Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution

TL;DR

This paper presents a deep learning model called Multi-residual U-Net that enhances the spatial resolution of MODIS Land Surface Temperature images from 1km to 250m, enabling finer-scale environmental analysis.

Contribution

The paper introduces a novel deep learning architecture specifically designed for super-resolving MODIS LST images, improving resolution from 1km to 250m.

Findings

Multi-residual U-Net outperforms existing super-resolution methods.

Enhanced resolution enables better analysis of heterogeneous environments.

Deep learning approach effectively captures fine-scale temperature details.

Abstract

Nowadays, thermal infrared satellite remote sensors enable to extract very interesting information at large scale, in particular Land Surface Temperature (LST). However such data are limited in spatial and/or temporal resolutions which prevents from an analysis at fine scales. For example, MODIS satellite provides daily acquisitions with 1Km spatial resolutions which is not sufficient to deal with highly heterogeneous environments as agricultural parcels. Therefore, image super-resolution is a crucial task to better exploit MODIS LSTs. This issue is tackled in this paper. We introduce a deep learning-based algorithm, named Multi-residual U-Net, for super-resolution of MODIS LST single-images. Our proposed network is a modified version of U-Net architecture, which aims at super-resolving the input LST image from 1Km to 250m per pixel. The results show that our Multi-residual U-Net outperforms other state-of-the-art methods.