Junjun Wu

High accuracy and time synchronous aerosol optical depth (AOD) is essential for atmospheric correction (AC) of medium and high spatial resolution (MHSR) remote sensing data. However, existing high-resolution AOD retrieval methods often rely on sparsely distributed ground-based measurements, which limits their capacity to resolve fine-scale spatial heterogeneity and consequently constrains retrieval performance. To address this limitation, we propose a framework that takes GF-1 top-of-atmosphere (TOA) reflectance as input, where the model is first pre-trained using MCD19A2 as Pseudo-labels, with high-confidence samples weighted according to their spatial consistency and temporal stability, and then fine-tuned using Aerosol Robotic Network (AERONET) observations. This approach enables improved retrieval accuracy while better capturing surface variability. Validation across multiple regions demonstrates strong agreement with AOD measurements, achieving the correlation coefficient (R) of 0.941 and RMSE of 0.113. Compared to models without pretraining, the proportion of AOD retrievals within EE improves by 13%. While applied to AC, the corrected surface reflectance also shows strong consistency with in situ observations (R > 0.93, RMSE < 0.04). The proposed Trans-AODnet significantly enhances the accuracy and reliability of AOD inputs for AC of high-resolution wide-field sensors (e.g., GF-WFV), offering robust support for regional environmental monitoring and exhibiting strong potential for broader remote sensing applications.

The Qinling Mountains ecosystem serves as a vital ecological barrier and geographic demarcation line in China. Monitoring long-term land cover changes in the Qinling Mountains is essential for ecosystem pattern evaluation, environmental protection, and sustainable development. Focusing on the Qinling Mountains in Shaanxi Province, this study aimed to quantify the land cover changes from 1986 to 2020 using remote sensing and GIS technologies. An optimized Support Vector Machine (SVM) classification method was developed using Landsat satellite images and historical field samples. The method was employed to conduct land cover classification across eight discrete time periods: 1986, 1990, 1995, 2000, 2005, 2010, 2015, and 2020. The average overall accuracy (OA) of the classification results for the eight time periods was 96.42%, with a Kappa coefficient (K) of 0.9230, thus confirming the reliability of the mapping results. We subsequently developed a spatiotemporal Geo-information Tupu that facilitated a detailed analysis of land cover changes in the study area across different periods. The results show the following: (1) Forest was the dominant land cover type, followed by cropland. From 1986 to 2020, the forest, impervious surface, and water body areas showed overall increasing trends, although fluctuations were observed over time, and the increase was estimated at 6677.30 km², 557.57 km², and 135.71 km², respectively. In contrast, the areas of cropland, grassland, and bare soil showed a fluctuating decreasing trend, with a decrease in areal coverage of 2790.57 km², 1528.76 km², and 3042.66 km², respectively. During the study period, the forest area experienced the greatest increase but maintained the lowest dynamic degree. In contrast, bare soil showed the largest decrease and the highest dynamic degree. (2) A total of 30.74% of the area underwent dynamic changes during the study period, with the most active transformation occurring after 2010; these changes were mainly manifested in the outflow of cropland (4997.27 km²), the transfer of forest (8557.43 km²), and the expansion of impervious surfaces (771.33 km²). In conclusion, the overall ecological environment is improving. The results demonstrate a land cover reconstruction process that enables the management department to rationally utilize natural resources in the Qinling Mountains.

We addressed rising drinking water risks in tropical tourism catchments by selecting Sanya as a representative case and developing an integrated 10–16 m remote sensing framework (Sentinel-2, GF-1) with a fuzzy evaluation, combining NDVI, WET, and NDBSI, K–T + NDVI eutrophication mapping, and event-sensitive RUSLE (30 m DEM, nonlinear LS, monthly NDVI-driven C, localized R). Land use mapping shows orchards at 736.46 km² (38.37%) and tourism land at 2.64% (mostly golf), with 86.52% overall accuracy (Kappa 0.84). Basin-wide, 91% of the area experiences slight–mild erosion, intensified near reservoirs; relative to forests (FVC > 80%), orchards (FVC 60–70%) have a 3.2× higher median erosion risk (IQR 2.8–3.6, 95% CI 2.7–3.7). On 10–25° slopes during flood seasons, orchard pesticide/nutrient runoff indices rise 28–46%, and in the Dalong watershed, high-erosion orchard pixels co-locate with pesticide residues by 62% (95% CI 58–66%). Tourism is associated with elevated nearshore chlorophyll-a (Chl-a); the area is generally mesotrophic (0.25–0.75 mg/L), with localized nearshore hotspots > 1.0 mg/L; across monthly composites, nearshore Chl-a exceeds center waters by 130–210%, and in the Dalong Reservoir, the shoreline-to-center ratio is 2.3–3.1 (median 2.7, 95% CI 2.1–3.3) during 2023–2024 flood seasons. Overall, this source-to-sink framework supports forward-looking governance of drinking water sources under dual monsoon and tourism pressures.

Satellite-derived aerosol optical depth (AOD) products from MODIS and VIIRS sensors are vital for monitoring global aerosol distributions. However, inconsistencies in quality control algorithms and spatial resolution introduce errors that complicate validation processes and reduce the accuracy of satellite-to-ground comparisons. This study proposes the “optimal” spatial matching method to minimize these errors and enable a more accurate evaluation of retrieval algorithm performance. Using AERONET ground observations from 2012 to 2021, MODIS and VIIRS AOD products were systematically validated with three spatial matching methods—“direct”, “average”, and “optimal”. Results demonstrate that the “optimal” method consistently outperformed the other methods by selecting pixel values. The study highlights significant quality control disparities across AOD products and demonstrates that high-resolution products, with purer pixels, achieve superior accuracy under the “optimal” method. These insights provide valuable guidance for optimizing dataset applications and refining aerosol retrieval algorithms.

The identification of roads from satellite imagery plays an important role in urban design, geographic referencing, vehicle navigation, geospatial data integration, and intelligent transportation systems. The use of deep learning methods has demonstrated significant advantages in the extraction of roads from remote sensing data. However, many previous deep learning-based road extraction studies overlook the connectivity and completeness of roads. To address this issue, this paper proposes a new high-resolution satellite road extraction network called FERDNet. In this paper, to effectively distinguish between road features and background features, we design a Multi-angle Feature Enhancement module based on the characteristics of remote sensing road data. Additionally, to enhance the extraction capability for narrow roads, we develop a High–Low-Level Feature Enhancement module within the directional feature extraction branch. Furthermore, experimental results on three public datasets validate the effectiveness of FERDNet in the task of road extraction from satellite imagery.

Accurate and timely extraction and evaluation of sandy land are essential for ecological environmental protection; it is urgent to do the research to support the sustainable development goals (SDGs) of Land Degradation Neutrality. This study used Sentinel-1 Synthetic Aperture Radar (SAR) data and Landsat 8 OLI multispectral data as the main data sources. Combining the rich spectral information from optical data and the penetrating advantages of radar data, a feature-level fusion method was employed to unveil the intrinsic nature of vegetative cover and accurately identify sandy land. Simultaneously, leveraging the results obtained from training with measured data, a comprehensive desertification assessment model was proposed, which combines multiple indicators to achieve a thorough evaluation of sandy land. The results showed that the method based on feature-level fusion achieved an overall accuracy of 86.31% in sandy land detection in Gansu Province, China. The integrated multi-indicator model C22_C/FVC is the ratio of correlation texture features of VH to vegetation cover based on which sandy land can be classified into three categories. When C22_C/FVC is less than 2.2, the pixel is classified as fixed sandy land. Pixels of semi-fixed sandy land have an indicator value between 2.2 and 5.2. Shifting sandy land has values greater than 5.2. Results showed that shifting sandy land and semi-fixed sandy land are the predominant types in Gansu Province, with 85,100 square kilometers and 87,100 square kilometers, respectively. The acreage of fixed sandy land was the least, 51,800 square kilometers. The method presented in this paper is robust for the detection and evaluation of sandy land from satellite imageries, which can potentially be applied for conducting high-resolution and large-scale detection and evaluation of sandy land.

Long time series of annual land cover with fine spatio-temporal resolutions play a crucial role in studying environmental climate change, biophysical modeling, carbon cycling models, and land management. Despite a strong consistency exhibited by several publicly available medium to fine resolution global land cover datasets, significant discrepancies exist at the regional scale; moreover, only every 5/10 year land cover were available. Consequently, high-quality annual land cover datasets before 2000 are unavailable in China. In this study, we proposed a deep learning-based method by integrating multiple remote sensing data from different platforms with historical high spatial resolution land cover datasets (CNLUCC) to derive the 30 m annual land cover maps from 1980 to 1990 for Qilian Mountain. First, the super-resolution generative adversarial network models for upscaling the 5.5 km AVHRR NDVI to 250 m were established by employing the AVHRR and MODIS NDVI data with the same year as input, and the early time series AVHRR NDVI data were subsequently upscaled to 250 m through the above models. Second, the breaks for the additive seasonal and trend (BFAST) change detection algorithm was applied to the upscaled time series NDVI data to detect the change time of different land cover types. Third, the CNLUCC data in 1980 and 1990 were updated to annual land cover datasets from 1980 to 1990 and the annual mapping results provided insights into the dynamic processes of urbanization, deforestation, water bodies, and farmland from 1980 to 1990. Finally, comprehensive analysis and validation were carried out for evaluation and an overall accuracy of 77.26% for the land cover product in 1986 was achieved.

Shrubs are important ecological barriers in desert regions and an important component of global carbon estimation. However, the shrubland in deserts has been hardly presented, although many high-quality land cover datasets with a 10 m scale based on remote-sensing data have been publicly released products. Therefore, the underestimation of carbon storage is inevitable with the absence of desert shrublands. The existing land-cover datasets have been analyzed and compared, and it has been found that the reason for missing the shrubland in deserts is mainly indued by the absence of shrubland samples, which are easy to neglect and difficult to retrieve. In this study, we developed a semi-automatic method to extract shrubland samples in deserts as the updated input for the machine-learning method. Firstly, the initial samples of desert shrublands were identified from the very high spatial-resolution (0.3~0.5 m) imagery on GEE, and the maximum NDVI from Sentinel-2 was used for double-checking. Secondly, a feature-based method was used to learn the feature from the initial samples and a similarity-based searching method was employed to automatically expand the samples. Finally, the expanded samples and their corresponding time-series satellite images were inputted into different machine-learning methods at a large region (1.63 × 10⁶ km²) for extracting the shrubland in the desert. It was found that different combinations of feature variables and time-series combinations have different impacts on the overall accuracy (OA) of the classification results, as well as the performance of identifying and classifying the different land-cover types. Compared to the existing global-scale land-cover products, the proposed method can better identify the shrubland in deserts and show better overall accuracy.

With the rapid development of digital intelligent agriculture, the accurate extraction of field information from remote sensing imagery to guide agricultural planning has become an important issue. In order to better extract fields, we analyze the scale characteristics of agricultural fields and incorporate the multi-scale idea into a Transformer. We subsequently propose an improved deep learning method named the Multi-Swin Mask Transformer (MSMTransformer), which is based on Mask2Former (an end-to-end instance segmentation framework). In order to prove the capability and effectiveness of our method, the iFLYTEK Challenge 2021 Cultivated Land Extraction competition dataset is used and the results are compared with Mask R-CNN, HTC, Mask2Former, etc. The experimental results show that the network has excellent performance, achieving a bbox_AP50 score of 0.749 and a segm_AP50 score of 0.758. Through comparative experiments, it is shown that the MSMTransformer network achieves the optimal values in all the COCO segmentation indexes, and can effectively alleviate the overlapping problem caused by the end-to-end instance segmentation network in dense scenes.

Accurate identification of sandy land plays an important role in sandy land prevention and control. It is difficult to identify the nature of sandy land due to vegetation covering the soil in the sandy area. Therefore, HJ-2A hyperspectral data and GF-3 Synthetic Aperture Radar (SAR) data were used as the main data sources in this article. The advantages of the spectral characteristics of a hyperspectral image and the penetration characteristics of SAR data were used synthetically to carry out mixed-pixel decomposition in the “horizontal” direction and polarization decomposition in the “vertical” direction. The results showed that in the study area of the Otingdag Sandy Land, in China, the accuracy of sandy land detection based on feature-level fusion and single GF-3 data was verified to be 92% in both cases by field data; the accuracy of sandy land detection based on feature-level fusion was verified to be 88.74% by the data collected from Google high-resolution imagery, which was higher than that based on single HJ-2A (74.17%) and single GF-3 data (88.08%). To further verify the universality of the feature-level fusion method for sandy land detection, Alxa sandy land was also used as a verification area and the accuracy of sandy land detection was verified to be as high as 88.74%. The method proposed in this paper made full use of the horizontal and vertical structural information of remote sensing data. The problem of mixed pixels in sparse-vegetation scenes in the horizontal direction and the problem of vegetation covering sandy soil in the vertical direction were both well solved. Accurate identification of sandy land can be realized effectively, which can provide technical support for sandy land prevention and control.