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        <title>Frontiers in Remote Sensing | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/remote-sensing</link>
        <description>RSS Feed for Frontiers in Remote Sensing | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-04-06T06:56:14.78+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1704067</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1704067</link>
        <title><![CDATA[A novel building metric for PlanetScope© data optimization]]></title>
        <pubdate>2026-04-01T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Mona Morsy</author><author>Silas Michaelides</author><author>Peter Dietrich</author>
        <description><![CDATA[PlanetScope© data are being widely used by the scientific community in addressing important environmental issues, including cropland and tree cover loss, burned area mapping post-wildfire, imagery for river water masking, and snow-covered area mapping. However, building indices’ applications on PlanetScope data for use in separating blocks and to identify the difference between altered/unaltered constructed areas are not available, primarily due to the absence of the Short-Wave Infrared (SWIR) band. In this research, three types of remote sensing data series are employed, namely, PlanetScope images, Sentinel-2A data, and Google Earth Pro data, to separate the blocks and to capture the differences between damaged and undamaged blocks in the Khan Younis town of the Gaza Strip, utilizing a novel building metric purposely developed to be used with PlanetScope data. Additionally, three change detection methodologies are utilized to assess the efficacy of the new metric, namely, image differencing, Principal Component Analysis (PCA), and metrics, including the one proposed in this paper. The proposed metric exhibited high performance with the PlanetScope data compared to Sentinel-2A data. The results of the three methodologies indicate a strong correlation, with an R-value of 0.793,263 and a P-value of 0.0002456. The average sizes of the affected areas, derived from PlanetScope data from October 2023 to April 2024, are 0, 0.05, 2.4, 2.9, 5.2, 12.8, and 13.1 km2. The Sentinel data from October to January shows that the average sizes of the devastated regions are 0, 0.9, 2.1, and 7 km2, while the rest of the data series was concealed and unavailable by Google Earth Engine (GEE). The accuracy assessment test conducted with the Google Earth Pro scenes to evaluate the strength of the metric showed a considerably strong percentage of compatibility with PlanetScope data. The new proposed building metric yields results that are very close to the other techniques adopted, with slight variations but clearer visual outputs.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1765013</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1765013</link>
        <title><![CDATA[Benchmarking machine learning classifiers for urban mapping in arid environments: a google earth engine analysis of Riyadh’s expansion (1990–2025)]]></title>
        <pubdate>2026-03-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Amal Abdelsattar</author>
        <description><![CDATA[Monitoring urban expansion in arid regions is complicated by the spectral similarity between impervious surfaces and bare soil. Although machine learning classifiers on platforms like Google Earth Engine (GEE) offer effective solutions, their performance in these environments has not been systematically benchmarked. This study addresses this gap by comparing five supervised ML algorithms—Random Forest (RF), Support Vector Machine (SVM), Gradient Boosted Trees (GBT), Classification and Regression Tree (CART), and k-Nearest Neighbor (KNN)—for binary urban and non-urban mapping. We applied this analysis to Riyadh, Saudi Arabia, using a 35-year Landsat time series from 1990 to 2025. Annual, radiometrically consistent median composites were generated in GEE, with the 2025 composite based on imagery from January to September 2025. A custom ten-band feature stack, including indices such as the Bare Soil Index (BSI), was used for classification. The Random Forest model (RF-100) achieved the highest accuracy (Overall Accuracy = 0.977, Kappa = 0.954) and was selected for final land-use and land-cover mapping. Validation with 600 independent samples per epoch and comparison to the ESA WorldCover 2020 dataset confirmed the robustness of the results. The analysis found a 293% increase in Riyadh’s built-up area, from 416 km2 in 1990 to 1,219 km2 in 2025, with a notable slowdown in growth after 2010. Variable importance analysis showed that the Bare Soil Index (BSI) and the Normalized Difference Built-up Index (NDBI) were the most significant features for class separation, offering key methodological insights for arid urban remote sensing. This work provides a transferable methodological framework for classifier and feature selection in arid environments and a high-accuracy spatiotemporal dataset establishing a baseline for assessing sustainable urban development under Saudi Vision 2030.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1759371</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1759371</link>
        <title><![CDATA[Estimating flavonoids using radiative transfer model inversion on imaging spectroscopy data]]></title>
        <pubdate>2026-03-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bongokuhle S’phesihle Sibiya</author><author>Moses Azong Cho</author><author>Onisimo Mutanga</author><author>John Odindi</author><author>Cecilia Masemola</author><author>Johannes Van Staden</author>
        <description><![CDATA[Assessing flavonoid content is crucial for understanding plant responses to biotic and abiotic stresses. However, pigments such as chlorophylls, carotenoids, and anthocyanins, absorb light between 410 and 430 nm, thus makes it difficult to quantify flavonoid content in this region. To improve the sensitivity of flavonoids have been developed, but they are largely empirical and lack transferability across environments. Therefore, this study compares the effectiveness of the PROSAIL-D radiative transfer model (RTM) and an empirical approach in mapping flavonoids using AVIRIS data in the Fynbos biome through indices. Results show that PROSAIL-D consistently outperformed the empirical method, with the spectral index FI417,693 achieving an R2 of 0.63 and RMSE of 0.24 (mg catechin (CAE)/g). The empirical approach with the same index yielded an R2 of 0.46 and RMSE of 5.11 mg CAE/g. Overall, the RTM model demonstrated superior accuracy, with RMSE values ranging from 0.24 to 1.70, compared to 5.11 to 6.28 for empirical methods. These findings highlight the potential of integrating AVIRIS data with PROSAIL for non-destructive, remote sensing-based assessment of flavonoids, offering a scalable approach for monitoring plant health and stress responses at the canopy level.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1786848</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1786848</link>
        <title><![CDATA[Study of the changes in the Curonian Lagoon shoreline strip (1995–2024), Lithuania]]></title>
        <pubdate>2026-03-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sérgio Lousada</author><author>Dainora Jankauskienė</author><author>Giedrė Ivavičiūtė</author><author>Lina Kuklienė</author><author>Indrius Kuklys</author><author>Birutė Ruzgienė</author><author>Vivita Pukite</author>
        <description><![CDATA[Coastal lagoons are among the most vulnerable aquatic environments to climate change and human pressures; therefore, studying the Curonian Lagoon is crucial to support evidence-based management and improve understanding of shoreline responses to hydro-meteorological forcing and local land-use pressures. This research investigates the evolution of the Curonian Lagoon shoreline near Preila (Neringa municipality) from 1995 to 2024 using a multi-temporal orthophotographic series (ORT10LT) complemented by a very high-resolution UAV orthomosaic produced in 2024. Shoreline position was consistently delineated and compared across eight observation periods to quantify section-based displacement and hotspot area changes. The analysis reveals a spatially organized pattern, with a persistent accumulation-prone stretch in the mid-profile (250–350 m) and a persistent erosion hotspot toward the latter shoreline (400–566 m). The maximum shoreline retreat reached 32.80 m (425 m section, 2024 relative to 1995–1999), while the maximum shoreline advance reached 18.22 m (275 m section, 2021–2023). Area-based hotspot metrics indicate erosion losses up to 654 m2 (2018–2020) and accumulation gains up to 395 m2 (2009–2010) relative to the baseline. These results provide a reproducible, decision-oriented shoreline-change characterization that supports targeted monitoring and management of this culturally and environmentally significant lagoon margin.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1710758</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1710758</link>
        <title><![CDATA[Case 2 Regional Coast Colour: a neural network-based framework for atmospheric correction and in-water retrievals across multiple ocean colour satellite sensors]]></title>
        <pubdate>2026-03-18T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Dagmar Müller</author><author>Martin Hieronymi</author><author>Ana B. Ruescas</author><author>Marco Peters</author><author>Rüdiger Röttgers</author><author>Marcel König</author><author>Carole Lebreton</author><author>Kerstin Stelzer</author><author>Carsten Brockmann</author><author>Roland Doerffer</author>
        <description><![CDATA[Since the 1990s, Doerffer and Schiller have been developing physics-based neural network algorithms for analyzing ocean colour in satellite imagery of optically complex coastal waters. At its core, the approach uses neural networks to solve the inversions in various aspects of solar radiative transfer in both the atmosphere and water, including atmospheric correction, towards the estimation of inherent optical properties (IOPs) of the water constituents. Empirical bio-optical models are then applied to derive constituent concentrations from these IOPs. Over the years, this algorithm has evolved significantly and is now widely recognized as Case-2 Regional CoastColour (C2RCC), a trusted tool within the ocean colour research community. Originally designed for the MERIS sensor aboard ENVISAT, C2RCC is now the operational ground segment processor for generating Case-2 (complex) water products from Sentinel-3 OLCI data and from Sentinel-2 MSI data in the Copernicus Marine High Resolution Ocean Colour Service. Adaptations of the algorithm have also been developed for other satellite missions, including SeaWiFS, MODIS, VIIRS, Landsat OLI, and Sentinel-2 MSI. The C2RCC processor is freely accessible through the Sentinel Application Platform (SNAP). This article provides an overview of the background and evolution of the C2RCC algorithm, presenting validation results at coastal sites and in land waters alongside user performance evaluations analyzing the influence of system vicarious calibration gains. It highlights cases where the algorithm delivers reliable results as well as its limitations and areas for future improvement. In its current iteration for Sentinel-3 OLCI, C2RCC performs effectively, particularly in moderately absorbing or scattering Case-2 waters.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1711426</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1711426</link>
        <title><![CDATA[Spectral assessment of nutrient limitation in the savanna landscape: selection of spectral indices towards Sentinel-2 upscaling]]></title>
        <pubdate>2026-03-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nasiphi Ngcoliso</author><author>Abel Ramoelo</author><author>Philemon Tsele</author><author>Mcebisi Qabaqaba</author><author>Siyamthanda Gxokwe</author>
        <description><![CDATA[Nutrient limitations can significantly impact the ecosystem services provided by the savanna biome, potentially leading to degradation and reduced grazing capacity if not detected in time. A key indicator of growth-limiting nutrients is the Nitrogen to Phosphorus (N:P) ratio. However, grass foliar phosphorus content has rarely been studied in African savannas, especially using remote sensing approaches. As a result, there is limited information on the spatial distribution of nutrient limitations in these ecosystems. This study aimed to develop a Sentinel-2-based machine learning regression model to predict and map the distribution of the N:P ratio in the northern region of Kruger National Park (KNP), South Africa, which is dominated by the savanna rangeland biome. Fieldwork was conducted between 15 March and 30 April 2008 to collect grass samples and spectral data using an Analytical Spectral Device (ASD). The hyperspectral field data were then resampled to match the multispectral configuration of Sentinel-2 imagery. A Random Forest Regression (RFR) technique was applied to the simulated Sentinel-2 datasets to develop predictive models of the N:P ratio. Model accuracy was evaluated using the Root Mean Square Error (RMSE) Relative Root Mean Square Error (RRMSE), Percent Bias (PBIAS), and the coefficient of determination (R2). The results showed that vegetation indices (VIs), particularly the Normalized Difference Red Edge (NDRE) derived from Sentinel-2 bands B8 and B5, was optimal for estimating N:P ratio. This index explained over 80% of the N:P variability, with the lowest PBIAS of 0.02%. The best-performing model was used to map nutrient limitations across the study area using Sentinel-2 imagery. The spatial analysis indicated consistent nitrogen limitation and co-limitation across the investigated regions, with no evidence of phosphorus limitation. The high-accuracy models demonstrate the effectiveness of Sentinel-2 imagery for estimating nutrient limitations in heterogeneous savanna landscapes. This study offers a cost-effective, scalable tool for decision-makers involved in the management, sustainability, and restoration of the savanna biome. Future research should consider incorporating textural and environmental variables to enhance model performance and understanding of nutrient dynamics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1659305</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1659305</link>
        <title><![CDATA[Mapping small-sized logging disturbances in tropical forests using Sentinel-1 time series and an extensive ground truth dataset]]></title>
        <pubdate>2026-03-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Audrey Mercier</author><author>Julie Betbeder</author><author>Frédéric Mortier</author><author>Nicolas Barbier</author><author>Pierre Ploton</author><author>Guillaume Cornu</author><author>Pierre Couteron</author>
        <description><![CDATA[Deforestation and forest degradation are the main threats to biodiversity and carbon stocks in tropical forests. Advances in optical and SAR satellite sensors have enabled the development of real-time monitoring of deforestation on a global scale. SAR is particularly appealing in tropical areas due to its insensitivity to cloud cover. However, the automatic detection of small disturbed areas (such as individual tree felling gaps) remains a major challenge. Thanks to a unique dataset consisting of 23,759 locations of individual tree felling gaps and multi-date drone lidar acquisitions, we evaluated the potential of Sentinel-1 dense time series for monitoring small-sized forest disturbances substantially smaller than 0.1 ha on both FSC-certified and artisanal logging sites in the Congo Basin. We designed a new method for forest monitoring using the fused-lasso technique optimized to detect abrupt changes of at least 0.02 ha in Sentinel-1 time series using the fused-lasso technique (Fused-Lasso Change Detection, FLCD). We assessed our new method along with the Cumulative Sum (CuSum) that also proved promising for detecting small impacts, referring for the first time to precise disturbance dates over large areas. Both approaches reached similar rates of confirmed felling gaps that were similarly increasing with gap size, and similar rates of unconfirmed detected gaps. The FLCD method estimates the dates of tree felling more accurately in FSC-certified areas (−2 days difference for FLCD and −19 for CuSum on average). The effective resolution of the S-1 images limits detection for the smallest gaps, yet the approach can help detect and monitor degradation fronts. Fused lasso regression is relevant for modeling the temporal trajectories of the radar signal, which will allow taking advantage of both the increasing availability of UAV-borne data and the lengthening of the S-1 image series.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1751006</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1751006</link>
        <title><![CDATA[A fully satellite-driven workflow for hydrodynamic modeling in data-scarce coastal systems: integrating ICESat-2, Sentinel-2, SWOT and reanalysis models]]></title>
        <pubdate>2026-03-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ali Reza Payandeh</author><author>Marc Simard</author><author>Daniel Jensen</author><author>Anthony Daniel Campbell</author><author>Heidi van Deventer</author><author>Alexandra Christensen</author>
        <description><![CDATA[Hydrodynamic models in coastal and estuarine systems are typically constrained by sparse bathymetry, boundary, and validation data, especially in regions where field campaigns are costly or impractical. Here we develop and test a fully satellite-driven framework for hydrodynamic modeling in South Africa’s Langebaan Lagoon without using any local in situ measurements. Bathymetry is derived by training multispectral Sentinel-2 reflectance against ICESat-2 ATL24 photon-derived depths using an XGBoost model optimized with Bayesian search. The final satellite derived bathymetry reproduces independent ATL24 points with RMSE = 0.45 m and R2 = 0.97. This bathymetry was used in a depth-averaged Delft3D Flexible Mesh model driven at the open boundary by TPXO tidal harmonics and by ERA5 winds. We validate modeled water surface elevation against 16 SWOT low-rate (250 m, unsmoothed) passes in 2023. SWOT–model comparisons yield an overall RMSE of 0.11 m and R2 = 0.61, with typical point differences <0.10 m (∼5% of the 2 m tidal range), and showed consistent spatial gradients in water level from the offshore boundary, through Saldanha Bay, and into the lagoon. At the offshore boundary, TPXO and SWOT sea surface heights agree closely (R2 = 0.86). A ∼26 min phase lag, determined using a lag-correlation analysis, reduces the TPXO–SWOT RMSE from 0.18 m to 0.11 m, indicating that phase differences explain some of the mismatch, with remaining differences likely linked to non-tidal signals. Our results demonstrate that combining passive optical, photon-counting LiDAR, radar interferometry, and global tidal/atmospheric models enables robust, transferrable hydrodynamic modeling in data-scarce coastal systems, offering a cost-effective pathway for monitoring.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1804992</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1804992</link>
        <title><![CDATA[Editorial: Multibeam echosounder backscatter: advances and applications]]></title>
        <pubdate>2026-03-05T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Craig J. Brown</author><author>Marc Roche</author><author>Vanessa Lucieer</author><author>Alexandre C. G. Schimel</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1774149</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1774149</link>
        <title><![CDATA[Low-cost mobile laser scanning for urban tree assessment: accuracy evaluation and application potential]]></title>
        <pubdate>2026-03-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jozef Výbošťok</author><author>Juliána Chudá</author><author>Daniel Tomčík</author><author>Michal Skladan</author><author>Arunima Singh</author><author>Roman Kadlečík</author><author>František Chudý</author><author>Daniel Kükenbrink</author><author>Martin Mokroš</author><author>Janusz Bedkowski</author>
        <description><![CDATA[Recent advances in mobile laser scanning (MLS) have enabled rapid three-dimensional data acquisition for urban tree monitoring, providing an alternative to traditional terrestrial laser scanning (TLS) and photogrammetric approaches. However, the high cost of commercial handheld mobile laser scanning (HMLS) systems limits their routine use in urban green-space inventories. This study evaluates the performance of a low-cost wearable MLS prototype based on a Livox MID-360 sensor and compares it with two commercial HMLS systems (Stonex X120GO and Stonex X200GO) for urban tree assessment. The analysis was conducted in an urban park environment and included 80 individual trees. Tree detection rate (TDR), diameter at breast height (DBH), tree height (TH), crown projection, and point cloud quality were evaluated using commonly applied processing workflows (RayCloud with ITSMe, FSCT, and 3DFin). Using the best-performing workflow, the prototype achieved a DBH RMSE of 2.47 cm and a TH RMSE of 0.43 m, compared to 1.25–2.08 cm (DBH RMSE) and 0.31–0.40 m (TH RMSE) for the commercial systems. Mean cross-section quality metrics further supported data reliability, with Cross Section Quality Index (CSQI) values of 0.78 for the prototype and up to 0.83 for the high-end system, and corresponding Standard Deviation of Radial Distances (SDRD) values of 0.034 m and 0.018 m, respectively. Despite lower point density and increased noise, the low-cost wearable MLS prototype provided comparable TDR, DBH, and TH estimates. Differences in processing time were mainly driven by the selected workflow rather than by the scanning device. Overall, the results demonstrate that low-cost wearable MLS systems can deliver reliable urban tree metrics when combined with suitable processing methods, offering a cost-effective alternative for urban tree inventories and operational monitoring.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1801687</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1801687</link>
        <title><![CDATA[Editorial: Detection and characterization of unidentified underwater biological sounds, their spatiotemporal patterns, and possible sources]]></title>
        <pubdate>2026-03-03T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Lucia Di Iorio</author><author>Audrey Looby</author><author>Francis Juanes</author><author>Tzu-Hao Lin</author><author>Zhongchang Song</author><author>Jenni Stanley</author><author>Miles J. G. Parsons</author>
        <description><![CDATA[Graphical AbstractInfographic illustrating research themes in unidentified underwater biological sounds, featuring animal silhouettes surrounded by arrows pointing to images representing identifying, finding, understanding, and using sounds, along with maps, graphs, and labeled research categories and references.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1810164</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1810164</link>
        <title><![CDATA[Editorial: Earth observations from the deep space: 10 years of the DSCOVR mission]]></title>
        <pubdate>2026-03-02T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>A. Lyapustin</author><author>A. Marshak</author><author>A. Szabo</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1782148</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1782148</link>
        <title><![CDATA[Crop type mapping in the pre-Sentinel era using variable-length Landsat time-series and self-supervised learning]]></title>
        <pubdate>2026-02-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jayan Wijesingha</author><author>Ilze Beila</author>
        <description><![CDATA[Crop type mapping is crucial for agricultural land cover monitoring and decision-making. State-of-the-art methods developed using recent Sentinel satellite data have already demonstrated their ability to accurately map crop types. However, crop type mapping for the pre-Sentinel era remains challenging due to the limited availability of higher spatial- and temporal-resolution data. This study addresses this knowledge gap by leveraging variable-length Landsat satellite time-series (L-SITS) data in combination with a self-supervised learning model, SITS-BERT, for crop type mapping. This case study, conducted in two German districts, demonstrates the potential of mapping two different crop type levels (CTL1 - 5 and CTL2 - 9 classes) in the pre-Sentinel era. The SITS-BERT model, pre-trained on unlabelled L-SITS data, was fine-tuned on single-year and 3-year datasets and evaluated using past and future years’ data, compared with the model’s training data. The SITS-BERT model achieved overall accuracies of 0.78–0.83 and 0.64–0.76 for CTL1 and CTL2, respectively, with fine-tuning on single-year data. The model fine-tuned with 3 years achieved higher accuracies (0.81–0.85 and 0.72–0.78). The results showed that the SITS-BERT model finetuned with single-year data outperforms the baseline random forest model trained on single-year fixed-length L-SITS data. The study highlighted that, with this approach, limited number of available SITS observations can still be useful. The findings of this study demonstrated the potential of the SITS-BERT model with L-SITS data for crop-type mapping in the pre-Sentinel era, contributing to a more comprehensive understanding of agricultural land cover dynamics and to the evaluation of agricultural policy impacts.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1710331</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1710331</link>
        <title><![CDATA[Spatiotemporal assessment and driving factors analysis of eco-environmental quality in Xi’an’s main urban area using the remote sensing ecological index]]></title>
        <pubdate>2026-02-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qing Tang</author><author>Youqing Wu</author><author>Zhigang Lu</author><author>Yu Li</author><author>Rui Wang</author><author>Fang Li</author><author>Huineng Yan</author>
        <description><![CDATA[IntroductionRapid urbanization and industrialization in Xi’an have precipitated a sharp conflict between spatial expansion and environmental conservation, necessitating a rigorous spatiotemporal assessment of regional ecological quality.MethodsThis study evaluates the eco-environmental quality of Xi’an’s main urban area from 2021 to 2024 by synergizing the Remote Sensing Ecological Index (RSEI) with advanced spatial statistical models, including Geodetector and spatial autocorrelation algorithms. Specifically, the Coefficient of Variation (CV) is introduced to quantify the temporal stability and volatility intensity of ecological quality under top-down policy interventions.ResultsEmpirical results indicate that: (1) The region exhibited a generally stable trajectory, with approximately 65% of the study area maintaining a steady state. Notably, the proportion of areas showing slight improvement consistently surpassed those exhibiting degradation, signaling a cessation of historical decline; (2) Spatiotemporal stability analysis identifies a distinct “Matrix-Spot” configuration via the CV metric, characterized by a dominant low-volatility background punctuated by discrete, spatially confined high-volatility anomalies. This structure confirms that anthropogenic perturbations remained localized, preventing the propagation of systemic instability; (3) Pronounced spatial heterogeneity was corroborated by Global Moran’s I indices consistently exceeding 0.79. This distribution is predominantly characterized by high-high (H-H) clusters in ecological barriers and low-low (L-L) agglomerations in central districts, with minimal transitional patterns; (4) Factor detection identified Land Use/Land Cover change as the primary determinant of ecological differentiation. Furthermore, interaction analysis elucidated a significant “Enhancement Effect” between Land Use/Land Cover and temperature, highlighting the amplification of climatic sensitivity by urbanization.DiscussionThese findings provide empirical evidence for the “immediate efficacy” of strategic interventions such as the “14th Five-Year Plan” and Ecological Red Lines. The observed “policy-driven resilience“ demonstrates that state-led governance has effectively buffered the negative externalities of rapid urban development within a constrained temporal window.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1730222</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1730222</link>
        <title><![CDATA[A multi-feature fusion based remote sensing inversion method for farmland shelterbelts]]></title>
        <pubdate>2026-02-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qi Zhang</author><author>Yuncheng Zhou</author><author>Hongge Zhao</author><author>Wenhao Wu</author><author>Yuekun Huang</author>
        <description><![CDATA[Precise segmentation of farmland shelterbelts in high-resolution remote sensing imagery represents a crucial yet challenging task for establishing a quantifiable farmland quality evaluation system. The core difficulties arise from two principal issues: (1) effectively distinguishing cultivated land from shelterbelts with similar textural characteristics while suppressing interference from complex backgrounds such as roads and ditches; and (2) accurately segmenting narrow, elongated, and discontinuously distributed single-row shelterbelts with blurred boundaries. Conventional semantic segmentation methods, primarily designed for large-scale objects in natural scenes, generally underperform when confronted with the distinctive characteristics of remote sensing targets. To overcome these challenges, we propose a novel remote sensing inversion framework based on multi-feature fusion. For the first challenge, we designed a Multi-Feature Fusion Block (MFFB) that utilizes a Spatial Gated Fusion Mechanism (SGFM) to adaptively integrate global contextual features captured by Mamba-like linear attention, local details extracted through convolutional operators, and frequency-domain information obtained via Fast Fourier Transform (FFT), thereby significantly enhancing the model’s capacity to represent and discriminate complex features. To address the second challenge, we introduced a super-resolution preprocessing strategy along with a Multi-Scale Contextual feature Extraction (MSCE) module within an encoder-decoder architecture. The former effectively increases the pixel width of narrow shelterbelts through enhanced image detail reconstruction, while the latter ensures segmentation continuity for elongated features by integrating multi-scale contextual information. Experimental results on our self-constructed farmland shelterbelt dataset demonstrate that our method achieves segmentation accuracies of 96.42% for cultivated land and 82.83% for shelterbelts, outperforming both mainstream general-purpose semantic segmentation models and specialized remote sensing methods, thus validating the effectiveness of the proposed framework for precise farmland shelterbelt extraction.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1691652</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1691652</link>
        <title><![CDATA[NISTAR measurements confirm basic aspects of EPIC-derived global-scale dayurnal variability in Earth’s reflected radiation]]></title>
        <pubdate>2026-02-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Andrew A. Lacis</author><author>Gary L. Russell</author><author>Barbara E. Carlson</author><author>Wenying Su</author><author>Yinan Yu</author>
        <description><![CDATA[A unique model/data comparison capability is made possible by the unique viewing geometry from NASA’s DSCOVR Mission Lissajous orbital location around the Lagrangian L1 point. The key point of this unique location is the large orbital inclination relative to the perpendicular of the Sun-Earth line-of-sight. This circumstance enables periodic Sun-Earth-Satellite phase angle shifts ranging from 2-degrees to 12-degrees with repeating ∼3-month periodicity. At such extreme phase angles, backscattered radiation for spherical cloud-top particles is strongly phase angle dependent, but not for irregular-shaped ice particles. Also key, are the near-hourly high-resolution EPIC images that have been converted to radiative solar fluxes by extensive use of ancillary satellite data and CERES-based ADMs. These EPIC-derived SW fluxes, integrated over the Earth’s sunlit hemisphere, constitute the EPIC Composite dataset of 1-day resolution global-scale reflected SW fluxes, which have been shown to agree well with CERES reflected SW fluxes. Using the EPIC data as a template, the DSCOVR satellite ephemeris enables aggregation of climate GCM run-time output over the sunlit hemisphere with the same viewing geometry as EPIC. Generating the GCM-equivalent global-scale SW flux dataset, together with the EPIC data, forms the basis for a new paradigm in model/data comparisons. The key advantages of this DSCOVR-style approach are the (1) identical space-time sampling with identical viewing geometry and complex, but identical averaging over the diurnal cycle between observations and climate GCM output data, (2) preservation of short-period variability at 1-day resolution due to the Earth’s rotation, and (3) self-consistent weather noise suppression by identical averaging over the sunlit hemisphere. Early examples of the EPIC data variability drew concerns from colleagues worried that the variability in the EPIC data might be modeling noise. There is no other way to resolve this concern but to find another data source that shows the same degree of variability. Definitive comparisons to NISTAR measurements presented in this study unequivocally confirm that the global EPIC-derived variability is indeed real, and not a data artifact.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1572674</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1572674</link>
        <title><![CDATA[Estimating biomass volumes on aquaculture dropper lines using multibeam water column data]]></title>
        <pubdate>2026-02-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Thomas Vandorpe</author><author>Samira Lashkari</author><author>Kobus Langedock</author><author>Alexia Semeraro</author><author>Gert Van Hoey</author><author>Tomas Sterckx</author><author>Ine Moulaert</author>
        <description><![CDATA[Assessing the biomass on longline setups based on acoustic data hold significant potential for improving the efficiency and accuracy of monitoring and management of aquaculture setups. Traditional assessment methods, such as manual sampling and visual inspections, are not only labor-intensive and time-consuming but are also subject to variability, often leading to under- or overestimations. Acoustic data, particularly multibeam water column (MBWC) data, provide a non-invasive alternative that can significantly enhance biomass estimation. Within this paper, we demonstrate that 2D and 3D visualizations based on MBWC data can effectively display aquaculture longline structures. To facilitate processing of MBWC data, we have developed scripts that allow to filter and cluster the data into individual dropper lines, enabling an estimation of the biomass volume on each dropper line individually. Our approach offers a scalable and cost-effective solution for aquaculture monitoring, reducing the reliance on destructive sampling and improving decision-making capabilities. Future improvements, such as enhanced data density, refined filtering techniques and automated acquisition workflows, will further increase the accuracy and usability of this method. Ultimately, this research provides aquaculture managers with an innovative tool for rapid volume assessments, contributing to the optimization of sustainable aquaculture practices.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1753296</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1753296</link>
        <title><![CDATA[SuperDove radiometric data assessment in coastal and inland waters]]></title>
        <pubdate>2026-02-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ilaria Cazzaniga</author><author>Ana I. Dogliotti</author><author>Susanne Kratzer</author><author>Frédéric Mélin</author>
        <description><![CDATA[The use of high-resolution data in aquatic applications increased significantly in the last decade with the launch of decametre-scale optical sensors. More recently, commercial very-high resolution (VHR) sensors, offering finer spatial and temporal resolutions, have shown the potential of complementing data from high-resolution missions. Planet SuperDove (SD), with a band-setting similar to the Copernicus Sentinel-2 MultiSpectral Instrument (S2-MSI), a 3-m spatial resolution and quasi-daily revisiting time, show the potential for widening water monitoring applications to smaller water basins, and finer-scale phenomena. However, the uncertainties in SD products need to be quantified, to assess their fitness-for-purpose for these applications. This work aims to provide uncertainty estimates for SD-derived aquatic remote sensing reflectance (RRS) in different water types, benefitting from the radiometric measurements of the AERONET-OC network. RRS was derived from both Surface Reflectance (SR) products, distributed by Planet, or from data processed with ACOLITE. The comparability between SD and S2-MSI products was also assessed comparing RRS and Rayleigh-corrected reflectance (RRC) from S2-MSI and SD. The results indicate generally low performance across all bands for both SD RRS products, except in the most turbid waters, and highlight the lack of a publicly available robust atmospheric correction processor for SD data for most optical water types. The comparison to S2-MSI shows promising results only when comparing RRC values, but differences still suggest issues associated with calibration and radiometry of the SD sensors. The results also highlight the need for a harmonization strategy to ensure consistent integration of these datasets within multi-source monitoring systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1731775</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1731775</link>
        <title><![CDATA[Research on automatic mosaicking and synthesis processing technology for multi-source remote sensing images]]></title>
        <pubdate>2026-01-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jing Cai</author><author>Feng Ye</author><author>Jingyu Sun</author><author>Hangan Wei</author><author>Zichuang Li</author><author>Pengao Li</author>
        <description><![CDATA[Multi - source remote sensing image automatic mosaic and synthesis processing technology is the key to improving the utilization efficiency of remote sensing data. With the rapid develop-ment of diversified imaging platforms such as satellites, unmanned aerial vehicles and ground sensors, the heterogeneity of image data sources has become increasingly prominent, which makes the difficulty of mosaic and synthesis increase. This paper focuses on the auto-matic mosaic and synthesis processing technology of multi - source remote sensing images. Firstly, an adaptive block - weighted Wallis parallel color equalization algorithm fusing specific scene constraints is designed. It dynamically adjusts the block size of color equalization pro-cessing through the coefficient of variation, and optimizes the calculation of local color param-eters combined with bilinear interpolation, which avoids the color distortion of traditional glob-al algorithms and significantly improves the efficiency of radiometric correction. Moreover, an adaptive mosaic algorithm is introduced, and a space - constrained Markov Random Field - Graph Cut seamline generation model is used to generate seamless synthetic images, which supports large - area coverage. This technology can be extended to environmental monitoring, disaster assessment and urban planning. It can automatically process massive multi - source da-ta and achieve high - precision synthesis.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1756531</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1756531</link>
        <title><![CDATA[Moon observations from the Lagrange point L1 by the EPIC/DSCOVR spectrometer]]></title>
        <pubdate>2026-01-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nick Gorkavyi</author><author>Nickolay Krotkov</author><author>Alexander Marshak</author>
        <description><![CDATA[DSCOVR/EPIC, located at the Sun-Earth Lagrange point (L1) around 1.5 million kilometers away from Earth, can capture images of the near and far sides of the Moon in the multiple UV-VIS-NIR wavelengths. These observations were previously used only for calibration purposes. In this study, for the first time, images of the Moon taken by EPIC are treated as scientific data with a unique set of characteristics: 1. They were acquired under full-disk illumination of the Moon. 2. They were taken in 10 narrow wavelength bands—from the ultraviolet (317 nm) to the near-infrared (780 nm). 3. At each wavelength the entire lunar disk is imaged simultaneously. 4. The images can be oversampled to reduce noise levels and increase spatial resolution. These features of the lunar images allow the creation of high-quality maps of the far and near sides of the Moon in 10 quasi-monochromatic wavelength channels. These maps will serve as a reference for comparison with data from other satellites in lunar orbit. The study of multispectral images of the Moon presented in this paper reveals a significant mineralogical difference between the farside and the nearside of the Moon. We interpret the studied spectral features of the Moon as indicating an increased concentration of ilmenite (a titanium-iron oxide mineral, FeTiO3) on the nearside of the Moon, particularly in the Sea of Tranquility.]]></description>
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