EnMAP-Box 3 Documentation
The EnMAP-Box is a python plugin for QGIS, designed to process and visualise hyperspectral remote sensing data.
Get going with the Installation and the Getting Started chapter. Have a look at the Cookbook for usage examples!
Key features
Extend your QGIS for remote sensing image analysis
Add powerful tools to process and analyse imaging spectroscopy data
Integrate machine learning algorithms into your image classification and regression with Random Forests, Support Vector Machines and many more
Create and manage spectral libraries with attribute data
Develop your own image processing algorithms using a powerful Python API

News
FOSSGIS 2023 Berlin
The EnMAP-Box will be present at the FOSSGIS with a demosession: Visualisierung und Analyse von Satellitenbildern mit der EnMAP-Box (https://pretalx.com/fossgis2023/talk/9WAMJ9/)
Looking forward meeting you there!
UPDATE - Here is the recording of the presentation:
EnMAP-Box Version 3.11 released
Oct. 10, 2022
A new EnMAP-Box version has been released. A list of all improvements can be found in the changelog.
EnMAP-Box Version 3.10 released
June 09, 2022
A new EnMAP-Box version has been released. A list of all improvements can be found in the changelog.
EnMAP-Box Version 3.9 released
Oct. 10, 2021
A new EnMAP-Box version has been released. A list of all improvements can be found in the changelog.
EnMAP-Box Workshop 2021
June 07, 2021
The next EnMAP-Box workshop will be held online on 21-23 June, 2021. The workshop aims to demonstrate the current state of the EnMAP-Box by way of presentations, live demos with Q&A and self-paced tutorials. Registration for up to 250 participants is now open at the workshop website.

FOSSGIS 2021
June 09, 2021
Benjamin Jakimow presented how to use Spectral Libraries in QGIS using the EnMAP-Box (german).
EnMAP-Box Version 3.7 released
Oct. 27, 2020
A new EnMAP-Box version has been released. It includes product readers for EnMAP L1B, L2C and L2A, PRISMA L2D and DESIS L2A images, and a first version of Spectral Math in the QGIS Expression Builder. A list of all improvements can be found in the changelog.
FOSSGIS 2020
Andreas Rabe presented the EnMAP-Box at the FOSSGIS 2020 in Freiburg. See the full live-demo session here (german):
QGIS User Conference 2019
Two Presentations by EnMAP-Box developers Andreas Rabe and Benjamin Jakimow at the QGIS User conference in Coruña
General
User Section
- Installation
- Installation (NEW)
- Getting Started
- Cookbook
- User Manual
- The GUI
- Tools
- Add Product
- Add Web Map Services (WMS)
- Band Statistics
- Bivariate Color Raster Renderer
- Class Fraction/Probability Renderer and Statistics
- Classification Statistics
- CMYK Color Raster Renderer
- Color Space Explorer
- Decorrelation Stretch Renderer
- Enhanced Multiband Color Renderer
- HSV Color Raster Renderer
- Image Cube
- Metadata Viewer
- Multisource Multiband Color Raster Renderer
- Raster Layer Styling
- Raster Source Band Properties Editor
- Reclassify
- Scatter Plot
- Virtual Raster Builder
- Applications
- Agricultural Applications
- Classification Dataset Manager
- Classification workflow
- Classification Workflow (advanced)
- Classification Workflow (deprecated)
- EO Time Series Viewer
- EnPT (EnMAP Processing Tool)
- GFZ EnGeoMAP
- Image Math (deprecated)
- Raster math
- Regression Dataset Manager
- Regression Workflow (deprecated)
- Spectral Index Creator
- Earth Observation for QGIS (EO4Q)
- Example Data
- Processing Algorithms
- Auxilliary
- Classification
- Class fraction layer from categorized vector layer
- Class separability report
- Classification layer accuracy and area report (for simple random sampling)
- Classification layer accuracy and area report (for stratified random sampling)
- Classification layer from class probability/fraction layer
- Classification layer from rendered image
- Classification workflow
- Classifier performance report
- Fit CatBoostClassifier
- Fit GaussianProcessClassifier
- Fit generic classifier
- Fit LGBMClassifier
- Fit LinearSVC
- Fit LogisticRegression
- Fit RandomForestClassifier
- Fit SpectralAngleMapper
- Fit SVC (polynomial kernel)
- Fit SVC (RBF kernel)
- Fit XGBClassifier
- Fit XGBRFClassifier
- Predict class probability layer
- Predict classification layer
- Reclassify raster layer
- Clustering
- Convolution, morphology and filtering
- Spatial convolution Airy Disk filter
- Spatial convolution Box filter
- Spatial convolution custom filter
- Spatial convolution Gaussian filter
- Spatial convolution Moffat filter
- Spatial convolution Ricker Wavelet filter
- Spatial convolution ring filter
- Spatial convolution Savitsky-Golay filter
- Spatial convolution Top-Hat filter
- Spatial convolution Trapezoid filter
- Spatial Gaussian Gradient Magnitude filter
- Spatial generic filter
- Spatial Laplace filter
- Spatial Maximum filter
- Spatial Median filter
- Spatial Minimum filter
- Spatial morphological Binary Closing filter
- Spatial morphological Binary Dilation filter
- Spatial morphological Binary Erosion filter
- Spatial morphological Binary Fill Holes filter
- Spatial morphological Binary Opening filter
- Spatial morphological Binary Propagation filter
- Spatial morphological Black Top-Hat filter
- Spatial morphological Gradient filter
- Spatial morphological Grey Dilation filter
- Spatial morphological Grey Erosion filter
- Spatial morphological Grey Opening filter
- Spatial morphological Laplace filter
- Spatial morphological White Top-Hat filter
- Spatial Percentile filter
- Spatial Prewitt filter
- Spatial Sobel filter
- Spectral convolution Box filter
- Spectral convolution Gaussian filter
- Spectral convolution Ricker Wavelet filter
- Spectral convolution Savitsky-Golay filter
- Spectral convolution Trapezoid filter
- Dataset creation
- Create classification dataset (from categorized raster layer and feature raster)
- Create classification dataset (from categorized spectral library)
- Create classification dataset (from categorized vector layer and feature raster)
- Create classification dataset (from categorized vector layer with attribute table)
- Create classification dataset (from JSON file)
- Create classification dataset (from Python code)
- Create classification dataset (from table with categories and feature fields)
- Create classification dataset (from text files)
- Create regression dataset (from continuous-valued raster layer and feature raster)
- Create regression dataset (from continuous-valued spectral library)
- Create regression dataset (from continuous-valued vector layer and feature raster)
- Create regression dataset (from continuous-valued vector layer with attribute table)
- Create regression dataset (from JSON file)
- Create regression dataset (from Python code)
- Create regression dataset (from table with target and feature fields)
- Create regression dataset (from text files)
- Create regression dataset (SynthMix from classification dataset)
- Create unsupervised dataset (from feature raster)
- Create unsupervised dataset (from JSON file)
- Create unsupervised dataset (from Python code)
- Create unsupervised dataset (from spectral library)
- Create unsupervised dataset (from text file)
- Create unsupervised dataset (from vector layer with attribute table)
- Merge classification datasets
- Random samples from classification dataset
- Random samples from regression dataset
- Select features from dataset
- Export data
- Feature selection
- Import data
- Import DESIS L1B product
- Import DESIS L1C product
- Import DESIS L2A product
- Import EMIT L2A product
- Import EnMAP L1B product
- Import EnMAP L1C product
- Import EnMAP L2A product
- Import Landsat L2 product
- Import PRISMA L1 product
- Import PRISMA L2B product
- Import PRISMA L2C product
- Import PRISMA L2D product
- Import Sentinel-2 L2A product
- Masking
- Raster analysis
- Raster conversion
- Raster miscellaneous
- Raster projections
- Regression
- Fit CatBoostRegressor
- Fit GaussianProcessRegressor
- Fit generic regressor
- Fit KernelRidge
- Fit LGBMRegressor
- Fit LinearRegression
- Fit LinearSVR
- Fit PLSRegression
- Fit RandomForestRegressor
- Fit SVR (polynomial kernel)
- Fit SVR (RBF kernel)
- Fit XGBRegressor
- Fit XGBRFRegressor
- Predict regression layer
- Receiver operating characteristic (ROC) and detection error tradeoff (DET) curves
- Regression layer accuracy report
- Regression workflow
- Regressor performance report
- Spectral Index Optimizer
- Spectral resampling
- Spectral resampling (to custom sensor)
- Spectral resampling (to DESIS)
- Spectral resampling (to EnMAP)
- Spectral resampling (to Landsat 4/5 TM)
- Spectral resampling (to Landsat 7 ETM+)
- Spectral resampling (to Landsat 8/9 OLI)
- Spectral resampling (to PRISMA)
- Spectral resampling (to response function library)
- Spectral resampling (to Sentinel-2A MSI)
- Spectral resampling (to Sentinel-2B MSI)
- Spectral resampling (to wavelength)
- Spectral resampling (to wavelength and FWHM)
- Transformation
- Unmixing
- Vector conversion
- Vector creation
- Metadata Guide
- EnMAP Data
- Application Tutorials
- Regression-based unmixing of urban land cover
- Regression-based mapping of forest aboveground biomass
- Ocean Colour analysis with ONNS
- EnGeoMAP 3.1 Tutorial
- Manual Retrieval of Vegetation Variables using IVVRM
- SIO and DASF for N Estimation
- EnPT
- Workshop Tutorials
Developer Section