Land Surface Temperature from Landsat on Google Earth Engine
A Google Earth Engine API (interactive dashboard) for satellite-based global climate hazard analysis (urban heat, landcover changes, etc). Project under World Bank Group. ⬇️ ⬇️
This is the github repository for the article: Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution
Practical split-window algorithm estimating Land Surface Temperature from Landsat 8 OLI/TIRS imagery
Figuring out what the hottest villages in Kerala are with the help of Microsoft's Planetary Computer
Python package to estimate Land Surface Temperatures from Google Earth Engine's Landsat imagery
Image-to-Image Training for Spatially Seamless Air Temperature Estimation with Satellite Images and Station Data
Extracts data from one or more landSAF LST HDF5s, resamples them, stacks them, and adds them to a new netCDF4.
Predicting urban heat island for the city of Pune using MODIS LST, PM2.5 and GLC_FCS30D Landcover dataset. The neural network consists of three covolution streams and attention unet for feature fusion and UHI map creation.
Data-driven deep learning framework designed to predict future Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) maps conditioned on proposed changes in land cover, using a Metadata-Augmented U-Net
Global MODIS NDVI and LST python image display and 20+ year time-series analysis program
S. Liu et al., 2025. Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification. IEEE Transactions on Geoscience and Remote Sensing (TGRS) 2025
Ground stations - satelite data comparison using low-cost temperature sensors and MODIS Land Surface Temperature data
Heat vulnerability of ZCTAs within the Boston Metropolitan Region based on demographic and environmental indicators.
This repository contains the Python codes used in the short paper "Sensitivity of Land Surface Temperature to Emissivity Retrieved from Landsat 8 Data", submitted to the XXIV Brazilian Symposium on GeoInformatics.
D'Agostini et al. (2026) "A Bayesian Spatially Varying Coefficient Model for Surface Urban Heat Island Analysis"
Convert Raw Landsat Data from Digital Number to Surface Reflectance using the Visible Infra-red bands and composite (band stacking) the scene bands. Estimate LST from thermal bands
Machine-learning workflow for sharpening NASA ECOSTRESS land surface temperature (LST) data from 70 m to 10 m resolution over Toronto
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