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A reduction of methane emissions could help to mitigate global warming on a relatively short time scale. Monitoring of local and regional anthropogenic CH4 emissions is crucial in order to increase our understanding of the methane budget which is still subject to scientific debate. The study compares various retrieval schemes that estimate localized CH4 emissions from ventilation shafts in the Upper Silesian Coal Basin (USCB) in Poland using short-wave infrared nadir observations of the airborne imaging spectrometer HySpex. The examined methods are divided into nonlinear and linear schemes. The former class are of iterative nature and encompass various nonlinear least squares setups while the latter are represented by the Matched Filter (MF), Singular Value Decomposition (SVD) and Spectral Signature Detection (SSD) algorithms. Particular emphasis is put on strategies to rem- edy the problem of albedo related biases due to correlation with broad band absorption features caused by the hyperspectral instrument's low spectral resolution. It was found that classical nonlinear least squares fits based on the Beer InfraRed Retrieval Algorithm (BIRRA) suffers from surface-type dependent biases. The effect is more pronounced for retrievals from single spectral intervals but can be mitigated when multiple intervals are combined. The albedo related correlation is also found in the BIRRA solutions for the separable least squares. A new BIRRA setup that exploits the inverse of a scene's covariance structure to account for reflectivity statistics significantly reduces the albedo bias and enhances the CH4 signal so that the method infers two- to threefold higher methane concentrations. The linear estimators turned out to be very fast and well suited to detect enhanced levels of methane. The linearized BIRRA forward model turned out to be sensitive to the selected retrieval interval and in the default setup only works for very narrow windows. Other well established linear methods such as the MF and SVD identified the methane pattern as well and largely agree with the BIRRA fitted enhancements hence the methods allow quantitative estimates of methane. The latter two methods yielded increased performance when the scene was further divided into clusters by applying k-means in a preprocessing step. Methane plumes detected with the simple SSD method were faint and found rather sensitive to the polynomial used to compute the method's residuum ratio.

Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper, we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixel assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications.

In some types of imaging systems, such as imaging spectrometers, the spectral and geometric pixel properties like center wavelength, center angle, response shape and resolution change rapidly between adjacent pixels. Image transformation techniques are required to either correct these effects or to compare images acquired by different systems. In this paper we present a novel image transformation method that allows to manipulate geometric and spectral properties of each pixel individually. The linear transformation employs a transformation matrix to associate every pixel of a target sensor $B$ with all related pixels of a source sensor $A$. The matrix is derived from the cross-correlations of all sensor $A$ pixels and cross-correlations of sensor $A$ and sensor $B$ pixels. We provide the mathematical background, discuss the propagation of uncertainty, demonstrate the use of the method in a case study, and show that the method is a generalization of the Wiener deconvolution filter. In the study, the transformation of images with random, non-uniform pixel properties to distortion-free images leads to errors that are one order of magnitude smaller than those obtained with a conventional approach.

The user service Optical Airborne Remote Sensing & Calibration Homebase (OpAiRS) operates airborne sensors for the development and validation of novel optical remote sensing applications. In close cooperation with DLR’s Research Flight Facilities the ISO certified entity offers expertise throughout the entire systems chain from flight planning via data acquisition to generation of value-added products. OpAiRS aims to provide high quality products with reliable uncertainties especially suited for challenging remote sensing applications and Cal/Val activities. Calibration has been at the focus of OpAiRS activities ever since its foundation and remains a topic of active research. For this purpose a laboratory especially dedicated to the calibration of push-broom sensors has been established and continuously expanded over the last decade. The Calibration Home Base for APEX (CHB) was partially funded by ESA for the calibration of the APEX instrument and is also used for the calibration of the OpAiRS sensor suite. The CHB has been a constant source of innovative methods for improved sensor characterization methods over the last few years. These methods have mainly been developed for the airborne hyperspectral sensor system HySpex acquired by DLR from the Norwegian company NEO in 2011. The same calibration techniques are also employed for the pre-flight calibration of the German earth observation satellite EnMAP in cooperation with the satellite manufacturer OHB. OpAiRS contributions include the concept for radiometric, geometric and spectral calibration of the instrument including a sophisticated stray light correction. The HySpex sensor system is very well suited for pre-launch simulation of EnMAP data and post-launch instrument validation, because the two instruments have similar characteristic properties and are both calibrated based on the same standards and methods developed in the CHB. The presentation will focus on current OpAiRS activities regarding calibration, validation and preparation of the EnMAP mission with HySpex and the CHB. Additionally we aim to present the OpAiRS capabilities for the calibration and validation of current and future satellite missions as a basis for potential cooperation.

The German Aerospace Center’s (DLR) Remote Sensing Technology Institute (IMF) operates a laboratory for the characterisation of imaging spectrometers. Originally designed as Calibration Home Base (CHB) for the imaging spectrometer APEX, the laboratory can be used to characterise nearly every airborne hyperspectral system. Characterisation methods will be demonstrated exemplarily with HySpex, an airborne imaging spectrometer system from Norsk Elektro Optikks A/S (NEO). Consisting of two separate devices (VNIR-1600 and SWIR-320me) the setup covers the spectral range from 400 nm to 2500 nm. Both airborne sensors have been characterised at NEO. This includes measurement of spectral and spatial resolution and misregistration, polarisation sensitivity, signal to noise ratios and the radiometric response. The same parameters have been examined at the CHB and were used to validate the NEO measurements. Additionally, the line spread functions (LSF) in across and along track direction and the spectral response functions (SRF) for certain detector pixels were measured. The high degree of lab automation allows the determination of the SRFs and LSFs for a large amount of sampling points. Despite this, the measurement of these functions for every detector element would be too time-consuming as typical detectors have 105 elements. But with enough sampling points it is possible to interpolate the attributes of the remaining pixels. The knowledge of these properties for every detector element allows the quantification of spectral and spatial misregistration (smile and keystone) and a better calibration of airborne data. Further laboratory measurements are used to validate the models for the spectral and spatial properties of the imaging spectrometers. Compared to the future German spaceborne hyperspectral Imager EnMAP, the HySpex sensors have the same or higher spectral and spatial resolution. Therefore, airborne data will be used to prepare for and validate the spaceborne system’s data.