University of Twente Student Theses


Downscaling Land Surface Temperature using SAR images : A Machine Learning framework

Patel, Nishit (2023) Downscaling Land Surface Temperature using SAR images : A Machine Learning framework.

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Abstract:Land Surface Temperature (LST) is significant for climatological and environmental studies. LST products acquired 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 for constructing such spatial downscaling models. Weather-dependent nature of optical imagery data, however, can influence downscaling models and render them ineffective during bad weather conditions like high cloud cover. To cope with this issue, in this research, we involve predictors derived from the weather-independent Sentinel-1 Synthetic Aperture Radar (SAR) imagery to downscale Landsat-8 LST and MODIS LST products. In this context, we propose to use machine learning techniques, namely Random Forest (RF) and Convolutional Neural Networks (CNN) as base regression algorithms to develop radar-based LST downscaling models. 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 both quantitatively and qualitatively to those achieved using optical predictors. This confirms that the proposed method indeed paves a new way for mapping LST using SAR images.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Subject:38 earth sciences, 54 computer science
Programme:Geoinformation Science and Earth Observation MSc (75014)
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