Preprints
https://doi.org/10.5194/egusphere-2023-11
https://doi.org/10.5194/egusphere-2023-11
13 Mar 2023
 | 13 Mar 2023
Status: this preprint is open for discussion.

HSR-GEE: A 1-m GEE Automated Land Surface Temperature Downscaling System over CONUS

Mario Mhawej and Yaser Abunnasr

Abstract. Downscaling remote sensing-based Land Surface Temperature (LST) datasets is of paramount importance for multiple research fields including urban heating, irrigation scheduling, volcanic monitoring, to name a few. Even with the constant development in technology, especially improvements in spatial, temporal and spectral resolutions of satellite sensors, global satellites are limited to a 60-m LST datasets at best. In this study, we use the massive computation power and large dataset directory found in the Google Earth Engine (GEE) platform to design a 1-m fully-automated, open-source and user-friendly LST downscaling system, named High Spatial Resolution-GEE or HSR-GEE. It has the ability to downscale Landsat-8 LST, combined with the National Agriculture Imagery Program (NAIP) images, into 1-m HSR LSTs over the CONUS and at Landsat-8 overpass time. Using only red, green, blue and near infrared bands, HSR-GEE implements multiple machine learning approaches, including, Robust Least Square (RLS), Random Forests (RF), and Support Vector Machine (SVM), along with comparison to two commonly-known and classical methods: the disaggregation procedure for radiometric surface temperature (DisTrad) and thermal sharpening (TsHARP). We validate HSR-GEE outputs against multiple airborne thermal images over the USA. We obtained a MAE of 1.92 °C, 2.53 °C, 1.33 °C, 3.42 °C and 3.4 °C for the RLS, RF, SVM, DisTrad and TsHARP, respectively. With RF showing visually salt and pepper effect and SVM a Land Cover/Use form, the RLS appears to be most suited for 1-m LST downscaling. HSR-GEE is proposed as a high-potential system aiding researchers from different backgrounds to advance their research. HSR-GEE remains the only available 1-m GEE-based downscaling system that is able to derive the needed high resolution LST information in a matter of seconds and in five different approaches (i.e., RLS, RF, SVM, DisTrad and TsHARP). The research community is invited to implement this dynamic system over CONUS and enhance it if deemed necessary.

Mario Mhawej and Yaser Abunnasr

Status: open (until 08 May 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Mario Mhawej and Yaser Abunnasr

Mario Mhawej and Yaser Abunnasr

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Short summary
We design a 1-m fully-automated LST downscaling system, named HSR-GEE. It has the ability to downscale LST Into 1-m HSR LSTs over the CONUS. HSR-GEE implements multiple machine learning approaches along with comparison to two commonly-known and classical methods. We have successfully validated HSR-GEE outputs against multiple airborne thermal images over the USA. The research community is invited to implement this dynamic system over CONUS and enhance it if deemed necessary.