Search Results (6,550)

The presence of melt ponds on the surface of Arctic Sea ice affects its albedo, thermal properties, and overall melting rate; thus, the detection of melt pond onset is of significant importance for understanding the Arctic’s changing climate. This study investigates the utility of a novel method for detecting the onset of melt ponds on sea ice using a satellite-based, dual-sensor C-band approach, whereby Sentinel-1 provides horizontally polarized (HH) data and Advanced SCATterometer (ASCAT) provides vertically polarized (VV) data. The co-polarized ratio (VV/HH) is used to detect the presence of melt ponds on landfast sea ice in the Canadian Arctic Archipelago in 2017 and 2018. ERA-5 air temperature and wind speed re-analysis datasets are used to establish the VV/HH threshold for pond onset detection, which have been further validated by Landsat-8 reflectance. The co-polarized ratio threshold of three standard deviations from the late winter season (April) mean co-pol ratio values are used for assessing pond onset detection associated with the air temperature and wind speed data, along with visual observations from Sentinel-1 and cloud-free Sentinel-2 imagery. In 2017, the pond onset detection rates were 70.59% for FYI and 92.3% for MYI. Results suggest that this method, because of its dual-platform application, has potential for providing large-area coverage estimation of the timing of sea ice melt pond onset using different earth observation satellites. Full article

Over the past decade, surface ozone has emerged as a significant air pollutant in China, especially in the North China Plain (NCP). For effective ozone management in the NCP, it is crucial to accurately estimate the surface ozone levels and identify the primary influencing factors for ozone pollution in this region. This study utilized ozone precursors such as volatile organic compounds (VOCs) and nitrogen oxides (NO_X), meteorological data, land cover, normalized difference vegetation index (NDVI), terrain, and population data to build an extreme gradient boosting (XGBoost)-based ozone estimation model in the NCP during 2019 to 2021. Four ozone estimation models were developed using different NO₂ and formaldehyde (HCHO) datasets from the Sentinel-5 TROPOMI observations and Copernicus Atmosphere Monitoring Service (CAMS) reanalysis data. Site-based validation results of these four models showed high accuracy with R² values above 0.86. Among these four models, two models with higher accuracy and higher spatial coverage ratio were selected, and their results were averaged to produce the final ozone estimation products. The results indicated that VOCs and NO_X were the two main pollutants causing ozone pollution in the NCP, and their relative contributions accounted for more than 23.34% and 10.23%, respectively, while HCHO also played a significant role, contributing over 5.64%. Additionally, meteorological factors also had a notable impact, contributing 28.63% to ozone pollution, with each individual factor contributing more than 2.38%. The spatial distribution of ozone pollution identified the Hebei–Shandong–Henan junction as a pollution hotspot, with the peak occurring in summer, particularly in June. Therefore, for this hotspot region in the NCP, promoting the reduction in VOCs and NOx can play an important role in the mitigation of O₃ pollution and the improvement in air quality in this region. Full article

Most studies in the field of land use and land cover (LULC) classification in remote sensing rely on supervised classification, which requires a substantial amount of accurate label data. However, reliable data are often not immediately available, and are obtained through time-consuming manual labor. One potential solution to this problem is the use of already available classification maps, which may not be the true ground truth and may contain noise from multiple possible sources. This is also true for the classification maps of the MapBiomas project, which provides land use and land cover (LULC) maps on a yearly basis, classifying the Amazon basin into more than 24 classes based on the Landsat data. In this study, we utilize the Sentinel-2 data with a higher spatial resolution in conjunction with the MapBiomas maps to evaluate a proposed noise removal method and to improve classification results. We introduce a novel noise detection method that relies on identifying anchor points in feature space through clustering with self-organizing maps (SOM). The pixel label is relabeled using nearest neighbor rules, or can be removed if it is unknown. A challenge in this approach is the quantification of noise in such a real-world dataset. To overcome this problem, highly reliable validation sets were manually created for quantitative performance assessment. The results demonstrate a significant increase in overall accuracy compared to MapBiomas labels, from 79.85% to 89.65%. Additionally, we trained the L2HNet using both MapBiomas labels and the filtered labels from our approach. The overall accuracy for this model reached 93.75% with the filtered labels, compared to the baseline of 74.31%. This highlights the significance of noise detection and filtering in remote sensing, and emphasizes the need for further research in this area. Full article

Due to the multi-scale and spectral features of remote sensing images compared to natural images, there are significant challenges in super-resolution reconstruction (SR) tasks. Networks trained on simulated data often exhibit poor reconstruction performance on real low-resolution (LR) images. Additionally, compared to natural images, remote sensing imagery involves fewer high-frequency components in network construction. To address the above issues, we introduce a new high–low-resolution dataset GF_Sen based on GaoFen-2 and Sentinel-2 images and propose a cascaded network CSWGAN combined with spatial–frequency features. Firstly, based on the proposed self-attention GAN (SGAN) and wavelet-based GAN (WGAN) in this study, the CSWGAN combines the strengths of both networks. It not only models long-range dependencies and better utilizes global feature information, but also extracts frequency content differences between different images, enhancing the learning of high-frequency information. Experiments have shown that the networks trained based on the GF_Sen can achieve better performance than those trained on simulated data. The reconstructed images from the CSWGAN demonstrate improvements in the PSNR and SSIM by 4.375 and 4.877, respectively, compared to the relatively optimal performance of the ESRGAN. The CSWGAN can reflect the reconstruction advantages of a high-frequency scene and provides a working foundation for fine-scale applications in remote sensing. Full article

Mediterranean grasslands provide different ecosystems and social and economic services to the Mediterranean basin. Specifically, in Spain, pastures occupy more than 55% of the Spanish surface. Farmers and policymakers need to estimate the Gross Primary Production (GPP) to make sustainable management of these ecosystems and to study the role of grasslands acting as sinks or sources of Carbon in the context of climate change. High-frequency satellites, such as Sentinel-2, have opened the door to study GPP with a higher spatial and lower revisit time (10 m and 5 days). Therefore, the overall objective of this research is to estimate an ecosystem light use efficiency (eLUE) GPP model for a Mediterranean grassland in central Spain using Sentinel-2 NDVI Normalized Difference Vegetation Index (NDVI), complemented with meteorological information at the field scale for a relatively long period (from January 2018 to July 2020). The GPP models studied in this research were the MODIS GPP product, as well as the four eLUE models built with MODIS or Sentinel-2 NDVI and complemented by the inclusion of minimum temperature (T_min) and soil water content (SWC). The models were validated through the GPP obtained from an eddy-covariance flux tower located in the study site (GPP_T). Results showed that the MODIS GPP product underestimated the GPP_T of the grassland ecosystem. Besides this, the approach of the eLUE concept was valid for estimating GPP in this Mediterranean grassland ecosystem. In addition, the models showed an improvement using Sentinel-2 NDVI compared to MODIS GPP product and compared to the models that used MODIS NDVI due to its higher spatial and temporal resolution. The inclusion of T_min and SWC was also a determinant in improving GPP models during winter and summer periods. This work also illustrates how the main wind directions of the study area must be considered to appropriately estimate the footprint of the eddy covariance flux tower. In conclusion, this study is the first step to efficiently estimating the GPP of Mediterranean grasslands using the Sentinel-2 NDVI with complementary meteorological field information to make the management of these ecosystems sustainable. Full article

Recent advances in neoadjuvant systemic therapy (NST) have significantly improved pathologic complete response rates in early breast cancer, challenging the role of axillary lymph node dissection in nose-positive patients. Targeted axillary dissection (TAD) integrates marked lymph node biopsy (MLNB) and tracer-guided sentinel lymph node biopsy (SLNB). The introduction of new wire-free localisation markers (LMs) has streamlined TAD and increased its adoption. The primary endpoints include the successful localisation and retrieval rates of LMs. The secondary endpoints include the pathological complete response (pCR), SLNB, and MLNB concordance, as well as false-negative rates. Seventeen studies encompassing 1358 TAD procedures in 1355 met the inclusion criteria. The localisation and retrieval rate of LMs were 97% and 99%. A concordance rate of 67% (95% CI: 64–70) between SLNB and MLNB was demonstrated. Notably, 49 days (range: 0–272) was the average LM deployment time to surgery. pCR was observed in 46% (95% CI: 43–49) of cases, with no significant procedure-related complications. Omitting MLNB or SLNB would have under-staged the axilla in 15.2% or 5.4% (p = 0.0001) of cases, respectively. MLNB inclusion in axillary staging post-NST for initially node-positive patients is crucial. The radiation-free Savi Scout, with its minimal MRI artefacts, is the preferred technology for TAD. Full article

Continuously monitoring and mapping glacial lake variation is of great importance for determining changes in water resources and potential hazards in alpine cryospheric regions. The semi-automated glacial lake mapping methods used currently are hampered by inherent subjectivity and inefficiency. This study used improved YOLOv5 strategies to extract glacial lake boundaries from Sentinel-2 imagery. These strategies include using the space-to-depth technique to identify small glacial lakes, and adopting the coordinate attention and the convolution block attention modules to improve mapping performance and adaptability. In terms of glacial lake extraction, the improved YOLOv5-seg network achieved values of 0.95, 0.93, 0.96, and 0.94 for precision (P), recall (R), mAP_0.5, and the F1 score, respectively, indicating an overall improvement in performance of 12% compared to that of the newest YOLOv8 networks. In High Mountain Asia (HMA), 23,108 glacial lakes with a total area of 1847.5 km² were identified in imagery from 2022 using the proposed method. Compared with the use of manual interpretation for lake boundary extraction in test sites of HMA, the proposed method achieved values of 0.89, 0.87, and 0.86 for P, R, and the F1 score, respectively. Our proposed deep learning method has improved accuracy in glacial lake extraction because it can address the challenge represented by frozen or high-turbidity glacial lakes in HMA. Full article

Background: To support clinical decision-making at the point of care, the “best next step” based on Standard Operating Procedures (SOPs) and actual accurate patient data must be provided. To do this, textual SOPs have to be transformed into operable clinical algorithms and linked to the data of the patient being treated. For this linkage, we need to know exactly which data are needed by clinicians at a certain decision point and whether these data are available. These data might be identical to the data used within the SOP or might integrate a broader view. To address these concerns, we examined if the data used by the SOP is also complete from the point of view of physicians for contextual decision-making. Methods: We selected a cohort of 67 patients with stage III melanoma who had undergone adjuvant treatment and mainly had an indication for a sentinel biopsy. First, we performed a step-by-step simulation of the patient treatment along our clinical algorithm, which is based on a hospital-specific SOP, to validate the algorithm with the given Fast Healthcare Interoperability Resources (FHIR)-based data of our cohort. Second, we presented three different decision situations within our algorithm to 10 dermatooncologists, focusing on the concrete patient data used at this decision point. The results were conducted, analyzed, and compared with those of the pure algorithmic simulation. Results: The treatment paths of patients with melanoma could be retrospectively simulated along the clinical algorithm using data from the patients’ electronic health records. The subsequent evaluation by dermatooncologists showed that the data used at the three decision points had a completeness between 84.6% and 100.0% compared with the data used by the SOP. At one decision point, data on “patient age (at primary diagnosis)” and “date of first diagnosis” were missing. Conclusions: The data needed for our decision points are available in the FHIR-based dataset. Furthermore, the data used at decision points by the SOP and hence the clinical algorithm are nearly complete compared with the data required by physicians in clinical practice. This is an important precondition for further research focusing on presenting decision points within a treatment process integrated with the patient data needed. Full article

As one of the important types of forest resources, mapping forest stock volume (FSV) in larch (Larix decidua) forests holds significant importance for forest resource management, carbon cycle research, and climate change monitoring. However, the accuracy of FSV mapping using common spectral and texture features is often limited due to their failure in fully capturing seasonal changes and growth cycle characteristics of vegetation. Phenological features can effectively provide essential information regarding the growth status of forests. In this study, multi-temporal Sentinel-2 satellite imagery were initially acquired in the Wangyedian Forest Farm in Chifeng City, Inner Mongolia. Subsequently, various phenological features were extracted from time series variables constructed by Gaussian Process Regression (GPR) using Savitzky–Golay filters, stepwise differentiation, and Fourier transform techniques. The alternative features were further refined through Pearson’s correlation coefficient analysis and the forward selection algorithm, resulting in six groups of optimal subsets. Finally, four models including the Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multiple Linear Regression (MLR) algorithms were developed to estimate FSV. The results demonstrated that incorporating phenological features significantly enhanced model performance, with the SVM model exhibiting the best performance—achieving an R² value of 0.77 along with an RMSE value of 46.36 m³/hm² and rRMSE value of 22.78%. Compared to models without phenological features, inclusion of these features led to a 0.25 increase in R² value while reducing RMSE by 10.40 m³/hm² and rRMSE by 5%. Overall, integration of phenological feature variables not only improves the accuracy of larch forest FSV mapping but also has potential implications for delaying saturation phenomena. Full article

Vineyards and olive groves are two of the most important Mediterranean crops, not only for their economic value but also for their cultural and environmental significance, playing a crucial role in global agriculture. This systematic review, based on an adaptation of the 2020 PRISMA statement, focuses on the use of satellite remote sensing tools for the detection of drought in vineyards and olive groves. This methodology follows several key steps, such as defining the approach, selecting keywords and databases, and applying exclusion criteria. The bibliometric analysis revealed that the most frequently used terms included “Google Earth Engine” “remote sensing” “leaf area index” “Sentinel-2”, and “evapotranspiration”. The research included a total of 81 articles published. The temporal distribution shows an increase in scientific production starting in 2018, with a peak in 2021. Geographically, the United States, Italy, Spain, France, Tunisia, Chile, and Portugal lead research in this field. The studies were classified into four categories: aridity and drought monitoring (ADM), agricultural water management (AWM), land use management (LUM), and water stress (WST). Research trends were analysed in each category, highlighting the use of satellite platforms and sensors. Several case studies illustrate applications in vineyards and olive groves, especially in semi-arid regions, focusing on the estimation of evapotranspiration, crop coefficients, and water use efficiency. This article provides a comprehensive overview of the current state of research on the use of satellite remote sensing for drought assessment in grapevines and olive trees, identifying trends, methodological approaches, and opportunities for future research in this field. Full article

This study evaluated the performance of remote sensing (RS) algorithms for the estimation of actual maize evapotranspiration (ET_a) using different spaceborne, airborne, and proximal multispectral data in a semi-arid climate region to identify the optimal platform that provides the best ET_a estimates to improve irrigation water management and help make irrigated agriculture sustainable. The RS platforms used in the study included Landsat-8 (30 m pixel spatial resolution), Sentinel-2 (10 m), Planet CubeSat (3 m), multispectral radiometer or MSR (1 m), and a small uncrewed aerial system or sUAS (0.03 m). Two-source surface energy balance (TSEB) models, implementing the series and parallel surface resistance approaches, were used in this study to estimate hourly maize ET_a. The data used in this study were obtained from two maize research sites in Greeley and Fort Collins, CO, USA, in 2020 and 2021. Each research site had different irrigation systems. The Greeley site had a subsurface drip system, while the Fort Collins site had surface irrigation (furrow). Maize ET_a predictions were compared to observed maize ET_a data from an eddy covariance system installed at each research site. Results indicated that the MSR5 proximal platform (1 m) provided optimal RS data for the TSEB algorithms. The MSR5 “point-based” nadir-looking surface reflectance data and surface radiometric temperature combination resulted in the smallest error when predicting hourly (mm/h) maize ET_a. The mean bias and root mean square errors (MBE and RMSE, respectively), when predicting maize hourly ET_a using the MSR5 sensor data, were equal to −0.02 (−3%) ± 0.07 (11%) mm/h MBE ± RMSE and −0.02 (−3%) ± 0.09 (14%) mm/h for the TSEB parallel and series approaches, respectively. The poorest performance, when predicting hourly TSEB maize ET_a, was from Landsat-8 (30 m) multispectral data combined with its original thermal data, since the errors were −0.03 (−5%) ± 0.16 (29%) mm/h and −0.07 (−13%) ± 0.15 (29%) mm/h for the TSEB parallel and series approaches, respectively. These results indicate the need to develop methods to improve the quality of the RS data from sub-optimal platforms/sensors/scales/calibration to further advance sustainable irrigation water management. Full article

(1) Background: Sentinel lymph node (SLN) mapping represents an accurate and feasible technique for the surgical staging of endometrial and cervical cancer. This is commonly performed by conventional laparoscopy or robotic-assisted laparoscopy, but in recent years, a new retroperitoneal transvaginal natural orifice transluminal endoscopic surgery (vNOTES) approach has been described and developed by Jan Baekelandt. This technique provides easy visualization of lymphatic afferent vessels and pelvic lymph nodes, early SLN assessment, and a coherent mapping methodology following the lymphatic flow from caudal to cranial. However, only a few publications have reported it. Following the IDEAL (Idea Development Exploration Assessment Long-term follow-up) framework, research concerning this technique is in Stage 2a, with only small case series as evidence of its feasibility. Its standardized description appears necessary to provide the surgical homogeneity required to move further. (2) Methods: Description of a standardized approach for retroperitoneal pelvic SLN mapping by vNOTES. (3) Results: We describe a 10-step approach to successfully perform retroperitoneal vNOTES SLN mapping, including pre-, intra-, and postoperative management. (4) Conclusions: This IDEAL Stage 2a study could help other surgeons approach this new technique, and it proposes a common methodology necessary for evolving through future IDEAL Stage 2b (multi-center studies) and Stage 3 (randomized controlled trials) studies. Full article

Recently, the earth’s climate has changed considerably, leading to several hazards, including flash floods (FFs). This study aims to introduce an innovative approach to mapping and identifying FF exposure in the city of Tetouan, Morocco. To address this problem, the study uses different machine learning methods applied to remote sensing imagery within the Google Earth Engine (GEE) platform. To achieve this, the first phase of this study was to map land use and land cover (LULC) using Random Forest (RF), a Support Vector Machine (SVM), and Classification and Regression Trees (CART). By comparing the results of five composite methods (mode, maximum, minimum, mean, and median) based on Sentinel images, LULC was generated for each method. In the second phase, the precise LULC was used as a related factor to others (Stream Power Index (SPI), Topographic Position Index (TPI), Slope, Profile Curvature, Plan Curvature, Aspect, Elevation, and Topographic Wetness Index (TWI)). In addition to 2024 non-flood and flood points to predict and detect FF susceptibility, 70% of the dataset was used to train the model by comparing different algorithms (RF, SVM, Logistic Regression (LR), Multilayer Perceptron (MLP), and Naive Bayes (NB)); the rest of the dataset (30%) was used for evaluation. Model performance was evaluated by five-fold cross-validation to assess the model’s ability on new data using metrics such as precision, score, kappa index, recall, and the receiver operating characteristic (ROC) curve. In the third phase, the high FF susceptibility areas were analyzed for two-way validation with inundated areas generated from Sentinel-1 SAR imagery with coherent change detection (CDD). Finally, the validated inundation map was intersected with the LULC areas and population density for FF exposure and assessment. The initial results of this study in terms of LULC mapping showed that the most appropriate method in this research region is the use of an SVM trained on a mean composite. Similarly, the results of the FF susceptibility assessment showed that the RF algorithm performed best with an accuracy of 96%. In the final analysis, the FF exposure map showed that 2465 hectares were affected and 198,913 inhabitants were at risk. In conclusion, the proposed approach not only allows us to assess the impact of FF in this study area but also provides a versatile approach that can be applied in different regions around the world and can help decision-makers plan FF mitigation strategies. Full article

The Haiyuan fault system plays a crucial role in accommodating the eastward expansion of the Tibetan Plateau (TP) and is currently slipping at a rate of several centimeters per year. However, limited seismic activities have been observed using geodetic techniques in this area, impeding the comprehensive investigation into regional tectonics. In this study, the geometric structure and source models of the 2022 Mw 6.7 and the 2016 Mw 5.9 Menyuan earthquakes were investigated using Sentinel-1A SAR images. By implementing an atmospheric error correction method, the signal-to-noise ratio of the 2016 interferometric synthetic aperture radar (InSAR) coseismic deformation field was significantly improved, enabling InSAR observations with higher accuracy. The results showed that the reliability of the source models for those events was improved following the reduction in observation errors. The Coulomb stress resulting from the 2016 event may have promoted the strike-slip movement of the western segment of the Lenglongling fault zone, potentially expediting the occurrence of the 2022 earthquake. The coseismic slip distribution and the spatial distribution of aftershocks of the 2022 event suggested that the seismogenic fault may connect the western segment of the Lenglongling fault (LLLF) and the eastern segment of the Tuolaishan fault (TLSF). Additionally, the western segment of the surface rupture zone of the northern branch may terminate in the secondary branch close to the Sunan-Qilian fault (SN-QL) strike direction, and the earthquake may have triggered deep aftershocks and accelerated stress release within the deep seismogenic fault. Full article

Coastal lagoons are highly productive transitional water bodies threatened by human factors and vulnerable to global climate change effects. Monitoring biophysical parameters in these ecosystems is crucial for their preservation. In this work, we used Sentinel-2 and Landsat imagery combined with in situ data to (1) develop preliminary algorithms for retrieving the Chl-a concentration and water surface temperature of six lagoons located in the Ebro Delta (NE Mediterranean Sea, Spain), and to (2) compute maps and trend lines for analysing their spatiotemporal evolution from 2015 to 2022. Our findings showed that the algorithms’ accuracy ranged from 72% to 78% and had limited potential under high Chl-a concentration regimes. Even so, they revealed the lagoons’ trophic status, usual fluctuations, and deviations of both parameters attributed to seasonal (i.e., light and temperature) and short-term physical (i.e., winds) forcing, as well as valuable spatial patterns potentially useful for conservation efforts and land use planning. Future work will focus on the acquisition of a larger in situ data sample under a range of environmental conditions to improve the algorithms’ robustness, which in turn will allow the investigation of natural and human factors controlling the dynamics of the two investigated parameters. Full article