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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - EnMAP-Box 3 a free and open source Python plug-in for QGIS
摘要： The EnMAP-Box is a toolbox designed to process imaging spectroscopy data and particularly developed to handle data from the upcoming EnMAP (Environmental Mapping and Analysis Program) sensor. In near future, it will offer algorithms for user-defined Level-2a preprocessing, for advanced processing of spectral imagery as well as for EnMAP-specific product generation. It serves as a platform for sharing and distributing algorithms and methods among scientists and potential end-users. Starting with version 3.0 the EnMAP-Box is a free and open source (FOSS) Python plug-in for the geographic information system QGIS.
关键词： QGIS Plug-In,algorithm development,hyperspectral,FOSS,EnMAP
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Developing a Sandbox Environment for Prosail, Suitable for Education and Research
摘要： We introduce the Interactive Visualization of Vegetation Reflectance Models (IVVRM) tool as a sandbox environment for the PROSAIL family of radiative transfer models. Every interaction with the Graphical User Interface (GUI) invokes a new model run of the updated parameter set and the results are instantly plotted on the screen. The quasi-simultaneous response allows easy hands-on practice with PROSAIL for education and training as well as straightforward inversions of biophysical variables from spectra by manual curve fitting. It is shown that expert knowledge can improve the quality of parameter retrieval and reveal sources of uncertainties in the field data and the models. IVVRM is free of charge and available as an application through the EnMAP-Box 3.0.
关键词： reflectance modelling,model environment,PROSAIL,radiative transfer,EnMAP-Box
更新于2025-09-23 15:21:21
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Coupled retrieval of the three phases of water from spaceborne imaging spectroscopy measurements
摘要： Measurements of reflected solar radiation by imaging spectrometers can quantify water in different states (solid, liquid, gas) thanks to the discriminative absorption shapes. We developed a retrieval method to quantify the amount of water in each of the three states from spaceborne imaging spectroscopy data, such as those from the German EnMAP mission. The retrieval couples atmospheric radiative transfer simulations from the MODTRAN5 radiative transfer code to a surface reflectance model based on the Beer-Lambert law. The model is inverted on a per-pixel basis using a maximum likelihood estimation formalism. Based on a unique coupling of the canopy reflectance model HySimCaR and the EnMAP end-to-end simulation tool EeteS, we performed a sensitivity analysis by comparing the retrieved values with the simulation input leading to an R2 of 0.991 for water vapor and 0.965 for liquid water. Furthermore, we applied the algorithm to airborne AVIRIS-C data to demonstrate the ability to map snow/ice extent as well as to a CHRIS-PROBA dataset for which concurrent field measurements of canopy water content were available. The comparison between the retrievals and the ground measurements showed an overall R2 of 0.80 for multiple crop types and a remarkable clustering in the regression analysis indicating a dependency of the retrieved water content from the physical structure of the vegetation. In addition, the algorithm is able to produce smoother and more physically-plausible water vapor maps than the ones from the band ratio approaches used for multispectral data, since biases due to background reflectance are reduced. The demonstrated potential of imaging spectroscopy to provide accurate quantitative measures of water from space will be further exploited using upcoming spaceborne imaging spectroscopy missions like PRISMA or EnMAP.
关键词： Atmospheric correction,EnMAP,Canopy water content,Water vapor,Imaging spectroscopy
更新于2025-09-23 15:21:01
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Capability of Spaceborne Hyperspectral EnMAP Mission for Mapping Fractional Cover for Soil Erosion Modeling
摘要： Soil erosion can be linked to relative fractional cover of photosynthetic-active vegetation (PV), non-photosynthetic-active vegetation (NPV) and bare soil (BS), which can be integrated into erosion models as the cover-management C-factor. This study investigates the capability of EnMAP imagery to map fractional cover in a region near San Jose, Costa Rica, characterized by spatially extensive coffee plantations and grazing in a mountainous terrain. Simulated EnMAP imagery is based on airborne hyperspectral HyMap data. Fractional cover estimates are derived in an automated fashion by extracting image endmembers to be used with a Multiple End-member Spectral Mixture Analysis approach. The C-factor is calculated based on the fractional cover estimates determined independently for EnMAP and HyMap. Results demonstrate that with EnMAP imagery it is possible to extract quality endmember classes with important spectral features related to PV, NPV and soil, and be able to estimate relative cover fractions. This spectral information is critical to separate BS and NPV which greatly can impact the C-factor derivation. From a regional perspective, we can use EnMAP to provide good fractional cover estimates that can be integrated into soil erosion modeling.
关键词： EnMAP,Costa Rica,imaging spectroscopy,soil erosion modeling,spectral mixture analysis
更新于2025-09-19 17:15:36
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Unsupervised Feature Selection Based on Ultrametricity and Sparse Training Data: A Case Study for the Classification of High-Dimensional Hyperspectral Data
摘要： In this paper, we investigate the potential of unsupervised feature selection techniques for classification tasks, where only sparse training data are available. This is motivated by the fact that unsupervised feature selection techniques combine the advantages of standard dimensionality reduction techniques (which only rely on the given feature vectors and not on the corresponding labels) and supervised feature selection techniques (which retain a subset of the original set of features). Thus, feature selection becomes independent of the given classification task and, consequently, a subset of generally versatile features is retained. We present different techniques relying on the topology of the given sparse training data. Thereby, the topology is described with an ultrametricity index. For the latter, we take into account the Murtagh Ultrametricity Index (MUI) which is defined on the basis of triangles within the given data and the Topological Ultrametricity Index (TUI) which is defined on the basis of a specific graph structure. In a case study addressing the classification of high-dimensional hyperspectral data based on sparse training data, we demonstrate the performance of the proposed unsupervised feature selection techniques in comparison to standard dimensionality reduction and supervised feature selection techniques on four commonly used benchmark datasets. The achieved classification results reveal that involving supervised feature selection techniques leads to similar classification results as involving unsupervised feature selection techniques, while the latter perform feature selection independently from the given classification task and thus deliver generally versatile features.
关键词： classification,EnMAP data,land cover,ultrametricity,AVIRIS data,sparse training data,ROSIS data,land use,hyperspectral imagery,unsupervised feature selection
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Hyperspectral Retrieval of Canopy Water Content Through Inversion of the Beer-Lambert Law
摘要： The retrieval of quantitative equivalent water thickness on canopy level (EWTc) is an agriculturally important task for hyperspectral remote sensing. In this study the Beer-Lambert law is applied to inversely determine water content from measured winter wheat spectra collected in 2015 and 2017. The spectral model is calibrated using a look-up-table (LUT) of 50.000 PROSPECT spectra. Validation was performed using two leaf optical properties datasets (LOPEX93 and ANGERS) and in-situ data acquired in Southern Germany. After considering destructive in-situ water content measurements separately for leaves, stems, and fruits, results indicate optically active plant water by plant component in the 930 to 1060 nm range of canopy reflectance. Results for spectrally derived EWTc were most promising for leaves and ears reaching coefficients of determination up to 0.75 and a normalized RMSE (nRMSE) of 24% between measured and estimated canopy water content.
关键词： EnMAP,hyperspectral,agriculture,canopy water content,spectroscopy
更新于2025-09-04 15:30:14
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Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data
摘要： Quantitative equivalent water thickness on canopy level (EWTcanopy) is an important land surface variable and retrieving EWTcanopy from remote sensing has been targeted by many studies. However, the effect of radiative penetration into the canopy has not been fully understood. Therefore, in this study the Beer-Lambert law is applied to inversely determine water content information in the 930 to 1060 nm range of canopy reflectance from measured winter wheat and corn spectra collected in 2015, 2017, and 2018. The spectral model was calibrated using a look-up-table (LUT) of 50,000 PROSPECT spectra. Internal model validation was performed using two leaf optical properties datasets (LOPEX93 and ANGERS). Destructive in-situ measurements of water content were collected separately for leaves, stalks, and fruits. Correlation between measured and modelled water content was most promising for leaves and ears in case of wheat, reaching coefficients of determination (R2) up to 0.72 and relative RMSE (rRMSE) of 26% and in case of corn for the leaf fraction only (R2 = 0.86, rRMSE = 23%). These findings indicate that, depending on the crop type and its structure, different parts of the canopy are observed by optical sensors. The results from the Munich-North-Isar test sites indicated that plant compartment specific EWTcanopy allows us to deduce more information about the physical meaning of model results than from equivalent water thickness on leaf level (EWT) which is upscaled to canopy water content (CWC) by multiplication of the leaf area index (LAI). Therefore, it is suggested to collect EWTcanopy data and corresponding reflectance for different crop types over the entire growing cycle. Nevertheless, the calibrated model proved to be transferable in time and space and thus can be applied for fast and effective retrieval of EWTcanopy in the scope of future hyperspectral satellite missions.
关键词： EnMAP,hyperspectral,spectroscopy,equivalent water thickness,canopy water content,agriculture
更新于2025-09-04 15:30:14