Mapping Soil Properties in Tropical Rainforest Regions Using Integrated UAV-Based Hyperspectral Images and LiDAR Points DOI Open Access
Yiqing Chen,

Tiezhu Shi,

Qipei Li

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(12), P. 2222 - 2222

Published: Dec. 17, 2024

For tropical rainforest regions with dense vegetation cover, the development of effective large-scale soil mapping methods is crucial to improve management practices replace time-consuming and laborious conventional approaches. While machine learning (ML) algorithms demonstrate superior predictability properties over linear models, their practical automated application for predicting using remote sensing data requires further assessment. Therefore, this study aims integrate Unmanned Aerial Vehicles (UAVs)-based hyperspectral images Light Detection Ranging (LiDAR) points predict indirectly in two mountains (Diaoluo Limu) Hainan Province, China. A total 175 features, including texture indices, forest parameters, were extracted from sites. Six ML Partial Least Squares Regression (PLSR), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Decision Trees (GBDT), Extreme (XGBoost), Multilayer Perceptron (MLP), constructed properties, acidity (pH), nitrogen (TN), organic carbon (SOC), phosphorus (TP). To enhance model performance, a Bayesian optimization algorithm (BOA) was introduced obtain optimal hyperparameters. The results showed that compared default parameter tuning method, BOA always improved models’ performances achieving average R2 improvements 202.93%, 121.48%, 8.90%, 38.41% pH, SOC, TN, TP, respectively. In general, effectively determined complex interactions between hyperparameters prediction leading an performance models. GBDT generally outperformed other pH while XGBoost achieved highest accuracy SOC TP. fusion LiDAR resulted better each single source. models utilizing integration features derived those relying on one summary, highlights promising combination UAV-based advance digital property forested areas, monitoring.

Language: Английский

Interpretability Research of Deep Learning: A Literature Survey DOI

Biao Xu,

Guanci Yang

Information Fusion, Journal Year: 2024, Volume and Issue: 115, P. 102721 - 102721

Published: Oct. 9, 2024

Language: Английский

Citations

21
Advanced chemometrics toward robust spectral analysis for fruit quality evaluation DOI
Xiaolei Zhang, Jie Yang

Trends in Food Science & Technology, Journal Year: 2024, Volume and Issue: 150, P. 104612 - 104612

Published: July 2, 2024

Language: Английский

Citations

20
Fine-resolution mapping of cropland topsoil pH of Southern China and its environmental application DOI Creative Commons
Bifeng Hu, Modian Xie, Zhou Shi

et al.

Geoderma, Journal Year: 2024, Volume and Issue: 442, P. 116798 - 116798

Published: Feb. 1, 2024

Soil pH is one of the critical indicators soil quality. A fine resolution map urgently required to address practical issues agricultural production, environmental protection, and ecosystem functioning, which often fall short meeting demands for local applications. To fill this gap, we used data from an extensive survey 13,424 surface samples (0–0.2 m) across cropland Jiangxi Province in Southern China. Using digital mapping techniques with 46 covariates, produced a 30 m topsoil We integrate different variable selection algorithms machine learning methods. Our results indicate Random Forest covariates selected by recursive feature had best performance r 0.583 RMSE 0.41. The prediction interval coverage probability our was 0.92, indicating low estimated uncertainty. Climate identified as most predicting contribution 37.42 %, followed properties (29.09 %), management (21.86 parent material (6.22 biota (5.39 %) factors. mean 5.21, great pressure acidification region. high values were mainly distributed Northern, Western, Eastern parts region while majorly located central part. Compared past surveys 1980 s, there no significant change surveyed can provide important implications guidance decisions on heavy metal pollution remediation, precision agriculture, prevention acidification.

Language: Английский

Citations

12
Potential of solar-induced chlorophyll fluorescence (SIF) to access long-term dynamics of soil salinity using OCO-2 satellite data and machine learning method DOI Creative Commons

Ruiqi Du,

Youzhen Xiang, Junying Chen

et al.

Geoderma, Journal Year: 2024, Volume and Issue: 444, P. 116855 - 116855

Published: March 14, 2024

The accumulation of soil salt becomes a worldwide widespread phenomenon, being major threat to global production. As an environmental stress, salinity can reduce the vegetation photosynthetic activity. Solar-induced chlorophyll fluorescence (SIF) is electromagnetic signal actively released by during photosynthesis. SIF not only capture lower activity due stress promptly, but also less affected atmosphere and background. However, ability observation detect remains unclear. Here, we use standardized solar-induced iluorescence index (SIFI) from time series (2000 ∼ 2020) OCO-2 based product (GOSIF) develop model. results show that: identify (EC ≥ 2 4 dS m−1) class scale. SIFI calculated at May August (hereafter SIFI5-8) optimal sensitivity indices for rainfed cropland, herbaceous cover, irrigated shrubland, grassland. SIFI10-11 forest sparse vegetation; (2) By comparison, ovrerall classification accuracy predicted above 70 %. order cropland > bare area grassland shrubland cover (3) During least three-quarters period 2000 2020, was 4.9 Mkm2; (4) annual change rate content generally between −0.05 0.05 m−1 yr−1. Soil in South Africa West Asia increased greatly with 0.02 0.03 These demonstrate estimate salinity, providing new perspective explaining evaluating variation.

Language: Английский

Citations

10
Predictive Modeling of soil salinity integrating remote sensing and soil variables: An ensembled deep learning approach DOI Creative Commons
Sana Arshad, Syed Jamil Hasan Kazmi, Endre Harsányi

et al.

Energy Nexus, Journal Year: 2025, Volume and Issue: unknown, P. 100374 - 100374

Published: Feb. 1, 2025

Language: Английский

Citations

1
Estimating and mapping the dynamics of soil salinity under different crop types using Sentinel-2 satellite imagery DOI Creative Commons
Xin Cui, Wenting Han, Huihui Zhang

et al.

Geoderma, Journal Year: 2023, Volume and Issue: 440, P. 116738 - 116738

Published: Dec. 1, 2023

Soil salinization is one of the main factors contributing to land degradation, affecting ecological equilibrium, environmental health, and sustainable development agriculture. Due spatial temporal heterogeneity soil properties conditions in a large-scale region, monitoring accuracy can be challenging. This study investigated whether classification diverse crop types on time series improve prediction regional salinity levels. Specifically, we evaluated changes salt content (SSC) under vegetation cover over Hetao Irrigation District (HID) using multi-phase Sentinel-2 imagery ground-truth data collected from June September 2021 2022. Focused sunflower maize fields, this analyzed impact classifying these two examining four distinct SSC estimation. Five indices were selected as characteristic parameters pool 17 (VIs) 13 (SIs) derived satellite images. Moreover, three machine learning algorithms used establish estimation models. The findings underscored efficacy considering different enhancing response sensitivity spectral improving modeling accuracy. Among indices, VIs made more contributions model than SIs, achieving highest coefficient determination (R2) 0.71. artificial neural networks algorithm outperformed other terms stability, yielding an optimal R2 0.72 Root Mean Square Error (RMSE) 0.15%. proposed mapping approach that considers various series, offering valuable insights for accurately assessing salinization, guiding strategies its prevention remediation.

Language: Английский

Citations

16
Advancements and Perspective in the Quantitative Assessment of Soil Salinity Utilizing Remote Sensing and Machine Learning Algorithms: A Review DOI Creative Commons
Fei Wang, Lili Han, Lulu Liu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4812 - 4812

Published: Dec. 23, 2024

Soil salinization is a significant global ecological issue that leads to soil degradation and recognized as one of the primary factors hindering sustainable development irrigated farmlands deserts. The integration remote sensing (RS) machine learning algorithms increasingly employed deliver cost-effective, time-efficient, spatially resolved, accurately mapped, uncertainty-quantified salinity information. We reviewed articles published between January 2016 December 2023 on sensing-based prediction synthesized latest research advancements in terms innovation points, data, methodologies, variable importance, trends, current challenges, potential future directions. Our observations indicate innovations this field focus detection depth, iterations data conversion methods, application newly developed sensors. Statistical analysis reveals Landsat most frequently utilized sensor these studies. Furthermore, deep remains underexplored. ranking accuracy across various study areas follows: lake wetland (R2 = 0.81) > oasis 0.76) coastal zone 0.74) farmland 0.71). also examined relationship metadata accuracy: (1) Validation accuracy, sample size, number variables, mean exhibited some correlation with modeling while sampling type, time, maximum did not influence accuracy. (2) Across broad range scales, large sizes may lead error accumulation, which associated geographic diversity area. (3) inclusion additional environmental variables does necessarily enhance (4) Modeling improves when area exceeds 30 dS/m. Topography, vegetation, temperature are relatively covariates. Over past years, affected by has been increasing. To further we provide several suggestions for challenges directions research. While sole solution, it provides unique advantages salinity-related studies at both regional scales.

Language: Английский

Citations

5
Spatiotemporal Variation and Future Predictions of Soil Salinization in the Werigan–Kuqa River Delta Oasis of China DOI Open Access
Baozhong He, Jianli Ding, Wenjiang Huang

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(18), P. 13996 - 13996

Published: Sept. 21, 2023

Soil salinization is a serious global issue; by 2050, without intervention, 50% of the cultivated land area will be affected salinization. Therefore, estimating and predicting future soil salinity crucial for preventing investigating potential arable resources. In this study, several machine learning methods (random forest (RF), Light Gradient Boosting Machine (LightGBM), Decision Tree (GBDT), eXtreme (XGBoost)) were used to estimate in Werigan–Kuqa River Delta Oasis region China from 2001 2021. The cellular automata (CA)–Markov model was predict types 2020 2050. LightGBM method exhibited highest accuracy, overall prediction accuracy had following order: > RF GBRT XGBoost. Moderately saline, severely saline soils dominant east south research area, while non-saline mildly widely distributed inner oasis area. A marked decreasing trend salt content observed 2021, with rate 4.28 g/kg·10 a−1. primary change included conversion soil. generalized difference vegetation index (51%), Bio (30%), temperature drought (27%) greatest influence, followed variables associated attributes (soil organic carbon stock) terrain (topographic wetness index, slope, aspect, curvature, topographic relief index). Overall, CA–Markov simulation resulted suitable (kappa = 0.6736). Furthermore, areas increase other levels continue decrease From 2046 numerous converted These results can provide support control, agricultural production, investigations future. gradual decline past 20 years may have large-scale reclamation, which has turned alkali into also related effective measures taken local government control

Language: Английский

Citations

12
Monitoring Soil Salinity Classes through Remote Sensing-Based Ensemble Learning Concept: Considering Scale Effects DOI Creative Commons
Huifang Chen, Jingwei Wu, Chi Xu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(4), P. 642 - 642

Published: Feb. 9, 2024

Remote sensing (RS) technology can rapidly obtain spatial distribution information on soil salinization. However, (1) the scale effects resulting from mismatch between ground-based “point” salinity data and remote pixel-based “spatial” often limit accuracy of monitoring salinity, (2) same RS model usually provides inconsistent or sometimes conflicting explanations for different data. Therefore, based Landsat 8 imagery synchronously collected ground-sampling two typical study regions (denoted as N S, respectively) Yichang Irrigation Area in Hetao District May 2013, this used geostatistical methods to “relative truth values” corresponding pixel scale. Additionally, multispectral data, 14 indices were constructed. Subsequently, Correlation-based Feature Selection (CFS) method was select sensitive features, a strategy similar concept ensemble learning (EL) adopted integrate single-feature-sensitive Bayesian classification (BC) order construct an salinization (Nonsaline, Slightly saline, Moderately Strongly Solonchak). The research results indicated that exhibits moderate strong variability within 30 m scale, heterogeneity needs be considered when developing models; theoretical models variance functions S conform exponential spherical model, with R2 values 0.817 0.967, respectively, indicating good fit characteristics suitability Kriging interpolation; (3) compared single-feature BC identification constructed using EL demonstrated better potential robustness effectiveness.

Language: Английский

Citations

4
The daily soil water content monitoring of cropland in irrigation area using Sentinel-2/3 spatio-temporal fusion and machine learning DOI Creative Commons

Ruiqi Du,

Youzhen Xiang, Junying Chen

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 132, P. 104081 - 104081

Published: Aug. 1, 2024

Understanding soil moisture dynamics is crucial for crop growth. The digital mapping of field distribution provides valuable information agricultural water management. optical satellite data fine scale a region. However, these are greatly limited due to cloud contamination and revisit period. Despite the reported beneficial effects spatiotemporal fusion methods, accurate estimates high-resolution through still unclear, particularly when using Sentinel-2/3 images. This study introduces new estimation framework integrating spatio-temporal spectral from images machine learning algorithm,and thus provide spatiotemporally continuous estimation. includes four methods (ESTARRFM, Fit-FC, FSDAF STFMF) models (PLSR, SVM, RF GBRT). feasibility was validated in Hetao Irrigation Area Inner Mongolia, China. results showed that fused image generated by Fit-FC visually closest true image, followed ESTARFM, FSDAF, STFMF. fusion-machine provided reliable multi-layer (0 ∼ 20, 20 40 60 cm) irrigation area. dense time series facilitated detection events irrigated farmland. Our findings highlighted effectiveness providing daily monitoring farmland on large scale. These high spatial–temporal resolution growth resource management, contributing further expanding application remote sensing precision agriculture.

Language: Английский

Citations

4