Minimizing vegetation influence on soil salinity mapping with novel bare soil pixels from multi-temporal images DOI Creative Commons
Danyang Wang, Haichao Yang,

Hao Qian

и другие.

Geoderma, Год журнала: 2023, Номер 439, С. 116697 - 116697

Опубликована: Окт. 24, 2023

Optical remote sensing satellites provide rapid access to regional topsoil salinization mapping. However, mapping based on spectral reflectance is always affected by background material like vegetation cover, straw mulching and soil types. In light of these challenges, this study investigates the potential image fusion, where images original bare pixels were combined, minimize impact cover salinity A case was presented for typical area using synchronized Sentinel-2 MSI (named image) 255 ground-truth data collected in October 2020, aligning with periods salt return. Furthermore, obtain novel pixels, multi-temporal acquired during two distinct intervals: March May September November, spanning years from 2018 2021. The synthetic (SYSI) obtained extracting images. Two (original, SYSI) fused non-negative matrix factorization (NMF) method, named SYSIfused. Then, stacking machine algorithm used under different types, evaluating SYSIfused accuracy prediction. results showed outperformed (the R2 best models increased 0.054–0.242, RMSE MAE decreased 0.049–0.780 0.012–0.546, respectively). Based SYSIfused, order effect types coastal bog solonchaks > alluvial cinnamon coral saline overall samples, their roles improving model 0.141, 0.085, 0.022, 0.012, respectively. Besides, provided prediction performances (R2 = 0.742, 0.377, 0.362). This introduces concept merging SYSI, resulting a significant improvement areas covered vegetation.

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

42
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

35
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

31
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
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

10
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

22
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
Study on the Extraction of Topsoil-Loss Areas of Cultivated Land Based on Multi-Source Remote Sensing Data DOI Creative Commons
Xinle Zhang, Chuan Qin, S. Ma

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(3), С. 547 - 547

Опубликована: Фев. 6, 2025

Soil, a crucial natural resource and the cornerstone of agriculture, profoundly impacts crop growth, quality, yield. However, soil degradation affects over one-third global land, with topsoil loss emerging as significant form this degradation, posing grave threat to agricultural sustainability socio-economic development. Therefore, accurate monitoring topsoil-loss distribution is essential for formulating effective protection management strategies. Traditional survey methods are limited by time-consuming labor-intensive processes, high costs, complex data processing. These limitations make it particularly challenging meet demands large-scale research efficient information imperative develop more extraction method. This study focuses on Heshan Farm in Heilongjiang Province, China, subject utilizes remote sensing technology machine learning methods. It introduces multi-source data, including Sentinel-2 satellite imagery Digital Elevation Model (DEM) design four schemes. (1) spectral feature extraction; (2) + topographic (3) index (4) extraction. Models identification based Random Forest (RF) Support Vector Machine (SVM) algorithms developed, Particle Swarm Optimization (PSO) algorithm introduced optimize models. The performance models evaluated using overall accuracy Kappa coefficient indicators. results show that Scheme 4, which integrates features, various indices, performs best effects. RF model demonstrates higher classification than SVM model. optimized PSO-RF PSO-SVM improvements accuracy, especially model, an 0.97 0.94. 4 improves OA 34.72% 38.81% compared 1. Topsoil has negative impact severely restricting normal growth development crops. provides technical means black-soil regions offers scientific basis ecological strategies, thereby promoting sustainable resources.

Язык: Английский

Процитировано

1
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

16
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

15