Human activities and natural phenomena continually transform the Earth’s surface, presenting ongoing challenges to the environment. Therefore, the accurate and timely monitoring and prediction of these alterations are essential for devising effective solutions and mitigating environmental impacts in advance. This study introduces a novel framework, called HCD-Net, for detecting changes using bi-temporal hyperspectral images. HCD-Net is built upon a dual-stream deep feature extraction process, complemented by an attention mechanism. The first stream employs 3D convolution layers and 3D Squeeze-and-Excitation (SE) blocks to extract deep features, while the second stream utilizes 2D convolution and 2D SE blocks for the same purpose. The deep features from both streams are then concatenated and processed through dense layers for decision-making. The performance of HCD-Net is evaluated against existing state-of-the-art change detection methods. For this purpose, the bi-temporal Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral dataset was utilized to assess the change detection performance. The findings indicate that HCD-Net achieves superior accuracy and the lowest false alarm rate among the compared methods, with an overall classification accuracy exceeding 96%, and a kappa coefficient greater than 0.9.

Understanding the impacts of the spatial non-stationarity of environmental factors on surface soil moisture (SSM) in different seasons is crucial for effective environmental management. Yet, our knowledge of this phenomenon remains limited. This study introduces an interpretable machine learning framework that combines the SHapley Additive exPlanations (SHAP) method with two-step clustering to unravel the spatial drivers of SSM across Iran. Due to the limited availability of in situ SSM data, the performance of three global SSM datasets—SMAP, MERRA-2, and CFSv2—from 2015 to 2023 was evaluated using agrometeorological stations. SMAP outperformed the others, showing the highest median correlation and the lowest Root Mean Square Error (RMSE). Using SMAP, we estimated SSM across 609 catchments employing the Random Forest (RF) algorithm. The RF model yielded R² values of 0.89, 0.83, 0.70, and 0.75 for winter, spring, summer, and autumn, respectively, with corresponding RMSE values of 0.076, 0.081, 0.098, and 0.061 m³/m³. SHAP analysis revealed that climatic factors primarily drive SSM in winter and autumn, while vegetation and soil characteristics are more influential in spring and summer. The clustering results showed that Iran’s catchments can be grouped into five categories based on the SHAP method coefficients, highlighting regional differences in SSM controls.

Understanding the impacts of spatial non-stationarity of environmental factors on surface soil moisture (SSM) in different seasons is crucial for effective environmental management. Yet, our knowledge of this phenomenon remains limited. To fill this gap, we have introduced a framework combining the model agnostic SHapley Additive exPlanations (SHAP) technique with a two-step clustering analysis to provide spatial interpretations for machine learning models. Due to spatial and temporal limitations of in-situ SSM data in Iran, we evaluated the performance of global daily datasets, including SMAP, MERRA-2, and CFSv2, from 2015 to 2023 using the agrometeorological stations to select the best dataset for the machine learning process. Subsequently, SSM values were calculated for Iran’s 609 catchments using the most optimal dataset. Furthermore, we selected a set of climatological, hydrological, topographical, vegetation, and soil factors influencing SSM. While Random Forest (RF) was employed to estimate SSM, the SHAP technique was used for RF spatial interpretation in conjunction with the two-step cluster analysis. The results revealed that among the datasets, SMAP exhibited the highest median correlation and the lowest median root mean square error when compared to in-situ stations. Findings indicated that the RF model can offer SSM estimates with R2 values of 0.89, 0.83, 0.70, and 0.75 for the winter, spring, summer, and autumn seasons, respectively. The results of SHAP and two-step clustering demonstrated that climatic factors primarily influence winter and autumn SSM. In contrast, in spring and summer, SSM is predominantly affected by vegetation and soil characteristics. These findings provide valuable reference points and policy recommendations for effectively managing SSM in various catchments.

Seasonal snowpack is a crucial water resource, making accurate Snow Water Equivalent (SWE) estimation essential for water management and environmental assessment. This study introduces a novel approach to Passive Microwave (PMW) SWE estimation, leveraging the strong, unexpected correlation between SWE and the Spatial Standard Deviation (SSD) of PMW Calibrated Enhanced-Resolution Brightness Temperatures (CETB). By integrating spatial statistics, linear correlation, machine learning (Linear Regression, Random Forest, GBoost, and XGBoost), and SHapley Additive exPlanations (SHAP) analysis, this research evaluates CETB SSD as a key feature to improve SWE estimations or other environmental retrievals by investigating environmental drivers of CETB SSD. Analysis at three sites—Monument Creek, AK; Mud Flat, ID; and Jones Pass, CO—reveals site-specific SSD variability, showing correlations of 0.64, 0.82, and 0.72 with SNOTEL SWE, and 0.67, 0.89, and 0.67 with PMW-derived SWE, respectively. Among the sites, Monument Creek exhibits the highest ML model accuracy, with Random Forest and XGBoost achieving test R² values of 0.89 and RMSEs ranging from 0.37 to 0.39 [K] when predicting CETB SSD. SHAP analysis highlights SWE as the driver of CETB SSD at Monument Creek and Mud Flat, while soil moisture plays a larger role at Jones Pass. In snow-dominated regions with less surface heterogeneity, such as Monument Creek, SSDs can improve SWE estimation by capturing snow spatial variability. In complex environments like Jones Pass, SSDs aid SWE retrievals by accounting for factors such as soil moisture that impact snowpack dynamics. PMW SSDs can enhance remote sensing capabilities for snow and environmental research across diverse environments, benefiting hydrological modeling and water resource management.

Human activities and natural events alter the earth’s surface and pose a constant threat to the environment. Thus, accurately monitoring and predicting these changes in a timely manner is crucial to provide solutions and mitigate the environmental consequences beforehand. This research introduces a novel framework, called HCD-Net, for change detection using bi-temporal hyperspectral images. The framework is based on double-stream deep feature extraction and an attention mechanism. The first stream focuses on deep feature extraction through 3D convolution layers and 3D Squeeze-and-Excitation (SE) blocks. The second stream focuses on deep feature extraction through 2D convolution and 2D SE blocks. The extracted deep features are then concatenated and fed to dense layers for decision-making. The efficiency of HCD-Net is compared to the state-of-the-art change detection methods. Additionally, the bi-temporal Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) hyperspectral dataset was used for further evaluation of the change detection results. The results show that HCD-Net has a higher accuracy and the lowest false alarm rate, where the overall classification accuracy is above 96%, and the kappa coefficient is over 0.9.