Updating of the Archival Large-Scale Soil Map Based on the Multitemporal Spectral Characteristics of the Bare Soil Surface Landsat Scenes

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(18), P. 4491 - 4491

Published: Sept. 12, 2023

For most of the arable land in Russia (132–137 million ha), dominant and accurate soil information is stored form map archives on paper without coordinate reference. The last traditional map(s) (TSM, TSMs) were created over 30 years ago. Traditional and/or archival (ASM, ASMs) are outdated terms storage formats, dates, methods production. technology constructing a multitemporal line (MSL) makes it possible to update ASMs TSMs based processing big remote-sensing data (RSD). To construct an MSL, spectral characteristics bare surface (BSS) used. BSS RSD distinguished within framework conceptual apparatus neighborhood line. filtering deep machine learning. In course work, vector georeferenced version ASM updated coefficient “C” MSL. maps verified field surveys (76 pits). called interpretation (SIC “C”). SIC has more detailed legend compared (7 sections/chapters instead 5), greater accuracy (smaller errors first second kind), potential suitability for calculating organic matter/carbon (SOM/SOC) reserves (soil types/areals statistically significant divided according thickness organomineral horizon content SOM plowed layer). When updating, systematic underestimation numbers contours areas soils with manifestations negative/degradation processes (slitization erosion) TSM was established. process all three shortcomings ASMs/TSMs (archaic storage, creation) eliminated. digital (thematic raster), modern, methods. time, actualization carried out MSL (coefficient

Language: Английский

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Soil Data Cube and Artificial Intelligence Techniques for Generating National-Scale Topsoil Thematic Maps: A Case Study in Lithuanian Croplands

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(22), P. 5304 - 5304

Published: Nov. 9, 2023

There is a growing realization among policymakers that in order to pave the way for development of evidence-based conservation recommendations policy, it essential improve capacity soil-health monitoring by adopting multidimensional and integrated approaches. However, existing ready-to-use maps are characterized mainly coarse spatial resolution (>200 m) information not up date, making their use insufficient EU’s policy requirements, such as common agricultural policy. This work, utilizing Soil Data Cube, which self-hosted custom tool, provides yearly estimations soil thematic (e.g., exposed soil, organic carbon, clay content) covering all area Lithuania. The pipeline exploits various Earth observation data time series Sentinel-2 satellite imagery (2018–2022), LUCAS (Land Use/Cover Area Frame Statistical Survey) topsoil database, European Integrated Administration Control System (IACS) artificial intelligence (AI) architectures prediction accuracy well (10 m), enabling discrimination at parcel level. Five different models were tested with convolutional neural network (CNN) model achieve best both targeted indicators (SOC clay) related R2 metric (0.51 SOC 0.57 clay). predictions supported uncertainties based on PIR formula (average 0.48 0.61 provide valuable model’s interpretation stability. application final carried out national bare-soil-reflectance composite layers, generated employing pixel-based approach overlaid annual bare-soil using combination vegetation indices NDVI, NBR2, SCL. findings this work new insights generation large scale, leading more efficient sustainable management, supporting agri-food private sector.

Language: Английский

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Exploiting Soil and Remote Sensing Data Archives for 3D Mapping of Multiple Soil Properties at the Swiss National Scale

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(15), P. 2712 - 2712

Published: July 24, 2024

Soils play a central role in ecosystem functioning, and thus, mapped soil property information is indispensable to supporting sustainable land management. Digital Soil Mapping (DSM) provides framework spatially estimate properties. However, broad-scale DSM remains challenging because of non-purposively sampled data, large data volumes for processing extensive covariates, high model complexities due varying soil–landscape relationships. This study presents three-dimensional Switzerland, targeting the properties clay content (Clay), organic carbon (SOC), pH value (pH), potential cation exchange capacity (CECpot). The approach based on machine learning comprehensive exploitation remote sensing archives. Quantile Regression Forest was applied link sample from national base with covariates derived LiDAR-based elevation model, climate raster multispectral time series satellite imagery. covariate set comprises multiscale terrain attributes, patterns their temporal variation, temporarily use features, spectral bare signatures. predictions were evaluated respect different landcovers depth intervals. All reference sets found be clustered towards croplands, showing an increasing density lower upper According R2 independent overall accuracy amounts 0.69 Clay, 0.64 SOC, 0.76 pH, 0.72 CECpot. Reduced accuracies accompanied by limited sizes (e.g., CECpot), uneven statistical distributions SOC), low spatial densities woodland subsoils). Multiscale highly influential all models; particularly important Clay model; showed enhanced importance modeling pH; reflectance major driver SOC CECpot models.

Language: Английский

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Contribution of the Sentinel-2 spring seedbed spectra to the digital mapping of soil organic carbon concentration

Geoderma, Journal Year: 2024, Volume and Issue: 449, P. 116984 - 116984

Published: Aug. 1, 2024

Soil organic carbon (SOC) is central to the functioning of terrestrial ecosystems, has climate mitigation potential and provides several benefits for soil health. Understanding spatial distribution SOC can help formulate sustainable management practices. Digital mapping (DSM) uses advanced statistical geostatistical methods estimate properties across large areas. DSM integrates data, topographic features, geology, legacy maps, land remote sensing data. Bare spectra may reflect presence particular components, making satellite derived suitable predictors SOC. from Sentinel-2 were used concentration (SOC%) granulometric fractions in plough layer (0–30 cm) agricultural parcels northern Belgium. Thereafter, estimation performance SOC% was compared three models: one with bare spectra, environmental covariates (topography, granulometry vegetation), a combined model covariates. The sand, silt clay using spring seedbed (R2: 0.53–0.74; RPD: 1.49–2.05; RPIQ: 1.52–2.39) higher than that 0.16; 1.08; 1.32). highest obtained including all 0.28; 1.18; 1.44), but contribution containing small. results provide valuable insights refining property spectral

Language: Английский

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Estimates of Dust Emissions and Organic Carbon Losses Induced by Wind Erosion in Farmland Worldwide from 2017 to 2021

Agriculture, Journal Year: 2023, Volume and Issue: 13(4), P. 781 - 781

Published: March 28, 2023

Wind erosion can cause high dust emissions from agricultural land and lead to a significant loss of carbon nutrients the soil. The balance farmland soil is an integral part cycle, especially under current drive develop carbon-neutral practices. However, amount global lost due wind unknown. In this study, were estimated emission inventory (0.1° × 0.1°, daily) built using improved Community Multiscale Air Quality Modeling System–FENGSHA (CMAQ-FENGSHA), organic losses by combining with concentration data. average annual 2017 2021 1.75 109 g/s. Global are concentrated in UK, Ukraine, Russia Europe; southern Canada central US North America; area around Buenos Aires, capital Argentina, South northeast China Asia. was 2970 Gg for 2017–2021. spatial distribution roughly consistent that emissions, which mainly world’s four major black regions. These estimates essential references inform responses conservation.

Language: Английский

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A novel remote sensing-based approach to determine loss of agricultural soils due to soil sealing — a case study in Germany

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(6)

Published: May 4, 2024

Abstract Soils provide habitat, regulation and utilization functions. Therefore, Germany aims to reduce soil sealing 30 ha day $$^{-1}$$ - 1 by 2030 eliminate it 2050. About 55 of are damaged (average 2018–2021), but detailed information on its quality is lacking. This study proposes a new approach using geo-information remote sensing data assess agricultural loss in Lower Saxony Brandenburg. Soil assessed based erosion resistance, runoff regulation, filter functions, yield potential the Müncheberg Quality Rating from 2006 2015. Data German Map at scale 1:200,000 (BÜK 200), climate, topography, CORINE Land Cover (CLC) Imperviousness Layer (IMCC), both provided Copernicus Monitoring Service (CLMS), used generate potentials due sealing. For first time, losses under arable land spatially, quantitatively qualitatively. An estimate qualitative between 2015 obtained intersecting evaluation results with quantitative according IMCC. Between 2015, about 73,300 were sealed Germany, affecting 37,000 soils. corresponds rate 11 per for Germany. In Brandenburg, soils 1.9 0.8 respectively, removing these primary use. Saxony, 75% moderate or better biotic have been removed use, while Brandenburg this figure as high 88%. Implementing can help decision-makers reassess support Germany’s sustainable development strategy.

Language: Английский

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Leveraging legacy data with targeted field sampling for low-cost mapping of soil organic carbon stocks on extensive rangeland properties

Geoderma, Journal Year: 2024, Volume and Issue: 448, P. 116952 - 116952

Published: July 5, 2024

Accurately quantifying high-resolution field-scale soil organic carbon (SOC) stocks is challenging yet crucial for improving site-specific land management and accounting. This challenge even greater when the study units are large heterogenous ranches. utilizes a digital mapping (DSM) approach U.S. legacy dataset, combined with soil, climate, biotic, topographic covariate datasets, to design targeted sampling plan acquiring local samples. The resulting samples were then used in combination data build optimal ranch-scale SOC stock models. We provide an example of this using ranch western as case study. In our we first applied clustering analysis generate spatial clusters. was followed by adopting conditioned Latin hypercube scheme within each cluster, sets strategically selected points. required improved estimates determined have sample size 15 40 cores, respective 13 36 km2 parcels. While modeling results concentrations at relatively homogeneous site eastern Montana showed significant two-fold improvement model fit individually calibration datasets point, opposed selecting dataset whole level, disparity between pixel- ranch-based models inconsequential other two sites Colorado that more spatially diverse terms vegetation cover. Compared concentration (R2 0.3 0.7), performance bulk density (BD) < 0.4) 0.2) poor. Strategies including utilizing subset covariates, incorporating broader-scale national depths did not further improve BD Future work should explore whether addition temporally dynamic environmental covariates can estimates, DSM-supported field strategy be successfully elsewhere.

Language: Английский

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Spatiotemporal Monitoring of Cropland Soil Organic Carbon Changes From Space

Global Change Biology, Journal Year: 2024, Volume and Issue: 30(12)

Published: Dec. 1, 2024

ABSTRACT Soil monitoring requires accurate and spatially explicit information on soil organic carbon (SOC) trends changes over time. Spatiotemporal SOC models based Earth Observation (EO) satellite data can support large‐scale but often lack sufficient temporal validation long‐term data. In this study, we used repeated samples from 1986 to 2022 a time series of multispectral bare observations (Landsat Sentinel‐2) model high‐resolution cropland for almost four decades. An in‐depth the uncertainty accuracy derived was conducted network 100 sites that were continuously resampled every 5 years. While general prediction high ( R 2 = 0.61; RMSE 5.6 g kg −1 ), direct revealed significantly greater 0.16; p < 0.0001), even though predicted measured values showed similar distributions. Classifying results into declining increasing trends, found 95% all either correctly identified or as stable 0.001), highlighting potential our findings. Increased accuracies in soils with higher contents 0.4) reduced tillage 0.26). Based signal‐to‐noise ratio uncertainty, able show necessary frame detect strongly depends absolute present soils. Our findings highlight generate significant trend maps EO underline necessity measurements. This study marks an important step toward usability integration EO‐based monitoring.

Language: Английский

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Prediction of Soil Organic Carbon Content Using Machine Learning Based Fuzzy C-Means Clustering

Published: April 26, 2024

Soil organic carbon is a fundamental component of soil health, in this paper proposed Principle Component Analysis based Fuzzy C-Means clustering and Partial least squares regression (PCA-FCM-PLSR) for predicting the component. In research facing they offered limited insights into underlying relationships between input variables predicted outcome problem. Apply preprocessing technique on LUCAS dataset increase model accuracy model, then using FCM randomly selected initial cluster centers assigns closest samples to these centers. The PCA method solely utilized process. Finally, Least Square Regression PLSR effective prediction carbon, can built clusters calibration set that validation sample belonged order validate modelling technique. This archive better outcomes compare other existing models such as Root Mean Error (RMSE) 1.20, R ^{^} 2, 6.800 Ratio Performance Deviation (RPD) 2.70, inter quartile (RPI) 2.850. are k-means (k-Means-PLSR), Transferability Different Covariates (TDC) Deep Neural Network (DNN). Modify sentences present teens

Language: Английский

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Spatial Soil Moisture Prediction from In-Situ Data Upscaled to Landsat Footprint Across Heterogeneous Agricultural Landscapes

Published: Jan. 1, 2024

Language: Английский

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