Groundwater depletion is a critical environmental and agricultural issue in the Texas High Plains (THP), largely due to the region's dependence on the Ogallala Aquifer for irrigation. This study presents a data-driven approach integrating remote sensing and in-situ groundwater data to monitor and project groundwater level changes in Castro and Hale Counties from 2000 to 2030, supporting sustainable water management. To attribute water sources, a crop classification and field segmentation method was developed based on the phenological characteristics of sorghum, cotton, and maize from 1995 to 2024. An ensemble analysis of deep learning models, including the Segment Anything Model (SAM), revealed that the one-dimensional convolutional neural network (1DCNN) integrated with a Long Short-Term Memory (LSTM) network achieved the highest crop mapping performance, with an overall accuracy of approximately 0.90, an F1-score of 0.89, and a Matthews Correlation Coefficient (MCC) of 0.80. Water source attribution was further assessed using a water balance approach (precipitation minus evapotranspiration from ensemble OpenET), namely water difference, over agricultural areas in Castro and Hale County from 2000 to 2023. The greatest groundwater consumption occurred during the peak growing season (July-August), ranging from 60 to 80 mm/month. Time series analysis showed that all crop types contributed to groundwater depletion, with maize cultivation exhibiting a statistically significant correlation (p < 0.05; RMSE = 36-38 mm). Additionally, cumulative water difference correlated strongly with in-situ groundwater levels (R² = 0.72; RMSE ≈ 8258 mm). Future projections indicate an annual groundwater level decline of approximately 1.9-2.0 m, decreasing from 109-111 m in 2024 to 119-121 m by 2030. These results demonstrate that integrating remote sensing with deep learning and hydrological modeling offers a robust framework for monitoring and projecting groundwater depletion, with potential applicability of scaling to other regions facing similar challenges.

Mangrove forests are critical coastal ecosystems that store carbon, support marine life, and serve as natural barriers, protecting shorelines from erosion and reducing the impact of storms by absorbing wave energy. However, the rise of human activities and sea levels has led to their destruction over the past decades. It is important to know how the areas of mangrove forests change and adapt every year to plan for their restoration and protection and to support future trends like using carbon credits to help developing countries generate income. This study aims to map and monitor mangrove forest area changes over four decades in the Mekong region, comprising Myanmar, Thailand, Cambodia, and Vietnam, from 1984 to 2023 using a time series of Landsat data together with random forest (RF) classification. This analysis implemented multiple approaches, including creating stabilized Landsat imagery composites from the LandTrendr algorithm, Otsu edge detection, Minimum Mapping Unit (MMU), and RF classifier. The study found the map accuracy based on the RF model classifier achieved an overall accuracy between 86.2% and 88.8%, providing reliable data for analysis. Country-level analysis revealed increasing mangrove forest cover in Thailand (12.9%) and Vietnam (28.4%) since 1984. Conversely, mangrove areas in Cambodia and Myanmar have decreased significantly from 1984 to 2023 by about 14.6% and 22.7%, respectively. These findings have significant implications for resource allocation, investment strategies, and the development of carbon credits to support mangrove conservation efforts. This comprehensive dataset offers valuable insights for stakeholders involved in mangrove management and restoration in the Mekong region. By understanding the spatial-temporal distribution patterns of mangrove forest change, decision-makers can make informed decisions to safeguard these critical ecosystems for future generations.

Seasonal fires in northern Thailand are a persistent environmental and public health concern, yet existing fire probability mapping approaches in Thailand rely heavily on subjective multi-criteria analysis (MCA) methods and temporally static data aggregation methods. To address these limitations, we present a flexible, replicable, and operationally viable seasonal fire probability mapping methodology using a Random Forest (RF) machine learning model in the Google Earth Engine (GEE) platform. We trained the model on historical fire occurrence and fire predictor layers from 2016–2023 and applied it to 2024 conditions to generate a probabilistic fire prediction. Our novel approach improves upon existing operational methods and scientific literature in several ways. It uses a more representative sample design which is agnostic to the burn history of fire presences and absences, pairs fire and fire predictor data from each year to account for interannual variation in conditions, empirically refines the most influential fire predictors from a comprehensive set of predictors, and provides a reproducible and accessible framework using GEE. Predictor variables include both socioeconomic and environmental drivers of fire, such as topography, fuels, potential fire behavior, forest type, vegetation characteristics, climate, water availability, crop type, recent burn history, and human influence and accessibility. The model achieves an Area Under the Curve (AUC) of 0.841 when applied to 2016–2023 data and 0.848 when applied to 2024 data, indicating strong discriminatory power despite the additional spatial and temporal variability introduced by our sample design. The highest fire probabilities emerge in forested and agricultural areas at mid elevations and near human settlements and roads, which aligns well with the known anthropogenic drivers of fire in Thailand. Distinct areas of model uncertainty are also apparent in cropland and forests which are only burned intermittently, highlighting the importance of accounting for localized burning cycles. Variable importance analysis using the Gini Impurity Index identifies both natural and anthropogenic predictors as key and nearly equally important predictors of fire, including certain forest and crop types, vegetation characteristics, topography, climate, human influence and accessibility, water availability, and recent burn history. Our findings demonstrate the heavy influence of data preprocessing and model design choices on model results. The model outputs are provided as interpretable probability maps and the methods can be adapted to future years or augmented with local datasets. Our methodology presents a scalable advancement in wildfire probability mapping with machine learning and open-source tools, particularly for data-constrained landscapes. It will support Thailand’s fire managers in proactive fire response and planning and also inform broader regional fire risk assessment efforts.

Mangrove ecosystems act as highly efficient carbon sinks and provide critical ecological services for climate regulation; however, they are increasingly threatened by anthropogenic pressures and climate change. Recent advances in annual satellite-based Earth Observation (EO) embedding (EM) datasets have enabled the integration of multi-source data fusion, improving land use and land cover analyses. Nevertheless, EM datasets have rarely been applied for the monitoring of mangrove ecosystems. Within this context, the present study compares the performance of EM with combined Sentinel-1, Sentinel-2, and Landsat 8/9 datasets (S2S1L8/9) through the integration of a pretrained ResNet152–U-Net architecture and Random Forest (RF) modeling to assess spatio-temporal patterns of mangrove intactness and degradation from 2017 to 2024. A ResNet152 encoder pretrained on ImageNet was employed to train a U-Net model using the Global Mangrove Dataset (2020) for the mapping of potential mangrove extent. The EM dataset outperformed the S2S1L8/9 combination, achieving validation Intersection over Union (IoU) scores of 0.85 and 0.84, and Dice coefficients of 0.89 and 0.88, respectively. Spatial comparison further indicated that EM yielded a lower Root Mean Square Error (RMSE) (6.51 ha) compared to S2S1L8/9 (7.27 ha). The RF model, trained on intact and non-intact mangrove samples across multiple years, confirmed the superior performance of EM in delineating intact mangrove areas along coastlines, with an overall accuracy of 0.97, an F1-score of 0.97, and a Matthews Correlation Coefficient of 0.93. Degraded mangrove areas were identified by masking intact regions, and gain–loss analysis revealed a decline of approximately 3% in 2021 and 2022 relative to the 2017 baseline. These findings demonstrate that EM provides a more accurate and spatially consistent approach than conventional multisource datasets for mapping mangrove intactness and degradation. By minimizing classification errors and improving coastline delineation, EM establishes a robust framework for large-scale monitoring of mangrove dynamics, supporting conservation planning, carbon accounting, and climate resilience strategies across diverse coastal and terrestrial systems at global scales.