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. ⬇️ ⬇️
Practical split-window algorithm estimating Land Surface Temperature from Landsat 8 OLI/TIRS imagery
This is the github repository for the article: Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution
Figuring out what the hottest villages in Kerala are with the help of Microsoft's Planetary Computer
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.
Python package to estimate Land Surface Temperatures from Google Earth Engine's Landsat imagery
Global MODIS NDVI and LST python image display and 20+ year time-series analysis program
Ground stations - satelite data comparison using low-cost temperature sensors and MODIS Land Surface Temperature data
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.
Heat vulnerability of ZCTAs within the Boston Metropolitan Region based on demographic and environmental indicators.
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
Online version of a training manual for reading netcdf GOES-16 LST data in MATLAB.
This is fast-response software package for environmental applications
Contains python notebooks used for clipping of satellite data using Geojson AOI, calculating LST from Landsat 8 Images and Correlation analysis between CO and LST
A current project contributing to Omdena's initiative to develop an AI-based system for wildfire spread prediction and early warning in Uttarakhand. This contribution focuses on using Land Surface Temperature (LST) data, geospatial analysis with Google Earth Engine, and Python to predict wildfire behaviors and identify potential hotspots.
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