Search

Current and planned satellite missions such as the Japanese GOSAT (Greenhouse Gases Observing Satellite) and NASA’s OCO (Orbiting Carbon Observatory) series and the upcoming Copernicus Carbon Dioxide Monitoring (CO2M) mission aim to constrain national and regional-scale emissions down to scales of urban agglomerations and large point sources. The CO2Image demonstrator mission of the German Aerospace Center (DLR) is specifically designed to detect and quantify carbon dioxide (CO2) and methane (CH4) emissions from medium-size point sources. To this end its COSIS (Carbon dioxide Sensing Imaging Spectrometer) push-broom grating spectrometer measures reflected solar radiation with a high spatial resolution of 50x50 m2, covering tiles of ~50x50 km2 extent. The instrument has a moderate spectral resolution of approximately ~1 nm and observes in a single spectral window in the 2 µm region.Here we present and discuss the impact of the expected COSIS performance on the retrieved level-2 data. The level-1 data (spectra) are generated using the Py4CAtS (Python for Computational ATmospheric Spectroscopy) line-by-line radiative transfer model and the COSIS SIMulator (COSIS-SIM). Based on the COSIS instrument parameters the analysis examines the retrieval errors related to noise which allows to estimate the detection and quantification limit of CO2 and CH4 emission rates at the instrument’s spatial and spectral resolution. We further discuss the effect of heterogeneous scenes, i.e. high contrast surfaces that cause an effective distortion of the spectral response function by non-uniform illumination of the entrance slit. Finally, we assess the influence of initial guess values for the plume's vertical extent and shape on the retrieval.

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.

The DLR Earth Observation Center (EOC) in Oberpfaffenhofen has been an active contributor to the hyperspectral remote sensing community for more than 25 years. Since 2007 it operates the ISO-certified Optical Airborne Remote Sensing (OpAiRS) user service which aims to provide highly accurate airborne hyperspectral data to DLR scientists and their cooperation partners for the development of the next generation of remote sensing applications. The preparation of upcoming hyperspectral satellite missions and the validation of current space-borne earth observation instruments has been a core activity of OpAiRS since its foundation. From 2012 onwards, we have been operating two HySpex sensors (VNIR-1600 & SWIR 320m-e) as demonstrator for the national German EnMAP mission. Since then, more than 100 datasets have been collected over central Europe for various applications including preparatory work for the EnMAP mission as well as validation measurements for the DESIS spectrometer on-board the ISS and the ESA Copernicus mission Sentinel-2. In order to ensure a continuous high data quality, we maintain a calibration laboratory for airborne sensors, which allows us to characterize the spectral, geometric and radiometric properties of our sensors on a regular basis with benchmark accuracy traceable to national standards. The laboratory was originally set-up in 2008 as Calibration Home Base (CHB) for the airborne imager APEX funded by ESA and it has been actively extended for our calibration related research since then. The presentation will focus on Cal/Val activities for DESIS and Sentinel-2 based on OpAiRS data, and we will also present selected results from OpAiRS campaigns within the scope of the EnMAP mission preparation. Additionally, we are going to provide an overview of current advances in calibration techniques and their influence on the HySpex product quality. Most notably we will discuss a novel method for the homogenization of spectral and geometric response functions, which we developed to reduce systematic errors in inter-instrument comparisons. We also use this method to generate an improved HySpex Level 1C product, which minimizes many typical instrument artifacts and thus simplifies working with the product.

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.

In order to produce accurate and meaningful results from airborne hyperspectral data analysis, it is essential to have exact knowledge about the quality of the image data itself. Small differences in reflectance already produce diverse results particularly when it comes to vegetation analysis. Especially within the complex environment of forested areas having strong shading effects and large inter- and intra-class spectral variability, there are many naturally occurring aspects influencing the accuracy of image-retrieved parameters. Also in addition to the sensors signal-to-noise ratio and atmospheric conditions during data acquisition, the quality of the data analysis is also dependent on the variable viewing and illumination geometry. In the case of implicating multi-temporal data sets, the co-registration issue is another major quality indicator. The Earth Observation Center (EOC) of the German Aerospace Center (DLR) has developed a processing chain which generates standardised data products automatically, allowing the data to be reproduced easily at any time. This includes systematic and radiometric correction, direct georeferencing of the data and atmospheric correction. These processing steps as well as further necessary pre-processing of the data (e.g. spectral filtering, BRDF correction, masking, band selection) will be investigated to allow efficient and appropriate data extraction for forest applications. In this contribution, we will present the results of the pre-processing optimization exemplary for the case of tree species classification. This involves the mentioned pre-processing steps of radiometric correction, ortho-rectification including the image-to-image matching of the VNIR and SWIR cubes, atmospheric correction and class-specific BRDF correction, mosaicking with fine-adjustment between flightlines, as well as the corresponding data quality control issues. In addition to the airborne data itself, training endmember selection and field validation will complete the good practice of “Optimized Processing of Airborne Hyperspectral Data for Forest Studies”. This study is based on a large scale multitemporal, hyperspectral survey flown in 2013, 2015 and 2016 over the Bavarian Forest National Park in the south-eastern part of Germany. The National Park is a unique area of forest stands that developed with low anthropogenic interference into a landscape with remnants of a primeval forest. The park covers an elevation ranging from approximately 600 m to 1450 m above sea level. It therefore represents a heterogenic and complex study area. The data was acquired by DLR’s OpAiRS service using a HySpex system, comprising two imaging spectrometers with spectral ranges of 400-1000 nm and 1000-2500 nm and up to 416 spectral channels.

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.

This thesis treats the optical properties of mixed mineral dust and biomass burning aerosol in the thermal infrared (TIR) based on Fourier Transform infrared spectrometer (FTIR) measurements and radiative transfer simulations. The measurements were part of the Saharan Mineral Dust Experiment 2 (SAMUM-2) conducted from January to February 2008 at Praia, Cape Verde. The large amount of different instruments co-located at the main field site during the campaign resulted in a unique dataset comprising in-situ information and remote sensing data perfectly suited for column closure studies. The ultimate goal of this work is to investigate the consistency of microphysical and TIR remote sensing data. This is achieved by reproducing the measured radiances at top and bottom of the atmosphere (TOA, BOA) with a radiative transfer model, which assimilates the microphysical aerosol information gathered during SAMUM-2. The first part of the thesis describes several experimental efforts, including a novel calibration method and a drift correction algorithm for the ground-based FTIR instrument operated within the scope of SAMUM-2 by the author. The second part introduces the concurrent radiative transfer library PIRATES, which has been developed in the framework of this thesis for the analysis of TIR aerosol optical properties. The third and final part of the treatise compares measured and simulated spectra for various typical scenarios encountered during SAMUM-2. It is demonstrated in three case studies, that measured radiances in the TIR atmospheric window region (8-12 µm) can be reproduced at BOA and TOA by radiative transfer simulations assuming spheroidal model particles. Moreover, spherical particles are shown to be an inadequate model for mineral dust aerosol in this spectral region unless the aerosol optical depth is small.

This thesis treats the optical properties of mixed mineral dust and biomass burning aerosol in the thermal infrared (TIR) based on Fourier Transform infrared spectrometer (FTIR) measurements and radiative transfer simulations. The measurements were part of the Saharan Mineral Dust Experiment 2 (SAMUM-2) conducted from January to February 2008 at Praia, Cape Verde. The large amount of different instruments co-located at the main field site during the campaign resulted in a unique dataset comprising in-situ information and remote sensing data perfectly suited for column closure studies. The ultimate goal of this work is to investigate the consistency of microphysical and TIR remote sensing data. This is achieved by reproducing the measured radiances at top and bottom of the atmosphere (TOA, BOA) with a radiative transfer model, which assimilates the microphysical aerosol information gathered during SAMUM-2. The first part of the thesis describes several experimental efforts, including a novel calibration method and a drift correction algorithm for the ground-based FTIR instrument operated within the scope of SAMUM-2 by the author. The second part introduces the concurrent radiative transfer library PIRATES, which has been developed in the framework of this thesis for the analysis of TIR aerosol optical properties. The third and final part of the treatise compares measured and simulated spectra for various typical scenarios encountered during SAMUM-2. It is demonstrated in three case studies, that measured radiances in the TIR atmospheric window region (8-12 µm) can be reproduced at BOA and TOA by radiative transfer simulations assuming spheroidal model particles. Moreover, spherical particles are shown to be an inadequate model for mineral dust aerosol in this spectral region unless the aerosol optical depth is small.

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.

The German Aerospace Center (DLR) operates the Calibation Home Base (CHB) as a facility for the calibration of airborne imaging spectrometers such as APEX, HySpex and ROSIS, and for field spectrometers. Absolute radiometric calibration is performed so far using an integrating sphere that is traceable to SI units through calibration at the German Metrology Institute PTB. However, the stability of the radiance output is not monitored regularily and reliably since the small size (diameter 50 cm, exit port 20 cm x 4 cm) and the design make it difficult to install a device at a reproducible position which is suited to measure accurately changes of the spectral radiance. This drawback was the motivation to develop a new radiance standard (RASTA) which allows monitoring of small changes in the wavelength range from 380 to 2500 nm. The realisation is presented here.