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

arXiv: https://arxiv.org/abs/2202.10753 · HTML: https://mlsanity.com/paper/2202.10753/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network](https://mlsanity.com/paper/2010.00472/) (2020)
- [Super-resolving multiresolution images with band-independant geometry of multispectral pixels](https://mlsanity.com/paper/1609.07986/) (2016)

## Built on by (most cited first, 6 of 6)

- [Super-Resolution of BVOC Maps by Adapting Deep Learning Methods](https://mlsanity.com/paper/2302.07570/) (2023)
- [Multi-BVOC Super-Resolution Exploiting Compounds Inter-Connection](https://mlsanity.com/paper/2305.14180/) (2023)
- [Improving sub-seasonal wind-speed forecasts in Europe with a non-linear model](https://mlsanity.com/paper/2411.19077/) (2024)
- [Leveraging Land Cover Priors for Isoprene Emission Super-Resolution](https://mlsanity.com/paper/2503.18658/) (2025)
- [High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator](https://mlsanity.com/paper/2602.13416/) (2026)
- [Mitigating Spectral Bias in Neural Operators for Underwater Transmission Loss Prediction](https://mlsanity.com/paper/2608.18141/) (2026)

## Abstract (arXiv)

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.