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Our soils are the largest terrestrial carbon storage of the planet. With proceeding climate change and to retain food security for a growing human population, the pressure rises to increase the knowledge about the soil’s current status and especially its carbon content. Mapping the status of the soil carbon content presents a challenge, especially for larger areas, since conventional methods require extensive sample collection and expensive laboratory analyses. To fully utilise the soil’s capacity and preserve the its health in the future, it is required to monitor key soil parameters thereby adding a temporal dimension. Therefore, suitable approaches are needed to estimate the carbon content regularly and on larger scales. Soil spectroscopy has the potential to support this aim. At laboratory and airborne scale soil spectroscopy already proved to be an accurate method to estimate certain soil properties. Recently, advanced optical hyperspectral spaceborne imaging sensors have become available that have the potential to create quantitative soil carbon maps. These Earth observation (EO) sensors can also be a future monitoring tool to e.g. combat soil degradation. In this thesis, approaches were developed and applied to evaluate these new hyperspectral EO sensors to map the spectrally active soil organic carbon (SOC) content at different scales. Ground reference data represent a bottle neck but are essential for model generation and evaluation. A potential solution is the additional usage of existing large scale soil spectral libraries (SSLs) which contain information on physiochemical and spectral properties. Thereby, the challenge is the decreasing model accuracy with increasing size of the study site. Therefore, the first aim was to improve the prediction accuracy of SOC content using laboratory spectra and the European wide LUCAS SSL. Especially a memory-based learning approach (local partial least square regression: local PLSR) could improve the prediction accura

In this work, we develop a software suite for studies of atmosphere–underlying SNOW-spaceborne optical receiver light TRANsmission calculations (SNOWTRAN) with applications for the solution of forward and inverse radiative transfer problems in polar regions. Assuming that the aerosol load is extremely low, the proposed theory does not require the numerical procedures for the solution of the radiative transfer equation and is based on analytical equations for the spectral nadir reflectance and simple approximations for the local optical properties of atmosphere and snow. The developed model is validated using EnMAP and PRISMA spaceborne imaging spectroscopy data close to the Concordia research station in Antarctica. A new, fast technique for the determination of the snow grain size and assessment of the snowpack vertical inhomogeneity is then proposed and further demonstrated on EnMAP imagery over the Aviator Glacier and in the vicinity of the Concordia research station in Antarctica. The results revealed a large increase in precipitable water vapor at the Concordia research station in February 2023 that was linked to a warming event and a four times larger grain size at Aviator Glacier compared with Dome C.

Methane (CH4) mitigation from anthropogenic sources such as in the production and transport of fossil fuels has been found as one of the most promising strategies to curb global warming in the near future. Satellite-based imaging spectrometers have demonstrated to be well-suited to detect and quantify these emissions at high spatial resolution, which allows the attribution of plumes to sources. The PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite mission (ASI, Italy) has been successfully used for this application, and the recently launched Environmental Mapping and Analysis Program (EnMAP) mission (DLR/GFZ, Potsdam, Germany) presents similar spatial and spectral characteristics (30-m spatial resolution, 30-km swath, about 8-nm spectral sampling at 2300 nm). In this work, we investigate the potential and limitations of EnMAP for CH4 remote sensing, using PRISMA as a benchmark to deduce its added value. We analyze the spectral and radiometric performance of EnMAP in the 2300-nm region used for CH4 retrievals acquired using the matched-filter method. Our results show that in arid areas, EnMAP spectral resolution is about 2.7 nm finer and the signal-to-noise ratio values are approximately twice as large, which leads to an improvement in retrieval performance. Several EnMAP examples of plumes from different sources around the world with flux rate values ranging from 1 to 20 t/h are illustrated. We show plumes from sectors such as onshore oil and gas (O&G) and coal mining, but also from more challenging sectors such as landfills and offshore O&G. We detect two plumes in a close-to-sunglint configuration dataset with unprecedented flux rates of about 1 t/h, which suggests that the detection limit in offshore areas can be considerably lower under favorable conditions.

Volcanic flanks subject to hydrothermal alteration become mechanically weak and gravitationally unstable, which may collapse and develop far-reaching landslides. The dynamics and trajectories of volcanic landslides are hardly preserved and challenging to determine, which is due to the steep slopes and the inherent instability. Here we analyze the proximal deposits of the 21 July 2014, landslide at Askja (Iceland), by combining high-resolution imagery from satellites and Unoccupied Aircraft Systems. We performed a Principal Component Analysis in combination with supervised classification to identify different material classes and altered rocks. We trained a maximum-likelihood classifier and were able to distinguish 7 different material classes and compare these to ground-based hyperspectral measurements that we conducted on different rock types found in the field. Results underline that the Northern part of the landslide source region is a hydrothermally altered material class, which bifurcates halfway downslope and then extends to the lake. We find that a large portion of this material is originating from a lava body at the landslide headwall, which is the persistent site of intense hydrothermal activity. By comparing the classification result to in-situ hyperspectral measurements, we were able to further identify the involved types of rocks and the degree of hydrothermal alteration. We further discuss associated effects of mechanical weakening and the relevance of the heterogeneous materials for the dynamics and processes of the landslide. As the study demonstrates the success of our approach for identification of altered and less altered materials, important implications for hazard assessment in the Askja caldera and elsewhere can be drawn.

Soil spectral libraries (SSLs) are important big-data archives (spectra associated with soil properties) that are analyzed via machine-learning algorithms to estimate soil attributes. Since different spectral measurement protocols are applied when constructing SSLs, it is necessary to examine harmonization techniques to merge the data. In recent years, several techniques for harmonization have been proposed, among which the internal soil standard (ISS) protocol is the most largely applied and has demonstrated its capacity to rectify systematic effects during spectral measurements. Here, we postulate that a spectral transfer function (TF) can be extracted between existing (old) SSLs if a subset of samples from two (or more) different SSLs are remeasured using the ISS protocol. A machine-learning TF strategy was developed, assembling random forest (RF) spectral-based models to predict the ISS spectral condition using soil samples from two existing SSLs. These SSLs had already been measured using different protocols without any ISS treatment the Brazilian (BSSL, generated in 2019) and the European (LUCAS, generated in 2009-2012) SSLs. To verify the TF's ability to improve the spectral assessment of soil attributes after harmonizing the different SSLs' protocols, RF spectral-based models for estimating organic carbon (OC) in soil were developed. The results showed high spectral similarities between the ISS and the ISS-TF spectral observations, indicating that post-ISS rectification is possible. Furthermore, after merging the SSLs with the TFs, the spectral-based assessment of OC was considerably improved, from R2 = 0.61, RMSE (g/kg) = 12.46 to R2 = 0.69, RMSE (g/kg) = 11.13. Given our results, this paper enhances the importance of soil spectroscopy by contributing to analyses in remote sensing, soil surveys, and digital soil mapping.

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Remote sensing and soil spectroscopy applications are valuable techniques for soil property estimation. Soil organic matter (SOM) and calcium carbonate are important factors in soil quality, and although organic matter is well studied, calcium carbonates require more investigation. In this study, we validated the performance of laboratory soil spectroscopy for estimating the aforementioned properties with referenced in situ data. We also examined the performance of imaging spectroscopy sensors, such as the airborne HySpex and the spaceborne PRISMA. For this purpose, we applied four commonly used machine learning algorithms and six preprocessing methods for the evaluation of the best fitting algorithm.. The study took place over crop areas of Amyntaio in Northern Greece, where extensive soil sampling was conducted. This is an area with a very variable mineralogical environment (from lignite mine to mountainous area). The SOM results were very good at the laboratory scale and for both remote sensing sensors with R2 = 0.79 for HySpex and R2 = 0.76 for PRISMA. Regarding the calcium carbonate estimations, the remote sensing accuracy was R2 = 0.82 for HySpex and R2 = 0.36 for PRISMA. PRISMA was still in the commissioning phase at the time of the study, and therefore, the acquired image did not cover the whole study area. Accuracies for calcium carbonates may be lower due to the smaller sample size used for the modeling procedure. The results show the potential for using quantitative predictions of SOM and the carbonate content based on soil and imaging spectroscopy at the air and spaceborne scales and for future applications using larger datasets.

The paper presents the first retrievals of clean snow properties using spaceborne hyperspectral observations via the Environmental Mapping and Analysis Program (EnMAP). The location close to the Concordia station at the Dome C Plateau (Antarctica) was selected. At this location, the atmospheric effects (except molecular light scattering and absorption) are weak, and the simplified atmospheric correction scheme could be applied. The ice grain size, snow specific surface area, and snow spectral and broadband albedos were retrieved using single-view EnMAP measurements. In addition, we propose a technique to retrieve trace gas concentrations (e.g., water vapor and ozone) from EnMAP observations over the snow surfaces. A close correspondence of satellite and ground-measured parameters was found.