Wildfires are natural hazards that threaten ecosystems, communities, and economies, and they have shown a significant increase in frequency and intensity in recent years. Accurate wildfire spread prediction is critical for efficient management and mitigation strategies. Due to limitations in current wildfire spread datasets, this study introduces a novel dataset for Alberta, Canada, covering 616 wildfire events from 2001 to 2019, called “ABNextFire.” The dataset incorporates Landsat 7 (L7) and Landsat 8 (L8) data at 30-m spatial resolution and daily environmental variables at 250-m resolution, providing finer spatial detail than existing datasets. ABNextFire combines constant variables (e.g., vegetation indices, topography) and daily variables (e.g., weather, fire indices) in a 2D raster format, providing enhanced spatial detail and temporal consistency compared to prior datasets. Four baseline deep learning models, including UNet, DeepLabV3, residual network (ResNet), and VGG16, were trained and evaluated on ABNextFire to predict wildfire spread. UNet outperformed others, achieving an F1-score (F1) of up to 0.75, driven by its ability to consider complex spatial patterns via skip connections. DeepLabV3 achieved strong results but struggled with larger fires, whereas ResNet and VGG16 exhibited poorer generalization. Performance declined with increasing wildfire size (>30 km2), highlighting challenges in modeling large-scale fire behavior. ABNextFire's flexible format supports future variable integration, enhancing its utility.

High-resolution land cover classification is critical for monitoring environmental change and managing natural resources. This study presents an unsupervised framework with externally guided feature prioritization that integrates Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery at 10 m spatial resolution. A cloud-native export protocol in Google Earth Engine (GEE) enables the generation of consistent, cloud-free, and snow-free seasonal composites across Ontario, Canada. A comprehensive feature engineering pipeline combines spectral indices, radar backscatter metrics, terrain derivatives from digital elevation models (DEMs), and temporal statistics to create a rich multi-sensor input space. Dimensionality reduction is performed using Sparse Principal Component Analysis (SparsePCA) and mutual-information-based feature selection. Clustering is conducted using three complementary algorithms: centroid-based K-means, density-based Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), and reachability-based Ordering Points To Identify the Clustering Structure (OPTICS). Final land cover labels are assigned via a majority-voting ensemble, with prediction ties resolved deterministically using OPTICS. OPTICS is particularly effective for modeling heterogeneous landscapes due to its ability to detect clusters of varying density without requiring a global threshold. This study is designed as a pilot-site methodological demonstration using three representative 2 km × 2 km regions in Ontario, rather than a full provincial-scale land cover product. The resulting classification maps are validated against reference land cover data, demonstrating the effectiveness and potential scalability of the proposed external-label guided unsupervised mapping approach.

Large-scale land cover (LC) mapping is critical for monitoring Earth's surface and managing environmental changes. While machine learning (ML) and deep learning (DL) methods have significantly advanced LC mapping efforts, the reliance on high-quality training labels remains a major limitation due to their time-intensive and costly collection process. To address this challenge, we propose HiResNet, a novel DL framework that combines a multiscale feature extractor (MSFE) within convolutional neural networks (CNNs), a vision transformer (ViT) architecture, and a weakly-supervised loss function, to produce a 10 m LC map using an existing low-resolution (LR) 30 m LC map. In particular, this framework reduces the dependence on high-resolution (HR) training labels while generating HR (10 m) LC maps using Sentinel-2 imagery and LR 30 m labels. The capability of the proposed method is evaluated in the province of Ontario (ON), Canada, a region characterized by a diverse landscape and different LC types, covering approximately 1.07 million km(2). The results demonstrate a remarkable capability of the proposed model to enhance spatial resolution and achieve high classification accuracy, with an overall accuracy (OA) of 89% for large-scale LC classification. Compared to conventional methods and advanced DL architectures, such as Random Forest (RF), ViT, HRNet, SegFormer, DeepLabV3, and L2HNet, the proposed method exhibits a superior performance by handling class imbalances and extracting complex features. Furthermore, uncertainty estimation using Shannon entropy and class disagreement metrics demonstrates that the highest levels of uncertainty are associated with object edges, narrow features (e.g., rivers and roads), and complex LC classes like forests.

High-resolution land cover classification is critical for monitoring environmental change and managing natural resources. This study presents a fully unsupervised framework that integrates Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery at 10-meter spatial resolution. A cloud-native export protocol in Google Earth Engine (GEE) enables the generation of consistent, cloud-free, and snow-free seasonal composites across Ontario, Canada. A comprehensive feature engineering pipeline combines spectral indices, radar backscatter metrics, terrain derivatives from digital elevation models (DEMs), and temporal statistics to create a rich multi-sensor input space. Dimensionality reduction is performed using Sparse Principal Component Analysis (SparsePCA) and mutual-information based feature selection. Clustering is conducted using three complementary algorithms: centroid-based K-means, density-based Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), and reachability-based Ordering Points To Identify the Clustering Structure (OPTICS). Final land cover labels are assigned via a majority-voting ensemble, with prediction ties resolved deterministically using OPTICS. OPTICS is particularly effective for modeling heterogeneous landscapes due to its ability to detect clusters of varying density without requiring a global threshold. The resulting classification maps are validated against reference land cover data, demonstrating the scalability and effectiveness of the proposed label-free mapping approach.

Accurate and scalable wetland classification remains a challenging task due to spectral similarity among wetland types, landscape heterogeneity, and limited labeled data. This study presents a geo-foundation-model (GFM)-based framework for multi-class wetland classification using high resolution Planet multispectral imagery and spectral indices in St. John's, Newfoundland and Labrador, Canada. The proposed approach integrates the pretrained Clay GFM with a task specific convolutional neural network (CNN) branch. Model performance was evaluated using overall accuracy (OA), precision, recall, and F1-score, and compared with different deep learning models used in wetland mapping, including CNN-Atrous Spatial Pyramid Pooling (ASPP), CVTNet, spatiotemporal (ST) ViT, and Wet-ConViT across training data proportions (25%, 50%, 75%, and 100%). The proposed model achieved an OA of 0.92 and an F1-score of 0.90 on the full training dataset and consistently outperformed baseline models across all training scenarios, particularly under limited-data conditions. Spatial evaluation further demonstrated coherent classification outputs with uncertainty primarily concentrated along class boundaries and heterogeneous transition zones. The findings highlight the effectiveness of integrating pretrained GFM representations with task-specific feature extraction for robust, high-resolution wetland mapping and support the potential of foundation-model based approaches for scalable Earth observation (EO) applications.

Arctic-boreal wetlands play a central role in global carbon cycles, biodiversity, and hydrological regulation, yet their extent and dynamics remain poorly characterized. Static wetland maps often fail to capture short-term fluctuations in inundation caused by snowmelt, rainfall, river flooding, and permafrost degradation, contributing to an underestimation of wetland area and associated greenhouse gas emissions. This study evaluates a multi-sensor framework under two scenarios: (I) a typical static scenario based on multi-sensor median summer composites from 2021 to 2025, and (II) a dynamic scenario that integrates Sentinel-1 time-series backscatter with Sentinel-2 vegetation indices to represent seasonal and interannual inundation behavior. Results for two 500 & times; 500 km2 study areas indicate substantial improvements under the dynamic scenario. Overall accuracy reached 95.2% in the Northwest Territories, Canada, and 96.1% in northern Sweden, compared with 91% and 92% for the static products. Gains are most evident for seasonally flooded and vegetated classes, with User's and Producer's accuracies increasing by 3-7% relative to the static scenario. The dynamic scenario also identified that 14-18% of wetland area exhibits seasonal or episodic inundation, information that static methods fail to reveal. In addition to higher accuracy, the dynamic framework provides ecologically meaningful outputs such as maximum inundation extent, inundation frequency, and seasonal hydroperiod maps, offering direct value for methane modelling, biodiversity assessment, and water resource management.

Arctic–boreal wetlands and lakes are among the most significant and most uncertain natural sources of atmospheric methane. Rapid Arctic amplification, permafrost thaw, hydrological change, and increasing ecosystem productivity are expected to intensify methane emissions from high-latitude landscapes. Yet, significant uncertainties persist in quantifying their magnitude, seasonality, and spatial distribution. This review synthesizes the current state of the art in monitoring methane emissions from Arctic–boreal wetlands and lakes through complementary bottom-up and top-down approaches. We examine Earth observation (EO) capabilities, including optical, thermal infrared (TIR), and synthetic aperture radar (SAR) missions, as well as new emerging satellite platforms. We also assess in situ measurement networks, wetland and lake inventories, empirical and process-based models, and atmospheric inversion frameworks. Key gaps remain in representing small waterbodies, shoreline heterogeneity, winter emissions, inventory harmonization, and integration between atmospheric retrievals and surface-based flux models. Moreover, advances in multi-sensor data fusion, explainable artificial intelligence (XAI), physics-informed inversion methods, and geospatial foundation models offer strong potential to reduce these uncertainties. A coordinated integration of satellite observations, field measurements, and transparent modeling frameworks is essential to improve Arctic–boreal methane budgets and strengthen projections of climate feedback in a rapidly warming region.

Recent advances in foundation models, including large language models and advanced computer vision techniques, have opened new possibilities in remote sensing applications. One such model is the Segment Anything Model (SAM), which can perform image segmentation without task-specific training data. This is especially useful for detecting methane plumes in satellite imagery, where it is important to accurately separate methane column enhancements from complex background conditions. SAM’s prompt-based segmentation approach helps address these challenges and reduces the need for large annotated datasets. In this study, we introduce SAM4CH4, a zero-shot segmentation framework that applies SAM for methane plume detection using Sentinel-2 imagery, with segmentation prompts automatically generated by text encoder models including contrastive language-image pretraining (CLIP), CLIP Surgery, and Grounding DINO. We evaluate the approach on both a synthetically generated benchmark dataset and real Sentinel-2 images. Results show that bounding-box prompts from the Swin-L variant of Grounding DINO, combined with the latest version of SAM, consistently achieve high accuracy, exceeding 72% in F1-score and 95% in overall accuracy, and outperform a widely used statistical thresholding method by approximately 15% in F1-score. These results are also competitive with supervised deep learning methods, which typically require large labeled datasets and significant computational resources. By leveraging pretrained models and removing the need for manual annotation, the proposed SAM4CH4 framework offers a zero-shot scalable and efficient solution for operational methane plume detection and monitoring.

Methane (CH4) significantly contributes to global warming, with a global warming potential approximately 84 times greater than carbon dioxide (CO2) over 20 years. Numerous studies have shown that a small number of high-emitting point sources, known as super-emitters, account for a disproportionately large share of total anthropogenic CH4 emissions, underscoring the urgency of targeted detection strategies. Recently, a growing focus has been on using remote sensing technology for CH4 monitoring across various emission sources. As such, this study introduces an automated solution for identifying CH4 super-emitters using Sentinel-5P (S5P) satellite data. Specifically, a deep learning (DL) framework that integrates a Vision Transformer (ViT) and the Segment Anything Model (SAM) for CH4 plume detection is proposed. The ViT model is trained using CH4 plume locations reported by the Netherlands Institute for Space Research (SRON) to classify the presence or absence of CH4 plumes within image patches, achieving an overall accuracy (OA) of 0.92. Subsequently, SAM extracts plume boundaries from patches identified as plumes by the ViT. Integrated Mass Enhancement (IME) is then used to quantify emission rates based on the SAM-generated masks. This approach is applied to various known emission regions, including Turkmenistan, Spain, Algeria, Argentina, China, Iran, the United States, India, and Morocco, identifying significant CH4 plumes with emission rates up to 92 t/h. The reported rates align closely with SRON values for the same dates and locations. While the ViT model requires supervised training, the SAM component operates without mask-specific training data, enabling automated and generalizable mask extraction. This study demonstrates the potential of combining advanced AI models with satellite data for effective CH4 monitoring and environmental assessment.

Wetland ecosystems are vital for global carbon sequestration and environmental stability, highlighting the importance of accurately estimating aboveground biomass (AGB). This study leverages high-resolution PlanetScope satellite data to map AGB in the dynamic tidal marshes of the Mississippi River Delta (MRD). PlanetScope SuperDove surface reflectance data with 3-m spatial resolution was analyzed using machine learning regression models-Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN)-to estimate AGB during the spring and fall seasons of 2021. Field data collected during these seasons provided the reference samples for creating three datasets: one for spring, one for fall, and a combined multi-temporal dataset. For each machine learning method, three models were developed: two for individual seasons and one multi-temporal model. The study incorporated vegetation-sensitive spectral indices to enhance AGB prediction, and their importance was analyzed across the models. The multi-temporal ANN model achieved the highest accuracy, with an RMSE of 1.96 Mg/ha and an R-squared value of 0.84. Using this model, AGB maps for all seasons were generated, revealing seasonal variations in biomass across the wetlands. These results demonstrate the potential of high-resolution remote sensing data and machine learning models to provide accurate and detailed AGB maps, filling spatial and temporal gaps in biomass data. Using spaceborne highresolution data to produce wetland AGB maps is essential for informing wetland management and conservation policies, as these maps enable monitoring biomass distribution, prioritizing conservation areas, and assessing the effectiveness of conservation strategies to support biodiversity and climate change mitigation.

Wetlands mapping using remote sensing data is a challenging task due to the spectral similarity of wetlands, the fragmented nature of these landscapes, and seasonal variations in wetlands. To address these limitations, this study proposes a novel spatio-temporal vision transformer (ST-ViT) model for an accurate wetland classification using seasonal data. The ST-ViT model was trained using multi-seasonal Sentinel-1 (S1) and Sentinel-2 (S2) data acquired during the spring, summer, and fall of 2020 in a study area located in Newfoundland and Labrador, Canada. The performance of the ST-ViT model was evaluated against the validation dataset, achieving an overall accuracy (OA) of 0.950 and F1-score (F1) of 0.934, outperforming other deep learning models such as random forest (RF), hybrid spectral network (HybridSN), etc. The model demonstrated strong classification capabilities among most wetland classes, with some challenges in distinguishing between spectrally similar classes like bogs and fens. Moreover, the integration of spatio-temporal features enabled the reduction of feature mixing between wetland classes, particularly during different seasons. The ST-ViT model provides an accurate wetland distribution map in different seasons, supporting critical decision-making processes related to wetland conservation and environmental monitoring.

Wetlands are among the most ecologically significant yet threatened ecosystems, providing critical services such as carbon storage, water filtration, and biodiversity support. However, their mapping remains challenging due to their complex and heterogeneous nature. This study introduces a novel deeplearning (DL) framework for accurate wetland mapping by integrating convolutional neural networks (CNNs) and vision transformers (ViT). The model uses local features extracted through CNN blocks and global features extracted by ViT's self-attention mechanism, enhanced by spatial attention (SA) and channel attention (CA) modules. Using Sentinel-1 (S1) and Sentinel-2 (S2) data, the framework achieves high classification accuracy across diverse wetland types, including bogs, fens, marshes, swamps, and other classes. Experimental results demonstrate strong performance, with $\mathbf{F 1}$-scores of $\mathbf{0. 9 0}$ for bogs and 0.89 for swamps, highlighting the model's ability to distinguish spectrally similar classes. The gradient-weighted class activation mapping (Grad-CAM) analysis further shows the complementary roles of local and global features in capturing fine-grained details and large-scale contextual information.

Coastal wetlands play a vital role in climate change mitigation, and remote sensing tools offer a unique opportunity for monitoring carbon content. This study explores the use of high-resolution satellite data, specifically PlanetScope with 3m spatial resolution and eight spectral bands, for monitoring wetland carbon content. Vegetation-Sensitive spectral indices were calculated from the acquired surface reflectance product, and datasets for spring and fall seasons were created using field measurements, with 65% reserved for the training stage. Utilizing a Random Forest regression model, we mapped carbon content over coastal wetlands, producing seasonal maps for spring and fall 2021 with 3m spatial resolution. The analysis of the model revealed the importance of the employed spectral bands and indices. Evaluation metrics, including an RMSE of 90.35 and 325.88 mg g-1, along with R-squared values of 0.91 and 0.40 for the training and test stages, provide insights into the model's performance.

This study addresses the challenge of large-scale land cover mapping using advanced deep learning models. While state-of-the-art deep learning methods have demonstrated promising results in various remote sensing applications, their efficiency for large-scale semantic segmentation tasks remains underexplored. A key limitation is their reliance on extensive training datasets. To address this issue, we propose a two-stage classification approach that integrates random forest for initial land cover mapping and MobileUNetR, a lightweight hybrid convolution-transformer model, for a refined classification. Leveraging Sentinel-1 and Sentinel-2 data, the land cover map of Ontario at spatial resolution of 10 m, aligned with the North American Land Change Monitoring System Level I legend, encompassing 11 classes, is generated. The findings of this study reveal that MobileUNeTR surpasses widely used models such as UNet and PSPNet in terms of both accuracy and efficiency, underscoring its suitability for large-scale land cover mapping. In particular, an overall accuracy of 85% and a Kappa coefficient of 0.83 are achieved with MobileUNetR, which has only about one-fourth to one-fifth the number of parameters compared to other deep learning models examined in this study. As the only deep learning model, examined in this study, combining convolutional and transformer blocks, MobileUNetR demonstrates the superiority of hybrid architectures for large-scale semantic segmentation. This is due to its capability in capturing both local and global features, which are essential for semantic segmentation of heterogeneous land cover classes with varying sizes and spectral signatures.

Methane (CH4) stands out as the second-largest contributor to the global warming since pre-industrial era. Anthropogenic methane emissions (e.g., oil and gas, waste management, and coal mining) are the major sources of methane release and in the meantime provide an excellent opportunity for emission reduction. Regarding this, observations from satellite remote sensing paly pivotal role in methane detection and quantification, further enhancing the temporal and spatial extent of methane research. A key feature of satellite data is the presence of Shortwave Infrared (SWIR) methane absorption bands, which are essential for identifying methane plumes from space. Detection, monitoring, and characterization are the main components of satellite-based methane studies. In general, quantifying methane emissions from regional and point sources presents two major challenges in the literature and the past few years have witnessed a tremendous success in terms of research, development, and operationalization of new techniques for methane studies, particularly, in the latter domain. As such, there is a need for a systematic analysis and review of existing literature to identify current trends, techniques, earth observation data for methane point source monitoring from space. Accordingly, this study systematically reviews 77 studies and highlights the critical roles of satellite data in detecting methane point source emissions. The literature identifies oil and gas sector as a dominant source of reported methane emission, particularly from countries like Turkmenistan, the United States, and Algeria. The review also categorizes instruments with methane detection capabilities into three main types, namely hyperspectral, multispectral, and SWIR spectrometers, each offering their unique advantages and addressing the limitations of other source of data. The main processing steps for methane point source emission monitoring from space identified in the literature are methane column retrieval, masking/detection, and source rate quantification. The literature reveals while conventional techniques are still widely used for methane detection and quantification, AI-based models are emerging as useful tools in different stages of methane research and significantly address the limitations of conventional techniques. The general characteristics of methane point source studies reveal a diverse array of applications across both atmospheric science and remote sensing fields. The rising number of publications in these areas, especially in high-impact journals, underscores the importance and relevance of methane monitoring. These studies bridge the gap between atmospheric observations and remote sensing technologies, contributing to a more integrated understanding of methane emissions on a global scale.

Wetlands play a vital role in carbon sequestration, biodiversity conservation, and water regulation, making their accurate monitoring essential for environmental management. Synthetic Aperture Radar (SAR) is particularly effective for assessing wetland ecosystems due to its ability to penetrate vegetation and capture biomass dynamics under various weather conditions. This study leverages UAVSAR quad-polarization data to estimate aboveground biomass (AGB) in the wetlands of southern Louisiana, USA, a region with diverse wetland types and significant ecological importance. A total of 103 features were extracted from UAVSAR data using various polarimetric decomposition methods, including Zhang, Huynen, Van Zyl, and others. Three machine learning models, including Support Vector Machine (SVM), Random Forest (RF), and Histogram-based Gradient Boosting (HGB) were employed to evaluate the effectiveness of these decompositions. Results indicated that the Zhang decomposition, combined with HGB, achieved the highest accuracy with an R-2 of 0.74 and an RMSE of 183.95 g m(-2), outperforming other decomposition methods and classifiers. Additionally, RF showed strong performance, while SVM consistently underperformed. These findings highlight the potential of UAVSAR-derived polarimetric features for wetland biomass estimation, demonstrating that targeted decomposition selection and advanced machine learning models can enhance accuracy. This study provides valuable insights for improving wetland monitoring and conservation efforts, supporting ecosystem management, and climate change mitigation strategies.

Wetlands are vital ecosystems for biodiversity. However, these regions encounter global degradation from human activities and climate change. This study introduces a convolutional neural network (CNN) model with a channel attention (CA) mechanism, called ACNN, for wetland mapping using high-resolution Planet satellite data. While achieving an Overall Accuracy of 0.96 on the training set and 0.90 on the validation set, our focus is on evaluating the transparency of the model's decision-making process. We employed the gradient-weighted class activation mapping (GradCAM) algorithm to quantify the robustness, faithfulness, localization, complexity, and randomization. Our results show a direct relationship between model depth and localization, indicating a refined focus on critical channels and features. However, a concurrent decrease in faithfulness, particularly in later layers, suggests a complexity-faithfulness trade-off. This study shows the significance of adopting Explainable Artificial Intelligence (XAI) methods to enhance the interpretability of deep learning (DL) models in wetland mapping.

Wildfires significantly threaten ecosystems and human lives, necessitating effective prediction models for the management of this destructive phenomenon. This study integrates Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) modules to develop a novel deep learning model called CNN-BiLSTM for near-real-time wildfire spread prediction to capture spatial and temporal patterns. This study uses the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire product and a wide range of environmental variables, including topography, land cover, temperature, NDVI, wind informaiton, precipitation, soil moisture, and runoff to train the CNN-BiLSTM model. A comprehensive exploration of parameter configurations and settings was conducted to optimize the model’s performance. The evaluation results and their comparison with benchmark models, such as a Long Short-Term Memory (LSTM) and CNN-LSTM models, demonstrate the effectiveness of the CNN-BiLSTM model with IoU of F1 Score of 0.58 and 0.73 for validation and training sets, respectively. This innovative approach offers a promising avenue for enhancing wildfire management efforts through its capacity for near-real-time prediction, marking a significant step forward in mitigating the impact of wildfires.

Wetlands are amongst Earth’s most dynamic and complex ecological resources, serving productive and biodiverse ecosystems. Enhancing the quality of wetland mapping through Earth observation (EO) data is essential for improving effective management and conservation practices. However, the achievement of reliable and accurate wetland mapping faces challenges due to the heterogeneous and fragmented landscape of wetlands, along with spectral similarities among different wetland classes. The present study aims to produce advanced 10 m spatial resolution wetland classification maps for four pilot sites on the Island of Newfoundland in Canada. Employing a comprehensive and multidisciplinary approach, this research leverages the synergistic use of optical, synthetic aperture radar (SAR), and light detection and ranging (LiDAR) data. It focuses on ecological and hydrological interpretation using multi-source and multi-sensor EO data to evaluate their effectiveness in identifying wetland classes. The diverse data sources include Sentinel-1 and -2 satellite imagery, Global Ecosystem Dynamics Investigation (GEDI) LiDAR footprints, the Multi-Error-Removed Improved-Terrain (MERIT) Hydro dataset, and the European ReAnalysis (ERA5) dataset. Elevation data and topographical derivatives, such as slope and aspect, were also included in the analysis. The study evaluates the added value of incorporating these new data sources into wetland mapping. Using the Google Earth Engine (GEE) platform and the Random Forest (RF) model, two main objectives are pursued: (1) integrating the GEDI LiDAR footprint heights with multi-source datasets to generate a 10 m vegetation canopy height (VCH) map and (2) seeking to enhance wetland mapping by utilizing the VCH map as an input predictor. Results highlight the significant role of the VCH variable derived from GEDI samples in enhancing wetland classification accuracy, as it provides a vertical profile of vegetation. Accordingly, VCH reached the highest accuracy with a coefficient of determination (R2) of 0.69, a root-mean-square error (RMSE) of 1.51 m, and a mean absolute error (MAE) of 1.26 m. Leveraging VCH in the classification procedure improved the accuracy, with a maximum overall accuracy of 93.45%, a kappa coefficient of 0.92, and an F1 score of 0.88. This study underscores the importance of multi-source and multi-sensor approaches incorporating diverse EO data to address various factors for effective wetland mapping. The results are expected to benefit future wetland mapping studies.