A Parallel-Cascaded Ensemble of Machine Learning Models for Crop Type Classification in Google Earth Engine Using Multi-Temporal Sentinel-1/2 and Landsat-8/9 Remote Sensing Data

Remote Sensing, Journal Year: 2023, Volume and Issue: 16(1), P. 127 - 127

Published: Dec. 28, 2023

The accurate mapping of crop types is crucial for ensuring food security. Remote Sensing (RS) satellite data have emerged as a promising tool in this field, offering broad spatial coverage and high temporal frequency. However, there still growing need type classification methods using RS due to the intra- inter-class variability crops. In vein, current study proposed novel Parallel-Cascaded ensemble structure (Pa-PCA-Ca) with seven target classes Google Earth Engine (GEE). Pa section consisted five parallel branches, each generating Probability Maps (PMs) different multi-temporal Sentinel-1/2 Landsat-8/9 images, along Machine Learning (ML) models. PMs exhibited correlation within class, necessitating use most relevant information reduce input dimensionality Ca part. Thereby, Principal Component Analysis (PCA) was employed extract top uncorrelated components. These components were then utilized structure, final performed another ML model referred Meta-model. Pa-PCA-Ca evaluated in-situ collected from extensive field surveys northwest part Iran. results demonstrated superior performance achieving an Overall Accuracy (OA) 96.25% Kappa coefficient 0.955. incorporation PCA led OA improvement over 6%. Furthermore, significantly outperformed conventional approaches, which simply stack sources feed them single model, resulting 10% increase OA.

Language: Английский

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Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms

Water, Journal Year: 2024, Volume and Issue: 16(4), P. 553 - 553

Published: Feb. 11, 2024

Remote sensing technology applications for water quality inversion in large rivers are common. However, their application to medium/small-sized bodies within rural areas is limited due the low spatial resolution of remote images. In this work, a typical small river was selected, and high-resolution unmanned aerial vehicle (UAV) multispectral images ground monitoring data were obtained. Then, comparative analysis three univariate regression models nine machine learning (Ridge Regression (RR), Support Vector (SVR), Grid Search (GS-SVR), Random Forest (RF), (GS-RF), eXtreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Convolutional (CNN), Catboost (CBR)) accuracy prediction turbidity (TUB), total nitrogen (TN), phosphorus (TP) performed. TUB can be achieved by simple statistical models. The CBR model exhibited best performance index inversions on test set evaluation metrics: R2 (0.90~0.92), RMSE (7.57 × 10−3~1.59 mg/L), MAE (0.01~1.30 RPD (3.21~3.56), NSE (0.84~0.92). pollution study area closely related its land-use pattern, excessive irrational fertilizer application, distribution pollutant outlets.

Language: Английский

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Fuzzy Similarity Analysis of Effective Training Samples to Improve Machine Learning Estimations of Water Quality Parameters Using Sentinel-2 Remote Sensing Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2024, Volume and Issue: 17, P. 5121 - 5136

Published: Jan. 1, 2024

Continuous monitoring of Water Quality Parameters (WQPs) is crucial due to the global degradation water quality, primarily caused by climate change and population growth. Typically, Machine Learning (ML) models are employed retrieve WQPs, but they require a large amount training samples accurately capture data relationships. Even with sufficient data, discrepancies still exist between values predicted in-situ WQPs. This study proposes Fuzzy Similarity Analysis (FSA) technique enhance ML estimates WQPs using prediction errors in Effective Training Samples (ETS). The method was successfully applied Turbidity (Turb) Specific Conductance (SC) Lake Houston, USA, Sentinel-2 remote sensing data. Three algorithms, namely Mixture Density Networks, Support Vector Regression, Partial Least Squares were tested evaluate method's effectiveness. results showed that FSA significantly improved accuracy all predictions. improvement resulted up 9.15% reduction Mean Absolute Percentage Error (MAPE) 12% increase R2 for Turb, while SC, improvements 5.47% MAPE 7% R2. adaptability proposed other various satellite different promising quality inland waters.

Language: Английский

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Cropping intensity mapping in Sentinel-2 and Landsat-8/9 remote sensing data using temporal transfer of a stacked ensemble machine learning model within google earth engine

Geocarto International, Journal Year: 2024, Volume and Issue: 39(1)

Published: Jan. 1, 2024

This article aimed to map Cropping Intensity Patterns (CIPs) in the southwest region of Iran using Google Earth Engine and monthly composites Sentinel-2 Landsat-8/9 data. To detect CIPs with high inter- intra-class variability crops, a heterogeneous Stack ensemble machine learning model was developed. The incorporated Minimum Distance (MD) approach as meta-classifier, combining multiple base models, including Support Vector Machines (SVM), Random Forest (RF), Classification Regression Trees (CART), Gradient Boosted (GBT). In 2021, trained evaluated Ground Truth (GT) samples from same year, achieving an Overall Accuracy (OA) 94.24%. performance surpassed models by about 4% OA also reflected detection accuracies, User's (UA), Producer's (PA), F1-score, target classes. Subsequently, stack temporally transferred generate CIP maps for other years. achieved OAs 91.82% 90.97% based on GT 2020 2022, respectively. Finally, time series (2019-2023) were utilized Cellular Automata-Markov forecast 2024.

Language: Английский

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Continuous mapping of forest canopy height using ICESat-2 data and a weighted kernel integration of multi-temporal multi-source remote sensing data aided by Google Earth Engine

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(37), P. 49757 - 49779

Published: July 31, 2024

Language: Английский

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