Remote Sensing Data for Digital Soil Mapping in French Research—A Review

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(12), P. 3070 - 3070

Published: June 12, 2023

Soils are at the crossroads of many existential issues that humanity is currently facing. a finite resource under threat, mainly due to human pressure. There an urgent need map and monitor them field, regional, global scales in order improve their management prevent degradation. This remains challenge high often complex spatial variability inherent soils. Over last four decades, major research efforts field pedometrics have led development methods allowing capture nature As result, digital soil mapping (DSM) approaches been developed for quantifying soils space time. DSM monitoring become operational thanks harmonization databases, advances modeling machine learning, increasing availability spatiotemporal covariates, including exponential increase freely available remote sensing (RS) data. The latter boosted DSM, resolution assessing changes through We present review main contributions developments French (inter)national research, which has long history both RS DSM. Thanks SPOT satellite constellation started early 1980s, communities pioneered using sensing. describes data, tools, imagery support predictions wide range properties discusses pros cons. demonstrates data frequently used (i) by considering as substitute analytical measurements, or (ii) covariates related controlling factors formation evolution. It further highlights great potential provides overview challenges prospects future sensors. opens up broad use natural monitoring.

Language: Английский

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Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-year Periods for Soil Organic Carbon Content Mapping in Central France

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(9), P. 2410 - 2410

Published: May 4, 2023

Satellite-based soil organic carbon content (SOC) mapping over wide regions is generally hampered by the low sampling density and diversity of periods. Some unfavorable topsoil conditions, such as high moisture, rugosity, presence crop residues, limited amplitude SOC values area bare when a single image used, are also among influencing factors. To generate reliable map, this study addresses use Sentinel-2 (S2) temporal mosaics (S2Bsoil) 6 years jointly with moisture products (SMPs) derived from Sentinel 1 2 images, measurement data other environmental covariates digital elevation models, lithology maps airborne gamma-ray data. In study, we explore (i) dates periods that preferable to construct soils while accounting for management; (ii) which set more relevant explain variability. From four sets covariates, best contributing was selected, median along uncertainty at 90% prediction intervals were mapped 25-m resolution quantile regression forest models. The accuracy predictions assessed 10-fold cross-validation, repeated five times. models using all had model performance. Airborne thorium, slope S2 bands (e.g., 6, 7, 8, 8a) indices calcareous sedimentary rocks, “calcl”) “late winter–spring” time series most important in model. Our results indicated role neighboring topographic distances oblique geographic coordinates between remote sensing parent material. These contributed not only optimizing performance but provided information related long-range gradients spatial variability, makes sense pedological point view.

Language: Английский

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Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland

Geoderma, Journal Year: 2024, Volume and Issue: 444, P. 116850 - 116850

Published: March 19, 2024

National soil organic carbon (SOC) maps are essential to improve greenhouse gas accounting and support climate-smart agriculture. Large-scale SOC models based on wall-to-wall information from remote sensing remain a challenge due the high diversity of natural conditions difficulty for spatial location samples. In this study, we tested if implementation local ensemble (LEM) can be used predictions Landsat-based reflectance composites (SRC) Germany. For this, divided research area into 30 times km tiles calculated generalized linear (GLM) random, nearby observations. Based GLMs, were predicted aggregated using moving window approach. The variable importance was analyzed identify dependencies in correlation between SRC SOC. final map, Random Forest (RF) model trained predictions, SRC, full set training samples agricultural inventory. results show that LEM able accuracy (R2 = 0.68; RMSE 5.6 g kg−1), compared single, global 0.52; 6.8 kg−1). spectral bands showed clear patterns throughout area. Differences explained by conditions, influencing properties. Compared widely adopted integration distance covariates such as geographical coordinates, reduce autocorrelation greater extent prediction accuracy, especially underrepresented values. presents new method integrate increase interpretability DSM models.

Language: Английский

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Influence of Soil Texture on the Estimation of Soil Organic Carbon From Sentinel‐2 Temporal Mosaics at 34 European Sites

European Journal of Soil Science, Journal Year: 2025, Volume and Issue: 76(1)

Published: Jan. 1, 2025

ABSTRACT Multispectral imaging satellites such as Sentinel‐2 are considered a possible tool to assist in the mapping of soil organic carbon (SOC) using images bare soil. However, reported results variable. The measured reflectance surface is not only related SOC but also several other environmental and edaphic factors. Soil texture one factor that strongly affects reflectance. Depending on spatial correlation with SOC, influence may improve or hinder estimation from spectral data. This study aimed investigate these influences local models at 34 sites different pedo‐climatic zones across 10 European countries. were individual agricultural fields few close proximity. For each site, predict clay particle size fraction developed temporal mosaics images. Overall, predicting was difficult, prediction performances ratio performance deviation (RPD) > 1.5 observed 8 12 for clay, respectively. A general relationship between evident explained small part large variability we sites. Adding information additional predictors improved average, benefit varied average relative importance bands indicated red far‐red regions visible spectrum more important than prediction. opposite true region around 2200 nm, which models.

Language: Английский

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Optimized bare soil compositing for soil organic carbon prediction of topsoil croplands in Bavaria using Landsat

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2023, Volume and Issue: 202, P. 287 - 302

Published: June 29, 2023

Language: Английский

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Soil organic carbon mapping utilizing convolutional neural networks and Earth observation data, a case study in Bavaria state Germany

Geoderma, Journal Year: 2024, Volume and Issue: 444, P. 116867 - 116867

Published: March 28, 2024

The Copernicus Sentinel-2 multispectral imagery data may be aggregated to extract large-scale, bare soil, reflectance composites, which enable soil mapping applications. In this paper, approach was tested in the German federal state of Bavaria, provide estimations for organic carbon (SOC). Different temporal ranges were considered generation including multi-annual and seasonal ranges. A novel multi-channel convolutional neural network (CNN) is proposed. By leveraging advantages deep learning techniques, it utilizes complementary information from different spectral pre-treatment techniques. SOC predictions indicated little dissimilarity amongst with best performance attained six-year composite containing only spring months (RMSE = 12.03 g C · kg−1, R2 0.64, RPIQ 0.89). It has been demonstrated that these outcomes outperform other well-known machine An ablation analysis accordingly performed evaluate interplay CNN's components disentangle each aspect proposed framework. Finally, a DUal inPut LearnIng architecture, named DUPLICITE, proposed, concatenates features derived CNN mentioned earlier, as well topographical environmental covariates through an artificial (ANN) exploit their complementarity. improvement overall prediction 11.60 gC 0.67, 0.92).

Language: Английский

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Spectra-based predictive mapping of soil organic carbon in croplands: Single-date versus multitemporal bare soil compositing approaches

Geoderma, Journal Year: 2024, Volume and Issue: 449, P. 116987 - 116987

Published: Aug. 1, 2024

Sustainable cropland management requires quantitative and up-to-date information on the spatial pattern of soil organic carbon (SOC) at scales relevant for implementing targeted conservation measures. Spectra-based remote sensing SOC in croplands is promising, but it extraction high-quality bare pixels that enable spatially continuous coverage. Here, we aim to compare predictive capability single-date versus multitemporal compositing images an intensively cultivated region (4,700 km2) northeast China. A series 12 within 2017–2022 were processed passed onto three approaches (geometric median, univariate mean median) create mosaics. Both spectral images, together with laboratory-simulated Sentinel-2 benchmark data, used develop partial least squares regression, Cubist random forest models via 100 bootstrapped validations. With consistently being best performing algorithm all data sources, results show exhibited temporally unstable performance (R2: 0.30–0.67). Among approaches, high-dimensional geometric median composite was most suitable because (i) its close resemblance laboratory reference robustness outliers, which yielded a model 0.64; RMSE: 2.24 g/kg) outperforming 11 out models; (ii) ability retain between-band relationships allowed further incorporation SOC-relevant indexes, led 6.5 % increase prediction accuracy. The resultant map highlighted imaging reveal field-scale degradation patterns. Future work should explore possibility extending purely spectra-based framework integrated mapping monitoring additional biophysical information.

Language: Английский

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High-resolution soil erosion mapping in croplands via Sentinel-2 bare soil imaging and a two-step classification approach

Geoderma, Journal Year: 2024, Volume and Issue: 446, P. 116905 - 116905

Published: May 7, 2024

Erosion-induced lateral soil redistribution leads to spatially heterogenous composition, which can be captured through the distinctive spectral reflectance of soils under varying levels erosion influence. This points potential using remotely sensed spectra detect severe and deposition hotspots in exposed croplands and, importantly, further differentiate intra-class variability moderate that often occupies largest proportion. Here, we aim develop a two-step classification mapping approach based on multitemporal compositing Sentinel-2 bare images typical agricultural region (11,500 km2) northeast China. A random forest classifier was firstly trained against ground-truth data derived from very high resolution (VHR) imagery Google Earth, with an overall accuracy 91 % allowed for clear delineation areas their distinct topographic features particularly red red-edge bands. In second step, remaining area (60.30 %) differentiated Iterative Self-Organizing cluster unsupervised yield five-class map at 10 m spatial resolution. The predicted successfully validated by independent Caesium-137 (137Cs) organic carbon observations catchment regional scales, as revealed significant inter-class differences rates estimated 137Cs inventory. class had loss rate 5.5 mm yr−1, suggesting previous assessments have underestimated severity. accordance crop growth intensity, localized settings, highlighted imaging spatiotemporal development its response targeted sustainable cropland management efforts.

Language: Английский

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Remotely sensed inter-field variation in soil organic carbon content as influenced by the cumulative effect of conservation tillage in northeast China

Soil and Tillage Research, Journal Year: 2024, Volume and Issue: 243, P. 106170 - 106170

Published: May 30, 2024

Language: Английский

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A two-dimensional bare soil separation framework using multi-temporal Sentinel-2 images across China

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 134, P. 104181 - 104181

Published: Sept. 30, 2024

Language: Английский

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