awesome-spectral-indices / awesome-asi

A curated list of Awesome Spectral Indices (ASI) resources

A curated list of Awesome Spectral Indices (ASI) resources

Table of Contents

• Official Resources
• Python
• Julia
• R
• Google Earth Engine
• Apps
• YouTube
• Tutorials
• Podcasts
• Blogs
• Datasets
• Companies and Software
• Papers
• Attribution

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Official Resources

• Awesome Spectral Indices - Official GitHub Repository
• Espectro - Official Streamlit Web App
• spectral - Official Google Earth Engine Module
• spyndex - Official Python Package
• SpectralIndices.jl - Official Julia Package

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Python

• eoreader - Remote-sensing opensource python library reading optical and SAR sensors, loading and stacking bands, clouds, DEM and spectral indices in a sensor-agnostic way.
• openeo-python-client - Python client API for OpenEO. ASI provided as experimental feature.
• spyndex - Official ASI Python Package. Spectral Indices can be computed for Python objects with third party libraries (e.g., numpy, pandas, xarray, dask).

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Julia

• SpectralIndices.jl - Official ASI Julia Package. Spectral Indices can be computed for Python objects with third party libraries (e.g., DataFrames, YAXArrays).

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R

• rgeeExtra - An R package for high-level functions to process spatial and simple Earth Engine objects. ASI is available through the spectralIndices() function.

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Google Earth Engine

Python

• eeExtra - A ninja Python package that unifies the Google Earth Engine ecosystem. Provides ASI methods for eemont and rgeeExtra.
• eemont - A Python package that extends Google Earth Engine. ASI is available through the spectralIndices() method.
• spyndex - Official ASI Python Package. Spectral Indices can be computed for Google Earth Engine objects.

JavaScript

• spectral - Official ASI Google Earth Engine Module. Deploys ASI for the Code Editor.

R

• rgeeExtra - An R package for high-level functions to process spatial and simple Earth Engine objects. ASI is available through the spectralIndices() function.

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Apps

Streamlit

• Espectro - Official Streamlit Web App GitHub Repository

Visualization

• LexCube - Leipzig Explorer of Earth Data Cubes. Provides an interactive ASI example for a Sentinel-2 data cube.

Miscellaneous

• openEO Web Editor - Web-based UI for OpenEO. A wizard provides an easy way to generate products based on ASI.

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YouTube

Google Earth Engine

• ktmagar's YT | Awesome Spectral Indices in Earth Engine
• Open Source Remote Sensing & GIS | GEE tutorial 3: Compute multiple spectral indices easily in Google Earth Engine
• geodose video | How to Calculate Various Spectral Indices in Google Earth Engine Quickly
• MasterGIS | MasterGIS Days 2021 🌎: Procesamiento y visualización de datos geoespaciales con GEE [Spanish]

Miscellaneous

• openEO | openEO Web Editor Wizard

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Tutorials

Python

• David Montero Loaiza | Python Built-in Types
• David Montero Loaiza | Numpy
• David Montero Loaiza | Pandas
• David Montero Loaiza | Xarray
• David Montero Loaiza | Geopandas
• David Montero Loaiza | Dask
• David Montero Loaiza | Earth Engine
• David Montero Loaiza | Planetary Computer
• Digital Earth Africa | Calculating band indices with Spyndex packages

Google Earth Engine

Python

• David Montero Loaiza | Computing Spectral Indices in Landsat 8
• David Montero Loaiza | Computing Spectral Indices for MODIS
• David Montero Loaiza | Spectral Indices From the Awesome Spectral Indices for GEE
• David Montero Loaiza | Access to Awesome Spectral Indices v0.0.3
• David Montero Loaiza | Monthly Global kNDVI using eemont and wxee
• David Montero Loaiza | Landsat-9
• Aaron Zuspan | Combining wxee and eemont

JavaScript

• David Montero Loaiza | Example 1: Exploring spectral indices
• David Montero Loaiza | Example 2: Computing One Index
• David Montero Loaiza | Example 3: Computing Multiple Indices
• David Montero Loaiza | Example 4: Computing Kernel Indices
• David Montero Loaiza | Example 5: Mapping Indices Over an Image Collection

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Podcasts

• Scene From Above | S11E2: Disruption

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Blogs

• geodose | How to Calculate Various Spectral Indices in Google Earth Engine Quickly
• Akis Karagiannis | Spectral Reflectance Newsletter #5
• scicrop | O uso dos índices vegetativos e do sensoriamento remoto na agricultura [Portuguese]
• Cursos de Teledetección | Lanzamiento de una nueva versión de "Awesome Spectral Indices" para Google Earth Engine (GEE) [Español]

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Datasets

• UniNa - Machine Learning 22/23 - MiniContest n1 | Above Ground Biomass estimation from Sentinel-2

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Companies and Software

• Agriobs | "Agriobs is an application that accesses multiple sources of imagery with dynamic presentation and reporting tools."
• OpenEO | "openEO develops an open API to connect R, Python, JavaScript and other clients to big Earth observation cloud back-ends in a simple and unified way-"

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Papers

Official Papers

• A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research - How to cite. Montero, D., Aybar, C., Mahecha, M. D., Martinuzzi, F., Söchting, M., and Wieneke, S.: A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research, Scientific Data 10, 197, https://doi.org/10.1038/s41597-023-02096-0, 2023.
• spectral: Awesome Spectral Indices deployed via the Google Earth Engine JavaScript API - How to cite. Montero, D., Aybar, C., Mahecha, M. D., and Wieneke, S.: SPECTRAL: AWESOME SPECTRAL INDICES DEPLOYED VIA THE GOOGLE EARTH ENGINE JAVASCRIPT API, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W1-2022, 301–306, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-301-2022, 2022.

ASI Citations

• Deep Learning on Synthetic Data Enables the Automatic Identification of Deficient Forested Windbreaks in the Paraguayan Chaco - How to cite. Kriese, J.; Hoeser, T.; Asam, S.; Kacic, P.; Da Ponte, E.; Gessner, U. Deep Learning on Synthetic Data Enables the Automatic Identification of Deficient Forested Windbreaks in the Paraguayan Chaco. Remote Sens. 2022, 14, 4327. https://doi.org/10.3390/rs14174327
• BUSINESS INTELLIGENCE THROUGH MACHINE LEARNING FROM SATELLITE REMOTE SENSING DATA - How to cite. Kyriakos, C. 2022. BUSINESS INTELLIGENCE THROUGH MACHINE LEARNING FROM SATELLITE REMOTE SENSING DATA. Diploma Thesis, University of Thessaly.

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Attribution

The repository banner was created on top of this photo by Evie Shaffer.