Search Results (1,395)

Accurate coastal bathymetry is critical for navigation, environmental monitoring, and marine resource management. This study presents a deep learning-based approach that fuses Sentinel-2 multispectral imagery with airborne hyperspectral-derived reference data to generate high-resolution satellite-derived bathymetry (SDB). To address the spatial resolution mismatch between Sentinel-2 (10 m) and LiDAR reference data (1 m), three interpolation methods—Inverse Distance Weighting (IDW), Natural Neighbor (NN), and Spline—were employed to resample spectral reflectance data to a 1 m grid. Two spectral input configurations were evaluated: the log-ratio of Bands 2 and 3, and raw RGB composite reflectance (Bands 2, 3, and 4). A Fully Convolutional Neural Network (FCNN) was trained under each configuration and validated using LiDAR-based depth. The RGB + NN combination yielded the best performance, achieving an RMSE of 1.2320 m, MAE of 0.9381 m, bias of +0.0315 m, and R² of 0.6261, while the log-ratio + IDW configuration showed lower accuracy. Visual and statistical analyses confirmed the advantage of the RGB + NN approach in preserving spatial continuity and spectral-depth relationships. This study demonstrates that both interpolation strategy and input configuration critically affect SDB model accuracy and generalizability. The integration of spatially adaptive interpolation with airborne hyperspectral reference data represents a scalable and efficient solution for high-resolution coastal bathymetry mapping. Full article

The Mirim–Patos–Mangueira coastal lagoon system provides a wide range of ecosystem services. However, its vast territorial extent and the political boundaries that divide it hinder integrated assessments, especially during extreme hydrological events. This study is divided into two parts. First, we assessed the spatial and temporal patterns of water quality in the lagoon system using Sentinel-3/OLCI satellite imagery. Atmospheric correction was performed using ACOLITE, followed by spectral grouping and classification into optical water types (OWTs) using the Sentinel Applications Platform (SNAP). To explore the behavior of water quality parameters across OWTs, Chlorophyll-a and turbidity were estimated using semi-empirical algorithms specifically designed for complex inland and coastal waters. Results showed a gradual increase in mean turbidity from OWT 2 to OWT 6 and a rise in chlorophyll-a from OWT 2 to OWT 4, with a decline at OWT 6. These OWTs correspond, in general terms, to distinct water masses: OWT 2 to clearer waters, OWT 3 and 4 to intermediate/mixed conditions, and OWT 6 to turbid environments. In the second part, we analyzed the response of the Patos Lagoon to flooding in Rio Grande do Sul during an extreme weather event in May 2024. Satellite-derived turbidity estimates were compared with in situ measurements, revealing a systematic underestimation, with a negative bias of 2.6%, a mean relative error of 78%, and a correlation coefficient of 0.85. The findings highlight the utility of OWT classification for tracking changes in water quality and support the use of remote sensing tools to improve environmental monitoring in data-scarce regions, particularly under extreme hydrometeorological conditions. Full article

As part of the post-conflict reconstruction and recovery, the development of Kabul New City aims to bring relief to the existing capital city, Kabul, which has experienced exponential population growth, putting heavy pressure on its existing resources. Kabul New City is divided into four subsectors, and each of them is being developed and is expected to reach a target population by 2025, as defined by the master plan. The study’s objective is to determine which of the four zones are in demand and need to be prioritized for development, as per the model results. The data collection involves an online questionnaire, and the responses are collected from residents of Kabul and Herat. Agent-based modeling (ABM) is an emerging method of simulating urban dynamics. Cities are evolving continuously and are forming unique spatial patterns that result from the movement of residents in search of new locations that accommodate their needs and preferences. An agent-based model is developed using the weighted random selection process based on household size and income levels. The agents are the residents of Kabul and Herat, and the environment is the land use classification image using the Sentinel 2 image of Kabul New City. The barren class is treated as the developable area and is divided into four sub-sectors. The model simulates three alternative growth rate scenarios, i.e., ambitious, moderate, and steady. The results of the simulation reveal that the sub-sector Dehsabz South, being closer to Kabul city, is in higher demand. Barikab is another sub-sector high in demand, which has connectivity through the highway and is an upcoming industrial hub. Full article

Accurate estimation of forest carbon stock is critical for understanding ecosystem carbon dynamics and informing climate mitigation strategies. This study presents a deep learning framework that integrates Sentinel-2 multispectral imagery with an enhanced residual neural network for estimating aboveground forest carbon stock in the Liuchong River Basin, Bijie City, Guizhou Province, China. The proposed model incorporates multiscale residual blocks and channel attention mechanisms to improve spatial feature extraction and spectral dependency modeling. A dataset of 150 ground inventory plots was employed for supervised training and validation. Comparative experiments with Random Forest, Gradient Boosting Decision Trees (GBDT), and Vision Transformer (ViT) demonstrate that the enhanced ResNet achieves the best performance, with a root mean square error (RMSE) of 23.02 Mg/ha and a coefficient of determination ($R^2$) of 0.773 on the test set. Spatial mapping results further reveal that the model effectively captures fine-scale carbon stock variations across mountainous forested landscapes. These findings underscore the potential of combining multispectral remote sensing and advanced neural architectures for scalable, high-resolution forest carbon estimation in complex terrain. Full article

Fractional Vegetation Cover (FVC) is a crucial indicator describing vegetation conditions and provides essential data for ecosystem health assessments. However, due to the low and sparse vegetation in alpine meadows, it is challenging to obtain pure vegetation pixels from Sentinel-2 imagery, resulting in errors in the FVC estimation using traditional pixel dichotomy models. This study integrated Sentinel-2 imagery with unmanned aerial vehicle (UAV) data and utilized the pixel dichotomy model together with four machine learning algorithms, namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Deep Neural Network (DNN), to estimate FVC in an alpine meadow region. First, FVC was preliminarily estimated using the pixel dichotomy model combined with nine vegetation indices applied to Sentinel-2 imagery. The performance of these estimates was evaluated against reference FVC values derived from centimeter-level UAV data. Subsequently, four machine learning models were employed for an accurate FVC inversion, using the estimated FVC values and UAV-derived reference FVC as inputs, following feature importance ranking and model parameter optimization. The results showed that: (1) Machine learning algorithms based on Sentinel-2 and UAV imagery effectively improved the accuracy of FVC estimation in alpine meadows. The DNN-based FVC estimation performed best, with a coefficient of determination of 0.82 and a root mean square error (RMSE) of 0.09. (2) In vegetation coverage estimation based on the pixel dichotomy model, different vegetation indices demonstrated varying performances across areas with different FVC levels. The GNDVI-based FVC achieved a higher accuracy (RMSE = 0.08) in high-vegetation coverage areas (FVC > 0.7), while the NIRv-based FVC and the SR-based FVC performed better (RMSE = 0.10) in low-vegetation coverage areas (FVC < 0.4). The method provided in this study can significantly enhance FVC estimation accuracy with limited fieldwork, contributing to alpine meadow monitoring on the Qinghai–Tibet Plateau. Full article

Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. In addition, a canopy height model, constructed from a digital surface model (DSM) derived from Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and an ICESat-2-corrected SRTM DEM, is incorporated to quantify its impact on the accuracy of AGB estimation. The results indicate the following: (1) The incorporation of multi-source remote sensing data significantly improves the accuracy of AGB estimation, among which the RF model performs the best (R² = 0.69, RMSE = 24.26 t·ha⁻¹) compared with the single-source model. (2) The canopy height model (CHM) obtained from InSAR-LiDAR effectively alleviates the signal saturation effect of optical and SAR data in high-biomass areas (>200 t·ha⁻¹). When FCH is added to the RF model combined with multi-source remote sensing data, the R² of the AGB estimation model is improved to 0.74. (3) In 2018, AGB in Chenzhou City shows clear spatial heterogeneity, with a mean of 51.87 t·ha⁻¹. Biomass increases from the western hilly part (32.15–68.43 t·ha⁻¹) to the eastern mountainous area (89.72–256.41 t·ha⁻¹), peaking in Dongjiang Lake National Forest Park (256.41 t·ha⁻¹). This study proposes a comprehensive feature integration framework that combines red-edge spectral indices for capturing vegetation physiological status, SAR-derived texture metrics for assessing canopy structural heterogeneity, and canopy height metrics to characterize forest three-dimensional structure. This integrated approach enables the robust and accurate monitoring of carbon storage in subtropical forests. Full article

Understanding the time-lagged response of land subsidence to groundwater level fluctuations and subsurface strain variations is crucial for uncovering its underlying mechanisms and enhancing disaster early warning capabilities. This study focuses on Dangshan County, Anhui Province, China, and systematically analyzes the spatio-temporal evolution of land subsidence from 2018 to 2024. A total of 207 Sentinel-1 SAR images were first processed using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to generate high-resolution surface deformation time series. Subsequently, the seasonal-trend decomposition using the LOESS (STL) model was applied to extract annual cyclic deformation components from the InSAR-derived time series. To quantitatively assess the delayed response of land subsidence to groundwater level changes and subsurface strain evolution, time-lagged cross-correlation (TLCC) analysis was performed between surface deformation and both groundwater level data and distributed fiber-optic strain measurements within the 5–50 m depth interval. The strain data was collected using a borehole-based automated distributed fiber-optic sensing system. The results indicate that land subsidence is primarily concentrated in the urban core, with annual cyclic amplitudes ranging from 10 to 18 mm and peak values reaching 22 mm. The timing of surface rebound shows spatial variability, typically occurring in mid-February in residential areas and mid-May in agricultural zones. The analysis reveals that surface deformation lags behind groundwater fluctuations by approximately 2 to 3 months, depending on local hydrogeological conditions, while subsurface strain changes generally lead surface subsidence by about 3 months. These findings demonstrate the strong predictive potential of distributed fiber-optic sensing in capturing precursory deformation signals and underscore the importance of integrating InSAR, hydrological, and geotechnical data for advancing the understanding of subsidence mechanisms and improving monitoring and mitigation efforts. Full article

Remote sensing has emerged as a promising technology for large-scale detection and updating of global wind turbine databases. High-resolution imagery (e.g., Google Earth) facilitates the identification of offshore wind turbines (OWTs) but offers limited offshore coverage due to the high cost of capturing vast ocean areas. In contrast, medium-resolution imagery, such as 10-m Sentinel-2, provides broad ocean coverage but depicts turbines only as small bright spots and shadows, making accurate detection challenging. To address these limitations, We propose a novel deep learning approach to capture the variability in OWT appearance and shadows caused by changes in solar illumination and satellite viewing geometry. Our method learns intrinsic, imaging geometry-invariant features of OWTs, enabling robust detection across multi-seasonal Sentinel-2 imagery. This approach is implemented using Faster R-CNN as the baseline, with three enhanced extensions: (1) direct integration of imaging parameters, where Geowise-Net incorporates solar and view angular information of satellite metadata to improve geometric awareness; (2) implicit geometry learning, where Contrast-Net employs contrastive learning on seasonal image pairs to capture variability in turbine appearance and shadows caused by changes in solar and viewing geometry; and (3) a Composite model that integrates the above two geometry-aware models to utilize their complementary strengths. All four models were evaluated using Sentinel-2 imagery from offshore regions in China. The ablation experiments showed a progressive improvement in detection performance in the following order: Faster R-CNN < Geowise-Net < Contrast-Net < Composite. Seasonal tests demonstrated that the proposed models maintained high performance on summer images against the baseline, where turbine shadows are significantly shorter than in winter scenes. The Composite model, in particular, showed only a 0.8% difference in the F1 score between the two seasons, compared to up to 3.7% for the baseline, indicating strong robustness to seasonal variation. By applying our approach to 887 Sentinel-2 scenes from China’s offshore regions (2023.1–2025.3), we built the China OWT Dataset, mapping 7369 turbines as of March 2025. Full article

BE following RT after breast-conserving surgery is a common concern, impacting patients’ quality of life. This study introduces a clinically meaningful classification system for BE and retrospectively evaluates its incidence among patients treated with normofractionated (nfRT) and hypofractionated (hfRT) regimens. Data from 1156 patients treated between 2011 and 2021 were analyzed. BE was graded according to the CTC and a so-called “WST classification” (grade 1: lymphatic drainage performed by the patient; grade 2: professional lymphatic drainage; grade 3: surgery). A total of 33%/17% developed BE according to the WST classification/CTC. Grade III BE was not reported. About 70% experienced a remission of BE during follow-up. Risk factors for the development of BE included RT of lymphatic drainage, complete axillary dissection compared to sentinel node dissection, and CTX. CTX was not confirmed in multivariate analysis. The incidence of BE did not differ significantly between the nfRT and hfRT groups, affirming the safety and comparability of hfRT regarding BE risk (HR: 0.833, p = 0.1219). This study emphasizes the importance of precise and standardized BE classification for improved treatment outcomes. Given its comparable risk profile and potential for enhanced therapy adherence, the findings support hfRT as a preferred regimen aligned with the current guidelines. Full article

Soil available nitrogen (AN) is a critical nutrient for plant absorption and utilization. Accurately mapping its spatial distribution is essential for improving crop yields and advancing precision agriculture. In this study, 188 AN soil samples (0–20 cm) were collected at Heshan Farm, Nenjiang County, Heihe City, Heilongjiang Province, in 2023. The soil available nitrogen content ranged from 65.81 to 387.10 mg kg⁻¹, with a mean value of 213.85 ± 61.16 mg kg⁻¹. Sentinel-2 images and normalized vegetation index (NDVI) and enhanced vegetation index (EVI) time series data were acquired on the Google Earth Engine (GEE) platform in the study area during the bare soil period (April, May, and October) and the growth period (June–September). These remote sensing variables were combined with soil sample data, crop type information, and crop growth period data as predictive factors and input into a Random Forest (RF) model optimized using the Optuna hyperparameter tuning algorithm. The accuracy of different strategies was evaluated using 5-fold cross-validation. The research results indicate that (1) the introduction of growth information at different growth periods of soybean and maize has different effects on the accuracy of soil AN mapping. In soybean plantations, the introduction of EVI data during the pod setting period increased the mapping accuracy R² by 0.024–0.088 compared to other growth periods. In maize plantations, the introduction of EVI data during the grouting period increased R² by 0.004–0.033 compared to other growth periods, which is closely related to the nitrogen absorption intensity and spectral response characteristics during the reproductive growth period of crops. (2) Combining the crop types and their optimal period growth information could improve the mapping accuracy, compared with only using the bare soil period image (R² = 0.597)—the R² increased by 0.035, the root mean square error (RMSE) decreased by 0.504%, and the mapping accuracy of R² could be up to 0.632. (3) The mapping accuracy of the bare soil period image differed significantly among different months, with a higher mapping accuracy for the spring data than the fall, the R² value improved by 0.106 and 0.100 compared with that of the fall, and the month of April was the optimal window period of the bare soil period in the present study area. The study shows that when mapping the soil AN content in arable land, different crop types, data collection time, and crop growth differences should be considered comprehensively, and the combination of specific crop types and their optimal period growth information has a greater potential to improve the accuracy of mapping soil AN content. This method not only opens up a new technological path to improve the accuracy of remote sensing mapping of soil attributes but also lays a solid foundation for the research and development of precision agriculture and sustainability. Full article

The accurate mapping of crop planting patterns is vital for sustainable agriculture and food security, particularly in regions with complex cropping systems and limited cloud-free observations. This research focuses on the Jianghan Plain in southern China, where diverse planting structures and persistent cloud cover make consistent monitoring challenging. We integrated multi-temporal Sentinel-2 and Landsat-8 imagery from 2017 to 2021 on the Google Earth Engine platform and applied a sample migration strategy to construct multi-year training data. A random forest classifier was used to identify nine major planting patterns at a 10 m resolution. The classification achieved an average overall accuracy of 88.3%, with annual Kappa coefficients ranging from 0.81 to 0.88. A spatial analysis revealed that single rice was the dominant pattern, covering more than 60% of the area. Temporal variations in cropping patterns were categorized into four frequency levels (0, 1, 2, and 3 changes), with more dynamic transitions concentrated in the central-western and northern subregions. A multiscale geographically weighted regression (MGWR) model revealed that economic and production-related factors had strong positive associations with crop planting patterns, while natural factors showed relatively weaker explanatory power. This research presents a scalable method for mapping fine-resolution crop patterns in complex agroecosystems, providing quantitative support for regional land-use optimization and the development of agricultural policies. Full article

Using interferometric synthetic aperture radar (InSAR) to observe slow ground deformation can be challenging due to many sources of error, with tropospheric phase delay and unwrapping errors being the most significant. While analytical methods, weather models, and data exist to mitigate tropospheric error, most of these techniques are unsuitable for all InSAR applications (e.g., complex tropospheric mixing in the tropics) or are deficient in spatial or temporal resolution. Likewise, there are methods for removing the unwrapping error, but they cannot resolve the true phase when there is a high prevalence (>40%) of unwrapping error in a set of interferograms. Applying tropospheric delay removal techniques is unnecessary for C-band Sentinel-1 InSAR time series studies, and the effect of unwrapping error can be minimized if the full dataset is utilized. We demonstrate that using interferograms with long temporal baselines (800 days to 1600 days) but very short perpendicular baselines (<5 m) (LTSPB) can lower the velocity detection threshold to 2 mm y⁻¹ to 3 mm y⁻¹ for long-term coherent permanent scatterers. The LTSPB interferograms can measure slow deformation rates because the expected differential phases are larger than those of small baselines and potentially exceed the typical noise amplitude while also reducing the sensitivity of the time series estimation to the noise sources. The method takes advantage of the Sentinel-1 mission length (2016 to present), which, for most regions, can yield up to 300 interferograms that meet the LTSPB baseline criteria. We demonstrate that low velocity detection can be achieved by comparing the expected LTSPB differential phase measurements to synthetic tests and tropospheric delay from the Global Navigation Satellite System. We then characterize the slow (~3 mm/y) ground deformation of the Socorro Magma Body, New Mexico, and the Tampa Bay Area using LTSPB InSAR analysis. The method we describe has implications for simplifying the InSAR time series processing chain and enhancing the velocity detection threshold. Full article

The accurate estimation of evapotranspiration ($ET$) and its components are vital for water resource management and irrigation planning. This study models tree transpiration ($T$) and $ET$ for grapefruit, litchi, and mango orchards using light gradient-boosting machine (LightGBM) optimized using the Bayesian hyperparameter optimization. Grounds $T$ and $ET$ for these crops were measured using the heat ratio method of monitoring sap flow and the eddy covariance technique for quantifying $ET$. The Sentinel 2 satellite was used to compute field leaf area index ($LAI$). The modelled data were used to partition the orchard $ET$ into beneficial ($T$) and non-beneficial water uses (orchard floor evaporation—$E_s$). We adopted the 10-fold cross-validation to test the model robustness and an independent validation to test performance on unseen data. The 10-fold cross-validation and independent validation on $ET$ and $T$ models produced high accuracy with coefficient of determination ($R^2$) $\ge$ 0.88, Kling–Gupta efficiency ($KGE$) $\ge$ 0.91, root mean square error ($RMSE$) $\le$ 0.04 mm/h, and mean absolute error ($MAE$) $\le$ 0.03 mm/h for all the crops. The study demonstrates that LightGBM can accurately model the transpiration and evapotranspiration for subtropical tree crops using Sentinel 2 data. The study found that $E_s$ which combined soil evaporation and understorey vegetation transpiration contributed 35, 32, and 31% to the grapefruit, litchi and mango orchard evapotranspiration, respectively. We conclude that improvements on orchard floor management practices can be utilized to minimize non-beneficial water losses while promoting the productive water use ($T$). Full article

The southwestern mountainous region of China (SMRC), characterized by complex geological environments, experiences frequent landslide disasters that pose significant threats to local residents. This study focuses on the Qijiang District of Chongqing, where we conduct a systematic evaluation of wavelength and observation geometry effects on InSAR-based landslide monitoring. Utilizing multi-sensor SAR imagery (Sentinel-1 C-band, ALOS-2 L-band, and LUTAN-1 L-band) acquired between 2018 and 2025, we integrate time-series InSAR analysis with geological records, high-resolution topographic data, and field investigation findings to assess representative landslide-susceptible zones in the Qijiang District. The results indicate the following: (1) L-band SAR data demonstrates superior monitoring precision compared to C-band SAR data in the SMRC; (2) the combined use of LUTAN-1 ascending/descending orbits significantly improved spatial accuracy and detection completeness in complex landscapes; (3) multi-source data fusion effectively mitigated limitations of single SAR systems, enhancing identification of small- to medium-scale landslides. This study provides critical technical support for multi-source landslide monitoring and early warning systems in Southwest China while demonstrating the applicability of China’s SAR satellites for geohazard applications. Full article

This study addresses the challenges of redundant crop identification features and low computational efficiency in complex agricultural environments, particularly in arid regions. Focusing on the Hexi region of Gansu Province, we utilized the Google Earth Engine (GEE) to integrate Sentinel-2 optical imagery (10 bands) and Sentinel-1 radar data (VV/VH polarization), constructing a 96-feature set that comprises spectral, vegetation index, red-edge, and texture variables. The recursive feature elimination random forest (RF-RFE) algorithm was employed for feature selection and model optimization. Key findings include: (1) Variables driven by spatiotemporal differentiation were effectively selected, with red-edge bands (B5–B7) during the grain-filling stage in August accounting for 56.7% of the top 30 features, which were closely correlated with canopy chlorophyll content (p < 0.01). (2) A breakthrough in lightweight modeling was achieved, reducing the number of features by 69%, enhancing computational efficiency by 62.5% (from 8 h to 3 h), and decreasing memory usage by 66.7% (from 12 GB to 4 GB), while maintaining classification accuracy (PA: 97.69%, UA: 97.20%, Kappa: 0.89). (3) Multi-source data fusion improved accuracy by 11.54% compared to optical-only schemes, demonstrating the compensatory role of radar in arid, cloudy regions. This study offers an interpretable and transferable lightweight framework for precision crop monitoring in arid zones. Full article