Search Results (33,827)

High-resolution lunar gravity anomaly data are of great significance for the study of the lunar crust and lithosphere structure, asymmetric thermal evolution, impact basin subsurface structure and mass tumor genesis, breccia, and magmatism. However, due to errors in satellite orbit and instrument observation, correlation error in high-order spherical harmonic coefficients, and other factors, satellite observation gravity anomaly data present evident aliasing phenomena of stripe noise and random noise in the spatial domain, resulting in difficulties in practical application analysis. In this paper, a lunar satellite gravity anomaly denoising method based on prior knowledge deep learning is proposed. In one instance, the prior knowledge is fused into the data set, the manual processing results are labeled, and the six label-superimposed directions of the simulated stripe noise are used as the sample input data. Conversely, because the gravity field is a harmonic field with smooth characteristics, the Laplace constraint is added to the loss function, and the deep learning results are optimized through Gaussian filtering. Synthetic and real data tests demonstrate the effectiveness of the proposed method in removing complex noise from lunar satellite gravity anomaly data. Full article

Maize is an important crop used as food, feed, and industrial raw material. Therefore, it is critical to maximize maize yield on available land by using optimal inputs and adapting to challenges posed by climate change. The Leaf Area Index (LAI) is a key parameter that provides significant assistance in forecasting maize yields. This study focuses on modeling the Leaf Area Index for maize. Specifically, it compiles and systematizes the main findings of papers published over the past approximately 10–15 years. Our results are organized and presented based on the five most commonly used models: CERES-Maize, AquaCrop, WOFOST, APSIM, and RZWQM2. The limitations of these models’ applicability are also discussed. We present the limitations of these models and compare their minimum climate input requirements. Additionally, we evaluate the performance of the models across different climate zones, explore how the integration of remote sensing data sources can enhance model estimation accuracy, and examine the potential for spatial scalability in maize LAI modeling. Full article

The Shendong mining area is the largest coal production base in western China. Due to long-term mining activities, the ecological environment quality (EEQ) of the Shendong mining area has undergone significant changes. Investigating the evolution of EEQ during the process of mineral resource exploitation is of great importance for the sustainable development of the mining area. However, current research lacks a quantitative assessment of the contributions of climate change and human activities to the spatiotemporal variations in EEQ in the Shendong mining area. In this study, the Remote Sensing Ecological Index (RSEI) was used as an EEQ evaluation metric. The Theil–Sen slope estimation and Mann–Kendall test were applied to analyze the spatiotemporal changes of EEQ from 1990 to 2023. Additionally, the partial derivative method was used to investigate the response characteristics of EEQ to climatic factors and human activities and to quantify the relative contributions of these two driving factors. The results indicate that, over the past 34 years, the overall EEQ in the study area has shown an improving trend. Compared to 1990, the proportions of areas with good-grade and excellent-grade EEQ in 2023 increased by 28% and 23.78%, respectively. Additionally, in the second phase (2011–2023), the average RSEI time series value significantly increased compared to the first phase (1990–2010). Among the climatic factors, annual precipitation had the greatest impact on EEQ, with an average contribution rate of 0.085. The conversion of unused land to forestland significantly improved the EEQ, with the area showing a very significant increase in RSEI, accounting for 82.30%. The areas in the mining region showing very significant, significant, and slight increases in RSEI were smaller than the overall study area. In conclusion, the overall EEQ in the study area has shown an improving trend, with climate change being the dominant factor in 71.52% of the areas where RSEI increased, while human activities were the dominant factor in 26.89% of the areas where RSEI decreased. Full article

The forests in China’s temperate semi-arid region play a significant role in water conservation, carbon storage, and biodiversity protection. An accurate estimation of their aboveground biomass (AGB) is crucial for assessing key ecological characteristics, such as forest carbon storage capacity, biodiversity, and ecological productivity. This provides a scientific basis for forest resource management and ecological conservation in this region. In this study, we extract 17 features related to the dominant species (Larix gmelinii and Betula platyphylla), including 7 vegetation indices derived from remote sensing data, 14 indices from 7 satellite bands, and 3 forest site characteristics. We then analyze the correlations between the AGB and these features. We compare the performance of AGB estimation models using linear regression (LR), polynomial regression (PR), ridge regression (RR), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and random forest regression (RFR). The results show that for Larix gmelinii, the Landsat 8 bands TM4 and TM7 have a greater degree of correlation with the AGB than the other features, while for Betula platyphylla, bands TM3 and TM4 show a greater degree of correlation with the AGB, and elevation has a weaker correlation with the AGB. Although the linear regression (LR) demonstrates certain advantages for AGB estimation, particularly when the AGB values range from 40 to 70 t/ha, the RFR outperforms in overall performance, with estimation accuracies reaching 85% for Betula platyphylla and 89% for Larix gmelinii. This study reveals that both the species and environmental characteristics may significantly influence the selection of the remote sensing features for AGB estimation, and the choice of algorithm for model optimization is critical. This study innovatively extracts the features related to the dominant species in temperate forests, analyses their relationships with environmental factors, and optimizes the AGB estimation model using advanced regression techniques, offering a method that can be applied to other forest regions as well. Full article

Due to the inherent limitations in remote sensing image quality, seasonal variations, and radiometric inconsistencies, river extraction based on remote sensing image classification often results in omissions. These challenges are particularly pronounced in the detection of narrow and complex river networks, where fine river features are frequently underrepresented, leading to fragmented and discontinuous water body extraction. To address these issues and enhance both the completeness and accuracy of fine river identification, this study proposes an advanced fine river extraction and optimization method. Firstly, a linear river feature enhancement algorithm for preliminary optimization is introduced, which combines Frangi filtering with an improved GA-OTSU segmentation technique. By thoroughly analyzing the global features of high-resolution remote sensing images, Frangi filtering is employed to enhance the river linear characteristics. Subsequently, the improved GA-OTSU thresholding algorithm is applied for feature segmentation, yielding the initial results. In the next stage, to preserve the original river topology and ensure stripe continuity, a river skeleton refinement algorithm is utilized to retain critical skeletal information about the river networks. Following this, river endpoints are identified using a connectivity domain labeling algorithm, and the bounding rectangles of potential disconnected regions are delineated. To address discontinuities, river endpoints are shifted and reconnected based on structural similarity index (SSIM) metrics, effectively bridging gaps in the river network. Finally, nonlinear water optimization combined K-means clustering segmentation, topology and spectral inspection, and small-area removal are designed to supplement some missed water bodies and remove some non-water bodies. Experimental results demonstrate that the proposed method significantly improves the regularization and completeness of river extraction, particularly in cases of fine, narrow, and discontinuous river features. The approach ensures more reliable and consistent river delineation, making the extracted results more robust and applicable for practical hydrological and environmental analyses. Full article

In recent years, safety concerns in aquaculture have become increasingly prominent due to various factors. Concurrently, the emergence of artificial intelligence (AI) has offered novel approaches to addressing these challenges. This paper provides a comprehensive review and synthesis of AI applications in aquaculture safety over the past few decades, while also suggesting future directions. Utilizing bibliometric tools such as Citespace and VOSviewer, we analyzed 513 publications spanning from 1998 to 2025. Our analysis highlighted a growing global research interest in this emerging field. Furthermore, it is forecasted that the integration of remote sensing technology, immune response monitoring, and cross-disciplinary innovations will drive the transformation of aquaculture safety management toward a more intelligent, proactive, and sustainable approach. These advancements are expected to enhance the precision and efficiency of risk assessment and disease prevention in aquaculture systems. Full article

Accurately partitioning evapotranspiration (ET) of cropland into productive plant transpiration (T) and non-productive soil evaporation (E) is important for improving crop water use efficiency. Many methods, including machine learning methods, have been developed for ET partitioning. However, the applicability of machine learning models in cropland ET partitioning with diverse crop rotations is not clear. In this study, machine learning models are used to predict E, and T is obtained by calculating the difference between ET and E, leading to the derivation of the ratio of transpiration to evapotranspiration (T/ET). We evaluated six machine learning models (i.e., artificial neural networks (ANN), extremely randomized trees (ExtraTrees), gradient boosting decision tree (GBDT), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost)) on partitioning ET at 16 cropland flux sites during the period from 2000 to 2020. The evaluation results showed that the XGBoost model had the best performance (R = 0.88, RMSE = 6.87 W/m², NSE = 0.77, and MAE = 3.41 W/m²) when considering the meteorological data, ecosystem sensible heat flux, ecosystem respiration, soil water content, and remote sensing vegetation indices as input variables. Due to the unavailability of observed E or T data at the 16 cropland sites, we used three other widely used ET partitioning methods to indirectly validate the accuracy of our ET partitioning results based on XGBoost. The results showed that our T estimation results were highly consistent with their T estimation results (R = 0.83–0.91). Moreover, based on the XGBoost model and the three other ET partitioning methods, we estimated the ratio of transpiration to evapotranspiration (T/ET) for different crops. On average, maize had the highest T/ET of 0.619 ± 0.119, followed by soybean (0.618 ± 0.085), winter wheat (0.614 ± 0.08), and sugar beet (0.611 ± 0.065). Lower T/ET was found for paddy rice (0.505 ± 0.055), winter barley (0.590 ± 0.058), potato (0.540 ± 0.088), and rapeseed (0.522 ± 0.107). These results suggest the machine learning models are easy and applicable for cropland T/ET estimation with different crop rotations and reveal obvious differences in water use among different crops, which is crucial for the sustainability of water resources and improvements in cropland water use efficiency. Full article

As carbon dioxide (CO₂) concentrations continue to rise, climate change, characterized by global warming, presents a significant challenge to global sustainable development. Currently, most global shortwave infrared CO₂ retrievals rely on fully physical retrieval algorithms, for which complex calculations are necessary. This paper proposes a method to predict the concentration of column-averaged CO₂ (XCO₂) from shortwave infrared hyperspectral satellite data, using machine learning to avoid the iterative computations of the physical method. The training dataset is constructed using the Orbiting Carbon Observatory-2 (OCO-2) spectral data, XCO₂ retrievals from OCO-2, surface albedo data, and aerosol optical depth (AOD) measurements for 2019. This study employed a variety of machine learning algorithms, including Random Forest, XGBoost, and LightGBM, for the analysis. The results showed that Random Forest outperforms the other models, achieving a correlation of 0.933 with satellite products, a mean absolute error (MAE) of 0.713 ppm, and a root mean square error (RMSE) of 1.147 ppm. This model was then applied to retrieve CO₂ column concentrations for 2020. The results showed a correlation of 0.760 with Total Carbon Column Observing Network (TCCON) measurements, which is higher than the correlation of 0.739 with satellite product data, verifying the effectiveness of the retrieval method. Full article

Conventional object detection methods face challenges in addressing the complexity of targets in optical remote sensing images (ORSIs), including multi-scale objects, high aspect ratios, and arbitrary orientations. This study proposes a novel detection framework called Progressive Self-Modulating Detector (PSMDet), which incorporates self-modulation mechanisms at the backbone, feature pyramid network (FPN), and detection head stages to address these issues. The backbone network utilizes a reparameterized large kernel network (RLK-Net) to enhance multi-scale feature extraction. At the same time, the adaptive perception network (APN) achieves accurate feature alignment through a self-attention mechanism. Additionally, a Gaussian-based bounding box representation and smooth relative entropy (${\mathrm{smooth}}_{\mathrm{RE}}$) regression loss are introduced to address traditional bounding box regression challenges, such as discontinuities and inconsistencies. Experimental validation on the HRSC2016 and UCAS-AOD datasets demonstrates the framework’s robust performance, achieving the mean Average Precision (mAP) scores of 90.69% and 89.86%, respectively. Although validated on ORSIs, the proposed framework is adaptable for broader applications, such as autonomous driving in intelligent transportation systems and defect detection in industrial vision, where high-precision object detection is essential. These contributions provide theoretical and technical support for advancing intelligent image sensor-based applications across multiple domains. Full article

The COllaborative Carbon Column Observing Network has become a reliable source of high-quality ground-based remote sensing network data that provide column-averaged dry-air mole fractions of carbon dioxide (XCO₂), methane (XCH₄), and carbon monoxide (XCO). The fiducial reference measurements of these gases from the COCCON complement the TCCON and NDACC-IRWG data. This study shows the application of COCCON data for the validation of existing greenhouse gas satellite products. This study includes the validation of XCH₄ and XCO products from the European Copernicus Sentinel-5 Precursor (S5P) mission, XCO₂ products from the American Orbiting Carbon Observatory-2 (OCO-2) mission, and XCO₂ and XCH₄ products from the Japanese Greenhouse gases Observing SATellite (GOSAT). A total of 27 datasets contributed to this study; some of these were collected in the framework of campaign activities and covered only a short time period. In addition, several permanent stations provided long-term observations. The random uncertainties in the validation results, specifically for S5P with a lot of coincidences pairs, are found to be similar to the comparison with the TCCON. The comparison results of OCO-2 land nadir and land glint observation modes to the COCCON on a global scale, despite limited coincidences, are very promising. The stations can, therefore, expand on the coverage of the already existing ground-based reference remote sensing sites from the TCCON and the NDACC network. The COCCON data can be used for future satellite and model validation studies and carbon cycle studies. Full article

Non-point source pollution (NPSP) originates from domestic agricultural pollutants and deforestation. Agricultural NPSP discharges into rivers and oceans through precipitation and soil runoff. Awareness and research regarding NPSP and its harmful effects on human health and the environment are increasing. The Diffuse Pollution Estimation with Remote Sensing (DPeRS) model, a distributed NPSP model proposed by Chinese researchers, seeks to predict agricultural NPSP and includes modules estimating nitrogen and phosphorus balance, vegetation coverage, dissolved pollution, and absorbed pollution. By applying the DPeRS model, the present work aims to predict the distribution of all nitrogen and phosphorus pollutants in Henan Province, China in 2021. We used statistical yearbook, remotely sensed, and hydrological data as input. To facilitate uncertainty characterization in pollution predictions, we performed sensitivity analysis, which identified the model input variables that contributed most to uncertainty in model output. Specifically, we used ArcGIS for processing data for nitrogen and phosphorus balance equations, an ENVI 5.3 software system for deriving vegetation cover, and the RUSLE soil erosion model for predicting absorption pollution. Dissolved pollution was estimated using a unified approach to estimating agricultural runoff, urban runoff, rural resident, and livestock pollutants. Absorbed pollution was estimated by considering the soil erosion model and precipitation. Moreover, Sobol’s method was applied for sensitivity analysis. We found that regardless of the accumulation of nitrogen or phosphorus, indicators of the dissolved pollution of Zhoukou were relatively high. Sensitivity analysis of the models for estimating dissolved pollution and absorbed pollution revealed that the top four influential variables for dissolved pollution were standard runoff coefficient ${\mathsf{\epsilon }}_0$, natural factor correction coefficient ${\mathrm{N}}_{\mathrm{i}}$, the newly produced TN pollutants per area ${\mathrm{Q}}_{\mathrm{i}\mathrm{N}}$, and runoff coefficient $\mathsf{\epsilon }$. For absorbed pollution, influential variables were rainfall erosion factor R, water and soil conservation factor P, slope degree factor S, and slope length factor L. The total discharges of Henan Province were 9546.4649 t, 1061.8940 t, 6031.4577 t, and 3587.6113 t for TN, TP, ${\mathrm{N}\mathrm{H}}_4^{+}$-N, and COD, respectively, in 2021. This paper provides a valuable reference for understanding the status of NPSP in Henan province. The DPeRS approach presented in this paper provides strong support for policymakers in the field of environmental management in China. This study confirmed that the DPeRS model can be feasibly applied to larger areas for NPSP prediction enhanced with sensitivity analysis due to its fast computation and reliance on accessible and simple data sources. Full article

Small Target Detection and Identification (TDI) methods for Remote Sensing (RS) images are mostly inherited from the deep learning models of the Computer Vision (CV) field. Compared with natural images, RS images not only have common features such as shape and texture but also contain unique quantitative information such as spectral features. Therefore, RS TDI in the CV field, which does not use Quantitative Remote Sensing (QRS) information, has the potential to be explored. With the rapid development of high-resolution RS satellites, RS wind turbine detection has become a key research topic for power intelligent inspection. To test the effectiveness of integrating QRS information with deep learning models, the case of wind turbine TDI from high-resolution satellite images was studied. The YOLOv5 model was selected for research because of its stability and high real-time performance. The following methods for integrating QRS and CV for TDI were proposed: (1) Surface reflectance (SR) images obtained using quantitative Atmospheric Correction (AC) were used to make wind turbine samples, and SR data were input into the YOLOv5 model (YOLOv5_AC). (2) A Convolutional Block Attention Module (CBAM) was added to the YOLOv5 network to focus on wind turbine features (YOLOv5_AC_CBAM). (3) Based on the identification results of YOLOv5_AC_CBAM, the spectral, geometric, and textural features selected using expert knowledge were extracted to conduct threshold re-identification (YOLOv5_AC_CBAM_Exp). Accuracy increased from 90.5% to 92.7%, then to 93.2%, and finally to 97.4%. The integration of QRS and CV for TDI showed tremendous potential to achieve high accuracy, and QRS information should not be neglected in RS TDI. Full article

Mapping anthropic activities in aquatic environments is crucial to support their sustainable management. Aquatic traffic is one of the human-related activities gaining relevance nowadays, and remote sensing can support the description of the distribution of vessels, particularly small boats or other vessels not tracked with other tools. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we performed a systematic review of the literature to describe current trends, key methodologies, and gaps, with special regard to the challenges of monitoring small boats that are not equipped with Global Positioning System (GPS) transponders. A total of 133 studies published between 1992 and 2024 were included. The research effort is mainly dedicated to developing new methods or upgrading existing ones, with only a few studies focusing on applications in a contest of environmental studies and, among these, only a few focusing on small boats. To promote the use of remote sensing by environmental scientists, coastal, and fishery managers, explicative case studies are delineated, showing how boat identification through satellites can support environmental studies. Moreover, a guideline section for using remote sensing to integrate monitoring of small boats is given to promote newcomers to this field. Full article

A PhenoCam is a near-surface remote sensing system traditionally used for monitoring phenological changes in diverse landscapes. Although initially developed for forest landscapes, these near-surface remote sensing systems are increasingly being adopted in agricultural settings, with deployment expanding from 106 sites in 2020 to 839 sites by February 2025. However, agricultural applications present unique challenges because of rapid crop development and the need for precise phenological monitoring. Despite the increasing number of PhenoCam sites, clear guidelines are missing on (i) the phenological analysis of images, (ii) the selection of a suitable color vegetation index (CVI), and (iii) the extraction of growth stages. This knowledge gap limits the full potential of PhenoCams in agricultural applications. Therefore, a study was conducted in two soybean (Glycine max L.) fields to formulate image analysis guidelines for PhenoCam images. Weekly visual assessments of soybean phenological stages were compared with PhenoCam images. A total of 15 CVIs were tested for their ability to reproduce the seasonal variation from RGB, HSB, and Lab color spaces. The effects of image acquisition time groups (10:00 $\mathrm{h}$–14:00 $\mathrm{h}$) and object position (ROI locations: far, middle, and near) on selected CVIs were statistically analyzed. Excess green minus excess red (EXGR), color index of vegetation (CIVE), green leaf index (GLI), and normalized green red difference index (NGRDI) were selected based on the least deviation from their loess-smoothed phenological curve at each image acquisition time. For the selected four CVIs, the time groups did not have a significant effect on CVI values, while the object position had significant effects at the reproductive phase. Among the selected CVIs, GLI and EXGR exhibited the least deviation within the image acquisition time and object position groups. Overall, we recommend employing a consistent image acquisition time to ensure sufficient light, capture the largest possible image ROI in the middle region of the field, and apply any of the selected CVIs in order of GLI, EXGR, NGRDI, and CIVE. These results provide a standardized methodology and serve as guidelines for PhenoCam image analysis in agricultural cropping environments. These guidelines can be incorporated into the standard protocol of the PhenoCam network. Full article

The open-vocabulary understanding of UAV aerial images plays a crucial role in enhancing the intelligence level of remote sensing applications, such as disaster assessment, precision agriculture, and urban planning. In this paper, we propose an innovative open-vocabulary model for UAV images, which combines vision–language methods to achieve efficient recognition and segmentation of unseen categories by generating multi-view image descriptions and feature extraction. To enhance the generalization ability and robustness of the model, we adopted Mixup technology to blend multiple UAV images, generating more diverse and representative training data. To address the limitations of existing open-vocabulary models in UAV image analysis, we leverage the GPT model to generate accurate and professional text descriptions of aerial images, ensuring contextual relevance and precision. The image encoder utilizes a U-Net with Mamba architecture to extract key point information through edge detection and partition pooling, further improving the effectiveness of feature representation. The text encoder employs a fine-tuned BERT model to convert text descriptions of UAV images into feature vectors. Three key loss functions were designed: Generalization Loss to balance old and new category scores, semantic segmentation loss to evaluate model performance on UAV image segmentation tasks, and Triplet Loss to enhance the model’s ability to distinguish features. The Comprehensive Loss Function integrates these terms to ensure robust performance in complex UAV segmentation tasks. Experimental results demonstrate that the proposed method has significant advantages in handling unseen categories and achieving high accuracy in UAV image segmentation tasks, showcasing its potential for practical applications in diverse aerial imagery scenarios. Full article