A Comparison of Fractional Vegetation Cover in Camarena, Spain from DESIS and EnMAP Observations

Fractional vegetation cover (FVC) is an important measure for the conservation, restoration and maintenance of biodiverse environments, giving the spatial patterns and distributions of photosynthetically active (PV) and non-photosynthetically active (NPV) vegetation as well as bare soil (BS) in a given region. Using hyperspectral remote sensing observations from DESIS and EnMAP (Environmental Mapping and Analysis Program), we derive FVC for Camarena, Spain, a semi-arid region southwest of Madrid and an important test site for the upcoming Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and compare the results from both sensors. DESIS and EnMAP are both hyperspectral remote sensing instruments with spatial resolutions of 30 m but they differ in other key aspects. DESIS has a spectral range of 400-1000 nm and a maximum spectral resolution of 2.55 nm whilst EnMAP has a range of 400-2500 nm and a resolution of 6.5-10 nm. The SWIR bands of EnMAP make it far more useful for the derivation of FVC than DESIS due to characteristic absorption features above 1500 nm which help to disentangle NPV and BS spectra. Nevertheless, abundances can still be derived from the FVC processing, accepting that the RMSEs are higher for the DESIS results (13% for PV, 18% for NPV, 9% for BS) than for the EnMAP results (12% for PV, 14% for NPV, 4% for BS). The FVC processing of the DESIS and EnMAP images consists of three steps. After some pre-processing (band removal and smoothing), pure spectra are retrieved from the image using the spatial-spectral endmember extraction method developed by Rogge. This method creates a global set of endmembers from the image after the masking of pixels which are not vegetation or soil. Secondly, the extracted endmembers are classified with a Logistic Regression (for DESIS) or a Random Forest (for EnMAP) classifier which were trained from a spectral library containing 631 samples. Three classes are used for the classification: PV, NPV amd BS. Unmixing is the final stage which uses a MESMA approach where each pixel is considered to be a linear combination of one PV spectrum, one NPV spectrum and one BS spectrum from the labelled endmember library. The class abundance are the weights found in the linear unmixing and an extra shade component is considered. In this work, we will present FVC maps derived from EnMAP and DESIS of Camarena which is a semi-arid region covering approximately 75 km2 in the Province of Toledo, Spain, where the land is mainly used for rainfed agriculture. It has an undulating topography with vegetation growing on sloping areas that were either not considered good enough for farming or later abandoned. Since June 2019, 60 cloud free images were acquired by DESIS over the region and EnMAP has so far acquired 8 cloud free images in this area since launch in April 2022. Several EnMAP images in July-August 2022 coincide closely with a DESIS observation which will enable quantifiable comparisons to be made between the two sensors and allow for an evaluation of the results considering the different wavelength ranges of each sensor

Data Validation of the DLR Earth Sensing Imaging Spectrometer DESIS

Imaging spectrometry provides densely sampled and finely structured spectral information for each image pixel over large areas, enabling the characterization of materials on the Earth's surface by measuring and analyzing quantitative parameters allowing the user to identify and characterize Earth surface materials such as minerals in rocks and soils, vegetation types and stress indicators, and water constituents. The recently launched DLR Earth Sensing Imaging Spectrometer (DESIS) installed on the International Space Station (ISS) closes the long-term gap of sparsely available spaceborne imaging spectrometry data and will be part of the upcoming fleet of such new instruments in orbit. DESIS measures in the spectral range from 400 and 1000 nm with a spectral sampling distance of 2.55 nm and a Full Width Half Maximum (FWHM) of about 3.5 nm. The various DESIS data products available for users are described with the focus on specific processing steps. A summary of the data quality results are given. The product validation studies show that top-of-atmosphere radiance, geometrically corrected, and bottom-of-atmosphere reflectance products meet the mission requirements

The Spaceborne Imaging Spectrometer DESIS: Data Access and Scientific Applications

The DLR Earth Sensing Imaging Spectrometer (DESIS) is a space-based instrument installed and operated on the International Space Station (ISS). This space mission is the achievement of the collaboration between the German Aerospace Center (DLR) and the US company Teledyne Brown Engineering (TBE). DLR has developed the instrument and the software for data processing, while TBE provides the Multi-User System for Earth Sensing (MUSES) platform, where DESIS is installed, and the infrastructure for operation and data tasking

Integration of Spaceborne LiDAR and Imaging Spectroscopy in Vegetation Classification

In the face of a dramatically changing climate, the need to model, monitor, and respond to our environment has never been greater or so nearly within our grasps. Advances in remote sensing have made possible the rise of automated methods to study vegetation at a fine detail over previously unimaginable scales. The 2018 launch of the DLR Earth Sensing Imaging Spectrometer (DESIS) coincided with the beginning of NASA's Global Ecosystem Dynamic Investigation (GEDI) mission. For the first time, high resolution spaceborne LiDAR (GEDI) was onboard the International Space Station (ISS) in tandem with hyperspectral imaging instrumentation (DESIS). This occasion presents a unique opportunity in remote sensing to obtain temporally-proximal spectral and structural information from spaceborne sources. Through the integration of these two data sources, we constructed a random forest classification model to perform successional classification on three classes over a study site in upper Michigan. The classifier was trained over distinct datasets from each instrument, then over a combined dataset utilizing data from both instruments. Over this combined dataset, the model achieved 91.7% classification accuracy, greater than the 80.2% and 88.6% accuracies achieved from either instrument in isolation. These results suggest predictive variation between spectral imaging and structural information from LiDAR can be determined algorithmically as in a random forest classifier.No embargoAcademic Major: Computer Science and Engineerin

Temperate forest soil pH accurately Quantified with image spectroscopy

Forest canopies to some extent obscure passive reflectance of soil traits such as pH, as well as below-canopy vegetation, in the optical to middle infrared portions of the electromagnetic spectrum (approximately 400–2500 nm) which are typically used in airborne and spaceborne image spectrometers. In this study, we present, for the first time, an accurate estimation of soil pH across extensive areas using hyperspectral imaging data obtained from the DLR Earth Sensing Imaging Spectrometer (DESIS) satellite. Furthermore, we investigate the impact of predicted soil pH variation on the concentrations of micronutrients in both leaves and soil. Our modelling is based on a comprehensive in-situ field campaign conducted during the summers of 2020 and 2021. This campaign collected soil pH data for model calibration and validation from 197 plots located across three distinct temperate forest sites: Veluwezoom and Hoge Veluwe National Parks in the Netherlands, as well as the Bavarian Forest National Park in Germany. The soil pH for each test site was accurately predicted by means of a partial least squares regression (PLSR) model, root mean square error (RMSEcv) of 0.22 and the cross-validated coefficient of determination (R2CV) of 0.66. Our findings demonstrate that there are patches of extremely low soil pH possibly due to ongoing soil acidification processes. We saw a particularly significant decrease in soil pH (p ≤ 0.05) in the coniferous forests when compared to the deciduous forest. The acidification of forest soils had a profound impact on the variation of soil and leaf micronutrient content, particularly iron concentration. These results highlight the potential of image spectroscopy data from the DESIS satellite to monitor and estimate soil pH in forested areas over extensive areas given sufficient data. Our findings hold significant implications for soil pH monitoring programs, enabling forest managers to assess the impact of their management practices and gauge their effectiveness in maintaining soil and forest vitality

A Newly Developed Algorithm for Cloud Shadow Detection - TIP Method

The masking of cloud shadows in optical satellite imagery is an important step in automated processing chains. A new method (the TIP method) for cloud shadow detection in multispectral satellite images is presented and compared to current methods. The TIP method is based on the evaluation of thresholds, indices and projections. Most state-of-the-art methods solemnly rely on one of these evaluation steps or on a complex working mechanism. Instead, the new method incorporates three basic evaluation steps into one algorithm for easy and accurate cloud shadow detection. Furthermore the performance of the masking algorithms provided by the software packages ATCOR (“Atmospheric Correction”) and PACO (“Python-based Atmospheric Correction”) is compared with that of the newly implemented TIP method on a set of 20 Sentinel-2 scenes distributed over the globe, covering a wide variety of environments and climates. The algorithms incorporated in each piece of masking software use the class of cloud shadows, but they employ different rules and class-specific thresholds. Classification results are compared to the assessment of an expert human interpreter. The class assignment of the human interpreter is considered as reference or “truth”. The overall accuracies for the class cloud shadows of ATCOR and PACO (including TIP) for difference areas of the selected scenes are 70.4% and 76.6% respectively. The difference area encompasses the parts of the classification image where the classification maps disagree. User and producer accuracies for the class cloud shadow are strongly scene-dependent, typically varying between 45% and 95%. The experimental results show that the proposed TIP method based on thresholds, indices and projections can obtain improved cloud shadow detection performance

Influence of the Solar Spectra Models on PACO Atmospheric Correction

The solar irradiance is the source of energy used by passive optical remote sensing to measure the ground reflectance and, from there, derive the ground properties. Therefore, the precise knowledge of the incoming solar irradiance is fundamental for the atmospheric correction (AC) algorithms. These algorithms use the simulation results of a model of the interactions of the atmosphere with the incoming solar irradiance to determine the atmospheric contribution of the remote sensing observations. This study presents the differences in the atmospherically corrected ground reflectance of multi- and hyper-spectral sensors assuming three different solar models: Thuillier 2003, Fontenla 2011 and TSIS-1 HRS. The results show no difference when the solar irradiance model is preserved through the full processing chain. The differences appear when the solar irradiance model used in the atmospheric correction changes, and this difference is larger between some irrradiance models (e.g., TSIS and Thuillier 2003) than for others (e.g., Fontenla 2011 and TSIS)

Sampling Robustness in Gradient Analysis of Urban Material Mixtures

Many studies analyzing spaceborne hyperspectral images (HSIs) have so far struggled to deal with a lack of pure pixels due to complex mixtures of urban surface materials. Recently, an alternative concept of gradients in urban surface material composition has been proposed and successfully applied to map cities with spaceborne HSIs without the requirement for a previous determination of pure pixels. The gradient concept treats all pixels as mixed and aims to describe and quantify gradual transitions in the cover fractions of surface materials. This concept presents a promising approach to tackle urban mapping using spaceborne HSIs. However, since gradients are determined in a data-driven way, their transferability within urban areas needs to be investigated. For this purpose, we analyze the robustness of urban surface material gradients and their dependence across six systematic and three simple random sampling schemes. The results show high similarity between nine sampling schemes in the primary gradient feature space (Pspace) and individual gradient feature spaces (Ispaces). Comparing the Pspace with the Ispaces, the Mantel statistics show the resemblance of samples' distribution in the Pspace, and each Ispace is rather strong with high credibility, as the significance level is P < 0.01. Therefore, it can be concluded that the material gradients defined in the test area are independent of the specific sampling scheme. This study paves the way for subsequent analysis of the stability of urban surface material gradients and the interpretation of material gradients in other urban environments

Model-based quality assessment of tower-based field spectroscopy measurements

Recent and upcoming satellite missions providing high-quality spectrometric measurements are used for vegetation monitoring and studies of ecosystem functioning which are becoming increasingly important in the context of climate change. The calibration and validation of these measurements are crucial but remain a challenge. The need for in-situ references is high and is expected to increase with the trend toward mini-satellites without onboard calibration systems. In-situ measurements however need to be validated themselves before being used as a reference for air- or space-borne sensors. Crossvalidation of measurements with additional independent measurements is established but costly. Three approaches using two Radiative Transfer Models (RTM) namely the library for Radiative transfer (libRadtran) and the Soil Canopy Observation of Photosynthesis and Energy Fluxes Model (SCOPE) were built to validate in-situ irradiance and radiance measurements based on simulations. The performance of the approaches was assessed from summer to late autumn and over a single clear-sky day resulting in an average Root Mean Square Relative Error (RMSRE) of below 10% for irradiance simulations and 10%-38% RMSRE for radiance simulations compared to in-situ measurements. The higher RMSRE of radiance simulations originates in misspecifications of the reflectance spectrum which is either assumed constant (approach 1) or modelled (approach 2 & 3) based on vegetation parameters. The vegetation parameters however are themselves subject to large uncertainty. Shadowing on the vegetation canopy can additionally lead to ill-posed vegetation parameter selection. The experiments show the potential of coupled RTM-based quality assessment of high-frequency field measurements but also indicate the need for more accurate vegetation canopy parameter estimates and a more sophisticated optimization process to avoid the effects of ill-posedness