Features

Visualization

Like QGIS, just more maps

visualize raster and vector data interactively and in multiple maps, e.g. to compare different band combinations or satellite observations.
each map has it’s individual and fully customizable layer-tree
free arrangement of maps, e.g. side-by-side, horizontally, vertically or in nested-layouts
maps can be linked spatially, e.g. to always have the same map scale, show the same map-center, or both
raster layers can be linked spectrally to always show band combinations with similar wavelengths

Explore your raster data interactively

The EnMAP-Box provides new raster renderers that enhance the visualization of imaging spectroscopy data and other raster outputs, e.g.:

Renderer	Example
Bivariate Color Renderer Visualize two bands using a 2d color ramp.
Class-fraction or probability rendering Visualizes multiple class factions/probabilities at the same time using the original class colors.
HSV color rendering Visualizes 3 bands using the HSV (Hue, Saturation, Value/Black) color model
CMYK Color Raster Renderer Visualizes 4 bands using the CMYK (Cyan, Magenta, Yellow, and Key/Black) color model
Decorelation Stretch Renderer Removing the high correlation between 3 band for a more colorful color composite image.

Spectral Libraries

Your measurements, your data.

The EnMAP box offers a wide range of options for creating spectral libraries and to describe and visualize their spectral profiles.

Read spectral profiles measured with ASD, SVC (*.sig) or Spectral Evolution (*.sed) field spectrometers
Create profiles from raster images, e.g. for given vector locations (point or polygons)
Save spectral profiles in vector datasets and show their coordinates, e.g. using GeoPackage, GeoJSON or DBMS like PostgreSQL or HANA DB
Keep profiles together that belong together, e.g. reference and target radiances and reflectance derived from
Annotate your profiles as needed, e.g. using text (String, Varchar), numeric (int, float) or binary (BLOB) datatypes
Query your profiles using powerful SQL expressions
Plot profiles from different instruments simultaneously against wavelength units, e.g. nanometers, micrometers

Algorithms

The EnMAP-Box adds more that 190 processing algorithms to the QGIS Processing Framework. Start them from the QGIS/EnMAP-Box GUI, from python, command line interfaces, or connect them with algorithms from other plugins in the QGIS Model Builder.

<show python example>

Open the OSGeo4W or conda shell and call:

qgis_process run enmapbox:PredictClassificationLayer ^
      --raster="%data_dir%\enmap_potsdam.tif" ^
      --classifier="%output_dir%\rfc_fit.pkl" ^
      --matchByName=1 ^
      --outputClassification="%output_dir%\classification.tif"

qgis_process run enmapbox:PredictClassificationLayer \
      --raster="$data_dir/enmap_potsdam.tif" \
      --classifier="$output_dir/rfc_fit.pkl" \
      --matchByName=1 \
      --outputClassification="$output_dir/classification.tif"

Applications

Various applications enhance the EnMAP-Box to make it ready for different thematic uses, e.g.:

Application	Keywords	Description
EnMAP Preprocessing Tools (EnPT)	preprocessing	Scheffler et al. 2023, EnPT – an Alternative Pre-Processing Chain for Hyperspectral EnMAP Data, https://doi.org/10.1109/igarss52108.2023.10281805.
Regression-based unmixing	unmixing	Okujeni et al. 2017, Ensemble Learning From Synthetically Mixed Training Data for Quantifying Urban Land Cover With Support Vector Regression. https://doi.org/10.1109/jstars.2016.2634859
Plant Water Retrieval	vegetation	Wocher et al. 2018, Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data, Remote Sensing https://doi.org/10.3390/rs10121924.
Analyze Spectral Integral (ASI)	vegetation	Wocher et al. 2020, RTM-based dynamic absorption integrals for the retrieval of biochemical vegetation traits, doi: https://doi.org/10.1016/j.jag.2020.102219.
Vegetation Processor	vegetation	Danner et al. 2021, “Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops”, ISPRS J Photogramm Remote Sens, 09242716, 173 (2021), pp. 278-296, doi: https://doi.org/10.1016/j.isprsjprs.2021.01.017
Interactive Visualization of Vegetation Reflectance Models (IVVRM)	vegetation, data visualization	Danner et al. 2018, “Developing a sandbox environment for prosail, suitable for education and research” IEEE international geoscience and remote sensing symposium (2018), pp. 783-786, doi: https://doi.org/10.1109/IGARSS.2018.8519378
Interactive Red-Edge Inflection Point (iREIP)	vegetation	Hank et al. 2021, “Introducing the potential of the EnMAP-box for agricultural applications using desis and prisma data”, IEEE international geoscience and remote sensing symposium (2021), pp. 467-470, doi: https://doi.org/10.1109/IGARSS47720.2021.9554729
Vegetation Index Toolbox and Spectral Index Creator	spectral indices	Hank et al. 2021, “Introducing the potential of the EnMAP-box for agricultural applications using desis and prisma data”, IEEE international geoscience and remote sensing symposium (2021), pp. 467-470, doi: https://doi.org/10.1109/IGARSS47720.2021.9554729
EnMAP Soil Mapper (EnSoMap)	soil	Mielke et al. 2018, “Engeomap and Ensomap: Software Interfaces for Mineral and Soil Mapping under Development in the Frame of the Enmap Mission,” in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Jul. 2018, pp. 8369–8372. doi: https://doi.org/10.1109/igarss.2018.8517902
EnMAP Geological Mapper (EnGeoMap)	geology	Mielke et al. 2018, “Engeomap and Ensomap: Software Interfaces for Mineral and Soil Mapping under Development in the Frame of the Enmap Mission,” in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Jul. 2018, pp. 8369–8372. doi: https://doi.org/10.1109/igarss.2018.8517902
EO Time Series Viewer	timeseries	Jakimow et al. 2020, “Visualizing and labeling dense multi-sensor earth observation time series: The EO time series viewer”, Environ Model Softw, 13648152, 125 (2020), doi: https://doi.org/10.1016/j.envsoft.2020.104631
GEE Time Series Explorer	timeseries	Rufin Pet al. 2021, “GEE Timeseries Explorer for QGIS – Instant Access to Petabytes of Earth Observaton Data.” Int Arch Photogramm Remote Sens Spatial Inf Sci, 2194-9034, XLVI-4/W2-2021 (2021), pp. 155-158, doi: https://doi.org/10.5194/isprs-archives-XLVI-4-W2-2021-155-2021
Scatter Plots	data visualization
OLCI Neural Network Swarm (ONNS)	water	Ocean color analysis Hieronymi et al. 2017, “The OLCI neural network swarm (ONNS): A bio-geo-optical algorithm for open ocean and coastal waters” Front Mar Sci, 2296-7745, 4 (2017), p. 140, doi: https://doi.org/10.3389/fmars.2017.00140
OC-PFT	water	Retrieval of Phytoplankton Functional Types (PFTs) from satellite or in situ chlorophyll-a (Chl-a) measurements. Alvarado et al. 2022, “Retrievals of the main phytoplankton groups at Lake Constance using OLCI, DESIS, and evaluated with fieldobservations”. 12th EARSeL workshop. 2022.
Image Cube	general, data visualization
Raster Math	general
Classification Workflow	general, classification
Regression Workflow	general, regression