Multifaceted Spectral Feature Interaction Effects Enhance Remote Sensing Inversion of Chlorophyll in Cadmium-Stressed Rice DOI
Jie Liu, Zhao Zhang, Xingwang Liu

и другие.

Опубликована: Янв. 1, 2023

AbstractEnhancing the remote sensing inversion of chlorophyll (Chl) in rice under cadmium (Cd) stress can help improve accuracy and efficiency large-scale monitoring soil Cd pollution. Spectral characteristics capture subtle changes Chl content stress; however, a more comprehensive exploration relationship between multifaceted spectral features has not been fully conducted. Moreover, most studies have overlooked impact interaction term effects on effectiveness prediction. In this study, sensitive to were selected, including first-order derivatives, envelope removal, inverse logarithmic transformations, wavelet parameters, characteristic using an interpretable neural network (GAMI-Net) quantify screen interactive terms. The application GAMI-Net model elucidated mechanisms by which these their respond stress. robustness enhanced grid-search algorithm based k-Fold cross-validation technique (GS-kFCV). Comparisons made traditional Vegetation Index (VI), Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN) models. Subsequently, Sentinel-2 satellite data used optimal invert modeling area prediction area, was validated with actual data. results indicated that improved model, compared original, showed increase 18.4% coefficient determination (R2) 90.9% ratio performance deviation (RPD), 76.5% reduction root mean square error (RMSE) test set. when other machine learning models, achieved R2 value 0.90 This surpassed values VI, RF, SVM, ANN, 0.71, 0.74, 0.34, respectively. addition, outperformed terms RMSE RPD metrics, 0.09 3.2, respectively, indicating higher robustness. Interpretative analysis significant variables revealed red-edge position accounted for 25.3% 17.7% variation stress, whereas 39.4% variation. predicted measurements 0.7988, 0.7233. Therefore, novel method proposed study exhibited high robustness, providing new insights into use estimation

Язык: Английский

High-Accuracy Mapping of Soil Organic Carbon by Mining Sentinel-1/2 Radar and Optical Time-Series Data with Super Ensemble Model DOI Creative Commons

Zhibo Cui,

Songchao Chen, Bifeng Hu

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(4), С. 678 - 678

Опубликована: Фев. 17, 2025

Accurate digital soil organic carbon mapping is of great significance for regulating the global cycle and addressing climate change. With advent remote sensing big data era, multi-source multi-temporal techniques have been extensively applied in Earth observation. However, how to fully mine time-series high-accuracy SOC remains a key challenge. To address this challenge, study introduced new idea mining data. We used 413 topsoil samples from southern Xinjiang, China, as an example. By (Sentinel-1/2) 2017 2023, we revealed temporal variation pattern correlation between Sentinel-1/2 SOC, thereby identifying optimal time window monitoring using integrating environmental covariates super ensemble model, achieved Southern China. The results showed following aspects: (1) windows were July–September July–August, respectively; (2) modeling accuracy sensor integrated with was superior single-source alone. In model based on data, cumulative contribution rate Sentinel-2 51.71% higher than that Sentinel-1 data; (3) stacking model’s predictive performance outperformed weight average simple models. Therefore, covariates, driven represents strategy mapping.

Язык: Английский

Процитировано

1
Mapping Soil Organic Carbon by Integrating Time-Series Sentinel-2 Data, Environmental Covariates and Multiple Ensemble Models DOI Creative Commons

Zhibo Cui,

Songchao Chen, Bifeng Hu

и другие.

Sensors, Год журнала: 2025, Номер 25(7), С. 2184 - 2184

Опубликована: Март 30, 2025

Despite extensive use of Sentinel-2 (S-2) data for mapping soil organic carbon (SOC), how to fully mine the potential time-series S-2 still remains unclear. To fill this gap, study introduced an innovative approach mining data. Using 200 top samples as example, we revealed temporal variation patterns in correlation between SOC and subsequently identified optimal monitoring time window SOC. The integration environmental covariates with multiple ensemble models enabled precise arid region southern Xinjiang, China (6109 km2). Our results indicated following: (a) exhibited both interannual monthly variations, while July August is SOC; (b) adding properties texture information could greatly improve accuracy prediction models. Soil contribute 8.85% 61.78% best model, respectively; (c) among different models, stacking model outperformed weight averaging sample terms performance. Therefore, our proved that spectral from window, integrated has a high accurate mapping.

Язык: Английский

Процитировано

0
Improving in-situ spectral estimation of wetland soil organic carbon by integrating multiple optimization strategies DOI
Hongyi Li, Jiang-Tao Yang, Bifeng Hu

и другие.

CATENA, Год журнала: 2025, Номер 255, С. 109078 - 109078

Опубликована: Апрель 23, 2025

Язык: Английский

Процитировано

0
Spatiotemporal Responses of River Water Quality Characterization to Multi-Source Pollution: A Case Study of the Jinjing Watershed in Subtropical China DOI
Lingling Tong, Feng Liu, Fatimah Md. Yusoff

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Unraveling the threshold and interaction effects of environmental variables on soil organic carbon mapping in plateau watershed DOI Creative Commons
Chengqi Zhang, Yiyun Chen, Yujiao Wei

и другие.

Geoderma, Год журнала: 2024, Номер 450, С. 117032 - 117032

Опубликована: Сен. 23, 2024

Язык: Английский

Процитировано

3
A Novel Framework for Improving Soil Organic Carbon Mapping Accuracy by Mining Temporal Features of Time-Series Sentinel-1 Data DOI Creative Commons

Zhibo Cui,

Bifeng Hu, Songchao Chen

и другие.

Land, Год журнала: 2025, Номер 14(4), С. 677 - 677

Опубликована: Март 23, 2025

Digital soil organic carbon (SOC) mapping is used for ecological protection and addressing global climate change. Sentinel-1 (S-1) microwave radar remote sensing data offer critical insights into SOC dynamics through tracking variations in moisture vegetation characteristics. Despite extensive studies using S-1 mapping, most focus on either single or multi-date periods without achieving satisfactory results. Few have investigated the potential of time-series high-accuracy mapping. This study utilized from 2017 to 2021 analyze temporal correlation between southern Xinjiang, China. The primary objective was determine optimal monitoring period SOC. Within this period, feature subsets were extracted variable selection algorithms. performance partial least squares regression, random forest, convolutional neural network–long short-term memory (CNN-LSTM) models evaluated a 10-fold cross-validation approach. findings revealed following: (1) exhibited both interannual monthly variations, with July October. volume reduced by 73.27% relative initial dataset when determined. (2) Introducing significantly improved CNN-LSTM model (R2 = 0.80, RPD 2.24, RMSE 1.11 g kg⁻1). Compared single-date 0.23) 0.33) data, R2 increased 0.57 0.47, respectively. (3) newly developed vertical–horizontal maximum mean annual cumulative indices made significant contribution (17.93%) Therefore, integrating selection, deep learning offers enhancing accuracy digital

Язык: Английский

Процитировано

0
Prediction of soil heavy metal contents in urban residential areas and the strength of deep learning: A case study of Beijing DOI
Ying Hou,

Wenhao Ding,

Tian Xie

и другие.

The Science of The Total Environment, Год журнала: 2024, Номер 950, С. 175133 - 175133

Опубликована: Июль 30, 2024

Язык: Английский

Процитировано

2
Interpretable Digital Soil Organic Matter Mapping Based on Geographical Gaussian Process-Generalized Additive Model (GGP-GAM) DOI Creative Commons

Liangwei Cheng,

Mingzhi Yan,

Wenhui Zhang

и другие.

Agriculture, Год журнала: 2024, Номер 14(9), С. 1578 - 1578

Опубликована: Сен. 11, 2024

Soil organic matter (SOM) is a key soil component. Determining its spatial distribution necessary for precision agriculture and to understand the ecosystem services that provides. However, field SOM studies are severely limited by time costs. To obtain spatially continuous map of content, it conduct digital mapping (DSM). In addition, there vital need both accuracy interpretability in mapping, which difficult achieve with conventional DSM models. address above issues, particularly coefficient variation (SVC) regression model, Geographic Gaussian Process Generalized Additive Model (GGP-GAM), was used. The root mean squared error (RMSE), average (MAE), adjusted determination (adjusted R2) this model Leizhou area 7.79, 6.01, 0.33 g kg−1, respectively. GGP-GAM more accurate compared other three models (i.e., Geographical Random Forest, Geographically Weighted Regression, Regression Kriging). Moreover, patterns covariates affecting interpreted coefficients each predictor individually. results show can be used high-precision content good interpretability. This technique will turn contribute agricultural sustainability decision making.

Язык: Английский

Процитировано

2
Improved soil organic matter monitoring by using cumulative crop residue indices derived from time-series remote sensing images in the central black soil region of China DOI
Meiwei Zhang, Xiaolin Sun, Meinan Zhang

и другие.

Soil and Tillage Research, Год журнала: 2024, Номер 246, С. 106357 - 106357

Опубликована: Ноя. 13, 2024

Язык: Английский

Процитировано

2
A long-term global Mollisols SOC content prediction framework: Integrating prior knowledge, geographical partitioning, and deep learning models with spatio-temporal validation DOI
Xiangtian Meng, Yilin Bao, Xinle Zhang

и другие.

Remote Sensing of Environment, Год журнала: 2024, Номер 318, С. 114592 - 114592

Опубликована: Дек. 31, 2024

Язык: Английский

Процитировано

2