Special Issue "Remote Sensing for Soil Organic Carbon Mapping and Monitoring"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 31 July 2022.

Special Issue Editors

Prof. Dr. Bas van Wesemael
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Guest Editor
Georges Lemaître Centre for Earth and Climate Research, Université Catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium
Interests: soil organic carbon; VisNIR spectroscopy; Hyperspectral remote sensing; multivariate calibration; Digital soil mapping
Special Issues and Collections in MDPI journals
Dr. Sabine Chabrillat
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Guest Editor
Geoforschungszentrum Potsdam, Sect Remote Sensing 1 4, D-14473 Potsdam, Germany
Interests: soil spectroscopy; organic carbon; remote sensing
Special Issues and Collections in MDPI journals
Dr. Adrián Sanz Díaz
E-Mail Website
Guest Editor
GMV, Santiago Grisolía, 4, P.T.M. Tres Cantos, E-28760 Madrid, Spain
Interests: image processing; remote sensing; machine learning; cloud computing
Dr. Michael Berger
E-Mail Website
Guest Editor
European Space Agency, ESA—ESRIN, Largo Galileo Galilei 1, I-00044 Frascati, Italy
Interests: EO space missions; Remote sensing; thermal and optical EO systems; image processing; imaging spectroscopy

Special Issue Information

Dear Colleagues,

Recently, the availability and quality of optical satellite remote sensing data have dramatically changed the paradigm for soil mapping and monitoring. Remote sensing of soil organic carbon (SOC) becomes feasible in a coherent manner from regional to global scales. The change of SOC over time is an important indicator of CO2 sequestration in soils and is often cited as a natural climate solution (NCS). A new generation of space-based hyperspectral missions is under implementation, giving rise to an additional advancement to the already promising results obtained using the Sentinel-2 multispectral instrument. The DESIS and PRISMA instruments are already available; the ENMAP and EMIT are ready to be launched; and the Surface Biology and Geology (SBG) and Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) instruments are in the planning stage. Simultaneously, new methodologies and large soil spectral libraries are available or in development, which can be used for improved SOC modeling based on spectral data.

Promising results based on spaceborne sensors have been obtained by merging two types of techniques in order to map SOC from both permanently vegetated areas and exposed soils: i) for areas covered by permanent vegetation, Digital Soil Mapping (DSM) relying on empirical relationships between measured soil properties and spatially distributed co-variates, and ii) for exposed (mainly cropland) soils, imaging spectrometry based on chemometric techniques.

We welcome original manuscripts on the use of optical and thermal multi- or hyperspectral imagery for SOC mapping, as well as on the challenges involved in producing coherent SOC maps. Such challenges are the compositing of the images in order to increase the coverage of satellite imagery; the transfer of spectral models from spectral libraries to the remote sensing signal; dealing with mixed pixels and improved covariates for mapping soil properties in permanently vegetated areas.

Prof. Dr. Bas van Wesemael
Dr. Sabine Chabrillat
Dr. Adrián Sanz Díaz
Dr. Michael Berger
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hyperspectral satellites
  • imaging spectrometry
  • soil remote sensing
  • soil organic carbon maps
  • natural climate solutions
  • multispectral satellite missions
  • spectral modeling
  • digital soil mapping

Published Papers (3 papers)

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Research

Article
Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites
Remote Sens. 2021, 13(16), 3141; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163141 - 08 Aug 2021
Viewed by 683
Abstract
For food security issues or global climate change, there is a growing need for large-scale knowledge of soil organic carbon (SOC) contents in agricultural soils. To capture and quantify SOC contents at a field scale, Earth Observation (EO) can be a valuable data [...] Read more.
For food security issues or global climate change, there is a growing need for large-scale knowledge of soil organic carbon (SOC) contents in agricultural soils. To capture and quantify SOC contents at a field scale, Earth Observation (EO) can be a valuable data source for area-wide mapping. The extraction of exposed soils from EO data is challenging due to temporal or permanent vegetation cover, the influence of soil moisture or the condition of the soil surface. Compositing techniques of multitemporal satellite images provide an alternative to retrieve exposed soils and to produce a data source. The repeatable soil composites, containing averaged exposed soil areas over several years, are relatively independent from seasonal soil moisture and surface conditions and provide a new EO-based data source that can be used to estimate SOC contents over large geographical areas with a high spatial resolution. Here, we applied the Soil Composite Mapping Processor (SCMaP) to the Landsat archive between 1984 and 2014 of images covering Bavaria, Germany. Compared to existing SOC modeling approaches based on single scenes, the 30-year SCMaP soil reflectance composite (SRC) with a spatial resolution of 30 m is used. The SRC spectral information is correlated with point soil data using different machine learning algorithms to estimate the SOC contents in cropland topsoils of Bavaria. We developed a pre-processing technique to address the issue of combining point information with EO pixels for the purpose of modeling. We applied different modeling methods often used in EO soil studies to choose the best SOC prediction model. Based on the model accuracies and performances, the Random Forest (RF) showed the best capabilities to predict the SOC contents in Bavaria (R² = 0.67, RMSE = 1.24%, RPD = 1.77, CCC = 0.78). We further validated the model results with an independent dataset. The comparison between the measured and predicted SOC contents showed a mean difference of 0.11% SOC using the best RF model. The SCMaP SRC is a promising approach to predict the spatial SOC distribution over large geographical extents with a high spatial resolution (30 m). Full article
(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)
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Article
Sentinel-2 Exposed Soil Composite for Soil Organic Carbon Prediction
Remote Sens. 2021, 13(9), 1791; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091791 - 04 May 2021
Cited by 3 | Viewed by 903
Abstract
Pilot studies have demonstrated the potential of remote sensing for soil organic carbon (SOC) mapping in exposed croplands. However, the use of remote sensing for SOC prediction is often hindered by disturbing factors at the soil surface, such as photosynthetic active and non-photosynthetic [...] Read more.
Pilot studies have demonstrated the potential of remote sensing for soil organic carbon (SOC) mapping in exposed croplands. However, the use of remote sensing for SOC prediction is often hindered by disturbing factors at the soil surface, such as photosynthetic active and non-photosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, recent studies have focused on stabilizing the soil reflectance by building image composites. These composites tend to minimize the disturbing effects by applying sets of criteria. Here, we aim to develop a robust method that allows selecting Sentinel-2 (S-2) pixels with minimal influence of the following disturbing factors: crop residues, surface roughness and soil moisture. We selected all S-2 cloud-free images covering the Belgian Loam Belt from January 2019 to December 2020 (in total 36 images). We then built nine exposed soil composites based on four sets of criteria: (1) lowest Normalized Burn Ratio (NBR2), (2) Normalized Difference Vegetation Index (NDVI) < 0.25, (3–5) NDVI < 0.25 and NBR2 < threshold, (6) the ‘greening-up’ period of a crop and (7–9) the ‘greening-up’ period of a crop and NBR2 < threshold. The ‘greening-up’ period was selected based on the NDVI timeline, where ‘greening-up’ is considered as the last date of acquisition where the soil is exposed (NDVI < 0.25) before the crop develops (NDVI > 0.25). We then built a partial least square regression (PLSR) model with 10-fold cross-validation to estimate the SOC content based on 137 georeferenced calibration samples on the nine composites. We obtained non-satisfactory results (R2 < 0.30, RMSE > 2.50 g C kg–1, and RPD < 1.4, n > 68) for all composites except for the composite in the ‘greening-up’ stage with a NBR2 < 0.07 (R2 = 0.54 ± 0.12, RPD = 1.68 ± 0.45 and RMSE = 2.09 ± 0.39 g C kg–1, n = 49). Hence, the ‘greening-up’ method combined with a strict NBR2 threshold allows selecting the purest exposed soil pixels suitable for SOC prediction. The limit of this method might be its coverage of the total cropland area, which in a two-year period reached 62%, compared to 95% coverage if only the NDVI threshold is applied. Full article
(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)
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Article
Spectral Assessment of Organic Matter with Different Composition Using Reflectance Spectroscopy
Remote Sens. 2021, 13(8), 1549; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081549 - 16 Apr 2021
Viewed by 515
Abstract
Soil surveys are critical for maintaining sustainable use of natural resources while minimizing harmful impacts to the ecosystem. A key soil attribute for many environmental factors, such as CO2 budget, soil fertility and sustainability, is soil organic matter (SOM), as well as [...] Read more.
Soil surveys are critical for maintaining sustainable use of natural resources while minimizing harmful impacts to the ecosystem. A key soil attribute for many environmental factors, such as CO2 budget, soil fertility and sustainability, is soil organic matter (SOM), as well as its sequestration. Soil spectroscopy is a popular method to assess SOM content rapidly in both field and laboratory domains. However, SOM source composition differs from soil to soil, and the use of spectral-based models for quantifying SOM may present limited accuracy when applying a generic approach to SOM assessment. We therefore examined the extent to which the generic approach can assess SOM contents of different origin using spectral-based models. We created an artificial big dataset composed of pure dune sand as a SOM-free background, which was artificially mixed with increasing amounts of different organic matter (OM) sources obtained from commercial compost of different origins. Dune sand has high albedo and yields optimal conditions for SOM detection. This study combined two methods: partial least squares regression for the prediction of SOM content from reflectance values across the 400–2500 nm region and a soil spectral detection limit (SSDL) to judge the prediction accuracy. Spectral-based models to assess SOM content were evaluated with each OM source as well as with a merged dataset that contained all of the generated samples (generic approach). The latter was concluded to have limitations for assessing low amounts of SOM (<0.6%), even under controlled conditions. Moreover, some of the OM sources were more difficult to monitor than others; accordingly, caution is advised when different SOM sources are present in the examined population. Full article
(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)
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