Search Results (9,030)

Soil particle size fractions (PSFs)—sand, silt, and clay—are fundamental determinants of soil hydrological behavior, nutrient retention, and erodibility, yet their spatial prediction remains challenging due to the compositional nature of the data, unquantified prediction uncertainty, and limited interpretability of machine learning models. This study develops an integrated compositional mapping framework incorporating multi-source Sentinel-1/2 and topographic covariates, coupling the isometric log-ratio (ILR) transformation with Quantile Regression Forests (QRFs), a Monte Carlo simulation (MCS)-based latent-to-physical space uncertainty propagation strategy, and a Wrapper-SHAP attribution method to jointly address these challenges. The framework was evaluated across regional croplands in the central Shandong mountain-hilly region of China, using an elevation-stratified spatial cross-validation. Validations achieved R² values of 0.72, 0.61, and 0.59 for sand, silt, and clay, respectively, and a global Aitchison distance of 0.34. Critically, the MCS error propagation strategy effectively compensated for the probability distribution shift introduced by non-linear ILR back-transformation. This ensured that all predicted compositions strictly satisfied compositional closure and the [0, 100%] constraint, while aligning the prediction interval coverage probability (PICP) of each fraction closely with the 90% nominal level. Wrapper-SHAP overcame direct attribution limitations in compositional models, revealing the predictive associations of these multi-source covariates: high remote sensing-derived Bare Soil Index (BSI) and Moisture Stress Index (MSI) values primarily exhibited strong predictive associations with sand enrichment, whereas their lower values, combined with elevated Normalized Difference Moisture Index (NDMI), Enhanced Vegetation Index (EVI), and anthropogenic indicators, favored silt and clay accumulation. The proposed framework provides a transferable methodological reference for remote sensing-integrated compositional soil mapping with reliable uncertainty estimates and interpretable driver identification at regional scales. Full article

This study presents a multi-indicator remote sensing framework for assessing satellite-derived water-quality-related and trophic-state-related dynamics across four freshwater systems in Türkiye Egirdir Lake, Sapanca Lake, Catalan Dam, and Yuvacik Dam between the baseline (2015–2018) and recent (2023–2025) periods. Rather than providing a regulatory or use-specific satellite-based assessment of water-quality-related indicators, the study evaluates optically and thermally detectable surface water indicators derived from Sentinel-2 MSI and Landsat 8/9 imagery processed in Google Earth Engine. The Normalized Difference Chlorophyll Index (NDCI), the Normalized Difference Turbidity Index (NDTI), and land surface temperature (LST, applied to water surfaces) were used to detect change patterns through period-mean difference mapping (Δ-mask) and interannual time series analysis. Results reveal distinct spatial and temporal dynamics broadly consistent with the interplay of climatic, hydrological, and anthropogenic drivers. In the southern Mediterranean systems, positive ΔNDCI anomalies in littoral and inflow zones were associated with increasing summer LST, with Egirdir Lake exhibiting a statistically significant warming trend of +0.170 °C yr⁻¹ (Mann–Kendall τ = 0.53, p = 0.029), interpreted cautiously as a physically plausible signal consistent with regional climate trends, suggesting elevated thermally mediated eutrophication-related optical risk. In the northern Marmara systems, satellite-observed patterns were more strongly associated with anthropogenic nutrient loading and morphological constraints, with turbidity-related optical increases concentrated in western and marginal zones despite relatively stable thermal conditions. As concurrent in situ measurements were unavailable, cross-sensor consistency checks and literature-based benchmarking were applied as alternative validation strategies. Across all four systems, positive ΔNDCI anomalies were systematically concentrated in shallow marginal and inflow zones, while ΔNDTI patterns varied by system, underscoring the role of littoral dynamics as early indicators of optically detectable water-quality deterioration and trophic-state-related change. The proposed framework offers a scalable, cost-effective approach for freshwater quality surveillance in data-scarce environments and provides direct support for integrated water resource management under Türkiye’s National Water Plan (2026–2036). Full article

Accurate land use/land cover (LULC) mapping from remotely sensed data remains challenging in arid regions, particularly for spatial and seasonal generalization. This work proposes a novel exclude-one-city-out (EOCO) framework based on machine learning (ML) to achieve LULC generalization across summer and winter in arid environments. Four cities in Saudi Arabia witnessing rapid urban growth were selected: Riyadh, Madinah, Jeddah, and Dammam. The ML models were trained on three cities and tested on the unseen city. Sentinel-2 surface reflectance data for the visible (Blue, Green, and Red) and near-infrared bands (NIR, SWIR1, and SWIR2) were used. Spectral indices, texture features, and topographical data were used to form five feature sets, which were utilized as inputs for four ML algorithms: random forest, support vector machine, classification and regression trees, and K-nearest neighbors. Statistical tests (Friedman, Kendall’s W, and Wilcoxon signed rank) were conducted to assess differences across ML models, feature sets, and seasons. The random forest model consistently outperformed other models across the five feature sets, while the spectral texture and combined feature sets outperformed other feature combinations. Significant differences in feature importance were observed across cities and seasons for spectral texture during summer and winter (p-values: 1.25 × 10⁻⁴ and 9.2 × 10⁻⁵, respectively), with strong agreement (Kendall’s W = 0.9212 and 0.9424). The findings can support urban environmental monitoring in arid regions, contributing to sustainable urban development. Full article

Objective: We aimed to elucidate the epidemiological characteristics and co-infection status of HBoV in Guangzhou and to investigate the potential recombination events and alterations in antigenic properties among circulating HBoV strains. Methods: Utilizing respiratory specimens collected from patients at sentinel surveillance hospitals in Guangzhou between August 2023 and December 2025, multiplex pathogen detection was performed. We describe the temporal and demographic distribution of HBoV in Guangzhou and determine its co-infection patterns. Subsequent sequence analysis focused on identifying potential recombination events and characterizing antigenic properties. Results: The epidemiological features of HBoV in Guangzhou exhibited a primary epidemic peak around the autumn season, followed closely by a secondary peak. HBoV infection was predominantly observed in children under three years of age. Co-infections with rhinovirus and parainfluenza virus were common. Whole-genome sequencing yielded 15 complete HBoV genome sequences. Recombination analysis and verification suggested potential recombination events in two of these sequences. A comparative analysis of the antigenic characteristics of one identified recombinant strain, GZ-2024-20891, against its putative parental strains and domestic prevalent strains revealed potential alterations in its antigenic characteristic. Conclusions: Bocavirus is highly prevalent among young children under 3 years of age, with a secondary peak following the main epidemic peaks around autumn in Guangzhou. Genetic recombination and potential antigenic alteration were detected in bocavirus. Full article

High-resolution surface soil moisture (SM) is needed for local hydrological and agricultural applications, but reliable retrieval at 100 m remains challenging. Within this broader methodological context, radiometer-constrained SAR change detection remains a practical and interpretable option for high-resolution soil moisture retrieval. It uses SAR-derived temporal changes to describe fine-scale wetting and drying processes, while passive microwave observations provide volumetric moisture references. This study proposes an improved SMAP-anchored Sentinel-1 change-detection framework (ISSF) for 100 m SM mapping. ISSF addresses these limitations by fitting NDVI-binned upper-envelope samples with a nonlinear quadratic function to normalize the vegetation-dependent backscatter-change range and by using multi-year SMAP dry/wet quantiles to scale the normalized relative wetness into volumetric SM. ISSF was evaluated using in situ measurements, a near-concurrent airborne reference, SMAP-based products, and direct transfer to OzNet. In the Shandian River Basin, ISSF achieved R = 0.549 and ubRMSE = 0.062 m³ m⁻³ at the point scale. Relative to three benchmark change-detection methods, ISSF increased R by 11–53% and reduced ubRMSE by 7–15%. For the airborne-referenced event, ISSF showed R = 0.635 and ubRMSE = 0.027 m³ m⁻³. Under direct transfer to OzNet, ISSF achieved mean R = 0.55 and mean ubRMSE = 0.05 m³ m⁻³. These results indicate that ISSF provides a practical and interpretable approach for 100 m soil moisture mapping in semi-arid regions with sparse to moderate vegetation. Full article

Background/Objectives: Nowadays, lymph node assessment represents a key component in the surgical staging of endometrial cancer, as sentinel lymph node (SLN) mapping increased in adoption as an alternative to lymphadenectomy. This study aimed to compare SLN mapping and selective lymph node sampling (SLNS) in endometrial cancer cases managed in a tertiary oncologic center. Moreover, the study evaluated clinicopathological characteristics and the association between tumor stage and nodal involvement. Methods: This prospective observational cohort study included 137 patients with histologically confirmed endometrial cancer who underwent surgical staging between January 2020 and August 2025. Either SLN mapping using indocyanine green (ICG) or methylene blue (blue dye–BD) or SLNS was performed during the surgery. Clinical, surgical, and histopathological data were analyzed using descriptive and inferential statistics. Results: SLN mapping was performed in 86 patients (BD: 45; ICG: 41), while the other 51 underwent SLNS. Median lymph node yield was significantly higher in the SLNS group (10 nodes) in comparison to SLN mapping (four nodes for both BD and ICG; p < 0.001). The overall nodal metastasis rate was 9.5%, with no significant difference between techniques (SLNS: 9.8%, BD: 8.9%, ICG: 9.8%; p = 0.99). Bilateral nodal detection rates were higher in the BD group compared to the ICG group (73.3% vs. 51.2%; OR = 2.62, p = 0.045). Nodal involvement increased significantly in parallel with advancing pathological T stage (p < 0.001), ranging from 0% in T1a to 40.0% in T3a disease. Conclusions: Even though SLNS resulted in a higher number of lymph nodes retrieved, SLN mapping demonstrated similar observed rates of nodal metastases across groups within the limits of this observational study. BD demonstrates superior bilateral detection rates compared to ICG in this cohort. Tumor stage remains a predictor of lymph node involvement. All these findings justify the use of SLN mapping as an effective staging strategy in patients with endometrial cancer. Full article

Cystic echinococcosis (CE), caused by Echinococcus granulosus sensu lato (s.l.), is a significant zoonotic disease affecting livestock and public health worldwide, particularly in endemic regions such as La Araucanía, Chile. This study evaluated the role of cattle in the transmission dynamics of E. granulosus sensu stricto (s.s.) by morphologically and molecular characterizing hydatid cysts (HC) collected from cattle, sheep, pigs, and goats. A total of 123 cysts were obtained from a local slaughterhouse, with cattle contributing the majority of samples (n = 94). Fertility was highest in sheep (76.2%) and low in cattle (3.2%), while cysts from pigs and goats were infertile. PCR amplification and sequencing of the cox1 gene confirmed the predominance of genotype G1 (98.1%), with two additional haplotypes (Eg^B and Eg^C) identified in cattle and sheep. Two cattle samples harbored genotype G3. Phylogenetic analyses grouped all sequences within the E. granulosus s.s. complex. The results corroborate the role of cattle as important sentinels for environmental surveillance of CE due to their exposure and traceability but highlight their lower competence in parasite transmission to definitive hosts compared with sheep. The genetic diversity observed aligns with previous findings in Chile, underscoring the epidemiological significance of E. granulosus s.s. and genotype G1 in the region. Full article

Modeling crop development under different agrotechnologies is important not only for assessing the factors that affect their yields but also because of the role of vegetation in regulation of the hydrology regime. For this reason, interest in the plant module in the semi-distributed hydrological model SWAT is increasing. The model has to be supplied with a lot of information for running and testing, which can be achieved with ground-based, statistical and satellite data. The aim of the study is to determine the accuracy of the SWAT model to predict crop development by using ground-based and satellite data for LAI in the case of a 5-year field experiment. Two staple crops in rotation were monitored—winter wheat and maize—under different erosion control technologies (up-and-down conventional tillage, conventional contour tillage, and minimum contour tillage with inclusion of cover crop before maize) on sloping terrain on moderately eroded Epicalcic Chernozem in the region of Ruse, north Bulgaria. The remote sensing data from the Copernicus Sentinel-2 mission were used for estimation of LAI of both crops and verified against ground-based data in two ways—via a custom LAI script available through the Sentinel Hub cloud platform and as input to a machine learning quantile regression forests (QRF) model. The calibrated satellite-derived LAI, ground-based soil moisture and yields data were used to calibrate several SWAT model parameters (EPCO, ESCO, CN2, LAImax, HU, HI) and assess the model performance regarding these variables. Although a good temporal fit of the SWAT-modeled LAI data with the satellite data was achieved, the accuracy of predicted LAI is moderately high only in the last two years of the rotation (R² = 60.4%). The accuracy of calibrated yields (R² = 55.5%) is acceptable in four of the years. On average for the period, the applied erosion control agrotechnologies did not cause significantly different yields, but they are 14% higher compared to the up-and-down conventional tillage. The most sensitive SWAT parameters accounting for this effect are EPCO and ESCO. Full article

Estimation of rice crop yield at the village level is essential because village is the Insurance Unit (IU) for rice crop in many regions in India, and timely and accurate yield information at this scale supports timely and transparent claim settlements for farmers and supports local agricultural planning. To achieve this, a multi-source satellite data-based machine learning approach was used to estimate rice yield at the village level using optical and SAR data, climatic data and land surface model-derived parameters in Kakinada of Andhra Pradesh, India. The predictor dataset included seasonal cumulative rainfall, seasonal Normalized Difference Vegetation Index (NDVI)-Max, seasonal NDVI-Mean, seasonal Land Surface Water Index (LSWI)-Max, seasonal LSWI-Mean, season total Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and season total Root Zone Soil Moisture (RZSM), and season total backscatter of the Sentinel-1 VH polarization were used to represent crop greenness, moisture status, photosynthetic activity, soil water availability, canopy structure, and seasonal water supply. For model development and validation, village-level rice yield data from 2017 to 2023 was used, which was collected through Crop Cutting Experiment (CCE) at the maturity stage of Kharif season. In this study, four machine learning models such as Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (GB) were evaluated. The multi-source satellite data and yield data for the period 2017–2021 were used to train the models, which were independently tested on 2022 data and then applied to predict the rice yield in 2023. Leave-One-Year-Out (LOYO) cross-validation was also conducted on the 2017–2022 data to assess temporal robustness and generalization capability across years. Among the evaluated models, Random Forest exhibited the best overall performance. For the independent test year 2022, RF achieved an R² of 0.465, RMSE of 415.34 kg ha⁻¹, MAE of 322.22 kg ha⁻¹, and MAPE of 10.36%. For the prediction year 2023, RF achieved improved accuracy with an R² of 0.838, RMSE of 325.75 kg ha⁻¹, MAE of 262.21 kg ha⁻¹, and MAPE of 7.68%. Further, LOYO cross-validation also showed the robustness of RF, achieving the highest mean R² of 0.702 and mean RMSE of 384.73 kg ha⁻¹. The results illustrate that multi-source satellite data combined with machine learning can be a reliable and operationally useful tool in predicting village-level rice yield, which can be used for crop insurance claim settlement. Full article

Coastal zones are dynamic interfaces where land, ocean, and atmosphere interact, making them sensitive indicators of environmental change. However, quantifying shoreline movement across long distances and over multi-year timescales remains challenging using traditional ground-based methods alone. We conducted an analysis of environmental factors and shoreline dynamics along a 58 km stretch of the arid Cabo Pulmo shoreline in Mexico from 2020 to 2026 using the CoastSat tool. The landscape is characterized by a diverse array of geographical features, including sandy beaches, granite cliffs, estuarine systems, and various anthropogenic structures. Results indicated a sea-level rise of 2 mm/year over the last 27 years, which is consistent with the reported range for the Pacific (1.8 to 3.8 mm/year). Notably, we observed an increasing trend of Category 4 and 5 hurricanes in the Mexican Pacific, with an average of 1 additional hurricane per decade (1950–2023). A total of 457 Sentinel-2 satellite images were used for automated analysis using the CoastSat platform, all of which were acquired under tidal conditions not exceeding 1 m. Our findings indicate that the granite cliffs show no detectable horizontal changes in the satellite images; however, their minimal vertical erosion contributes sediment to adjacent beaches. The most significant shoreline erosion was observed north of a marina breakwater, measuring −19.7 m, attributed to the disruption of littoral transport toward the southeast. In contrast, sandy beaches located in front of streams and estuaries—characterized by a lack of infrastructure (houses and breakwaters) and gentle slopes of 2° to 4°—demonstrated positive accretion of up to 5.9 m. According to the autoregressive distributed lag model, wave energy and hurricane-driven wind gusts are the primary agents of shoreline retreat, displacing sediment seaward to the continental shelf. Sea level rise exacerbates this retreat, while rainfall plays a minor but contributing role by transporting sediment during hurricanes in this arid region. This study highlights the effectiveness of CoastSat as a neural network-based tool for analyzing shoreline changes; however, we faced certain limitations, such as the absence of in situ beach profiles due to restricted access. Full article

Most remote sensing extraction studies utilizing vegetation indices typically classify and extract land cover features based on the phenological characteristics of the study area or rely on a single vegetation index. When attempting to extract multiple land cover types simultaneously, classification accuracy often declines significantly because a single vegetation index is unsuitable for all features. While some recent studies employ deep learning and neural networks for classification and extraction, their complex mechanisms and “black-box effect” hinder clear explanations for accuracy outcomes. In response to the issues outlined above, this paper proposes a simpler and more intuitive method for the hierarchical extraction of typical land cover features. This approach analyzes the difficulty of separating these features based on spectral reflectance data to determine the following extraction order: first water bodies, followed by reed, then Suaeda salsa, and finally tidal flat. Furthermore, by selecting appropriate parameters and substituting vegetation indices for bands that perform better, high extraction accuracy is achieved. The classification and interpretation results were validated using a combination of field survey data and Google imagery, together with a validation sample. Accuracy assessments using overall accuracy and Kappa coefficient demonstrate the following optimal results for the hierarchical approach: NDWI for water, S2REP for reeds, and MSAVI for Suaeda salsa. Overall accuracy reached 98.5% with a Kappa coefficient of 0.9796, validating the effectiveness of this spectral-feature-based hierarchical extraction method using diverse vegetation indices. Using a hierarchical extraction approach to classify typical land cover features in the study area from 2020 to 2025, accuracy rates exceeded 98% in all cases. Based on these classification results, the INVEST model was employed to simulate carbon stock trends in the Liaohe Estuary region over the past five years. The study found that, although factors such as tides and the date of image acquisition had a certain impact on the study area compared with the problems caused by historical development, the ecological environment in the study area is gradually stabilizing at the present stage. Full article

Soil organic carbon (SOC) is closely linked to soil fertility, agricultural carbon cycling, and the functioning of cotton field ecosystems, and it provides essential information for sustainable soil management. Rapid and accurate SOC estimation is therefore important for assessing carbon sequestration potential and supporting low-carbon agricultural management. This study focused on cotton fields in northern Shawan City and used optical imagery, Synthetic Aperture Radar (SAR) imagery, and 140 ground-collected SOC samples to estimate SOC content with three machine learning models: Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost). The Kennard–Stone algorithm was applied to partition the 140 SOC samples into training and validation subsets at a 7:3 ratio, ensuring a more representative distribution of samples. Model performance was evaluated using the coefficient of determination (R²) and root mean square error (RMSE), and SHapley Additive exPlanations (SHAP) was used to interpret feature contributions and SOC spatial variability. The results showed that: (1) optical features performed better than SAR features, while fused optical-SAR features achieved the highest accuracy; (2) XGBoost consistently outperformed RF and LightGBM, with the optimal model achieving R² = 0.726 and RMSE = 1.252% on the validation set; (3) SHAP analysis confirmed the dominant contribution of optical features to SOC estimation; and (4) the predicted SOC distribution showed higher values in the central study area, lower values in the northern and southern parts, and high-value zones mainly along both sides of the Manas River. By comparing optical, SAR, and fused features for SOC estimation in arid-zone cotton fields, this study provides methodological support for rapid SOC monitoring and sustainable soil management, and offers practical guidance for variable-rate fertilization and soil carbon sequestration planning along the Manas River corridor. Full article

Endemic human coronaviruses (HCoVs) are important contributors to respiratory infections, yet genomic data from sub-Saharan Africa remain limited. We analyzed 13,530 nasopharyngeal samples collected through the national influenza sentinel surveillance network in Côte d’Ivoire between 2022 and 2024 to characterize the circulation and genomic diversity of endemic HCoVs. A subset of 52 RT-qPCR-positive samples with Ct values ≤ 28 was selected for targeted hybrid-capture sequencing using the Twist Bioscience Respiratory Virus Research Panel. Genome recovery metrics were available for 28 samples, including HCoV-NL63 (n = 9), HCoV-229E (n = 8), HCoV-OC43 (n = 9), and HCoV-HKU1 (n = 2). Endemic HCoVs circulated throughout the study period, with temporal variation across species and increased detections during several rainy-season months. No co-presence of multiple endemic HCoV species was identified in the final analytical dataset. Genome recovery differed by species, with broader and more consistent coverage for HCoV-OC43 and HCoV-NL63 than for HCoV-229E and HCoV-HKU1. Phylogenetic analysis showed that all recovered HCoV-229E genomes clustered within genotype L6 and all recovered HCoV-HKU1 genomes within genotype A, whereas HCoV-OC43 and HCoV-NL63 were distributed across multiple genotypes among recovered genomes. To our knowledge, these findings provide the first genomic data on endemic HCoVs from Côte d’Ivoire and support the feasibility and further targeted integration of targeted hybrid-capture sequencing into routine genomic surveillance of respiratory viruses. Full article

Background/Objectives: Systematic lymphadenectomy is recommended in apparent early-stage epithelial ovarian cancer (EOC) to assess nodal status, but it is associated with significant morbidity and lacks survival benefit. Sentinel lymph node (SLN) mapping may offer a less invasive alternative, although evidence remains limited due to the complexity of ovarian lymphatic drainage and methodological heterogeneity across studies. This prospective study evaluates the feasibility and diagnostic accuracy of a true hybrid ^99mTc–indocyanine green (ICG) tracer for SLN mapping in apparent early-stage EOC. Methods: A prospective observational study was conducted at a tertiary oncology center between 2021 and 2026. Patients presenting with a suspicious ovarian mass (Group A) or requiring restaging after adnexectomy for confirmed EOC (Group B) underwent SLN mapping using a hybrid ^99mTc–ICG tracer injected into the infundibulopelvic (IPL) and/or utero-ovarian ligament (UOL). SLNs were identified using gamma detection and near-infrared fluorescence imaging. All malignant cases underwent complete surgical staging including systematic pelvic and para-aortic lymphadenectomy. SLNs were ultrastaged and compared with the final nodal status. Results: Forty patients were included; 20 (50%) had malignant tumors. The overall SLN detection rate was 92.5% (37/40), with 100% in malignant cases. Among malignant tumors, 3/20 (15%) had metastatic SLNs, all accurately detected (false-negative rate 0%). Sensitivity and negative predictive value were 100%. Combined pelvic and para-aortic drainage was the most frequent pattern (75%). Conclusions: SLN mapping may represent a feasible and potentially accurate staging strategy in apparent early-stage EOC. In the present study, a hybrid ^99mTc–ICG tracer was associated with high detection rates and complete concordance with final nodal status. These findings support further multicenter validation to define its potential role as an alternative to systematic lymphadenectomy. Full article

The 2019 Mississippi River floods were among the most severe in recent U.S. history, impacting 11 states and driven by multiple tributary flood events rather than a single episode. This study focuses on the Lower Mississippi River Basin in Mississippi, examining how flood frequency interacts with land ownership patterns to influence agricultural losses in the Yazoo–Mississippi Delta. Using Sentinel-2 imagery within Google Earth Engine, land use and land cover were classified with a random forest algorithm, followed by change detection and a flood recurrence–persistence modeling framework to map and characterize inundation. Results indicate that mid-year floods (April–July) caused the greatest crop losses, particularly in soybeans (4475 ha), cotton (501 ha), and corn (546 ha). Most impacts were associated with short-duration, low-recurrence floods, which affected many structures (1812) and extensive agricultural areas due to their broad spatial reach. Small agricultural parcels (≤48 ha) experienced the highest proportional exposure across flood zones, while medium and large parcels showed comparatively lower vulnerability. These findings highlight the importance of targeted resilience and mitigation strategies that account for flood frequency, land use, and land ownership patterns across the Delta. Full article