SOC content of global Mollisols at a 30 m spatial resolution from 1984 to 2021 generated by the novel ML-CNN prediction model

Remote Sensing of Environment, Год журнала: 2023, Номер 300, С. 113911 - 113911

Опубликована: Ноя. 16, 2023

Язык: Английский

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Historical and future variation of soil organic carbon in China

Geoderma, Год журнала: 2023, Номер 436, С. 116557 - 116557

Опубликована: Июнь 12, 2023

Revealing historical changes in soil organic carbon (SOC) and exploring its future status are important for safeguarding health food security, giving full play to the service function of ecosystems, coping with climate change. However, there is still a gap our understanding SOC stocks China their spatial patterns response Therefore, we attempted fill this knowledge using large amount observation data, digital mapping technology, global circulation models from Coupled Model Inter-comparison Project phase 6 (CMIP6). In study, random forest model was selected construct relationship between top 0–20 cm (SOC020) 0–100 (SOC0100) 21 environmental factors. Spatiotemporal 1980 2100 were revealed at resolution 1 km five-year interval three scenarios CMIP6. The cross-validation results indicated acceptable predictions both depths SOC; however, relatively prediction uncertainties observed SOC0100 Tibetan Plateau northeastern China. mean values SOC020 over last four decades 35.77 84.62 Tg, respectively, showed sinks national scale, accumulation rates 0.05 Pg yr-1 0.036 y-1 two depths. Compared (1980–2020), will fluctuate significantly under different scenarios. Among them, slow increasing trend SSP1-1.9 low emission scenario, while presented decreasing SSP2-4.5 SSP5-8.5 medium–high particular, most larger likelihood being source. This study provides reference pools change evaluating effectiveness land management ecological protection.

Язык: Английский

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Optimizing the rate of straw returning to balance trade-offs between carbon emission budget and rice yield in China

Sustainable Production and Consumption, Год журнала: 2024, Номер 47, С. 166 - 177

Опубликована: Март 27, 2024

Язык: Английский

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Improving soil organic carbon mapping in farmlands using machine learning models and complex cropping system information

Environmental Sciences Europe, Год журнала: 2024, Номер 36(1)

Опубликована: Апрель 21, 2024

Abstract Obtaining accurate spatial maps of soil organic carbon (SOC) in farmlands is crucial for assessing quality and achieving precision agriculture. The cropping system an important factor that affects the cycle farmlands, different agricultural managements under systems lead to heterogeneity SOC. However, current research often ignores differences main controlling factors SOC systems, especially when pattern complex, which not conducive farmland zoning management. This study aims (i) obtain distribution map six by using multi-phase HJ-CCD satellite images; (ii) explore stratified heterogeneous relationship between environmental variables Cubist model; (iii) predict Xiantao, Tianmen, Qianjiang cities, are core areas Jianghan Plain, were selected as area. Results showed content rice–wheat rotation was highest among systems. model outperformed random forest, ordinary kriging, multiple linear regression mapping. results system, climate, attributes, vegetation index influencing farmlands. different. Specifically, summer crop types had a greater influence on variations than winter crops. Paddy–upland more affected river distance NDVI, while upland–upland irrigation-related factors. work highlights differentiated provides data support can improve prediction accuracy complex

Язык: Английский

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Optimizing the Nitrogen Fertilizer Management to Maximize the Benefit of Straw Returning on Early Rice Yield by Modulating Soil N Availability

Agriculture, Год журнала: 2024, Номер 14(7), С. 1168 - 1168

Опубликована: Июль 17, 2024

Straw returning has gradually been adopted as an effective approach to address the serious degradation of farmland. However, carbon/nitrogen (C/N) ratio rice straw is generally too high for microorganisms decompose organic materials and release nutrients, which may minimize benefits agricultural production system. This study aimed investigate effects on propose optimum nitrogen (N) management early under a The total N fertilizer that was evaluated 165 kg ha-1, urea (46% N), applied in different proportions three stages cultivation: basal, tillering, panicle. Using no with basal:tillering:panicle = 5:2:3 treatment (T1) control, four ratios basal:tillering:panicle, including (T2), 5:2:2 (T3), 5:4:1 (T4), 5:5:0 (T5) were set returning. return decreased available soil at tillering stage, impeded root growth crop canopy from establishing, panicles by 10.1% compared T1, limiting increases grain yield. Increasing 10–20% (T3 T4) stage effectively increased content ammonium nitrate nitrogen, improved growth, activities 16.0–40.5% stage. As result, panicle number 5.1–16.2%. Among these, T4 maximized most. Additionally, increasing shoot uptake across growing season synchronized accumulation carbon assimilates, enhanced rate yield 13.5–25.1%. It concluded 20% promising strategy increase availability phases demand this nutrient.

Язык: Английский

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Future soil organic carbon stocks in China under climate change

Cell Reports Sustainability, Год журнала: 2024, Номер 1(9), С. 100179 - 100179

Опубликована: Авг. 28, 2024

Язык: Английский

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Exploring the inter-decadal variability of soil organic carbon in China

CATENA, Год журнала: 2023, Номер 230, С. 107242 - 107242

Опубликована: Июнь 6, 2023

Язык: Английский

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Environmental variables improve the accuracy of remote sensing estimation of soil organic carbon content

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Авг. 16, 2024

Accurately and quickly estimating the soil organic carbon (SOC) content is crucial in monitoring of global carbon. Environmental variables play a significant role improving accuracy SOC estimation model. This study focuses on modeling methodologies environmental variables, which significantly influence The methods used this research comprise multiple linear regression (MLR), partial least squares (PLSR), random forest, support vector machines (SVM). analyzed include terrain, climate, soil, vegetation cover factors. original spectral reflectance (OSR) Landsat 5 TM images reflectivity after derivative processing were combined with above to estimate content. results showed that: (1) can be efficiently estimated using OSR TM, however, derived method cannot improve accuracy. (2) effectively estimation, climate factors producing most improvements. (3) Machine learning provide better than MLR PLSR, especially SVM model has highest According our observations, best area was "OSR + SVM" (R2 = 0.9590, RMSE 13.9887, MAE 10.8075), considered four highlights significance content, offering insights for more precise future assessments. It also provides data health sustainable agricultural development area.

Язык: Английский

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Soil Organic Carbon Estimation and Transfer Framework in Agricultural Areas Based on Spatiotemporal Constraint Strategy Combined with Active and Passive Remote Sensing

Remote Sensing, Год журнала: 2025, Номер 17(2), С. 333 - 333

Опубликована: Янв. 19, 2025

Mapping soil organic carbon (SOC) plays a crucial role in agricultural productivity and water management. This study discusses the potential of active passive remote sensing for SOC estimation modeling areas, incorporating synthetic aperture radar (SAR) data (L-band quad-polarization C-band dual-polarization), multi-spectrum (MS) data, brightness temperature (TB) data. The performance five advanced machine learning regression (MLR) models was assessed, focusing on spatial interpolation accuracy cross-spatial transfer accuracy, using two field observation datasets validation. Results indicate that when MS alone is comparable to TB alone, both perform slightly better than SAR Radar cross-polarization ratio index, microwave polarization difference shortwave infrared reflectance, parameters (elevation moisture) demonstrate high correlation with measured SOC. Incorporating temporal features, as opposed single-phase allows each model reach its upper limit accuracy. MLR algorithm satisfactory, Gaussian process (GPR) demonstrating optimal performance. When SAR, MS, or are used individually modeling, errors (RMSE) 0.637 g/kg, 0.492 0.229 g/kg SMAPVEX12 sampling campaign, 0.706 0.454 0.474 SMAPVEX16-MB respectively. After moisture topographic factors, above RMSEs further reduced by 57.8%, 35.6%, 3.5% SMAPVEX12, 18.4%, 8.8%, 3.4% SMAPVEX16-MB, However, remains limited (RMSE = 0.866–1.043 0.995–1.679 different sources). To address this, this reduces uncertainties introducing terrain factors sensitive 0.457–0.516 0.799–1.198 proposed framework, based provides guidance high-resolution regional-scale mapping applications.

Язык: Английский

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Multi-Scenario Simulation Assessment by utilising PLUS and InVEST Model on the effect of Land Use and Carbon Storage Changes in Hohhot

Environmental and Sustainability Indicators, Год журнала: 2025, Номер unknown, С. 100655 - 100655

Опубликована: Март 1, 2025

Язык: Английский

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