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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>2026-09-05T23:42:52.502+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1838735</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1838735</link>
        <title><![CDATA[Comparative assessment of YOLO-OBB models for AI-enabled mussel raft detection in VHR remote sensing imagery: insights from the Ría de Arousa, Spain]]></title>
        <pubdate>2026-09-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Farshid Dabestani</author><author>Armin Moghimi</author><author>Amin Azhir</author><author>Mario Welzel</author><author>Hamid Ebadi</author><author>Mahmod Reza Sahebi</author>
        <description><![CDATA[The rapid expansion of mussel raft aquaculture has increased the demand for accurate and scalable monitoring systems to support sustainable coastal management. Deep learning (DL) object detectors, particularly the YOLO family, enable automated extraction of aquaculture infrastructures from Very High-Resolution (VHR) satellite imagery. However, conventional detectors rely on horizontal bounding boxes that often include excessive background when objects are elongated or rotated, reducing localization accuracy. Oriented bounding box (OBB) detectors address this limitation by modeling object orientation and geometry. In this study, an open-source Python-based graphical user interface (GUI) was developed for automated mussel raft detection, and a comprehensive comparison of YOLOv8-OBB and YOLO11-OBB architectures was conducted using VHR imagery from Spain’s Ría de Arousa. A dedicated mussel raft dataset was compiled and annotated, and both model families were evaluated across five architectural scales (n, s, m, l, and x). Experimental results showed that the YOLO11 family generally outperformed YOLOv8 in terms of detection accuracy, localization quality, and performance under challenging maritime conditions. Among the evaluated models, YOLO11s achieved the highest F1-score (0.866) and mAP50–90 (85.21%), while YOLO11n achieved the highest Mean IoU (0.872) and mAP50 (96.87%). The results further demonstrated that compact architectures can provide a favorable balance between accuracy and computational efficiency, as larger models did not consistently yield superior performance. Large-scale experiments on a 60,309 × 61,569-pixel (50 cm resolution) VHR scene demonstrated the operational scalability of the proposed framework through a patch-based processing strategy. The comparative evaluation conducted in this study provides valuable insights into recent YOLO-OBB architectures and highlights their potential for scalable AI-based aquaculture monitoring.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1914197</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1914197</link>
        <title><![CDATA[Assessing the spatiotemporal dynamics of land-use/land-cover change and human-wildlife conflict around Hluhluwe-iMfolozi park, South Africa]]></title>
        <pubdate>2026-09-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ongeziwe Njomi</author><author>Oscar Z. Gwala</author><author>Mohammed B. Altoom</author><author>Abel Ramoelo</author><author>Remi Chandran</author><author>Ram Avtar</author><author>Michael Gebreslasie</author>
        <description><![CDATA[Human‐wildlife conflict (HWC) is increasingly associated with rapid land‐use and land‐cover (LULC) changes and broader environmental shifts. While previous studies have focused on causal factors and coexistence strategies, the spatiotemporal dynamics of human-wildlife interactions under shifting LULC remain poorly quantified. This study examines the spatiotemporal patterns of LULC and their association with HWC around the Hluhluwe‐iMfolozi Park in South Africa. The detailed landscape dynamics were captured using Sentinel‐2 imagery at 5‐year intervals (2015, 2020, and 2025) with machine‐ and deep‐learning classification algorithms. Among the evaluated algorithms, the Two‐dimensional Convolutional Neural Network outperformed the others, achieving an average overall accuracy of 99.92% and a kappa coefficient of 0.9991. The findings show substantial LULC changes during the study, including a notable decrease in cultivated land, changes in vegetation structure, and an increase in built-up areas. Shrubland grew significantly by 2025, suggesting continuous landscape change, while woodland increased between 2015 and 2020. Anthropogenic activities and vegetation gains dominated earlier transitions, while natural vegetation transitions drove later changes. Outside protected regions, these trends were more pronounced, indicating greater anthropogenic pressure. Integration of LULC transitions with HWC data revealed that conflict events were increasingly linked to grassland, woodland, and transitional landscapes and were concentrated along protected area boundaries. Hotspots were closely associated with regions undergoing active land-cover change, and the frequency and spatial extent of HWC increased over time. These results demonstrate how landscape dynamics spatially overlap with human‐wildlife interactions and emphasize the need for sophisticated geospatial technologies to pinpoint conflict hotspots and guide targeted conservation and mitigation efforts.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1915380</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1915380</link>
        <title><![CDATA[Multi-temporal land use/land cover dynamics (2005–2025) and machine learning algorithm comparison in and around kruger national park]]></title>
        <pubdate>2026-08-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Oscar Z. Gwala</author><author>Mohammed B. Altoom</author><author>Abel Rameolo</author><author>Michael Gebreslasie</author>
        <description><![CDATA[Land Use/Land Cover (LULC) change detection is fundamental for understanding landscape dynamics and supporting sustainable environmental management, particularly in ecologically sensitive and transboundary conservation areas such as Kruger National Park, South Africa. Rapid urban expansion, agricultural intensification, and environmental change have accelerated landscape transformation, highlighting the need for accurate and reliable LULC monitoring. This study aimed to evaluate long-term LULC dynamics in and around Kruger National Park during 2005, 2015, and 2025 using multi-temporal Landsat imagery and four machine learning classifiers: Random Forest (RF), Support Vector Machine (SVM), Gradient Tree Boosting (GTB), and K-Nearest Neighbors (KNN). In addition, RF-based transition matrices were employed to quantify long-term land cover transformations. The results showed that the Random Forest classifier consistently outperformed the other algorithms, achieving the highest Overall Accuracy (up to 92%) and Kappa coefficient (0.87) across all study years. Gradient Tree Boosting and K-Nearest Neighbors produced satisfactory results, whereas Support Vector Machine exhibited comparatively lower classification performance. Spatial analysis indicated that woodland/shrubland remained the dominant land cover class but experienced progressive fragmentation and conversion to grassland, agricultural land, and bare land. Built- up areas expanded substantially, particularly between 2005 and 2015, while agricultural land also increased, reflecting growing anthropogenic pressure. Water bodies exhibited a declining trend throughout the study period, suggesting increasing hydrological and environmental stress. These findings demonstrate that ensemble-based machine learning methods, particularly Random Forest, provide a robust and reliable approach for monitoring complex and heterogeneous savanna landscapes. The observed LULC changes highlight the increasing influence of human activities on ecosystem structure and emphasize the importance of continuous geospatial monitoring to support biodiversity conservation, sustainable land management, and evidence-based environmental planning. Overall, this study provides a reliable framework for long-term LULC mapping and change detection that can be applied to protected areas and other ecologically sensitive landscapes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1863721</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1863721</link>
        <title><![CDATA[Assessing temporal sampling uncertainty in hydrological modelling using multi-sensor observations from GPM and TROPICS]]></title>
        <pubdate>2026-08-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ajay Sharma</author><author>Dibyandu Roy</author><author>Indu J</author>
        <description><![CDATA[Satellite-based precipitation products are widely used for hydrological modelling; however, temporal sampling uncertainty caused by infrequent satellite overpasses can significantly affect runoff estimation, particularly in mountainous and flood-prone watersheds. This study evaluates the influence of temporal sampling uncertainty in precipitation on runoff simulation using multi-sensor combination data from two different satellite constellations (Global Precipitation Measuring Mission-GPM and Time Resolved Observations of Precipitation Structure and Storm Intensity with a Constellation of Smallsats - TROPICS) constellations over the Ranikhola watershed in the eastern Himalayas, India. Five GPM sensors (GMI, SSMIS, MHS, AMSR2, and ATMS) and three TROPICS CubeSats (T3, T5, and T6) were analysed individually and in different combinations. Sensor overpass times were collocated with IMERG precipitation data, and the derived rainfall was used to simulate runoff in the HYSIM (lumped model) hydrological model during 2016–2020. Results show that increasing the number of sensors improved sampling frequency, reduced revisit gaps, and enhanced runoff simulation accuracy. The combined TROPICS configuration (T3–T5–T6) achieved the best performance, with NSE = 0.6983, R2 = 0.9568, RMSE = 7.75 m3 s−1, and MAE = 5.62 m s−1, whereas the GMI–SSMIS combination showed poor performance with NSE = −1.7385 and RMSE = 23.36 m3 s−1. The TROPICS constellation provided more than 12 overpasses per day, compared to ∼1–2.5 overpasses per day for individual GPM sensors. The findings demonstrate that high-frequency CubeSat constellations substantially reduce temporal sampling uncertainty and improve runoff estimation, highlighting their potential for flood forecasting and hydrological applications in data-scarce mountainous regions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1895172</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1895172</link>
        <title><![CDATA[From foundational models to interpretability: a comparative study of embeddings and feature engineering for smallholder irrigation detection in Sub-Saharan Africa]]></title>
        <pubdate>2026-08-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hasan Siddiqui</author><author>Christopher Small</author><author>Vijay Modi</author>
        <description><![CDATA[Smallholder irrigation is crucial to livelihoods and growth in rural agro-economies. Identifying where it occurs provides valuable insights for agriculture extension, water management, infrastructure investments, marketing and energy provision. Remote sensing methods can aid but have been stymied by the small plot sizes, especially of irrigated plots. Humid tropical climates are characterized by high temperatures and heavy seasonal or year-round rainfall. This adds an additional layer of challenge due to fewer cloud-free imagery from persistent cloud cover. This study focuses on these settings to compare the performance, interpretability and transferability of classification using embeddings versus a feature engineering approach. Geospatial foundation models such as AlphaEarth Foundations (AEF) have emerged to address the contrasting scarcity of high-quality labels in comparison to increasing volumes of earth observation data. The embeddings from these models have been shown to generate maps from sparse labels outperforming traditional machine learning approaches in certain cases. In this study, we utilize a nation-wide smallholder irrigation survey data from Uganda as a data-rich region and pilot surveys from Zambia as a data-sparse region. We present a reproducible cloud-native Earth observation workflow in Google Earth Engine that discusses the tradeoffs and performance of the feature engineering approach compared to foundational model embeddings. The results show that a model trained on AEF embeddings outperforms a traditional feature engineering approach on a rich label dataset. The performance is similar in a data-sparse region. However, the embedding models do not lend themselves to auditable methods for applicability to regions where no data is available.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1894138</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1894138</link>
        <title><![CDATA[Analysis of atmospheric air pollutants (CO, NO2, and SO2), through Sentinel-5P images in google earth engine, apurimac region, period 2020–2023]]></title>
        <pubdate>2026-08-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Walquer Huacani Calsin</author><author>Hernán Yari</author><author>Edgar Vilca</author><author>German Espinoza</author><author>Franklin Lozano</author>
        <description><![CDATA[Atmospheric pollution associated with carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) represents an important environmental and public health concern in Andean regions influenced by urban growth, transportation, agricultural burning, mining-related activities, and complex topography. This study analyzed the spatiotemporal variability of CO, NO2, and SO2 in the Apurimac region, Peru, during 2020–2023 using Sentinel-5P/TROPOMI satellite products processed in Google Earth Engine. The original column-density data, expressed in mol/m2, were converted into column-derived estimated concentrations using a simplified effective lower-atmospheric layer height of 2,000 m. These values were used only as relative indicators and were not interpreted as direct ground-level air quality measurements. The results showed heterogeneous spatial patterns across Apurimac. CO presented relatively higher values mainly in Abancay, Chincheros, and Andahuaylas, while NO2 showed higher relative values in Cotabambas, Grau, Abancay, and Chincheros. SO2 exhibited a more irregular and uncertain behavior, with localized positive values, negative retrievals, and high variability, indicating greater sensitivity to satellite retrieval noise. Therefore, SO2 was interpreted as an exploratory indicator rather than robust evidence of province-level pollution hotspots. Temporal analysis showed seasonal fluctuations and isolated peaks, especially during dry-season months. Trend detection was performed using the Mann-Kendall test and Sen’s slope estimator. CO showed a statistically significant decreasing trend, NO2 showed a weak but statistically significant increasing trend, and SO2 did not show a statistically significant trend. Overall, Sentinel-5P/TROPOMI and Google Earth Engine proved useful for identifying relative pollutant patterns in a data-sparse Andean region. Future studies should integrate satellite observations with in situ measurements, meteorological data, boundary-layer information, emission inventories, and independent datasets to improve validation, source attribution, and regional air quality assessment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1863355</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1863355</link>
        <title><![CDATA[GF-2/sentinel-2 fusion for active fault detection: a case study of the yilan-yitong fault (liaoning segment)]]></title>
        <pubdate>2026-08-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chang Cao</author><author>Xiaodong Jia</author><author>Chao Wang</author><author>Bo Wan</author><author>Chenyang Gao</author><author>Meng Yuan</author><author>Yiyue Wang</author>
        <description><![CDATA[The Yilan-Yitong Fault Zone, a core part of the northern Tanlu Fault Zone, is a key seismogenic structure in Northeast Asia. Its Liaoning segment suffers from unclear surface distribution, insufficient late Quaternary activity evidence and lack of refined research based on optimized multi-source remote sensing data, which restricts regional seismic hazard assessment and major engineering safety layout. This study aimed to conduct fine detection and activity evaluation of this fault segment using high-resolution satellite data. We took GF-2 panchromatic and Sentinel-2 multispectral images as core data, compared four fusion algorithms (PCA, GS, HSV, Brovey) via qualitative and quantitative evaluation, and combined UAV remote sensing, field surveys of 28 key observation points and large-scale trench (25 m × 8 m) verification in Kaiyuan for systematic analysis. Results showed the PCA algorithm had the optimal comprehensive performance, suitable for fine geological interpretation. The fault was divided into three subsegments, with the Tieling-Kaiyuan segment showing distinct linear geomorphology and strong late Quaternary activity. The trench provides evidence suggestive of Holocene activity, and flower structures plus multi-phase faulting events reflected multistage activity characteristics, with preliminary identification of paleoseismic relics. This study verifies the application potential of domestic high-resolution satellites, fills the gap in refined activity research of the fault’s Liaoning segment, and provides high-precision scientific support for Liaoning’s seismic hazard assessment and major engineering safety layout.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1867220</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1867220</link>
        <title><![CDATA[Assessment of land suitability for agriculture production using hybrid approaches: trapezoidal fuzzy number analytic hierarchy process, remote sensing and GIS]]></title>
        <pubdate>2026-08-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Prabu Babu</author><author>Saurabh Chandra Maury</author>
        <description><![CDATA[IntroductionLand suitability assessment is crucial for identifying areas with high potential for agricultural production, promoting sustainable land use, and addressing food security challenges. In the study area, approximately 70% of the population depends on agriculture for their livelihood. Therefore, assessing land suitability is essential for ensuring efficient land utilization and enhancing agricultural productivity. Consequently, Dharmapuri District, Tamil Nadu, India, was selected as the study area. To evaluate agriculture production, several key parameters were analyzed, including Land Use and Land Cover (LULC), Slope (S), Depth to water level (DL), Soil Texture (ST), Annual Rainfall (AR), pH, Nitrogen (N), Organic Carbon (OC),Temperature (T), Soil Moisture (SM), Distance to River (DR), Aspect (A), Elevation (E), Proximity to road (PRD), Normalized Difference Built up Index (NDBI) and Salinity (S). The selected parameters were chosen based on their proven relevance in previous land suitability assessment studies. The study aims to evaluate Land Suitability Zone (LSZ) using hybrid approaches Trapezoidal Fuzzy Numbers Analytic Hierarchy Process (TRFN‐AHP), remote sensing and Geographic Information System (GIS).MethodsThe parameter weights were calculated using the TRFN‐AHP method.Result and DiscussionBased on the results, the land suitability for agriculture production was classified as not suitable (8.86%), marginally suitable (20.70%), moderately suitable (24.88%), highly suitable (27.12%) and very highly suitable (18.44%). The results of land suitability index for agriculture production was validated using Receiver Operating Characteristic (ROC) curve. In ROC curve, the area under the curve value is 0.76 indicates good predictive accuracy in the study area. Overall, the results demonstrate that the hybrid approach combining TRFN‐AHP, remote sensing, and Geographic Information System provides an effective decision-support framework for assessing land suitability for agricultural production, facilitating sustainable land‐use planning and improved agricultural productivity.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1803946</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1803946</link>
        <title><![CDATA[Integrated eco-environmental risk mapping in the mountainous tropical landscapes of the Western Ghats, Kerala]]></title>
        <pubdate>2026-08-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>J. Drisiya</author><author>Sarmistha Singh</author>
        <description><![CDATA[Rapid tourism expansion and infrastructure growth in the Western Ghats are reshaping ecological stability in steep, topographically complex landscapes that still retain substantial forest cover. These changes are intensifying habitat fragmentation, degrading ecosystems, and placing increasing pressure on biodiversity-rich environments. Focusing on Idukki district in southern India, this study integrates the Urban Nature Access (UNA) and Habitat Risk Assessment (HRA) modules within the InVEST framework to quantify how accessibility-driven development influences habitat vulnerability between 2011 and 2025. Results reveal a widening spatial imbalance between population demand and accessible natural areas, with strongly negative urban nature balance values expanding across valley settlements and plantation corridors by 2025. Built-up land exhibits the highest mean habitat risk (R̄ = 0.42), followed by plantations (R̄ = 0.38) and croplands (R̄ = 0.34), while deciduous forests (R̄ = 0.22) and water bodies (R̄ = 0.05) remain comparatively less vulnerable. More than one-third of built-up and plantation landscapes fall within medium to high-risk categories. Spatial overlap between high-risk zones and documented landslide-affected areas during the 2018–2020 extreme monsoon events highlight the cumulative impact of tourism-driven development and associated forest conversion on steep slopes. The findings demonstrate that ecological vulnerability in Idukki is closely linked to localized forest loss and land-use transformation driven by concentrated human activities in hazard-prone terrain. These patterns highlight how steep and environmentally sensitive landscapes are increasingly affected by development pressures. This underscores the need to integrate ecosystem accessibility metrics with slope-sensitive land-use planning in tropical mountain systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1843759</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1843759</link>
        <title><![CDATA[Testing a computed tomography imaging spectrometer for Earth observations on the HEIMDAL stratospheric balloon mission]]></title>
        <pubdate>2026-08-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mads Juul Ahlebæk</author><author>Albertino Antonio Almeida Bach</author><author>Loui Collin-Enoch</author><author>Christian Cordes</author><author>Boas Hermansson</author><author>Christian Hald Jessen</author><author>Søren Peter Jørgensen</author><author>Tobias Jørgensen</author><author>Viktor Ulrich Kanstrup</author><author>Laurits Tværmose Nielsen</author><author>Jes Enok Steinmüller</author><author>Mads Svanborg Peters</author><author>Jonathan Merrison</author><author>René Lynge Eriksen</author><author>Christoffer Karoff</author><author>Mads Toudal Frandsen</author>
        <description><![CDATA[Stratospheric high-altitude balloons (HABs) have great potential as remote sensing platforms for Earth observations, complementing orbiting satellites and low-flying drones. At altitudes of 20–35 km, HABs operate significantly closer to the ground than orbiting satellites but significantly higher than most drones. Therefore, HABs offer unique potential for high-spatial-resolution imaging with large-area coverage. Two other imaging parameters important for Earth observation applications are spectral resolution and spectral range. Hence, in this work, we present the development and testing of a hyperspectral imaging system capable of recording near-video-rate images in narrow contiguous spectral bands from a HAB platform. In particular, we present the first stratospheric environmental tests and HAB flight of a snapshot hyperspectral camera based on computed tomography imaging spectroscopy, which is well-suited to address the challenges posed by the motion of the HAB platform and stratospheric environment. We successfully acquired images with the system under both simulated stratospheric conditions in the Mars Simulation Laboratory at Aarhus University and a 5-h HAB flight mission named HEIMDAL from Kiruna in October 2024 as part of the REXUS/BEXUS 34/35 2024 campaign organized by DLR-SNSA. This study represents a step toward deploying the HAB platform for high-quality land-cover classification.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1931285</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1931285</link>
        <title><![CDATA[Editorial: Rising stars in remote sensing 2025: advancements in time series analysis]]></title>
        <pubdate>2026-08-11T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Jane Southworth</author><author>Nicolas Baghdadi</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1843209</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1843209</link>
        <title><![CDATA[AI-driven prediction of plant physiological traits in hemp using UAV-based multispectral imagery]]></title>
        <pubdate>2026-08-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Harjot Sidhu</author><author>Arnab Bhowmik</author><author>Harmandeep Sharma</author>
        <description><![CDATA[IntroductionIndustrial hemp (Cannabis sativa L.) is a multipurpose bio‐economy crop capable of producing fiber, grain, and biomass while contributing to soil health and carbon sequestration. Realizing this potential requires optimized nitrogen (N) management, as N strongly regulates plant growth, yield, and fiber quality, while inefficient or excessive N use can cause environmental harm. Conventional N diagnostics based on destructive sampling are labor‐intensive and lack the spatial resolution needed for precision management. Uncrewed aerial vehicle (UAV) multispectral imaging offers a high-throughput, non‐destructive alternative; however, hemp remains underrepresented in UAV‐based N studies, particularly in linking spectral data to physiological traits associated with N metabolism.MethodsTo address this gap, two field experiments were conducted at the North Carolina A&T State University research farm using dual‐purpose and fiber‐type hemp cultivars under contrasting N regimes during the 2024 and 2025 growing seasons. Multispectral imagery was collected using a WingtraOne GEN II UAV equipped with a MicaSense RedEdge‐P camera. From reflectance mosaics, 33 vegetation indices (VIs) were computed, and the top seven were selected using Spearman's correlation analysis. Ground measurements included SPAD chlorophyll readings and gas‐exchange traits, i.e., net photosynthetic rate (Pn), stomatal conductance (Gs), and transpiration rate (E), using a LI‐COR 6800 system. Using SAS Viya, multiple supervised learning models were developed to predict SPAD, Pn, Gs, and E from UAV‐derived VIs.ResultsRed‐edge and green‐based indices showed very strong correlations with SPAD (ρ = 0.9078−0.9375), while NDWI showed a strong negative relationship (ρ = −0.9623). For Pn, GNDVI and CIG were strongly correlated (ρ ≈ 0.89), with NDWI negatively associated (ρ = −0.8934). Gs and E exhibited moderate correlations (ρ ≈ 0.73−0.84 and 0.75−0.80). Linear regression achieved R2 = 0.88 for SPAD, while a generalized additive model predicted Pn with R2 = 0.87. Quantile regression performed best for Gs and E (R2 = 0.82 and 0.75; Gs: ASE ≈0.013, MAE ≈0.086; E: ASE ≈0.0003, MAE ≈0.014).DiscussionThese results demonstrate a scalable framework for UAV‐based phenotyping to support precision N management in hemp.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1888267</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1888267</link>
        <title><![CDATA[Estimation of GPP in northern hemisphere terrestrial ecosystems based on light use efficiency model and chlorophyll fluorescence]]></title>
        <pubdate>2026-08-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ziwei Deng</author><author>Meizhen Xiang</author><author>Shudan Chen</author>
        <description><![CDATA[Gross primary productivity (GPP) is a crucial indicator for understanding the global carbon cycle and climate change. Sun-Induced Chlorophyll Fluorescence (SIF) provides a direct link to plant photosynthesis, offering a novel approach for GPP estimation in terrestrial ecosystems. Given the complex factors influencing the canopy SIF–GPP relationship and the limited generality of empirical linear models, we developed a Light Use Efficiency (LUE) model incorporating the photochemical reflectance index (PRI) and structural vegetation indices, with a nonlinear function fitted using support vector machine regression. The model was evaluated across multiple vegetation types, SIF products, and vegetation indices to identify optimal SIF–VI combinations. Results indicate that training by vegetation type improves accuracy by 8.5%–14.4%, with shrublands and evergreen broadleaf forests showing the best performance. The combination of GOSIF with NDVI achieved the highest overall estimation accuracy across all vegetation types. Additionally, the combination of GOME-2 SIF with NDVI × NIRv performed best in inland arid low-GPP regions, while TCSIF with NDVI × NIRv was most accurate in cold high-latitude low-GPP regions. All three combinations effectively captured interannual GPP variations at individual sites. Using these combinations, monthly GPP from 2007 to 2014 was estimated and compared with GPP-MODIS and GPP-GLASS datasets, showing consistent temporal and spatial patterns that reflect seasonal dynamics in the Northern Hemisphere. These findings demonstrate that integrating SIF with targeted vegetation indices enhances GPP estimation and provides a robust framework for large-scale photosynthesis monitoring.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1919576</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1919576</link>
        <title><![CDATA[DBF-YOLO: a lightweight dual-branch fusion method for Visible-SAR cross-modal remote sensing target detection]]></title>
        <pubdate>2026-08-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaofeng Zhao</author><author>Chenxiao Li</author><author>Hui Zhang</author><author>Kehao Wang</author><author>Fan Zhang</author><author>Hanshuo Huo</author><author>Zhili Zhang</author>
        <description><![CDATA[In visible-SAR cross-modal remote sensing target detection tasks, due to the significant differences between the two modalities in imaging mechanisms and feature representations, as well as the susceptibility of SAR images to speckle noise interference and the inadequate utilization of structural information, existing methods often struggle to balance detection accuracy with model lightweighting and practical deployment requirements. This is particularly true in resource-constrained scenarios, such as space-borne or airborne platforms, where models need to have low parameter counts, reduced computational overhead, and robust adaptability to complex environments. To address these issues, this paper proposes a lightweight dual-branch fusion network, DBF-YOLO, for visible-SAR cross-modal remote sensing target detection. Based on the YOLOv10 framework, the method constructs a visible-SAR dual-branch feature extraction structure and designs an intermediate cross-modal fusion path at three levels (P3, P4, P5) to achieve progressive interaction of dual-modal features at different scales. To overcome the strong speckle noise and edge degradation in SAR images, a SAR Gradient Enhancement Module (SGM) is introduced to enhance the structural representation capability of SAR inputs. Additionally, an Adaptive Gated Dual-Modal Fusion Module (AGD) is proposed to enable dynamic selection and effective complementarity of dual-modal information based on different scales and spatial positions. Experimental results on the OGSOD 1.0 dataset show that DBF-YOLO achieves 94.4% mAP50% and 70.1% mAP50-95, with only 5.0 M parameters and 20.5 GFLOPs, striking a good balance between detection accuracy and computational complexity. Furthermore, experimental results on the OSPRC dataset demonstrate the robustness of DBF-YOLO under different resolutions, polarization modes, and cloud cover conditions. Particularly in low-contrast dense target scenes and cloud cover conditions, the model demonstrates stronger environmental adaptability. This method can provide a reference for designing lightweight multi-modal remote sensing target detection models and deploying them on resource-limited platforms.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1814582</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1814582</link>
        <title><![CDATA[Comparative machine learning classification of a heterogeneous Ramsar wetland using Sentinel-1 and Sentinel-2 imagery with quantity and allocation disagreement diagnostics]]></title>
        <pubdate>2026-07-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Cornelia Chifurira</author><author>Erwin Sieben</author><author>Irvin Shandu</author><author>Sifiso Xulu</author>
        <description><![CDATA[IntroductionStrategic and active management of protected and heterogeneous wetland landscapes requires timely, accurate, and spatially explicit land-use and land-cover (LULC) information. This study presents a comprehensive landscape-level characterisation of LULC across South Africa’s first proclaimed World Heritage and Ramsar site, the iSimangaliso Wetland Park (IWP), and its surrounding areas.MethodsMulti-season Sentinel-2 imagery acquired in 2025 (autumn: March‐May; spring: September‐November) was integrated with Sentinel-1 backscatter, topographic variables, and ancillary datasets to enhance class separability in this complex environment. A total of 6,588 training samples and 2,358 validation samples were used to classify eight LULC classes, including open water, wetland, planted forest, natural forest, cropland, grassland, barren, and built-up areas, using four machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and K-Nearest Neighbours (KNN). Classification performance was evaluated using confusion‐matrix metrics (overall, producer’s, and user’s accuracy), complemented by quantity disagreement (QD) and allocation disagreement (AD) to distinguish between proportional and spatial allocation errors.ResultsRF achieved the highest overall accuracy (0.91), followed by SVM (0.88), CART (0.87), and KNN (0.84). Class‐based analysis showed that spectrally distinct classes, such as open water, achieved very high accuracy (<0.90), whereas heterogeneous classes, including wetlands and grassland, exhibited lower accuracy due to spectral similarity. Misclassification errors were spatially clustered, primarily along wetland‐upland boundaries. Uncertainty metrics further indicated low classification confidence in areas with mixed vegetation and hydrologically dynamic conditions.DiscussionThe methodological novelty of this study lies in integrating full spatial coverage classification with disagreement-based accuracy analysis and spatially explicit uncertainty scrutiny, providing a robust evaluation and baseline for long-term LULC monitoring and conservation planning in complex wetland systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1839369</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1839369</link>
        <title><![CDATA[Leveraging large language models (LLMs) for GeoAI-enabled digital agro-advisory]]></title>
        <pubdate>2026-07-31T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Shalini Gakhar</author><author>Raj Kumar Singh</author>
        <description><![CDATA[Digital agriculture has undergone a profound transformation driven by rapid advances in Earth observation, unmanned aerial vehicles and advanced data processing techniques. Despite this progress, climate-smart agriculture management faces a critical challenge: a farmer-centric agro-advisory system. Farmers and extension workers have limited access to GeoAI-based agro-advisory services, primarily due to limited data access and insufficient technical support. This study synthesises the potential of Large Language Models to address this challenge by translating complex GeoAI data into actionable and human-centric advice. The review examines gaps, recent advancements, and the model architectures required to adapt general-purpose LLMs for remote-sensing-based crop monitoring and management. We distinguish between experimentally validated capabilities of LLMs and the broader prospective applications proposed for agricultural remote sensing, noting that many current advances are primarily driven by multimodal foundation models, computer vision, and GeoAI systems rather than standalone LLMs. The study highlights the importance of advanced Retrieval-Augmented Generation and Supervised Fine-Tuning in agronomic science and mitigating the risk of misinterpretation. Further, we examine the emerging capabilities of multimodal approaches, which can seamlessly integrate visualisation and textual reasoning to support stakeholders in assessing crop health conditions and biophysical anomalies. The representative case studies, spanning multiple geographies and including voice-based advisory systems, demonstrate the shift from static advisory tools to dynamic, interactive recommendation systems. We highlight current challenges, such as the need for region-specific fine-tuning, data governance, and operation in low-connectivity environments, and advocate for a “human-in-the-loop” approach to decision-making. Through this, the LLMs will function as co-pilots assisting multiple stakeholders rather than as autonomous decision-makers vulnerable to biased output or hallucination problems. The review concludes by outlining a forward-looking research scope and closed-loop systems that iteratively learn from field outcomes.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1819365</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1819365</link>
        <title><![CDATA[Hyperspectral indices and global fluorescence: how PACE vegetation indices correlate with terrestrial photosynthesis]]></title>
        <pubdate>2026-07-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Caleb S. Harmon</author><author>K. Fred Huemmrich</author><author>Randolph H. Wynne</author><author>Valerie A. Thomas</author><author>Rui Cheng</author>
        <description><![CDATA[Solar-induced chlorophyll fluorescence (SIF) is a direct remotely sensed indicator of photosynthetic activity and plant physiological status. However, its application is limited by coarse spatial resolution and data gaps. The NASA PACE mission offers hyperspectral observations that may help address these limitations through its Ocean Color Instrument and associated Land Vegetation Index (LANDVI) products. This study evaluates the strength of the relationship between PACE-derived vegetation indices and TROPOMI SIF across diverse global biomes. Using 8-day global composites from 2024, we demonstrate strong spatial and temporal correlations between PACE vegetation indices and TROPOMI SIF across fifteen biome-stratified regions, including forests, grasslands, agricultural areas, and xeric landscapes. Even in the absence of photosynthetically active radiation (PAR) and albedo corrections, simple univariate linear models show that the Enhanced Vegetation Index (EVI) and Chlorophyll Index Red Edge (CIRE) explain a large fraction of SIF variance (R2 = 0.80 and 0.77, respectively). The stability of these relationships across seasons highlights the key role of canopy structure and chlorophyll content in driving global SIF variability. Seasonal analyses further reveal that while EVI and CIRE perform robustly in most forests and agricultural systems, other moisture- and pigment-sensitive indices can provide better performance during dry seasons in water-limited ecosystems. Spatial residual analyses indicate minimal global bias, though systematic deviations occur in certain regions (e.g., boreal forests and parts of the tropics), consistent with known effects of canopy architecture and fluorescence escape probability. These strong correlations suggest that PACE vegetation indices capture key biophysical drivers of SIF and point to the potential for improved SIF prediction and downscaling in future studies, particularly when combined with additional variables such as PAR corrections, albedo, or escape probability factors. Given the availability of EVI from moderate-resolution sensors (e.g., Sentinel-2 and Landsat), such relationships also offer promising avenues for bridging coarse-resolution SIF to finer management-relevant scales.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1852249</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1852249</link>
        <title><![CDATA[Remote sensing-based detailed wetland classification: a review of advances from 2020 to 2025]]></title>
        <pubdate>2026-07-30T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Ying He</author><author>Muzi Li</author><author>Xiaoyan Tang</author><author>Ping Ren</author>
        <description><![CDATA[Wetlands play an irreplaceable role in maintaining ecological balance and conserving biodiversity. However, driven by both natural and human factors, vast wetland areas worldwide are being rapidly converted into agricultural or urban land, leading to a sharp decline in their extent and a degradation of their quality. Against this backdrop, remote sensing technology, with its unique advantages in macro-level monitoring and multi-temporal dynamic capture, provides indispensable technical support for the precise identification, classification, and change detection of wetlands, making it an essential tool for wetland monitoring, restoration assessment, conservation planning, and SDG-related evaluation. This review examines recent advances in remote sensing for detailed wetland classification over the past 5 years. It elaborates on commonly used remote sensing data sources, local and international wetland classification standards, diverse classification methods, accuracy evaluation metrics, and prospects. This review provides a comprehensive reference to remote sensing-based wetland classification studies and applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1856867</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1856867</link>
        <title><![CDATA[Comparative analysis on spectral characteristics of PRISMA and EnMAP hyperspectral missions]]></title>
        <pubdate>2026-07-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Valeria La Pegna</author><author>Sora Seo</author><author>Davide De Santis</author><author>Fabio Del Frate</author><author>Diego Loyola</author>
        <description><![CDATA[Given the similarities in their instrumental characteristics, a comparative analysis was conducted between hyperspectral missions of PRISMA, operated by the Italian Space Agency (ASI), and EnMAP, developed by the German Aerospace Center (DLR). This work investigates both the similarities and discrepancies between these two satellite optical data, providing an outline of the spectral regions where data from both sensors can be reliably interchanged. The analysis was performed by analyzing the full visible and near-infrared (VNIR) spectrum and by examining selected spectral ranges sensitive to key environmental parameters, such as nitrogen dioxide, chlorophyll and NDVI. A total of 11 PRISMA and EnMAP co-located hyperspectral image pairs acquired across different seasons over the Euro-Mediterranean region were analyzed, focusing on representative land cover classes (bare soil, vegetation, urban areas and waters). Preprocessing steps were applied to standardize the acquisition viewing geometries, and the top of atmosphere (TOA) level of products from the two sensors were used to avoid inconsistencies introduced by atmospheric correction schemes adopted by each operating agency. Overall, the quantitative and qualitative assessments confirm a strong correspondence between the PRISMA and EnMAP hyperspectral measurements across the VNIR spectrum, with SAM always below 0.1 rad. However, the statistical analysis reveals relatively low consistency between the two sensors for the vegetation sensitive spectral window (750–940 nm), where larger deviations are observed with STD of 0.067 and SAM of 0.097, while the NO2 spectral range (420–500 nm) shows lower SAM of 0.048 and STD of 0.045.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1931394</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1931394</link>
        <title><![CDATA[Editorial: Machine learning for advanced remote sensing: from theory to applications and societal impact]]></title>
        <pubdate>2026-07-24T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Rui Li</author><author>Shaoqing Dai</author><author>Bin Jiang</author><author>Cong Zhang</author><author>Haoyang Yang</author><author>Wufan Zhao</author>
        <description></description>
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