Sabine Chabrillat

Leibniz University Hannover

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Bas van Wesemael

UCLouvain

José Alexandre Melo Demattê

Forest Science and Research Institute

Maximilian Brell

Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences

Robert Milewski

Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences

Karl Segl

Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences

Eyal Ben‐Dor

Tel Aviv University

Fabio Castaldi

National Research Council

Eyal Ben Dor

Tel Aviv University

Asa Gholizadeh

Czech University of Life Sciences Prague

Saskia Foerster

Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences

Cooperative Institutions

Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences

Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

Centre National de la Recherche Scientifique

Universidade de São Paulo

Forest Science and Research Institute

Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung

National Research Council

University of Potsdam

Institut de Recherche pour le Développement

University of Helsinki

Author Statistics

Papers

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Hydrothermally altered deposits of 2014 Askja landslide, Iceland, identified by remote sensing imaging

Frontiers in Earth Science (2023)

Pouria Marzban Stefan Bredemeyer Thomas R. Walter Friederike Kästner Daniel Müller

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.

Caldera

10.3389/feart.2023.1083043

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Citations (4)

A Spectral Transfer Function to Harmonize Existing Soil Spectral Libraries Generated by Different Protocols

Applied and Environmental Soil Science (2023)

Nicolas Francos Daniela Heller Pearlshtien José Alexandre Melo Demattê Bas van Wesemael Robert Milewski

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.

Harmonization

Spectral Analysis

Merge (version control)

10.1155/2023/4155390

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Citations (3)

EnGeoMAP Test Data: Simulated EnMAP Satellite Data for Mountain Pass, USA and Rodalquilar, Spain

Nina Bösche Christian Mielke Karl Segl Sabine Chabrillat Christian Rogaß

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Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database

Remote Sensing (2018)

Fabio Castaldi Sabine Chabrillat Arwyn Jones Kristin Vreys Bart Bomans

The most commonly used approach to estimate soil variables from remote-sensing data entails time-consuming and expensive data collection including chemical and physical laboratory analysis. Large spectral libraries could be exploited to decrease the effort of soil variable estimation and obtain more widely applicable models. We investigated the feasibility of a new approach, referred to as bottom-up, to provide soil organic carbon (SOC) maps of bare cropland fields over a large area without recourse to chemical analyses, employing both the pan-European topsoil database from the Land Use/Cover Area frame statistical Survey (LUCAS) and Airborne Prism Experiment (APEX) hyperspectral airborne data. This approach was tested in two areas having different soil characteristics: the loam belt in Belgium, and the Gutland–Oesling region in Luxembourg. Partial least square regression (PLSR) models were used in each study area to estimate SOC content, using both bottom-up and traditional approaches. The PLSR model’s accuracy was tested on an independent validation dataset. Both approaches provide SOC maps having a satisfactory level of accuracy (RMSE = 1.5–4.9 g·kg−1; ratio of performance to deviation (RPD) = 1.4–1.7) and the inter-comparison did not show differences in terms of RMSE and RPD either in the loam belt or in Luxembourg. Thus, the bottom-up approach based on APEX data provided high-resolution SOC maps over two large areas showing the within- and between-field SOC variability.

Topsoil

Soil carbon

10.3390/rs10020153

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Citations (83)

Evaluation of Airborne HySpex and Spaceborne PRISMA Hyperspectral Remote Sensing Data for Soil Organic Matter and Carbonates Estimation

Remote Sensing (2023)

Theodora Angelopoulou Sabine Chabrillat Stefano Pignatti Robert Milewski Konstantinos Karyotis

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.

Soil test

10.3390/rs15041106

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Citations (24)

Minimizing soil moisture variations in multi-temporal airborne imaging spectrometer data for digital soil mapping

Geoderma (2018)

Sanne Diek Sabine Chabrillat Marco Nocita Michael E. Schaepman Rogier de Jong

Silt

Imaging spectrometer

Pedotransfer function

Soil texture

10.1016/j.geoderma.2018.09.052

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Citations (27)

Soil sampling strategies based on multispectral Sentinel-2 and hyperspectral EnMAP satellite data

EGUGA (2019)

Bas van Wesemael Fabio Castaldi Sabine Chabrillat

Source

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Citations (0)

Soil organic carbon estimation using VNIR–SWIR spectroscopy: The effect of multiple sensors and scanning conditions

Soil and Tillage Research (2021)

Asa Gholizadeh Carsten Neumann Sabine Chabrillat Bas van Wesemael Fabio Castaldi

VNIR

Spectroradiometer

Soil test

10.1016/j.still.2021.105017

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Citations (29)

Detection and mapping of shrink–swell clays in SW France, using ASTER imagery

Geological Society London Special Publications (2007)

Anne Bourguignon G. Delpont Stéphane Chevrel Sabine Chabrillat

Abstract Current mapping methods for shrink–swell clays in France are based on the use of existing 1:50 000 geological maps. However, stratigraphy is the primary basis of current published geological mapping, which is detrimental to the accurate mapping of clay minerals, argillaceous lithologies and clayey soils. In the study region, Pyrenean forefront molasse deposits have been mapped in a stratigraphy-dominated way, grouped into a single mapping unit, even though they are made up of eight sequences of continental sands, gravels, clays and lacustrine limestones. Mapping shrink–swell clay hazardous terrain can be improved by spectral methods, allowing rapid delineation of argillaceous units and the identification of their mineralogy, especially with regard to illite, chlorite and kaolinite. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) allows a new approach to regional clay mineral mapping, via the six spectral bands in its short-wave infrared domain, particularly wavelengths between 2.145 and 2.43 µm.

Swell

10.1144/sp283.10

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Citations (17)

Analyses of Namibian Seasonal Salt Pan Crust Dynamics and Climatic Drivers Using Landsat 8 Time-Series and Ground Data

Remote Sensing (2020)

Robert Milewski Sabine Chabrillat Bodo Bookhagen

Salt pans are highly dynamic environments that are difficult to study by in situ methods because of their harsh climatic conditions and large spatial areas. Remote sensing can help to elucidate their environmental dynamics and provide important constraints regarding their sedimentological, mineralogical, and hydrological evolution. This study utilizes spaceborne multitemporal multispectral optical data combined with spectral endmembers to document spatial distribution of surface crust types over time on the Omongwa pan located in the Namibian Kalahari. For this purpose, 49 surface samples were collected for spectral and mineralogical characterization during three field campaigns (2014–2016) reflecting different seasons and surface conditions of the salt pan. An approach was developed to allow the spatiotemporal analysis of the salt pan crust dynamics in a dense time-series consisting of 77 Landsat 8 cloud-free scenes between 2014 and 2017, covering at least three major wet–dry cycles. The established spectral analysis technique Sequential Maximum Angle Convex Cone (SMACC) extraction method was used to derive image endmembers from the Landsat time-series stack. Evaluation of the extracted endmember set revealed that the multispectral data allowed the differentiation of four endmembers associated with mineralogical mixtures of the crust’s composition in dry conditions and three endmembers associated with flooded or muddy pan conditions. The dry crust endmember spectra have been identified in relation to visible, near infrared, and short-wave infrared (VNIR–SWIR) spectroscopy and X-ray diffraction (XRD) analyses of the collected surface samples. According these results, the spectral endmembers are interpreted as efflorescent halite crust, mixed halite–gypsum crust, mixed calcite quartz sepiolite crust, and gypsum crust. For each Landsat scene the spatial distribution of these crust types was mapped with the Spectral Angle Mapper (SAM) method and significant spatiotemporal dynamics of the major surface crust types were observed. Further, the surface crust dynamics were analyzed in comparison with the pan’s moisture regime and other climatic parameters. The results show that the crust dynamics are mainly driven by flooding events in the wet season, but are also influenced by temperature and aeolian activity in the dry season. The approach utilized in this study combines the advantages of multitemporal satellite data for temporal event characterization with advantages from hyperspectral methods for the image and ground data analyses that allow improved mineralogical differentiation and characterization.

Endmember

VNIR

Halite

10.3390/rs12030474

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Citations (19)