modis

A ready-to-use curated list of Spectral Indices for Remote Sensing applications.

An R package 📦 making it easy to query, preview, download and preprocess multiple kinds of spatial data 🛰 via R. All beta.

Earth Observation Data Access Gateway

An "R" package for automatic download and preprocessing of MODIS Land Products Time Series

Awesome Spectral Indices for the Google Earth Engine JavaScript API (Code Editor).

cuSTARFM is a GPU-enabled Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM)

Download and processing framework for MODIS imagery. The package provides automated access to the global online data archives LP DAAC, LAADS and NSIDC as well as processing capabilities such as file conversion, mosaicking, subsetting and time series.

A ninja python package that unifies the Google Earth Engine ecosystem.

A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python

cuESTARFM is a GPU-enabled enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM)

Package designed to detect and quantify water quality and cyanobacterial harmful algal bloom (CHABs) from remotely sensed imagery

ActiveModel + Redis

Command-line application for visualizing data from CloudSat and CALIPSO satellites

Sentinel 2 and Landsat 8 Atmospheric correction

MODIS Assimilation and Processing Engine

cuSTNLFFM is a GPU-enabled Spatial and Temporal Non-Local Filter-based Fusion Model (STNLFFM)

Access data from the MODIS web service and perform quality filtering in Python

A highly modular Discord bot designed for anyone to customise and self-host.

FIRECAM: Fire Inventories - Regional Evaluation, Comparison, and Metrics

Remote sensing data processing

Webinar: NASA ORNL DAAC MODIS and VIIRS Data Tools and Services at your Fingertips

Google Earth Engine, Python and R scripts for analysing and visualising MODIS snow duration data in British Columbia

Python module to download modis data

卫星遥感-数据处理、分析与反演-Python实现

Source codes for groundwater pumping prediction using integrated remote sensing datasets and machine learning

The Optical Reef and Coastal Area Assessment (ORCAA) tool in Google Earth Engine allows users to monitor, track, and evaluate water parameters in the Belize and Honduras Barrier Reefs from January 2013 to present using Landsat 8, Sentinel-2, and Aqua/Terra MODIS imagery.

BRDF modelling using linear kernel models

MODIS Subsetting Tools and Services at ORNL DAAC

GEE, an open-source platform, for fast computation to Spatio-temporal analysis of satellite data.