Analyzing Spatial Distribution and Influencing Factors of Soil Organic Matter in Cultivated Land of Northeast China: Implications for Black Soil Protection DOI Creative Commons

Depiao Kong,

Nanchen Chu, Chong Luo

и другие.

Land, Год журнала: 2024, Номер 13(7), С. 1028 - 1028

Опубликована: Июль 9, 2024

Soil organic matter (SOM) in cultivated land is vital for quality and food security. This study examines SOM distribution influencing factors northeastern China, providing insights sustainable agriculture. Utilizing 10 m resolution data, the analysis covers regions including Greater Lesser Khingan Mountains, Liaohe Plain, Sanjiang Songnen northwest semi-arid region, low hilly areas of Paektu Mountain. The Geodetector method employed to assess various factors. key findings are as follows: (1) average content Northeast China (37.70 g/kg) surpasses national average, highest Mountains (49.32 g/kg), lowest region (26.15 g/kg). (2) maximized with high altitudes, steep slopes, temperatures, moderate precipitation. (3) annual temperature primary factor distribution, a combination administrative divisions better explanatory power. (4) trends vary across protected areas, slope being critical semi-humid plains, elevation arid regions, no dominant identified Plain. These underscore need tailored black soil protection policies effectively leverage local resources preserve ecosystem integrity.

Язык: Английский

Assessing the capability of Sentinel-2 time-series to estimate soil organic carbon and clay content at local scale in croplands DOI Creative Commons
Fabio Castaldi, Muhammed Halil Koparan, Johanna Wetterlind

и другие.

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2023, Номер 199, С. 40 - 60

Опубликована: Апрель 3, 2023

The use of remote sensing data methods is affordable for the mapping soil properties plowed layer over croplands. Carried out in framework ongoing STEROPES project European Joint H2020 Program SOIL, this work focused on feasibility Sentinel-2 based approaches high resolution topsoil clay and organic carbon (SOC) contents at within-farm or within-field scales, cropland sites contrasted climates types across Northern hemisphere. Four pixelwise temporal mosaicking methods, using a two years-Sentinel-2 time series several spectral indices (NDVI, NBR2, BSI, S2WI), were developed compared i) pure bare condition (maxBSI), ii) driest (minS2WI), iii) average (Median) iv) dry conditions excluding extreme reflectance values (R90). Three modeling approaches, bands output mosaics as covariates, tested compared: (i) Quantile Regression Forest (QRF) algorithm; (ii) QRF adding longitude latitude covariates (QRFxy); (iii) hybrid approach, Linear Mixed Effect Model (LMEM), that includes spatial autocorrelation properties. We pairs mosaic ten Türkiye, Italy, Lithuania, USA where samples collected SOC content measured lab. RPIQ best performances among test was 2.50 both (RMSE = 0.15%) 3.3%). Both accuracy level uncertainty mainly influenced by site characteristics cloud frequency, management. Generally, models including component (QRFxy LMEM) performing, while mostly Median R90. most frequent optimal combination model type R90 QRFxy SOC, LMEM estimation.

Язык: Английский

Процитировано

40
Mapping of soil organic matter in a typical black soil area using Landsat-8 synthetic images at different time periods DOI
Chong Luo, Wenqi Zhang, Xinle Zhang

и другие.

CATENA, Год журнала: 2023, Номер 231, С. 107336 - 107336

Опубликована: Июль 5, 2023

Язык: Английский

Процитировано

32
SOC content of global Mollisols at a 30 m spatial resolution from 1984 to 2021 generated by the novel ML-CNN prediction model DOI
Xiangtian Meng, Yilin Bao, Chong Luo

и другие.

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

Опубликована: Ноя. 16, 2023

Язык: Английский

Процитировано

30
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 DOI Open Access
Jing Geng, Qiuyuan Tan, Junwei Lv

и другие.

Soil and Tillage Research, Год журнала: 2023, Номер 235, С. 105897 - 105897

Опубликована: Сен. 22, 2023

Язык: Английский

Процитировано

27
Remote sensing of the Earth's soil color in space and time DOI
Rodnei Rizzo, Alexandre M.J.‐C. Wadoux, José Alexandre Melo Demattê

и другие.

Remote Sensing of Environment, Год журнала: 2023, Номер 299, С. 113845 - 113845

Опубликована: Окт. 25, 2023

Язык: Английский

Процитировано

19
Spectra-based predictive mapping of soil organic carbon in croplands: Single-date versus multitemporal bare soil compositing approaches DOI Creative Commons

Yuanli Zhu,

Lulu Qi,

Zihao Wu

и другие.

Geoderma, Год журнала: 2024, Номер 449, С. 116987 - 116987

Опубликована: Авг. 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.

Язык: Английский

Процитировано

8
A comprehensive review of soil organic carbon estimates: Integrating remote sensing and machine learning technologies DOI Creative Commons
Tong Li, Lizhen Cui, Matthias Kuhnert

и другие.

Journal of Soils and Sediments, Год журнала: 2024, Номер 24(11), С. 3556 - 3571

Опубликована: Окт. 5, 2024

Abstract Purpose Accurately assessing soil organic carbon (SOC) content is vital for ecosystem services management and addressing global climate challenges. This study undertakes a comprehensive bibliometric analysis of estimates SOC using remote sensing (RS) machine learning (ML) techniques. It showcases the historical growth thematic evolution in research, aiming to amplify understanding estimation themes provide scientific support change adaptation mitigation. Materials Methods Employing extensive literature database analysis, network clustering techniques, reviews 1,761 articles on RS technologies 490 employing both ML technologies. Results Discussion The results indicate that satellite-based RS, particularly Landsat series, predominant other associated studies, with North America, China, Europe leading evaluations Africa having low adopting technology. Trends research demonstrate an from basic mapping advanced topics such as (C) sequestration, complex modeling, big data utilization. Thematic clusters co-occurrence suggest interplay between technology development, environmental surveys, properties, dynamics. Conclusion highlights synergy ML, techniques proving be critical accurate estimation. These findings are crucial estimation, informed strategic decision-making.

Язык: Английский

Процитировано

8
National-scale spatial prediction of soil organic carbon and total nitrogen using long-term optical and microwave satellite observations in Google Earth Engine DOI
Tao Zhou, Wenhao Lv,

Yajun Geng

и другие.

Computers and Electronics in Agriculture, Год журнала: 2023, Номер 210, С. 107928 - 107928

Опубликована: Май 23, 2023

Язык: Английский

Процитировано

13
A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information DOI
Jiawen Wang, Chunhui Feng, Bifeng Hu

и другие.

The Science of The Total Environment, Год журнала: 2023, Номер 903, С. 166112 - 166112

Опубликована: Авг. 9, 2023

Язык: Английский

Процитировано

13
High-resolution soil erosion mapping in croplands via Sentinel-2 bare soil imaging and a two-step classification approach DOI Creative Commons

Lulu Qi,

Yue Zhou, Kristof Van Oost

и другие.

Geoderma, Год журнала: 2024, Номер 446, С. 116905 - 116905

Опубликована: Май 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.

Язык: Английский

Процитировано

5