Carbon farming: Are soil carbon certificates a suitable tool for climate change mitigation?

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 330, P. 117142 - 117142

Published: Jan. 4, 2023

Increasing soil organic carbon (SOC) stocks in agricultural soils removes dioxide from the atmosphere and contributes towards achieving neutrality. For farmers, higher SOC levels have multiple benefits, including increased fertility resilience against drought-related yield losses. However, increasing requires management changes that are associated with costs. Private certificates could compensate for these In schemes, farmers register their fields commercial certificate providers who certify increases. Certificates then sold as voluntary emission offsets on market. this paper, we assess suitability of an instrument climate change mitigation. From a soils' perspective, address processes enrichment, potentials limits, options cost-effective measurement monitoring. farmers' likely to increase SOC, discuss synergies trade-offs economic, environmental social targets. governance requirements guarantee additionality permanence while preventing leakage effects. Furthermore, questions legitimacy accountability. While is cornerstone more sustainable cropping systems, private fall short expectations mitigation sequestration cannot be guaranteed. Governance challenges include lack long-term monitoring, problems ensure additionality, safeguard effects, accountability if stored re-emitted. We conclude soil-based unlikely deliver offset attributed them benefit uncertain. Additional research needed develop standards metrics better understand impact term, non-permanent removals peaks atmospheric greenhouse gas concentrations probability exceeding climatic tipping points.

Language: Английский

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

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(12), P. 2917 - 2917

Published: June 18, 2022

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

Language: Английский

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High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic

CATENA, Journal Year: 2022, Volume and Issue: 212, P. 106024 - 106024

Published: Jan. 19, 2022

Language: Английский

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Assessing spatial variations in soil organic carbon and C:N ratio in Northeast China's black soil region: Insights from Landsat-9 satellite and crop growth information

Soil and Tillage Research, Journal Year: 2023, Volume and Issue: 235, P. 105897 - 105897

Published: Sept. 22, 2023

Language: Английский

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Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(4), P. 876 - 876

Published: Feb. 4, 2023

Precise knowledge about the soil organic carbon (SOC) content in cropland soils is one requirement to design and execute effective climate food policies. In digital mapping (DSM), machine learning algorithms are used predict properties from covariates derived traditional mapping, elevation models, land use, Earth observation (EO). However, such DSM models trained for a specific dataset region have so far only allowed limited general statements be made that would enable transferred different regions. this study, we test transferability of SOC using five covariate groups: multispectral reflectance composites (satellite), legacy data (soil), model derivatives (terrain), parameters (climate), combined (combined). The was analyzed two federal states southern Germany: Bavaria Baden-Wuerttemberg. First, baseline were each state with performing best both cases (R2 = 0.68/0.48). Next, tested samples other whose not during calibration. Only satellite transferable, but accuracy declined cases. final step, (mixed-data models) applied separately. This process significantly improved accuracies satellite, terrain, while it showed no effect on decreased based covariates. experiment underlines importance EO transfer extrapolation models.

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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Remote sensing-based prediction of organic carbon in agricultural and natural soils influenced by salt and sand mining using machine learning

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 352, P. 120107 - 120107

Published: Jan. 21, 2024

Language: Английский

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Earth Observation Data-Driven Cropland Soil Monitoring: A Review

Remote Sensing, Journal Year: 2021, Volume and Issue: 13(21), P. 4439 - 4439

Published: Nov. 4, 2021

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

Language: Английский

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Soil Reflectance Composites—Improved Thresholding and Performance Evaluation

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(18), P. 4526 - 4526

Published: Sept. 10, 2022

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

Language: Английский

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