Search Results (78)

Lake Taihu has highly turbid inland waters with complex optical properties. Due to the bottom effect of submerged aquatic plants in optically shallow waters, currently available phytoplankton chlorophyll-a retrieval algorithms tend to overestimate chlorophyll-a concentrations in the eastern part of Lake Taihu. This overestimation can distort the eutrophication evaluation of the entire lake. This paper identifies submerged and emergent plants, determines the retrieval models for the upwelling (K_u) and downwelling (K_d) irradiance attenuation coefficients, and proposes a phytoplankton chlorophyll-a retrieval model using a water depth optimization-based method to remove the bottom effect. The results show the following: (1) The normalized difference vegetation index (NDVI) method can distinguish the bottom mud (NDVI < −0.46) and submerged aquatic plants (−0.46 ≤ NDVI < 0.52) from the emergent plants (NDVI ≥ 0.52) with 90% accuracy. (2) The downwelling and upwelling irradiance attenuation coefficients are highly correlated with the suspended sediments, and retrieval models for these coefficients in three visible bands with high accuracy are presented. (3) Compared to traditional algorithms without bottom effect removal, the proposed chlorophyll-a concentration estimation algorithm based on the water depth-optimized bottom effect removal method efficiently reduces the bottom effect of the submerged aquatic plants. The root mean square error (RMSE) for the obtained chlorophyll-a concentrations decreases from 45.61 ${\mathsf{\mu }\mathrm{g}·\mathrm{L}}^{-1}$ to 8.69 ${\mathsf{\mu }\mathrm{g}·\mathrm{L}}^{-1}$, and the mean absolute percentage error (MAPE) is reduced from 245.12% to 19.58%. In the validation step, the obtained RMSE of 10.89 ${\mathsf{\mu }\mathrm{g}·\mathrm{L}}^{-1}$ and MAPE of 17.52% are consistent with the proposed algorithm. This research provides a good reference for the determination of chlorophyll-a concentrations in phytoplankton in complex inland water bodies. The findings are potentially useful for the operational monitoring of harmful algal blooms in the future. Full article

River channels are fundamental geomorphological and hydrological features that play a critical role in regulating the Earth’s water cycle and ecosystems and influencing human activities. This study utilized Digital Elevation Model (DEM) data and multi-source remote sensing imagery (including GF-1 WFV, Sentinel-1, and Sentinel-2) to determine river channel dimensions. River water masks were obtained from multiple remote sensing imagery sources and processed through triangulation and segmentation to generate river reach results. Based on these segmented river reaches, buffer analysis was conducted. The buffer analysis results were then used to refine and clip the 5 m DEM and 12.5 m DEM datasets. Finally, river channels were extracted from the clipped DEM data using the natural breaks classification method. The classification accuracy was assessed using a confusion matrix. Experimental results demonstrate a high overall classification accuracy, reaching or exceeding 0.985, with classification consistency (Kappa coefficient) ranging from 0.78 to 0.81. The 5 m resolution DEM exhibited superior performance compared to the 12.5 m resolution DEM in river channel extraction, especially regarding the classification consistency (Kappa coefficient), with the 5 m resolution model outperforming the latter. This approach effectively delineates the river channel boundaries, transcends the constraints of a singular data source, enhances the precision and resilience of river extraction, and possesses several practical applications. The extracted data can support analyses of river evolution, facilitate hydrological modeling at the basin scale, improve flood disaster monitoring, and contribute to various other research domains. Full article

Green tide area is a crucial indicator for monitoring green tide dynamics. However, scale effects arising from differences in image resolution can lead to estimation errors. Current pixel-level and sub-pixel-level methods often overlook the impact of morphological differences across varying resolutions. To address this, our study examines the influence of morphological diversity on green tide area estimation using GF-1 WFV data and the Virtual-Baseline Floating macroAlgae Height (VB-FAH) index at a 16 m resolution. Green tide patches were categorized into small, medium, and large sizes, and morphological features such as elongation, compactness, convexity, fractal dimension, and morphological complexity were designed and analyzed. Machine learning models, including Extra Trees, LightGBM, and Random Forest, among others, classified medium and large patches into striped and non-striped types, with Extra Trees achieving outstanding performance (accuracy: 0.9844, kappa: 0.9629, F1-score: 0.9844, MIoU: 0.9637). The results highlighted that large patches maintained stable morphological characteristics across resolutions, while small and medium patches were more sensitive to scale, with increased estimation errors at lower resolutions. Striped patches, particularly among medium patches, were more sensitive to scale effects compared to non-striped ones. The study suggests that incorporating morphological features of patches, especially in monitoring striped and small patches, could be a key direction for improving the accuracy of green tide monitoring and dynamic change analysis. Full article

The spatial prediction of soil CO₂ flux is of great significance for assessing regional climate change and high-quality agricultural development. Using a single satellite to predict soil CO₂ flux is limited by climatic conditions and land cover, resulting in low prediction accuracy. To this end, this study proposed a strategy of multi-source spectral satellite coordination and selected seven optical satellite remote sensing data sources (i.e., GF1-WFV, GF6-WFV, GF4-PMI, CB04-MUX, HJ2A-CCD, Sentinel 2-L2A, and Landsat 8-OLI) to extract auxiliary variables (i.e., vegetation indices and soil texture features). We developed a tree-structured Parzen estimator (TPE)-optimized extreme gradient boosting (XGBoost) model for the prediction and spatial mapping of soil CO₂ flux. SHapley additive explanation (SHAP) was used to analyze the driving effects of auxiliary variables on soil CO₂ flux. A scatter matrix correlation analysis showed that the distributions of auxiliary variables and soil CO₂ flux were skewed, and the linear correlations between them (r < 0.2) were generally weak. Compared with single-satellite variables, the TPE-XGBoost model based on multiple-satellite variables significantly improved the prediction accuracy (RMSE = 3.23 kg C ha⁻¹ d⁻¹, R² = 0.73), showing a stronger fitting ability for the spatial variability of soil CO₂ flux. The spatial mapping results of soil CO₂ flux based on the TPE-XGBoost model revealed that the high-flux areas were mainly concentrated in eastern and northern farmlands. The SHAP analysis revealed that PC2 and the TCARI of Sentinel 2-L2A and the TVI of HJ2A-CCD had significant positive driving effects on the prediction accuracy of soil CO₂ flux. The above results indicate that the integration of multiple-satellite data can enhance the reliability and accuracy of spatial predictions of soil CO₂ flux, thereby supporting regional agricultural sustainable development and climate change response strategies. Full article

A panchromatic and multispectral sensor (PMS) and a wide-field-of-view (WFV) sensor were fitted aboard the Gaofen6 (GF6) satellite, which was launched on 2 June 2018. This study used the Landsat9-Operational Land Imager 2 and Sentinel2-Multispectral Instrument as reference sensors to perform radiometric cross-calibration on GF6-PMS and WFV data at the Dunhuang calibration site. The four selected sensor images were all acquired on the same day. The results indicate that: the calibration results between different reference sensors can be controlled within 3%, with the maximum difference from the official coefficients being 8.78%. A significant difference was observed between the coefficients obtained by different reference sensors when spectral band adjustment factor (SBAF) correction was not performed; from the two sets of validation results, the maximum mean relative difference in the near-infrared band was 9.46%, with the WFV sensor showing better validation results. The validation of calibration coefficients based on synchronous ground observation data and the analysis of the impact of different SBAF methods on the calibration results indicated that Landsat9 is more suitable as a reference sensor for radiometric cross-calibration of GF6-PMS and WFV. Full article

This article extracts the Qiantang River tidal bore, analyzing the water environment characteristics in front of the tidal line of the Qiantang River tidal bore and behind it. The Qiantang River tidal bore Index (QRI) was established using HY-1C, HY-1D, and Gao Fen-1 wide field-of-view (GF-1 WFV) satellite data to precisely determine the location and details of the Qiantang River tidal bore. Comparative analyses of the changes on the two sides of the Qiantang River tidal bore were conducted. The results indicate the following: (1) QRI enhances the visibility of tidal bore lines, accentuating their contrast with the surrounding river water, resulting in a more vivid character. QRI proves to be an effective extraction method, with potential applicability to similar tidal lines in different regions. (2) Observable roughness changes occur at the tidal bore location, with smoother surface textures observed in front of the tidal line compared to those behind it. There is a discernible increase in suspended sediment concentration (SSC) as the tidal bore passes through. (3) This study reveals the mechanism of water environment change induced by the Qiantang River tidal bore, emphasizing its significance in promoting vertical water body exchange as well as scouring the bottom sediments. This effect increases SSC and surface roughness. Full article

Annual outbreaks of floating Ulva prolifera blooms in the Yellow Sea have caused serious local environmental and economic problems. Rapid and effective monitoring of Ulva blooms from satellite observations with wide spatial-temporal coverage can greatly enhance disaster response efforts. Various satellite sensors and remote sensing methods have been employed for Ulva detection, yet automatic and rapid Ulva detection remains challenging mainly due to complex observation scenarios present in different satellite images, and even within a single satellite image. Here, a reliable and fully automatic method was proposed for the rapid extraction of Ulva features using the Tasseled-Cap Greenness (TCG) index from satellite top-of-atmosphere reflectance (R_TOA) data. Based on the TCG characteristics of Ulva and Ulva-free targets, a local adaptive threshold (LAT) approach was utilized to automatically select a TCG threshold for moving pixel windows. When tested on HY1C/D-Coastal Zone Imager (CZI) images, the proposed method, termed the TCG-LAT method, achieved over 95% Ulva detection accuracy though cross-comparison with the TCG and VBFAH indexes with a visually determined threshold. It exhibited robust performance even against complex water backgrounds and under non-optimal observing conditions with sun glint and cloud cover. The TCG-LAT method was further applied to multiple HY1C/D-CZI images for automatic Ulva bloom monitoring in the Yellow Sea in 2023. Moreover, promising results were obtained by applying the TCG-LAT method to multiple optical satellite sensors, including GF-Wide Field View Camera (GF-WFV), HJ-Charge Coupled Device (HJ-CCD), Sentinel2B-Multispectral Imager (S2B-MSI), and the Geostationary Ocean Color Imager (GOCI-II). The TCG-LAT method is poised for integration into operational systems for disaster monitoring to enable the rapid monitoring of Ulva blooms in nearshore waters, facilitated by the availability of near-real-time satellite images. Full article

The GaoFen6 (GF-6) satellite, equipped with a wide full-swath (WFV) sensor, offers high spatial resolution and extensive coverage, making it widely utilized in agricultural and forestry classification, land resource monitoring, and other fields. Accurate on-orbit radiometric calibration of GF-6/WFV is crucial for these quantitative applications. Currently, the absolute radiometric calibration of GF-6/WFV relies primarily on vicarious calibration conducted by the China Center for Resources Satellite Data and Application (CRESDA). However, annual vicarious calibration may not adequately capture the radiometric performance of GF-6/WFV due to performance degradation. Therefore, increasing the frequency of on-orbit radiometric calibration throughout the lifetime of GF-6/WFV is essential. This study proposes a method for conducting long-term cross-radiometric calibrations of GF-6/WFV by taking the multispectral imager (MSI) onboard the Sentinel-2 satellite as a reliable reference sensor and the sites from RadCalNet as reference ground targets. Firstly, we conducted 62 on-orbit cross-radiometric calibrations of GF-6/WFV since its launch by tracking with the Sentinel-2/MSI sensor after correcting the discrepancy spectrum and solar zenith angle. Then, validation of cross-radiometric calibration results against RadCalNet products indicated an average absolute relative error between 3.55% and 4.64%. Cross-validation with additional reference sensors, including Landsat-8/OLI and MODIS, confirmed the reliability of calibration, demonstrating relative differences from GF-6/WFV of less than 5%. Furthermore, the overall uncertainty of the cross-radiometric calibration was estimated to be from 4.08% to 4.89%. Finally, trend analysis of the time-series radiometric performance was also conducted and revealed an annual degradation rate ranging from 0.57% to 2.31%. This degradation affects surface reflectance retrieval, introducing a bias of approximately 0.0073 to 0.0084. Our findings highlight the operational effectiveness of the proposed method in achieving long-time-series on-orbit radiometric calibration and degradation monitoring of GF-6/WFV. The study also demonstrates that the radiometric performance of GF-6/WFV is relatively stable and suitable for further quantitative applications, especially for long-term monitoring applications. Full article

Smoke, a byproduct of forest and grassland combustion, holds the key to precise and rapid identification—an essential breakthrough in early wildfire detection, critical for forest and grassland fire monitoring and early warning. To address the scarcity of middle–high-resolution satellite datasets for forest and grassland fire smoke, and the associated challenges in identifying smoke, the CAF_SmokeSEG dataset was constructed for smoke segmentation. The dataset was created based on GF-6 WFV smoke images of forest and grassland fire globally from 2019 to 2022. Then, an optimized segmentation algorithm, GFUNet, was proposed based on the UNet framework. Through comprehensive analysis, including method comparison, module ablation, band combination, and data transferability experiments, this study revealed that GF-6 WFV data effectively represent information related to forest and grassland fire smoke. The CAF_SmokeSEG dataset was found to be valuable for pixel-level smoke segmentation tasks. GFUNet exhibited robust smoke feature learning capability and segmentation stability. It demonstrated clear smoke area delineation, significantly outperforming UNet and other optimized methods, with an F1-Score and Jaccard coefficient of 85.50% and 75.76%, respectively. Additionally, augmenting the common spectral bands with additional bands improved the smoke segmentation accuracy, particularly shorter-wavelength bands like the coastal blue band, outperforming longer-wavelength bands such as the red-edge band. GFUNet was trained on the combination of red, green, blue, and NIR bands from common multispectral sensors. The method showed promising transferability and enabled the segmentation of smoke areas in GF-1 WFV and HJ-2A/B CCD images with comparable spatial resolution and similar bands. The integration of high spatiotemporal multispectral data like GF-6 WFV with the advanced information extraction capabilities of deep learning algorithms effectively meets the practical needs for pixel-level identification of smoke areas in forest and grassland fire scenarios. It shows promise in improving and optimizing existing forest and grassland fire monitoring systems, providing valuable decision-making support for fire monitoring and early warning systems. Full article

Clouds and cloud shadow cover cause missing data in some images captured by the Gaofen-1 Wide Field of View (GF-1 WFV) cameras, limiting the extraction and analysis of the image information and further applications. Therefore, this study proposes a methodology to fill GF-1 WFV images using the spatial autocorrelation and improved weighting (SAIW) method. Specifically, the search window size is adaptively determined using Getis-Ord Gi* as a metric. The spatial and spectral weights of the pixels are computed using the Chebyshev distance and spectral angle mapper to better filter the suitable similar pixels. Each missing pixel is predicted using linear regression with similar pixels on the reference image and the corresponding similar pixel located in the non-missing region of the cloudy image. Simulation experiments showed that the average correlation coefficient of the proposed method in this study is 0.966 in heterogeneous areas, 0.983 in homogeneous farmland, and 0.948 in complex urban areas. It suggests that SAIW can reduce the spread of errors in the gap-filling process to significantly improve the accuracy of the filling results and can produce satisfactory qualitative and quantitative fill results in a wide range of typical land cover types and has extensive application potential. Full article

To grasp the spatial distribution of soybean planting areas in time is the prerequisite for the work of growth monitoring, crop damage assessment and yield estimation. The research on remote sensing identification of soybean conducted in China mainly focuses on the major producing areas in Northeast China, while paying little attention to the Huang-Huai-Hai region and the Yangtze River Basin, where the complex planting structures and fragmented farmland landscape bring great challenges to soybean mapping in these areas. This study used Chinese GF-6 WFV imagery acquired during the pod-setting stage of soybean in the 2019 growing season, and two counties i.e., Guoyang situated in the northern plain of Anhui Province and Mingguang located in the Jianghuai hilly regionwere selected as the study areas. Three machine learning algorithms were employed to establish soybean identification models, and the distribution of soybean planting areas in the two study areas was separately extracted. This study adopted a stepwise hierarchical extraction strategy. First, a set of filtering rules was established to eliminate non-cropland objects, so the targets of subsequent work could thereby focus on field vegetation. The focal task of this study involved the selection of well-behaved features and classifier. In addition to the 8 spectral bands, a variety of texture features, color space components, and vegetation indices were employed, and the ReliefF algorithm was applied to evaluate the importance of each candidate feature. Then, a SFS (Sequential Forward Selection) method was applied to conduct feature selection, which was performed coupled with three candidate classifiers, i.e., SVM, RF and BPNN to screen out the features conductive to soybean mapping. The accuracy evaluation results showed that, the soybean identification model generated from SVM algorithm and corresponding feature subset outperformed RF and BPNN in both two study areas. The Kappa coefficients of the ground samples in Guoyang ranged from 0.69 to 0.80, while those in Mingguang fell within the range of 0.71 to 0.76. The near-infrared band (B4) and red edge bands (B5 and B6), the ‘Mean’ texture feature and the vegetation indices, i.e., EVI, SAVI and CI_green, demonstrated advantages in soybean identification. The feature selection operation achieved a balance between extraction accuracy and data volume, and the accuracy level could also meet practical requirements, showing a good application prospect. This method and findings of this study may serve as a reference for research on soybean identification in areas with similar planting structures, and the detailed soybean map can provide an objective and reliable basis for local agricultural departments to carry out agricultural production management and policy formulation. Full article

In response to the rapid changes in the chlorophyll-a concentration and eutrophication issues in lakes, with Dianchi Lake as an example, a remote sensing estimation model for chlorophyll-a, total phosphorus, and total nitrogen in Dianchi Lake was constructed using the three band method and ratio band method based on the visible-light shortwave infrared (AHSI) hyperspectral satellite data from Gaofen 5 (GF-5) and the water quality data collected at Dianchi Lake. The model results were compared with the multispectral data from the Gaofen 1 (GF-1) wide field-of-view (WFV) camera. The accuracy evaluation results indicate that the overall mean absolute percentage error of the remote sensing estimation models for chlorophyll a, total phosphorus, and total nitrogen are 7.658%, 4.511%, and 4.577%, respectively, which can meet the needs of lake water quality monitoring and evaluation. According to the remote sensing simulation results, chlorophyll a is mainly distributed in the northern part of Dianchi Lake, with phosphorus and nitrogen pollution throughout Dianchi Lake and relatively more abundant in the central and southern regions. The pollution is mainly concentrated in the northern and southern regions of Dianchi Lake, which is consistent with the actual situation. Further confirming the feasibility of using GF-5 satellite AHSI data for water quality parameter retrieval can provide new technical means for relevant departments to quickly and efficiently monitor the inland lake water environment. Full article

Due to the effects of global climate change and altered human land-use patterns, typical shrub encroachment in grasslands has become one of the most prominent ecological problems in grassland ecosystems. Shrub coverage can quantitatively indicate the degree of shrub encroachment in grasslands; therefore, real-time and accurate monitoring of shrub coverage in large areas has important scientific significance for the protection and restoration of grassland ecosystems. As shrub-encroached grasslands (SEGs) are a type of grassland with continuous and alternating growth of shrubs and grasses, estimating shrub coverage is different from estimating vegetation coverage. It is not only necessary to consider the differences in the characteristics of vegetation and non-vegetation variables but also the differences in characteristics of shrubs and herbs, which can be a challenging estimation. There is a scientific need to estimate shrub coverage in SEGs to improve our understanding of the process of shrub encroachment in grasslands. This article discusses the spectral differences between herbs and shrubs and further points out the possibility of distinguishing between herbs and shrubs. We use Sentinel-2 and Gao Fen-6 (GF-6) Wide Field of View (WFV) as data sources to build a linear spectral mixture model and a random forest (RF) model via space–air–ground collaboration and investigate the effectiveness of different data sources, features and methods in estimating shrub coverage in SEGs, which provide promising ways to monitor the dynamics of SEGs. The results showed that (1) the linear spectral mixture model can hardly distinguish between shrubs and herbs from medium-resolution images in the SEG. (2) The RF model showed high estimation accuracy for shrub coverage in the SEG; the estimation accuracy (R²) of the Sentinel-2 image was 0.81, and the root-mean-square error (RMSE) was 0.03. The R² of the GF6-WFV image was 0.72, and the RMSE was 0.03. (3) Texture feature introduced in RF models are helpful to estimate shrub coverage in SEGs. (4) Regardless of the linear spectral mixture model or the RF model being employed, the Sentinel-2 image presented a better estimation than the GF6-WFV image; thus, this data has great potential to monitor shrub encroachment in grasslands. This research aims to provide a scientific basis and reference for remote sensing-based monitoring of SEGs. Full article

The GaoFen-1 wide field of view (GF-1 WFV) has produced level 1 digital number data globally; however, most applications have focused on China, and data quality outside China has not been validated. This study presents a preliminary assessment of the 2020 GF-1 WFV surface reflectance data for Nepal, Azerbaijan, Kenya, and Sri Lanka along “the Belt and Road” route using Sentinel-2 Multi-Spectral Instrument (MSI), Landsat-8 Operational Land Image (OLI), and Moderate Resolution Imaging Spectroradiometer (MODIS) data. A method for obtaining the GF-1 WFV surface reflectance data was also proposed, with steps including atmospheric correction, cross-radiation calibration, and bidirectional reflectance distribution function correction. The results showed that WFV surface reflectance data was not significantly different from MSI, OLI, and MODIS surface reflectance data. In the visible and near-infrared bands, for most landcover types, the bias was less than 0.02, and the precision and root mean square error were less than 0.04. When the landcover types were forest and water, the MSI, OLI, and MODIS surface reflectance data were higher than that of WFV in the near-infrared band. The results of this study provide a basis for assessing the global application potential of GF-1 WFV. Full article

Cloud and cloud shadow detection in remote sensing images is an important preprocessing technique for quantitative analysis and large-scale mapping. To solve the problems of cloud and cloud shadow detection based on Convolutional Neural Network models, such as rough edges and insufficient overall accuracy, cloud and cloud shadow segmentation based on Swin-UNet was studied in the wide field of view (WFV) images of GaoFen-1 (GF-1). The Swin Transformer blocks help the model capture long-distance features and obtain deeper feature information in the network. This study selects a public GF1_WHU cloud and cloud shadow detection dataset for preprocessing and data optimization and conducts comparative experiments in different models. The results show that the algorithm performs well on vegetation, water, buildings, barren and other types. The average accuracy of cloud detection is 98.01%, the recall is 96.84% and the F1-score is 95.48%. The corresponding results of cloud shadow detection are 84.64%, 83.12% and 97.55%. In general, compared to U-Net, PSPNet and DeepLabV3+, this model performs better in cloud and cloud shadow detection, with clearer detection boundaries and a higher accuracy in complex surface conditions. This proves that Swin-UNet has great feature extraction capability in moderate and high-resolution remote sensing images. Full article