EnMap

Sensor simulation

This lecture describes the theoretical process chain of sensor forward simulation. In particular, the individual steps are explained as the signal undergoes a transformation from surface material reflectance via top of atmosphere radiance up to sensor DN (level-0 data), the raw product of real sensors.
This slide collection is one of the basic lectures offered by HYPERedu. It contains all essential information for understanding, but it is recommended to have worked through the lecture “Pre-processing of hyperspectral remote sensing data” beforehand.

Retrieval approaches of vegetation traits from imaging spectroscopy data

This slide collection focusses on quantification using regression analysis and RTMs. Following a short introduction about radiance regime of vegetation, we provide an overview about retrieval methods of vegetation bio-geophysical and biochemical traits from imaging spectroscopy data. Retrieval methods can be classified into the following four methodological categories: (1) parametric & (2) nonparametric regressions, (3) RTMs, and (4) hybrid methods. Further, emulation and quantification of uncertainties will be briefly discussed in this lecture.

Regression based mapping of forest aboveground biomass

This tutorial focuses on mapping forest Aboveground Biomass (AGB) from simulated EnMAP imagery. The slide collection provides the theoretical foundation for the tutorial, including general introductions into AGB, how it’s used, and why it’s important, as well as regression-based mapping of AGB using optical remote sensing data. The practical provides hands-on training for working with the EnMAP-Box, including a basic introduction into its functionalities, including built-in tools such as ImageMath, Scatterplot, and the Regression Workflow.

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Dimensionality reduction of imaging spectroscopy data

This unit provides an introduction to dimensionality reduction methods related to hyperspectral data or imaging spectroscopy. First, feature engineering methods using selected parametric and nonparametric regression methods are presented. Second, feature selection or extraction, using filter, wrapper and embedded methods will be discussed. Finally, some alternative methods are presented.

Regression-based unmixing of urban land cover

This tutorial focuses on regression-based unmixing of urban land cover from simulated EnMAP imagery. The slide collection provides the theoretical foundation for the tutorial, including general introductions into urban land cover mapping and regression-based unmixing using synthetically mixed training data. The practical provides hands-on training for working with the EnMAP-Box, including a basic introduction into its functionalities and spectral library handling, as well as the regression-based unmixing processing chain.

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EnMAP-Box

The EnMAP-Box is a free and open source plug-in for QGIS. It is designed to process imaging spectroscopy data and particularly developed to handle data from the upcoming EnMAP satellite. The EnMAP-Box provides state-of-the-art applications for the processing of high dimensional spectral and temporal remote sensing data and a graphical user interface that enhances the GIS oriented visualization capabilities in QGIS by applications for visualization and exploration of imaging spectroscopy raster data and spectral libraries.

Installation

Getting Started