Prediction of soil organic carbon in black soil based on a synergistic scheme from hyperspectral data: Combining fractional-order derivatives and three-dimensional spectral indices

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 220, С. 108905 - 108905

Опубликована: Апрель 6, 2024

Язык: Английский

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Comparison of global and zonal modeling strategies - A case study of soil organic matter and C:N ratio mapping in Altay, Xinjiang, China

Ecological Informatics, Год журнала: 2024, Номер 84, С. 102882 - 102882

Опубликована: Ноя. 17, 2024

Язык: Английский

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Improving SOC estimation in low-relief farmlands using time-series crop spectral variables and harmonic component variables based on minimum sample size

International Soil and Water Conservation Research, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Mapping Soil Organic Matter in Black Soil Cropland Areas Using Remote Sensing and Environmental Covariates

Agriculture, Год журнала: 2025, Номер 15(3), С. 339 - 339

Опубликована: Фев. 4, 2025

The accurate prediction of soil organic matter (SOM) content is important for sustainable agriculture and effective management. This task particularly challenging due to the variability in factors influencing SOM distribution across different cultivated land types, as well site-specific responses remote sensing data environmental covariates, especially black region northeastern China, where exhibits significant spatial variability. study evaluated variations on importance imagery covariates zones. A total 180 samples (0–20 cm) were collected from Youyi County, Heilongjiang Province, multi-year synthetic bare images 2014 2022 (focusing April May) acquired using Google Earth Engine. Combining three types such drainage, climate topography, area was categorized into dry field paddy field. Then, model constructed random forest regression method accuracy strategies by 10-fold cross-validation. findings indicated that, (1) overall analysis, combining drainage variables May could attain highest accuracy, ranked follows: (RS) > (CLI) (DN) Topography (TP). (2) Zonal analysis conducted with a high degree precision, evidenced an R2 0.72 impressively low RMSE 0.73%. time window monitoring More specifically, optimal frames dryland identified May, while those fields concentrated May. (3) In addition, diverse observed vary types. regions characterized intricate fields, contributions assumed heightened importance. Conversely, featuring flat terrain, roles played more substantial role outcomes. These underscore selecting appropriate inputs improving accuracy.

Язык: Английский

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Sweetpotato moisture content and textural property estimation using hyperspectral imaging and machine learning

Journal of Food Measurement & Characterization, Год журнала: 2025, Номер unknown

Опубликована: Март 3, 2025

Язык: Английский

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Comprehensive Evaluation of Cultivated Land Quality in Black Soil of Northeast China: Emphasizing Functional Diversity and Risk Management

Applied Sciences, Год журнала: 2025, Номер 15(7), С. 3753 - 3753

Опубликована: Март 29, 2025

The cultivated land in the black soil of Northeast China (BSNC), due to long-term high-input and high-output utilization, is facing a series challenges such as erosion, compaction, nutrient loss. However, existing quality evaluation (CLQE) lacks regional specificity, making it difficult accurately reflect (CLQ) characteristics across different areas. Therefore, this study proposes comprehensive framework that integrates both functionality degradation risk, establishing an assessment system consisting 18 indicators comprehensively evaluate CLQ BSNC from multiple perspectives. results indicate exhibits declining trend north south, with second- third-grade dominating, accounting for 75.68% total area. overall increases west east, Liaohe Plain Region (LHP) performing best. Low-risk primarily concentrated Songnen (SNP) Western Sandy (WS), covering 38.55% Additionally, finds trade-off between primary productivity function resource utilization efficiency regions, while synergistic relationship observed maintenance functions. This research emphasizes necessity balancing ecological protection achieve sustainable efficient use BSNC.

Язык: Английский

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Spatial Inversion of Soil Organic Carbon Content Based on Hyperspectral Data and Sentinel‐2 Images

Land Degradation and Development, Год журнала: 2025, Номер unknown

Опубликована: Апрель 3, 2025

ABSTRACT Given that Sentinel‐2 (S2) multispectral images provide extensive spatial information and ground‐based hyperspectral data capture refined spectral characteristics, their integration can enhance both the comprehensiveness precision of surface acquisition. This study seeks to leverage these sources develop an optimized estimation model for accurately monitoring large‐scale soil organic carbon (SOC) content, thereby addressing current limitations in multi‐source fusion research. In this study, using mathematical transformation discrete wavelet transform process ground delta oasis Weigan Kuqa rivers Xinjiang, China, combination with S2 image, machine learning algorithms were employed construct models SOC content total variables characteristic variables, inversion oases was carried out. We found R ‐DWT‐H9 significantly correlation between ( p < 0.001). The accuracy constructed based on feature selected by SPA IRIV generally higher than variable models. IRIV‐RFR had highest stable capability. values 2 training validation sets 0.66 0.64, respectively. RMSE 1.5 g∙kg −1 , RPD > 1.4. interior oasis, mainly deficient (61.35%) or relatively (8.17%), while periphery it extremely (30.48%). Combine providing a reference evaluating fertility arid regions.

Язык: Английский

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Variability analysis of soil organic carbon content across land use types and its digital mapping using machine learning and deep learning algorithms

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(5)

Опубликована: Апрель 10, 2025

Язык: Английский

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Improving soil organic carbon estimation in paddy fields using data augmentation algorithm and deep neural network model based on optimal image date

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 220, С. 108921 - 108921

Опубликована: Апрель 10, 2024

Язык: Английский

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Prediction of soil organic carbon stock combining Sentinel-1 and Sentinel-2 images in the Zoige Plateau, the northeastern Qinghai-Tibet Plateau

Ecological Processes, Год журнала: 2024, Номер 13(1)

Опубликована: Май 1, 2024

Abstract Background Soil organic carbon (SOC) is a critical component of the global cycle, and an accurate estimate regional SOC stock (SOCS) would significantly improve our understanding sequestration cycles. Zoige Plateau, locating in northeastern Qinghai-Tibet has largest alpine marsh wetland worldwide exhibits high sensitivity to climate fluctuations. Despite increasing use optical remote sensing predicting SOCS, obvious limitations Plateau due highly cloudy weather, knowledge on spatial patterns SOCS limited. Therefore, current study, distributions within 100 cm were predicted using XGBoost model—a machine learning approach, by integrating Sentinel-1, Sentinel-2 field observations Plateau. Results The results showed that content exhibited vertical distribution cm, with highest topsoil. tenfold cross-validation approach model satisfactorily efficiency 0.59 root mean standard error 95.2 Mg ha −1 . Predicted distinct heterogeneity average 355.7 ± 123.1 totaled 0.27 × 10 9 carbon. Conclusions High topsoil highlights risks significant loss from human activities Combining Sentinel-1 model, which demonstrates importance selecting modeling approaches satellite images at fine resolution m. Furthermore, study emphasizes potential radar (Sentinel-1) developing mapping, newly developed fine-resolution mapping having important applications land management, ecological restoration, protection efforts

Язык: Английский

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