Remote Sensing

Editorial

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3 pages, 195 KiB

Open AccessEditorial

Remote Sensing for Soil Organic Carbon Mapping and Monitoring

by Bas van Wesemael, Sabine Chabrillat, Adrian Sanz Dias, Michael Berger and Zoltan Szantoi

Remote Sens. 2023, 15(14), 3464; https://doi.org/10.3390/rs15143464 - 9 Jul 2023

Cited by 8 | Viewed by 4351

Abstract

Remote sensing soil properties in a coherent manner is now feasible from regional to global scales [...] Full article

(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)

Research

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19 pages, 5285 KiB

Open AccessArticle

Digital Mapping of Soil Organic Carbon Using UAV Images and Soil Properties in a Thermo-Erosion Gully on the Tibetan Plateau

by Mengkai Ding, Xiaoyan Li and Zongyi Jin

Remote Sens. 2023, 15(6), 1628; https://doi.org/10.3390/rs15061628 - 17 Mar 2023

Cited by 5 | Viewed by 2283

Abstract

Thermo-erosion gullies (TGs) are typical thermokarst features in upland permafrost; the soil organic carbon (SOC) of TGs has an important influence on soil quality in cold regions. The objectives of this study were to estimate the spatial distribution of SOC content in a [...] Read more.

Thermo-erosion gullies (TGs) are typical thermokarst features in upland permafrost; the soil organic carbon (SOC) of TGs has an important influence on soil quality in cold regions. The objectives of this study were to estimate the spatial distribution of SOC content in a typical TG on the northeastern Tibetan Plateau in China by using soil properties from seven different TGs and covariates from unmanned aerial vehicle (UAV) images, and to characterize the SOC content changes in four representative landscape regions (NO-Slumping, Slumping1, Slumping2, and Slumped) within this typical TG. The support vector machine (SVM) was the optimal machine learning algorithm for SOC content prediction, which explained 53.06% (R²) of the SOC content variation. Silt content was the most influential factor which demonstrated a positive relationship with SOC content in different TGs. In addition, the SOC content in the TGs was related to the landscapes. Severe Slumping (Slumping2: 150.79 g·kg⁻¹) had a lower SOC content than NO-Slumped (163.29 g·kg⁻¹) and the initial slumping stage (Slumping1: 169.08 g·kg⁻¹). The results suggested that SVM was an effective algorithm to obtain a profound understanding of the SOC content over space, while future research needs to pay more attention to the SOC content distribution in the different TGs. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)

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20 pages, 5342 KiB

Open AccessArticle

Detection of Soil Erosion Hotspots in the Croplands of a Typical Black Soil Region in Northeast China: Insights from Sentinel-2 Multispectral Remote Sensing

by Lulu Qi, Pu Shi, Klara Dvorakova, Kristof Van Oost, Qi Sun, Hanqing Yu and Bas van Wesemael

Remote Sens. 2023, 15(5), 1402; https://doi.org/10.3390/rs15051402 - 2 Mar 2023

Cited by 7 | Viewed by 3188

Abstract

Global efforts to restore the world’s degraded croplands require knowledge on the degree and extent of accelerated soil organic carbon (SOC) loss induced by soil erosion. However, the methods for assessing where and to what extent erosion takes place are still inadequate for [...] Read more.

Global efforts to restore the world’s degraded croplands require knowledge on the degree and extent of accelerated soil organic carbon (SOC) loss induced by soil erosion. However, the methods for assessing where and to what extent erosion takes place are still inadequate for precise detection of erosion hotspots at high spatial resolution. Drawing on recent advances in multitemporal Sentinel-2 remote sensing to create a bare soil composite that reflects erosion-induced variations in soil spectral signatures, this study attempted to develop a spectra-based soil erosion mapping approach to pinpoint eroded hotspots in a typical catchment located in the black soil region of northeast China as characterized by undulating landscapes. We built a ground-truth dataset consisting of three classes of soils representing Severe, Moderate and Low erosion intensity because of their inter-class contrasts in estimated erosion rates from ¹³⁷Cs tracing. The spectral separability of different erosion classes was first tested by a combined principal component and linear discriminant analysis (PCA-LDA) against laboratory hyperspectral data and then validated against Sentinel-2-derived broadband spectra. The results show that PCA-LDA produced excellent classification accuracy (Kappa coefficient > 0.9) for both data sources and even more so for Sentinel-2 spectra, highlighting the effectiveness of the multitemporal approach to extract bare soil pixels. Further investigations into the spectral curves enabled identification of distinctive spectral features representative of shifting soil albedo and biochemical composition due to erosion-induced SOC mobilization. A classification scheme comprising the spectral features was applied to the Sentinel-2 bare soil composite for pixel-wise soil erosion mapping, in which 15.9% of the cropland area was detected as erosion hotspots, while the Moderate class occupied 65.4%. Comparing the erosion map to a NDVI map demonstrated the negative impact of soil erosion on crop growth from a spatial perspective, highlighting the potential of the proposed approach to aid targeted cropland management for food security and climate. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)

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29 pages, 9416 KiB

Open AccessEditor’s ChoiceArticle

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

by Theodora Angelopoulou, Sabine Chabrillat, Stefano Pignatti, Robert Milewski, Konstantinos Karyotis, Maximilian Brell, Thomas Ruhtz, Dionysis Bochtis and George Zalidis

Remote Sens. 2023, 15(4), 1106; https://doi.org/10.3390/rs15041106 - 17 Feb 2023

Cited by 17 | Viewed by 4020

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)

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26 pages, 6859 KiB

Open AccessArticle

Soil Reflectance Composites—Improved Thresholding and Performance Evaluation

by Uta Heiden, Pablo d’Angelo, Peter Schwind, Paul Karlshöfer, Rupert Müller, Simone Zepp, Martin Wiesmeier and Peter Reinartz

Remote Sens. 2022, 14(18), 4526; https://doi.org/10.3390/rs14184526 - 10 Sep 2022

Cited by 18 | Viewed by 3573

Abstract

Reflectance composites that capture bare soil pixels from multispectral image data are increasingly being analysed to model soil constituents such as soil organic carbon. These temporal composites are used instead of single-date multispectral images to account for the frequent vegetation cover of soils [...] Read more.

Reflectance composites that capture bare soil pixels from multispectral image data are increasingly being analysed to model soil constituents such as soil organic carbon. These temporal composites are used instead of single-date multispectral images to account for the frequent vegetation cover of soils and, thus, to get broader spatial coverage of bare soil pixels. Most soil compositing techniques require thresholds derived from spectral indices such as the Normalised Difference Vegetation Index (NDVI) and the Normalised Burn Ratio 2 (NBR2) to separate bare soils from all other land cover types. However, the threshold derivation is handled based on expert knowledge of a specific area, statistical percentile definitions or in situ data. For operational processors, such site-specific and partly manual strategies are not applicable. There is a need for a more generic solution to derive thresholds for large-scale processing without manual intervention. This study presents a novel HIstogram SEparation Threshold (HISET) methodology deriving spectral index thresholds and testing them for a Sentinel-2 temporal data stack. The technique is spectral index-independent, data-driven and can be evaluated based on a quality score. We tested HISET for building six soil reflectance composites (SRC) using NDVI, NBR2 and a new index combining the NDVI and a short-wave infrared (SWIR) band (PV+IR2). A comprehensive analysis of the spectral and spatial performance and accuracy of the resulting SRCs proves the flexibility and validity of HISET. Disturbance effects such as spectral confusion of bare soils with non-photosynthetic-active vegetation (NPV) could be reduced by choosing grassland and crops as input LC for HISET. The NBR2-based SRC spectra showed the highest similarity with LUCAS spectra, the broadest spatial coverage of bare soil pixels and the least number of valid observations per pixel. The spatial coverage of bare soil pixels is validated against the database of the Integrated Administration and Control System (IACS) of the European Commission. Validation results show that PV+IR2-based SRCs outperform the other two indices, especially in spectrally mixed areas of bare soil, photosynthetic-active vegetation and NPV. The NDVI-based SRCs showed the lowest confidence values (95%) in all bands. In the future, HISET shall be tested in other areas with different environmental conditions and LC characteristics to evaluate if the findings of this study are also valid. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)

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15 pages, 1929 KiB

Open AccessArticle

No-Till Soil Organic Carbon Sequestration Patterns as Affected by Climate and Soil Erosion in the Arable Land of Mediterranean Europe

by Giorgio Baiamonte, Luciano Gristina, Santo Orlando, Salvatore Samuel Palermo and Mario Minacapilli

Remote Sens. 2022, 14(16), 4064; https://doi.org/10.3390/rs14164064 - 19 Aug 2022

Cited by 5 | Viewed by 2047

Abstract

No-tillage (NT) has been considered an agronomic tool to sequester soil organic carbon (SOC) and match the 4p1000 initiative requirements of conservative soil management. Recently, some doubts have emerged about the NT effect on SOC sequestration, often because observations and experimental data vary [...] Read more.

No-tillage (NT) has been considered an agronomic tool to sequester soil organic carbon (SOC) and match the 4p1000 initiative requirements of conservative soil management. Recently, some doubts have emerged about the NT effect on SOC sequestration, often because observations and experimental data vary widely depending on climate and geographic characteristics. Therefore, a suitable SOC accounting method is needed that considers climate and morphology interactions. In this study, the yearly ratio between SOC in NT and conventional tillage (CT) (RR_NT/CT) collected in a previous study for flat (96 samples) and sloping (44 samples) paired sites was used to map the overestimation of SOC sequestration. It was assumed that there would be an overestimation of NT capacity in sloping fields due to lower erosion processes with respect to CT. Towards this aim, Geographical Information System (GIS) techniques and an extensive input database of high spatial resolution maps were used in a simplified procedure to assess the overestimation of SOC stocks due to the sloping conditions and spatial variability of the Aridity Index (AI). Moreover, this also made it possible to quantify the effects of adopting NT practices on soil carbon sequestration compared to CT practices. The method was applied to the arable lands of five Mediterranean countries (France, Greece, Italy, Portugal and Spain) ranging between the 35° and 46° latitude. The results showed an overestimation of SOC sequestration, when the AI and soil erosion were considered. The average overestimation rate in the studied Mediterranean areas was 0.11 Mg ha⁻¹ yr⁻¹. Carbon stock overestimation ranged from 34 to 1417 Gg for Portugal and Italy, respectively. Even if overestimation is considered, 4p1000 goals are often reached, especially in the more arid areas. The findings of this research allowed us to map the areas suitable to meet the 4p1000 that could be achieved by adopting conservative practices such as NT. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)

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20 pages, 32747 KiB

Open AccessArticle

Scale-Specific Prediction of Topsoil Organic Carbon Contents Using Terrain Attributes and SCMaP Soil Reflectance Composites

by Markus Möller, Simone Zepp, Martin Wiesmeier, Heike Gerighausen and Uta Heiden

Remote Sens. 2022, 14(10), 2295; https://doi.org/10.3390/rs14102295 - 10 May 2022

Cited by 11 | Viewed by 3479

Abstract

There is a growing need for an area-wide knowledge of SOC contents in agricultural soils at the field scale for food security and monitoring long-term changes related to soil health and climate change. In Germany, SOC maps are mostly available with a spatial [...] Read more.

There is a growing need for an area-wide knowledge of SOC contents in agricultural soils at the field scale for food security and monitoring long-term changes related to soil health and climate change. In Germany, SOC maps are mostly available with a spatial resolution of 250 m to 1 km

^{2}

. The nationwide availability of both digital elevation models at various spatial resolutions and multi-temporal satellite imagery enables the derivation of multi-scale terrain attributes and (here: Landsat-based) multi-temporal soil reflectance composites (SRC) as explanatory variables. In the example of a Bavarian test of about 8000 km

^{2}

, relations between 220 SOC content samples as well as different aggregation levels of the explanatory variables were analyzed for their scale-specific predictive power. The aggregation levels were generated by applying a region-growing segmentation procedure, and the SOC content prediction was realized by the Random Forest algorithm. In doing so, established approaches of (geographic) object-based image analysis (GEOBIA) and machine learning were combined. The modeling results revealed scale-specific differences. Compared to terrain attributes, the use of SRC parameters leads to a significant model improvement at field-related scale levels. The joint use of both terrain attributes and SRC parameters resulted in further model improvements. The best modeling variant is characterized by an accuracy of R

^{2}

= 0.84 and RMSE = 1.99. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)

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19 pages, 8305 KiB

Open AccessArticle

Optimizing a Standard Spectral Measurement Protocol to Enhance the Quality of Soil Spectra: Exploration of Key Variables in Lab-Based VNIR-SWIR Spectral Measurement

by Zhengyuan Xu, Shengbo Chen, Peng Lu, Zibo Wang, Anzhen Li, Qinghong Zeng and Liwen Chen

Remote Sens. 2022, 14(7), 1558; https://doi.org/10.3390/rs14071558 - 23 Mar 2022

Cited by 6 | Viewed by 2531

Abstract

The method of proximal VNIR-SWIR (with a spectral region of 400–2500 nm) spectroscopy in a laboratory setting has been widely employed in soil property estimations. Increasing attention has been focused recently on establishing an agreed-upon protocol for soil spectral measurement, fueled by the [...] Read more.

The method of proximal VNIR-SWIR (with a spectral region of 400–2500 nm) spectroscopy in a laboratory setting has been widely employed in soil property estimations. Increasing attention has been focused recently on establishing an agreed-upon protocol for soil spectral measurement, fueled by the recognition that studies carried out under different laboratory settings have made future data sharing and model comparisons difficult. This study aimed to explore the key factors in a lab-based spectral measurement procedure to provide recommendations for enhancing the spectra quality and promoting the development of the spectral measurement protocol. To this aim, with the support of the standard spectral laboratory at Jilin University, China, we designed and performed control experiments on four key factors—the light interference in the measurement course, soil temperature, soil moisture, and soil particle size—to quantify the variation in the spectra quality by the subsequent estimation accuracies of different estimation models developed with different spectra obtained from control groups. The results showed that (1) the soil–probe contact measurement derived the optimum spectra quality and estimation accuracy; however, close-non-contact measurement also achieved acceptable results; (2) sieving the soil sample into particle sizes below 1 mm and drying before spectral measurement effectively enhanced spectra quality and estimation accuracy; (3) the variation in soil temperature did not have a distinct influence on spectra quality, and the estimation accuracies of models developed based on soil samples at 20–50 °C were all acceptable. Moreover, a 30-min warm-up of the spectrometer and contact probe was found to be effective. We carried out a complete and detailed control experiment process, the results of which offer a guide for optimizing the process of laboratory-based soil proximal spectral measurement to enhance spectra quality and corresponding estimation accuracy. Furthermore, we present theoretical support for the development of the spectral measurement protocol. We also present optional guidance with relatively lower accuracy but effective results, which are save time and are low cost for future spectral measurement projects. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)

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21 pages, 1473 KiB

Open AccessArticle

Estimating Forest Soil Properties for Humus Assessment—Is Vis-NIR the Way to Go?

by Felix Thomas, Rainer Petzold, Solveig Landmark, Hannes Mollenhauer, Carina Becker and Ulrike Werban

Remote Sens. 2022, 14(6), 1368; https://doi.org/10.3390/rs14061368 - 11 Mar 2022

Cited by 10 | Viewed by 3194

Abstract

Recently, forest management faces new challenges resulting from increasing temperatures and drought occurrences. For sustainable, site-specific management strategies, the availability of up to date soil information is crucial. Proximal soil sensing techniques are a promising approach for rapid and inexpensive collection of data, [...] Read more.

Recently, forest management faces new challenges resulting from increasing temperatures and drought occurrences. For sustainable, site-specific management strategies, the availability of up to date soil information is crucial. Proximal soil sensing techniques are a promising approach for rapid and inexpensive collection of data, and could facilitate the provision of the necessary information. This study evaluates the potential of visual and near-infrared spectroscopy (vis-NIRS) for estimating soil parameters relevant for humus mapping in Saxon forests. Therefore, soil samples from the organic layer are included. So far there is little knowledge about the applicability of vis-NIRS in the humus layer of forests. We investigate the spectral behaviour of samples from organic (Oh) and mineral (0–5 cm, Ah) horizons, pointing out differences in the occurring absorption features. Further, we identify and assess the accuracy of selected soil properties based on vis-NIRS for forest sites, compare the outcome of different regression methods, investigate the implications for forest soils due to the presence and different composition of the humus layer and organic horizons and interpret the results regarding their usefulness for soil mapping and monitoring purposes. For this, we used retained humus soil samples of forests from Saxony. Regression models were built with Partial Least Squares Regression, Support Vector Machine and Cubist. Investigated properties were carbon (C) and nitrogen (N) content, C/N ratio, pH value, cation exchange capacity (CEC) and base saturation (BS) due to their importance for assessing humus conditions in forests. In organic Oh horizons, prediction results for C and N content achieved R² values between 0.44 and 0.58, with corresponding RPIQ ranging from 1.58 to 2.06 depending on the used algorithm. Estimations of C/N ratio were more precise with R² = 0.65 and RMSE = 2.16. Best results were reported for pH value, with R² = 0.90 and RMSE = 0.20. Regarding BS, the best model accuracy was R² = 0.71, with RMSE = 13.97. In mineral topsoil, C and N content models achieved higher values of R² = 0.59 to 0.72, with RPIQ values between 2.22 and 2.54. However, prediction accuracy was lower for C/N ratio (R² = 0.50, RMSE = 3.52) and pH values (R² = 0.62, RMSE = 0.29). Models for CEC achieved R² = 0.65, with RPIQ = 2.81. In general, prediction precision varied dependent on the used algorithm, without showing clear tendencies. Classification into pH classes was exemplified since this offers a new perspective for humus mapping on forest soils. Balanced accuracy for the defined classes ranged from 0.50 to 0.87. We show that vis-NIR spectroscopy is suitable for assessing humus conditions in Saxon forests (Germany), in particular not only for mineral horizons but also for organic Oh horizons. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)

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22 pages, 6382 KiB

Open AccessArticle

Using Sentinel-2 Images for Soil Organic Carbon Content Mapping in Croplands of Southwestern France. The Usefulness of Sentinel-1/2 Derived Moisture Maps and Mismatches between Sentinel Images and Sampling Dates

by Diego Urbina-Salazar, Emmanuelle Vaudour, Nicolas Baghdadi, Eric Ceschia, Anne C. Richer-de-Forges, Sébastien Lehmann and Dominique Arrouays

Remote Sens. 2021, 13(24), 5115; https://doi.org/10.3390/rs13245115 - 16 Dec 2021

Cited by 22 | Viewed by 5393

Abstract

In agronomy, soil organic carbon (SOC) content is important for the development and growth of crops. From an environmental monitoring viewpoint, SOC sequestration is essential for mitigating the emission of greenhouse gases into the atmosphere. SOC dynamics in cropland soils should be further [...] Read more.

In agronomy, soil organic carbon (SOC) content is important for the development and growth of crops. From an environmental monitoring viewpoint, SOC sequestration is essential for mitigating the emission of greenhouse gases into the atmosphere. SOC dynamics in cropland soils should be further studied through various approaches including remote sensing. In order to predict SOC content over croplands in southwestern France (area of 22,177 km²), this study addresses (i) the influence of the dates on which Sentinel-2 (S2) images were acquired in the springs of 2017–2018 as well as the influence of the soil sampling period of a set of samples collected between 2005 and 2018, (ii) the use of soil moisture products (SMPs) derived from Sentinel-1/2 satellites to analyze the influence of surface soil moisture on model performance when included as a covariate, and (iii) whether the spatial distribution of SOC as mapped using S2 is related to terrain-derived attributes. The influences of S2 image dates and soil sampling periods were analyzed for bare topsoil. The dates of the S2 images with the best performance (RPD ≥ 1.7) were 6 April and 26 May 2017, using soil samples collected between 2016 and 2018. The soil sampling dates were also analyzed using SMP values. Soil moisture values were extracted for each sample and integrated into partial least squares regression (PLSR) models. The use of soil moisture as a covariate had no effect on the prediction performance of the models; however, SMP values were used to select the driest dates, effectively mapping topsoil organic carbon. S2 was able to predict high SOC contents in the specific soil types located on the old terraces (mesas) shaped by rivers flowing from the southwestern Pyrénées. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)

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25 pages, 9173 KiB

Open AccessArticle

Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites

by Simone Zepp, Uta Heiden, Martin Bachmann, Martin Wiesmeier, Michael Steininger and Bas van Wesemael

Remote Sens. 2021, 13(16), 3141; https://doi.org/10.3390/rs13163141 - 8 Aug 2021

Cited by 37 | Viewed by 5308

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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20 pages, 4300 KiB

Open AccessArticle

Sentinel-2 Exposed Soil Composite for Soil Organic Carbon Prediction

by Klara Dvorakova, Uta Heiden and Bas van Wesemael

Remote Sens. 2021, 13(9), 1791; https://doi.org/10.3390/rs13091791 - 4 May 2021

Cited by 39 | Viewed by 6110

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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17 pages, 12080 KiB

Open AccessArticle

Spectral Assessment of Organic Matter with Different Composition Using Reflectance Spectroscopy

by Nicolas Francos, Yaron Ogen and Eyal Ben-Dor

Remote Sens. 2021, 13(8), 1549; https://doi.org/10.3390/rs13081549 - 16 Apr 2021

Cited by 12 | Viewed by 4456

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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Review

Jump to: Editorial, Research

21 pages, 1819 KiB

Open AccessReview

Remote Sensing of Surface and Subsurface Soil Organic Carbon in Tidal Wetlands: A Review and Ideas for Future Research

by Rajneesh Sharma, Deepak R. Mishra, Matthew R. Levi and Lori A. Sutter

Remote Sens. 2022, 14(12), 2940; https://doi.org/10.3390/rs14122940 - 20 Jun 2022

Cited by 11 | Viewed by 4965

Abstract

Tidal wetlands, widely considered the most extensive reservoir of soil organic carbon (SOC), can benefit from remote sensing studies enabling spatiotemporal estimation and mapping of SOC stock. We found that a majority of the remote-sensing-based SOC mapping efforts have been focused on upland [...] Read more.

Tidal wetlands, widely considered the most extensive reservoir of soil organic carbon (SOC), can benefit from remote sensing studies enabling spatiotemporal estimation and mapping of SOC stock. We found that a majority of the remote-sensing-based SOC mapping efforts have been focused on upland ecosystems, not on tidal wetlands. We present a comprehensive review detailing the types of remote sensing models and methods used, standard input variables, results, and limitations for the handful of studies on tidal wetland SOC. Based on that synthesis, we pose several unexplored research questions and methods that are critical for moving tidal wetland SOC science forward. Among these, the applicability of machine learning and deep learning models for predicting surface SOC and the modeling requirements for SOC in subsurface soils (soils without a remote sensing signal, i.e., a soil depth greater than 5 cm) are the most important. We did not find any remote sensing study aimed at modeling subsurface SOC in tidal wetlands. Since tidal wetlands store a significant amount of SOC at greater depths, we hypothesized that surface SOC could be an important covariable along with other biophysical and climate variables for predicting subsurface SOC. Preliminary results using field data from tidal wetlands in the southeastern United States and machine learning model output from mangrove ecosystems in India revealed a strong nonlinear but significant relationship (r² = 0.68 and 0.20, respectively, p < 2.2 × 10⁻¹⁶ for both) between surface and subsurface SOC at different depths. We investigated the applicability of the Soil Survey Geographic Database (SSURGO) for tidal wetlands by comparing the data with SOC data from the Smithsonian’s Coastal Blue Carbon Network collected during the same decade and found that the SSURGO data consistently over-reported SOC stock in tidal wetlands. We concluded that a novel machine learning framework that utilizes remote sensing data and derived products, the standard covariables reported in the limited literature, and more importantly, other new and potentially informative covariables specific to tidal wetlands such as tidal inundation frequency and height, vegetation species, and soil algal biomass could improve remote-sensing-based tidal wetland SOC studies. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)

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22 pages, 3225 KiB

Open AccessReview

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

by Emmanuelle Vaudour, Asa Gholizadeh, Fabio Castaldi, Mohammadmehdi Saberioon, Luboš Borůvka, Diego Urbina-Salazar, Youssef Fouad, Dominique Arrouays, Anne C. Richer-de-Forges, James Biney, Johanna Wetterlind and Bas Van Wesemael

Remote Sens. 2022, 14(12), 2917; https://doi.org/10.3390/rs14122917 - 18 Jun 2022

Cited by 39 | Viewed by 8271

Abstract

There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is [...] Read more.

There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km²: dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of ~15 g·kg⁻¹ and a range of 30 g·kg⁻¹ in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)

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29 pages, 3914 KiB

Open AccessReview

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

by Nikolaos Tziolas, Nikolaos Tsakiridis, Sabine Chabrillat, José A. M. Demattê, Eyal Ben-Dor, Asa Gholizadeh, George Zalidis and Bas van Wesemael

Remote Sens. 2021, 13(21), 4439; https://doi.org/10.3390/rs13214439 - 4 Nov 2021

Cited by 40 | Viewed by 5640

Abstract

We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing [...] Read more.

We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Organic Carbon Mapping and Monitoring)

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