Cheng Shang

The Sentinel-3 Ocean and Land Colour Instrument (OLCI) is designed for water monitoring. Its 21-spectral bands serve as the basis for the precise retrieval of water quality parameters. However, its coarse resolution restricts the depiction of the spatial distribution of water quality parameters in small inland water bodies. Spatial–spectral fusion is a common method to address the inherent constraints between the spatial and spectral resolutions of sensors. Central to the popular methods is the deep learning-based method. Nonetheless, deep-learning-based models still face challenges in fusing Sentinel-2 Multi-Spectral Instrument (MSI) and Sentinel-3 OLCI data. Here, we propose a Multi-Scale-Attention-based Unsupervised Generative Adversarial Network (MSA-UGAN), which effectively integrates OLCI’s spectral advantage and MSI’s spatial resolution. Quantitative evaluation was conducted against five benchmark methods, including traditional approaches (GS, SFIM, MTF-GLP) and deep learning models (SRCNN, UCGAN). The results show that MSA-UGAN achieves the best overall performance: QNR (0.9709) and SSIM (0.9087) are the highest, while SAM (1.1331), spatial distortion (D_S = 0.0389), and spectral distortion (D_λ = 0.0252) are the lowest. This shows that MSA-UGAN can better preserve the spatial details of S2 MSI and the spectral features of S3 OLCI data. Moreover, ERGAS (2.2734) also performs excellently in the comparative experiments. The experiment of Chlorophyll-a inversion using the fused image in Chen Lake revealed a spatial gradient ranging from 3.25 to 19.33 µg/L, with the highest concentrations in the southwestern nearshore waters, likely associated with aquaculture. These results jointly indicate that MSA-UGAN can generate high-spatial-resolution multispectral images, and the fused images can be effectively utilized for water quality monitoring, thereby providing essential data support for the precision management and scientific decision-making regarding inland lakes.

The illegal wildlife trade (IWT) poses a significant global challenge that threatens biodiversity and ecosystem balance. This study addresses these complexities by proposing the Integrated Ecological Intervention Optimization Model (IEIOM). The model integrates three core metrics—habitat area, crime rate, and quantity of IWT—while incorporating multidimensional analysis and predictive modeling across ecological, social, and economic dimensions. To enhance predictive accuracy, we employed nonlinear regression, grey prediction, and autoregressive models. These predictive insights, combined with empirical data, were integrated into a multi-index intervention optimization framework using a sum-of-sines function. A simulated annealing algorithm was subsequently applied to achieve global optimization. Results indicate that the proposed IEIOM outperforms the traditional entropy weight method by providing a more dynamic, data-driven weight allocation. The optimal weights prioritized crime suppression (50%), habitat protection (28%), and trade regulation (22%), underscoring the critical roles of law enforcement and environmental preservation. Sensitivity analysis further demonstrated that technological innovation, community collaboration, and public awareness are pivotal to successful interventions. Overall, the IEIOM provides a robust decision-support tool for policymakers, enabling effective resource allocation to combat IWT and contributing to long-term sustainable development.

Accurate and continuous monitoring of flood dynamics is fundamental to understanding wetland hydrological processes and their ecological implications, yet it remains challenging due to the inherent trade-off between spatial and temporal resolution in remote sensing observations. This study advances flood monitoring methodology by developing and validating a spatiotemporal fusion framework specifically designed for multi-source Synthetic Aperture Radar (SAR) data—an approach that has remained underdeveloped despite its critical importance for all-weather wetland observation. We propose the Fusion SAR Operational Monitoring (FSOM) framework, which integrates three established components—the Flexible Spatiotemporal Data Fusion (FSDAF) model, the Sentinel-1 Dual-Polarized Water Index (SDWI), and automated thresholding classification—into a coherent processing chain that generates consistent high-resolution flood extent time series from multi-sensor SAR data (Sentinel-1 and GF-3). The FSOM was applied to the Chen Lake Wetland from 2020 to 2024, producing a monthly flood map dataset at 5 m spatial resolution. Quantitative validation demonstrated the superiority of the FSOM-derived products. Compared to water classifications using original Sentinel-1 data, the FSOM results achieved a significantly higher overall accuracy (exceeding 90%) and Kappa coefficient (>0.90) than the Sentinel-1 results, which had overall accuracy (exceeding 86%) and Kappa coefficient (>0.75). Critically, the producer’s accuracy for water bodies consistently surpassed 91%, indicating a substantial reduction in omission errors and markedly improved detection of small water bodies. These results confirm the effectiveness of the proposed FSOM framework in mitigating the spatiotemporal resolution trade-off, thereby providing a reliable high-fidelity data foundation to support precise wetland conservation and flood disaster emergency response. The framework thus offers a practical tool for scientists and water resource managers seeking to enhance monitoring capabilities in the world’s most dynamic and ecologically significant wetland ecosystems.

Super-resolution mapping (SRM) can effectively predict the spatial distribution of land cover classes within mixed pixels at a higher spatial resolution than the original remotely sensed imagery. The uncertainty of land cover fraction errors within mixed pixels is one of the most important factors affecting SRM accuracy. Studies have shown that SRM methods using deep learning techniques have significantly improved land cover mapping accuracy but have not coped well with spectral–spatial errors. This study proposes an end-to-end SRM model using a spectral–spatial generative adversarial network (SGS) with the direct input of multispectral remotely sensed imagery, which deals with spectral–spatial error. The proposed SGS comprises the following three parts: first, cube-based convolution for spectral unmixing is adopted to generate land cover fraction images. Second, a residual-in-residual dense block fully and jointly considers spectral and spatial information and reduces spectral errors. Third, a relativistic average GAN is designed as a backbone to further improve the super-resolution performance and reduce spectral–spatial errors. SGS was tested in one synthetic and two realistic experiments with multi/hyperspectral remotely sensed imagery as the input, comparing the results with those of hard classification and several classic SRM methods. The results showed that SGS performed well at reducing land cover fraction errors, reconstructing spatial details, removing unpleasant and unrealistic land cover artifacts, and eliminating false recognition.

The spatiotemporal reflectance fusion method is used to blend high-temporal and low-spatial resolution images with their low-temporal and high-spatial resolution counterparts that were previously acquired by various satellite sensors. Recently, a wide variety of learning-based solutions have been developed, but challenges remain. These solutions usually require two sets of data acquired before and after the prediction time, making them unsuitable for near-real-time predicting. The solutions are always trained band by band and thus do not consider the spectral correlation. High-resolution temporal changes are difficult to reconstruct accurately with the network structure used, which lowers the accuracy of the fusion result. To address these problems, this study proposes a novel spatiotemporal adaptive reflectance fusion model using a generative adversarial network (GASTFN). In GASTFN, an end-to-end network, including a generative and discriminative network, is simultaneously trained for all spectral bands. The proposed model can be applied to the one-pair case, consider the spectral correlation of each band, and improve the process of producing super-resolution imagery by adopting the discriminative network for image reflectance values rather than temporal changes in reflectance. The proposed model has been verified with two actual satellite data sets acquired in heterogeneous landscapes and areas with abrupt changes, with a comparison of the state-of-art methods. The results show that GASTFN can generate the most accurate fusion images with more detailed textures, more realistic spatial shapes, and higher accuracy, demonstrating that the GASTFN is effective for predicting near-real-time changes in image reflectance and preserves the most valuable spatial information.

Turbulent heat flux (THF) over leads is an important variable used for monitoring climate change in the Arctic. Presently, THF over leads is often calculated from satellite imagery. The accuracy of the estimated THF is low for mixed pixels that consist of ice and leads, because the mixed pixels along lead boundaries will lower the accuracy of the surface temperature measured over leads and the corresponding lead map. To address this problem, a deep residual convolutional neural network (CNN)-based framework is proposed to estimate THF over leads at the subpixel scale (DeepSTHF) with remotely sensed imagery. The DeepSTHF allows the production of a sea surface temperature (SST) image and a corresponding lead map with a finer spatial resolution than the input SST image using two CNNs, so that the subpixel scale THF can be estimated from them. The proposed approach is assessed using simulated and real MODIS imagery and compared against the conventional bicubic interpolation and pixel-based methods. The results demonstrate that the proposed CNN-based method can effectively estimate subpixel-scale information from the coarse data and performs well in producing fine spatial resolution SST images and lead maps, thereby allowing researchers to obtain more accurate and reliable THF over leads.

Superresolution mapping (SRM) is a commonly used method to cope with the problem of mixed pixels when predicting the spatial distribution within low-resolution pixels. Central to the popular SRM method is the spatial pattern model, which is utilized to represent the land cover spatial distribution within mixed pixels. The use of an inappropriate spatial pattern model limits such SRM analyses. Alternative approaches, such as deep-learning-based algorithms, which learn the spatial pattern from training data through a convolutional neural network, have been shown to have considerable potential. Deep learning methods, however, are limited by issues such as the way the fraction images are utilized. Here, a novel SRM model based on a generative adversarial network (GAN), GAN-SRM, is proposed that uses an end-to-end network to address the main limitations of existing SRM methods. The potential of the proposed GAN-SRM model was assessed using four land cover subsets and compared to hard classification and several popular SRM methods. The experimental results show that of the set of methods explored, the GAN-SRM model was able to generate the most accurate high-resolution land cover maps.