humid (UBKV) region (2021-2022). The calibration accuracy of the RF model was shown to better agreement with the coefficient of determination (R2) of 0.982, root means square error (RMSE) 0.181 and normalized root means square error (RMSE) 0.061 with their lower values of standard errors over three diverse agro-climatic zones. The findings demonstrate that the validation accuracy of models' varied according to the agroclimatic zones, although RF and TPS consistently outperformed as compared to the TsHARP models.

Keywords

LST downscaling; TsHARP; TPS; Random forest; spatial downscaling, agro-climate

Subject

Environmental and Earth Sciences, Remote Sensing

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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