Version 1
: Received: 19 June 2023 / Approved: 20 June 2023 / Online: 20 June 2023 (07:23:06 CEST)
How to cite:
Patel, N.; Aghababaei, H.; Osei, F.B.; Stein, A.; Mahour, M. Downscaling Land Surface Temperature Using SAR Images: A Machine Learning Framework. Preprints2023, 2023061391. https://doi.org/10.20944/preprints202306.1391.v1
Patel, N.; Aghababaei, H.; Osei, F.B.; Stein, A.; Mahour, M. Downscaling Land Surface Temperature Using SAR Images: A Machine Learning Framework. Preprints 2023, 2023061391. https://doi.org/10.20944/preprints202306.1391.v1
Patel, N.; Aghababaei, H.; Osei, F.B.; Stein, A.; Mahour, M. Downscaling Land Surface Temperature Using SAR Images: A Machine Learning Framework. Preprints2023, 2023061391. https://doi.org/10.20944/preprints202306.1391.v1
APA Style
Patel, N., Aghababaei, H., Osei, F.B., Stein, A., & Mahour, M. (2023). Downscaling Land Surface Temperature Using SAR Images: A Machine Learning Framework. Preprints. https://doi.org/10.20944/preprints202306.1391.v1
Chicago/Turabian Style
Patel, N., Alfred Stein and Milad Mahour. 2023 "Downscaling Land Surface Temperature Using SAR Images: A Machine Learning Framework" Preprints. https://doi.org/10.20944/preprints202306.1391.v1
Abstract
Land Surface Temperature (LST) is significant for climatological and environmental studies. LST products from satellites, however, suffer from the tradeoff between spatial and temporal resolution. Spatial downscaling has emerged as a well explored field aiming to overcome limitations arising from this tradeoff. Previous research on regression based LST downscaling models focused on utilizing predictors derived from optical imagery. Weather-dependency of optical imagery data, however, can influence downscaling models by the weather conditions. To cope this issue, in this study, we involve predictors derived from the weather-independent Sentinel-1 Synthetic Aperture Radar (SAR) imagery to downscale Landsat-8 LST data. In this context, we propose to use machine learning techniques, namely Random Forest (RF) and Convolutional Neural Networks (CNN). To demonstrate the applicability and performance of the proposed method, extensive experimental analyses were conducted over Zuid-Holland in the Netherlands. From the experiments, we found that the results obtained with radar predictors were comparable to those achieved using optical predictors. This confirms that the proposed method indeed paves a new way for mapping land surface temperature using SAR images.
Keywords
Land Surface Temperature; Synthetic Aperture Radar (SAR); Downscaling; Random Forest; Convolutional Neural Networks
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.
