<?xml version="1.0" encoding="utf-8"?>
    <rss version="2.0">
      <channel xmlns:content="http://purl.org/rss/1.0/modules/content/">
        <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>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-10-25T21:45:04.316+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1980112</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1980112</link>
        <title><![CDATA[Geospatial approaches to mapping invasive alien plants: the neglected role of soil in remote sensing and GIS-based distribution models]]></title>
        <pubdate>2026-10-22T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Mosa Koloko</author><author>Thabang Maphanga</author><author>Siyabonga Benett Madonsela</author><author>Karabo Malakane</author>
        <description><![CDATA[IntroductionInvasive alien plants (IAPs) pose significant ecological and socio‐economic threats by disrupting native biodiversity, ecosystem functioning, and land productivity, underscoring the need for robust spatial tools to guide management interventions. Using a systematic literature review approach guided by the PRISMA framework, this study examines geospatial techniques for modelling Invasive Alien Plant (IAP) distributions, focusing on how climatic, land use/land cover (LULC), and soil variables are integrated to explain habitat suitability.MethodsAcross reviewed studies, remote sensing and Geographic Information Systems (GIS) emerged as central tools for multi‐scale monitoring and predictor integration. Medium‐resolution satellite imagery (Sentinel‐2, Landsat) commonly supported regional assessments of vegetation change and land-use dynamics, while very‐high‐resolution imagery enabled finer species‐ and structure-level discrimination, though at greater cost. These data streams were increasingly coupled with machine learning and statistical approaches to improve classification accuracy, though performance remained dependent on input data quality and adequate representation of key environmental drivers.ResultsA notable methodological imbalance emerged in predictor selection. Among the 24 studies included in the soil‐variable integration analysis, 71% (n = 17) excluded soil variables, relying instead on spectral, thermal, and structural proxies, land surface temperature, and canopy characteristics. Only 29% (n = 7) incorporated measured edaphic variables, such as soil pH, texture, and soil classification, which typically require field integration if observations, soil survey data, and digital soil maps within GIS-based frameworks.DiscussionThis omission stemmed largely from remote sensing’s inability to directly observe below‐ground properties and the logistical challenges of acquiring spatially explicit soil data. The review suggests that the limited integration of soil variables may constrain the ecological realism of geospatial invasion models, particularly in environmentally heterogeneous landscapes. Greater integration of soil, climate and LULC frameworks has the potential to improve ecological realism and predictive capability and warrant further investigation, particularly in under‐studied regions such as Sub-Saharan Africa.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1944229</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1944229</link>
        <title><![CDATA[Fuzzy logic and frequency ratio based landslide susceptibility mapping using geo environmental and hydro meteorological factors in pithoragarh, uttarakhand]]></title>
        <pubdate>2026-10-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rakshi Pal</author><author>Punitha Kumar Akhas</author><author>Ponnusamy Porchelvan</author>
        <description><![CDATA[Mass movement events leads to severe infrastructural and socioeconomic damage across the Himalayan belt, particularly in Pithoragarh, Uttarakhand, India, where over nearly 1,000 events have been recorded in the last two decades. Modelling landslide susceptibility in such highly several, monsoon dominated terrains requires frameworks capable of handling significant spatial and lithological uncertainty. This study assesses and map out regional landslide vulnerability using a Multi Criteria Decision Analysis methodology based on fuzzy logic and frequency ratio. A spatial database comprising fourteen geo spatial and hydro meteorological conditioning factors was integrated to capture the region’s complex topographical and climatic changes. The analytical performances of the models were mathematically validated against a historical landslide inventory using the Receiver Operating Characteristic (ROC) curve. For this uncertain terrain, the Fuzzy Logic model produced a realistic and reliable susceptibility standard with an Area Under the Curve (AUC) of 0.686, in the other hand the frequency ratio model provides an AUC of 0.876. In this region, the middle and southern part, which are dominated by unconsolidated soils and heavy monsoon precipitation, are major hazard zones, while the northern side crystalline rock formations are still mostly stable, according to the spatial distribution analysis. So, this verified that the Frequency ratio hazard zonation map offers a data driven, optimal decision support tool compared to fuzzy logic for infrastructure development and disaster risk assessment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1880806</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1880806</link>
        <title><![CDATA[Mapping rainfed cereals and olive groves using Sentinel-1, Sentinel-2, and topographic data in northern Morocco]]></title>
        <pubdate>2026-10-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Meryem Qacami</author><author>Marc-André Bourgault</author><author>Mohamed Chikhaoui</author>
        <description><![CDATA[Accurate discrimination of agricultural crop categories in heterogeneous, rainfed landscapes remains challenging because crop spectral responses are strongly influenced by environmental variability and complex terrain. This study assessed the contribution of optical (Sentinel-2), radar (Sentinel-1), and topographic variables to the binary discrimination of annual rainfed cereals and perennial olive groves in the Tangier–Tétouan–Al Hoceima region of northern Morocco, a Mediterranean mountain environment characterized by fragmented agricultural systems and rainfed conditions. Using a Random Forest classifier, 38 candidate feature sets were evaluated, representing different combinations of Sentinel-2 spectral bands and vegetation indices, Sentinel-1 radar features, phenological information, and topographic variables to discriminate cereals and olive groves. The results showed that Sentinel-2 variables provided the strongest discriminative power, while Sentinel-1 variables alone yielded comparatively lower performance. The best-performing scenario combined Sentinel-2 spectral bands, Sentinel-1 VV and VH backscatter, Normalized Difference Red Edge Index 2 (NDRE2), Sentinel-1 VV/VH backscatter ratio (RATIO), and topographic variables (elevation, slope, and aspect), reaching an overall accuracy of 0.96 with balanced F1-scores for cereals and olives (0.92 and 0.91, respectively). Sentinel-2-only configurations already achieved high accuracies, particularly when spectral bands were combined with red-edge indices and topographic variables, whereas the addition of Synthetic Aperture Radar (SAR) data produced modest but consistent improvements in classification accuracy. SHAP-based interpretation revealed a hierarchical contribution of predictors, with elevation and slope emerging as the most influential variables, followed by NDRE2 and key spectral bands. These findings indicate that the discrimination between rainfed cereals and olive groves in this rainfed landscape is strongly influenced by topographic and environmental gradients, while spectral variables capture vegetation biophysical properties and SAR contributes complementary structural and moisture-related information. Overall, the study demonstrates that accurate mapping of rainfed cereals and olive groves in heterogeneous Mediterranean agro-ecosystems benefits from the integration of spectral, structural, and topographic information. The proposed framework provides a useful foundation for extending crop mapping and agricultural monitoring approaches in data-scarce and climate-sensitive regions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1919861</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1919861</link>
        <title><![CDATA[Where freshwater meets the sea: satellite–model insights into salinity dynamics in the Venice Lagoon]]></title>
        <pubdate>2026-10-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nagendra Jaiganesh Sankara Narayanan</author><author>Evangelos Spyrakos</author><author>Dalin Jiang</author><author>Federica Braga</author><author>Gian Marco Scarpa</author><author>Giorgia Manfè</author><author>Debora Bellafiore</author><author>Francesca De Pascalis</author><author>Michol Ghezzo</author><author>Stefan Simis</author><author>Andrew Tyler</author>
        <description><![CDATA[Salinity is a key descriptor of transitional water bodies at the land–sea interface, and resolving its spatiotemporal dynamics is essential for understanding water-mass exchange, habitat distribution, and ecosystem functioning. However, available observations remain limited in their ability to resolve dynamic freshwater–marine interactions at relevant spatial scales. The Venice Lagoon, a complex transitional system exposed to interacting natural and anthropogenic pressures, offers a valuable setting for investigating these dynamics. This study examines surface salinity variability using coloured dissolved organic matter (CDOM) as an optical proxy. Multi-year in situ measurements of remote sensing reflectance (Rrs), CDOM absorption, and salinity collected between 2018 and 2024 showed a persistent inverse relationship between CDOM and salinity, consistent with freshwater-marine mixing processes. A regionally calibrated model was developed from the in situ dataset, using the reflectance band ratio between 665 and 490 nm to estimate the CDOM absorption coefficient at 440 nm, aCDOM440, and showed good agreement across the lagoon–coastal gradient. The model was applied to atmospherically corrected Sentinel-3 Ocean and Land Colour Instrument (OLCI) imagery to generate satellite-derived aCDOM440. These estimates were then combined with concurrent salinity measurements from the continuous monitoring network (SAMANET) to develop a CDOM-based salinity retrieval model. The retrieval was further refined by accounting for wind and river discharge regimes, improving the representation of salinity variability under different environmental conditions. Resulting salinity maps captured short-term tidal redistribution, freshwater inputs during spring, and the persistent contrast between freshwater-influenced landward sectors and more saline waters near the tidal inlets. The System of Hydrodynamic Finite Element Module (SHYFEM), an established hydrodynamic model, captured the broader salinity patterns but underestimated values near the Dese river mouth. The proposed framework demonstrates the value of regionally calibrated ocean-colour observations for monitoring optically complex transitional waters and could support adaptive management strategies for environmentally sensitive lagoon systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1911652</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1911652</link>
        <title><![CDATA[Systematic vegetation index discovery for phenology-based crop mapping]]></title>
        <pubdate>2026-10-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ehsan Rahimi</author><author>Chuleui Jung</author>
        <description><![CDATA[IntroductionThe Systematic Vegetation Index Discovery (SVID) framework generates and evaluates large numbers of vegetation indices (VIs) by combining spectral bands with basic arithmetic operations. We evaluated whether systematically discovered indices could improve agricultural land-cover classification compared with conventional indices such as NDVI while reducing the dimensionality of multitemporal spectral data.MethodsWe applied SVID to a 323.9 ha agricultural landscape in Andong, South Korea, containing rice paddies, pepper fields, and plastic-covered greenhouses, using 32 cloud‐free Sentinel‐2A images acquired during 2022. Of 1,328 candidate expressions, 1,144 produced numerically valid rasters. A separate Random Forest model was trained for each index using its 32‐date temporal profile and evaluated against 500 independent reference points withheld from training. Before ranking, 388 expressions were identified as algebraically identical to another expression, and a further 355 were strictly monotone transformations and therefore indistinguishable to a rank‐based classifier.ResultsThe 1,144 indices consequently represented 401 distinct features. Several near‐infrared‐to‐visible ratio families outperformed NDVI. The best‐performing index, (NIR – Red)/(Green × Blue), achieved 97.0% overall accuracy and a Kappa coefficient of 0.873, compared with 94.4% and 0.734 for NDVI; this difference was significant by McNemar's test (p = 0.002). The improvement was concentrated in the greenhouse class, whose producer's accuracy increased from 58.6% to 84.5%. A model using all four raw bands across all dates (128 features) achieved 96.0% accuracy and was statistically indistinguishable from the best single index, demonstrating a four‐fold reduction in dimensionality without a detectable loss in accuracy. Rank positions were nevertheless unstable: ten of the 401 features were statistically tied with the best, and mathematically identical formulations differed by up to 3.4 percentage points because of finite‐precision effects.DiscussionSVID can identify compact spectral representations that perform as well as or better than conventional indices and full multiband temporal models. However, the instability among top‐ranked indices suggests that results should be reported as index families with confidence intervals rather than as a single winner. Limitations include the use of one site, one year, and four spectral bands; broader testing across sites, years, and spectral configurations is needed.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1912607</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1912607</link>
        <title><![CDATA[Assessing urban development trajectories using landscape expansion metrics and multivariate statistical approaches: evidences from rohtak city, NCR, India]]></title>
        <pubdate>2026-10-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bharat Bhushan</author><author>Surender Singh</author><author>Somnath Mandal</author><author>Vipasha Sharma</author><author>Pradeep Kumar</author><author>Attila Fülöp</author><author>Sara Elhdad</author>
        <description><![CDATA[Rohtak, which is a major city in the National Capital Region (NCR) of India, has experienced a fast-tracked transformation over the last 40 years as shown by the increased population growth through migration, urban development, and spatial restructuring. The research applies the landscape ecological metrics and the multivariate trajectory analysis to examine the distance and direction specific nature of urban development during 2000–2020, through the Mann-Kendall and Principal Component Analysis (PCA). The findings indicate that there is a high level of anisotropy in the growth patterns with the northern, western and north-western regions recording the highest growth due to the existence of educational hubs, industrial estates and improved transport connectivity leaving the eastern corridor relatively underdeveloped. A distance-gradient analysis suggests that infill saturation occurs between 2 and 4 km of the core, whereas leapfrog and edge-expansion processes occur between 6 and 10km, especially around the larger institutions and the Industrial Model Township. It is possible to identify three phases of urban development: the first fragmented phase (2000–2010), the transitional phase of urban development (2010–2015), and the coalescence phase (2015–2020), which can be characterized by the transition between dispersed urban forms and compact urban forms. Directional heterogeneity is also supported by trajectory metrics, whereby long, linear growth paths are oriented towards the north, southeast and south, and little expansion towards the west. On the whole, Rohtak is a typical ripple-effect urbanisation model, with a shift in the monocentric core to the polycentric, corridor-based growth at the edges. The results provide a critical role of the institutional hubs, industrial estates, and transport infrastructure in determining urban trajectories, hence providing important insights into sustainable urban planning in the fast-growing secondary cities in the NCR.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1873211</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1873211</link>
        <title><![CDATA[Quantifying the accuracy of estimating harmful algal bloom cell concentrations using OLCI imagery in the tributaries of lower Chesapeake Bay]]></title>
        <pubdate>2026-10-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>R. Wayne Litaker</author><author>Michelle C. Tomlinson</author><author>William C. Holland</author><author>Kimberly S. Reece</author><author>Todd A. Egerton</author><author>Travis O. Briggs</author><author>Gail P. Scott</author><author>Hamish J. Small</author><author>Sylvain Gaillard</author><author>Savannah A. Mapes</author>
        <description><![CDATA[IntroductionHarmful algal blooms of Alexandrium monilatum and Margalefidinium polykrikoides are a common occurrence in the tributaries of lower Chesapeake Bay, United States of America. This study evaluated whether daily processed imagery from the Ocean and Land Colour Instrument (OLCI) can accurately measure chlorophyll a (chla) concentrations in these turbid, bloom-prone tributaries. Since HAB cell concentrations provide a more direct measure of risk than chla, a second objective was to quantify the accuracy of bloom cell concentrations derived from satellite chla.MethodsField collected chla concentrations and processed OLCI data were used to evaluate the accuracy of satellite-derived chla concentrations in the York and James Rivers. Field, laboratory, and literature data were used to quantify the precision of satellite-based cell estimates.ResultsSatellite-derived chla estimates correlated well with laboratory extracted chla from the field and were stable over time for the entire OLCI satellite image dataset. The results of laboratory experiments and field studies showed that variations in the pg chla cell-1 used to convert chla to cell estimates, often varied five- to hundred-fold, depending on prevailing environmental growth conditions and the chla contributed by co-occurring phytoplankton.DiscussionThe satellite-derived chla concentrations proved accurate over a wide range of chla concentrations, confirming their usefulness in tracking high biomass blooms and guiding sampling efforts required to evaluate potential adverse impacts, and as a basis for potentially estimating relative HAB cell concentrations. However, field, literature, and laboratory data showed that chla cell-1 estimates at a given chla concentration can vary by up to 100-fold. Consequently, satellite-based cell concentrations will be highly inaccurate. More accurate HAB cell estimates might be possible if chla cell-1 is measured periodically during a bloom and used to estimate cell concentration from satellite chla in real-time.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1994165</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1994165</link>
        <title><![CDATA[Editorial: Instruments and Technologies for Earth Observation Satellite Missions]]></title>
        <pubdate>2026-10-07T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Oliver Lux</author><author>Gennadi Milinevsky</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1920951</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1920951</link>
        <title><![CDATA[A semi-supervised multi-sensor remote sensing pipeline for mapping irrigation dynamics in ground-truth deficient conflict zones: a case study of the Kakhovka Dam failure]]></title>
        <pubdate>2026-10-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sheila Baber</author><author>Sergii Skakun</author><author>Yuval Sadeh</author><author>Leonid Shumilo</author><author>Oleksandra Oliinyk</author><author>Mehdi Hosseini</author><author>Josef Wagner</author><author>Abdul Qadir Khan</author><author>Shabarinath Nair</author><author>Abhishek Kotcharlakota</author><author>Inbal Becker-Reshef</author>
        <description><![CDATA[The ongoing war in Ukraine has drawn international attention to the impact of conflict on global agricultural production and food security. On 6 June 2023, the Kakhovka Dam was destroyed. The loss of this critical agricultural and energy infrastructure has left most farmers on the East bank of the Dnipro River without irrigation in Kherson and Zaporizhzhia oblasts. This study develops a multi-sensor Earth Observation framework combining high resolution satellite imagery (Sentinel-2, Landsat) with meteorological data (CHIRPS) and water productivity data (FAO WaPOR), using a semi-supervised machine learning pipeline. Using a rigorous stratified random-sampling approach with photointerpretation, we found pre-war irrigated areas between 2019 to 2021 to be on average 187.94 ± 14.43 kHa, approximately 4% of pre-war baseline cropland and 8% of summer season cropland in Kherson and Zaporizhzhia oblasts. In 2022, these numbers contracted to 140.04 ± 17.44 kHa with the expanded occupation of these territories by Russia, seeing an additional decline in 2023 to 74.97 ± 17.84 kHa. Fields in 2023 also exhibited variability in irrigation, ranging from interrupted irrigation owing to the destruction of the Kakhovka reservoir, to those that were sufficiently irrigated with water remaining in the channels. Irrigated areas diminished further in 2024 to 15.95 ± 9.97 kHa, a ∼91% decline from pre-war averages. Based on inspection, formerly irrigated fields before 2023 were either systematically abandoned or converted to winter cropland with lower input requirements by 2024–a pattern that continued into 2025 (total irrigated area 5.76 ± 0.87 kHa). This study demonstrates the power of advanced EO workflows to precisely quantify agricultural transformation and infrastructural failure in ground-truth deficient, high-risk environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1927646</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1927646</link>
        <title><![CDATA[Remote estimation of plastic distribution in California agriculture using Sentinel-2 imagery: a case study]]></title>
        <pubdate>2026-10-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Eva Scrivner</author><author>Dexter Hamilton</author><author>Daniel Sousa</author><author>Margot Mattson</author><author>Nicholas E. Williams</author><author>Seeta Sistla</author>
        <description><![CDATA[Global application of agricultural plastic mulch is increasing. While these films offer benefits for crop production, they also are a large source of plastic waste and pollution in both terrestrial and aquatic systems. The current scale of global mulch application and its deleterious impacts are unclear. Current estimates of agricultural plastic use remain limited by field-specific growing patterns and data limitations. Satellite spectroscopy may offer a means to reduce this uncertainty and model agricultural plastic use on large spatial and temporal scales, though temporally variable crop canopy growth and spectrally diverse plastic targets complicate this approach. To build toward improved plasticulture estimates, this study presents an interpretable, spectral-based classification of bulk plastic use in a California agricultural site using Sentinel-2 imagery. In situ reflectance measurements of soil and white, green, and black plastic mulch endmembers were collected using a Spectra Vista Corporation HR-1024i Spectroradiometer from 350–2500 nm. A combination of field observations and high spatial resolution NearMap imagery (n = 416 spectra) were compiled and divided into 60% training and 40% testing data using a stratified sampling approach. Informed by field reflectance data, mixture analyses, and Sentinel-2 reflectance imagery, an empirical decision tree classification was parameterized to classify dark and bright plastic debris from soil and crop (accuracy = 0.982, kappa coefficient = 0.973, macro-averaged F1-score = 0.967). Modeled agricultural plastic distributions were then retrieved through a time series of Sentinel-2 imagery over Santa Maria, CA, from April 2023 to January 2025, elucidating patterns in plastic application and crop canopy growth without the need for prior knowledge of field growing and management patterns. These results are encouraging for the utility of optical remote sensing in modeling plastic use patterns and identifying contamination hotspots throughout California and, potentially, similarly mulched agroecological systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1991820</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1991820</link>
        <title><![CDATA[Editorial: Building resilient cities with remote sensing and AI]]></title>
        <pubdate>2026-09-29T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Sawaid Abbas</author><author>Faisal Mueen Qamer</author><author>Ali Zia</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1908768</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1908768</link>
        <title><![CDATA[Cross-sensor urban land-cover characterization using hyperspectral and multispectral satellite imagery]]></title>
        <pubdate>2026-09-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sawaid Abbas</author><author> Zohaib</author><author>Nawai Habib</author><author>Faisal Mueen Qamer</author><author>Majid Nazeer</author>
        <description><![CDATA[IntroductionThis study establishes a cross-sensor urban remote sensing baseline for future urban digital-intelligence applications by integrating separately classified and validated multispectral (Landsat, Sentinel-2) and hyperspectral (PRISMA) imagery using a common sampling and evaluation framework.Methods Eight land-cover classes — Dense Urban Area, Medium Urban Area, Sparse Urban Area, Tree Cover, Grass, Cropland, Bare Area, and Water — were mapped using Random Forest, Support Vector Machine (SVM), Gradient Boosting Machine, K-Nearest Neighbor, and Classification and Regression Tree algorithms. Performance was evaluated across all three sensors using overall accuracy, Cohen’s kappa, macro-averaged and class-wise F1-scores, class-area composition, spectral profile, Fisher scores, pairwise separability metrics, and principal component analysis (PCA).ResultsResults demonstrated clear sensor-dependent differences in urban representation and classification accuracy. Sentinel-2 improved spatial characterization of heterogeneous urban surfaces through enhanced spatial resolution and red-edge information. PRISMA produced the highest classification performance, with SVM achieving an overall accuracy of 91.26% and a macro-averaged F1-score of 89.72%. However, this advantage was class-specific rather than uniform across all urban-density classes. Spectral-profile and PCA analysis showed hyperspectral observations substantially improved separability between Medium and Sparse Urban classes (spectral angle: 0.54° Landsat, 1.58° Sentinel-2, 4.11° PRISMA). PRISMA classifications also allocated substantially less area to Sparse Urban Area and more to Bare Area than the multispectral classifications.DiscussionThis redistribution may reflect improved discrimination of mixed peri-urban surfaces but cannot be considered more accurate without an independent concurrent area-wide reference map. The findings do not establish universal sensor superiority; rather classification outcomes depend jointly on sensor properties, acquisition timing, preprocessing, class definitions, and classifier choice, supporting a complementary interpretation of multispectral and hyperspectral observations.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1860441</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1860441</link>
        <title><![CDATA[Leveraging Sentinel-2 and Sentinel-1 imagery for retrieving key wheat phenology stages in a dryland agricultural setting]]></title>
        <pubdate>2026-09-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ahmed S. Almalki</author><author>Marcel M. El Hajj</author><author>Kasper Johansen</author><author>Matthew F. McCabe</author>
        <description><![CDATA[Monitoring crop phenology is crucial for agricultural management, providing the basis for timely decisions related to irrigation and fertilization, while offering insights into crop condition. This is particularly important in drylands, where agriculture is constrained by water scarcity and harsh environmental conditions, making phenology monitoring essential for optimizing resources and sustaining productivity. Despite its importance, traditional ground-based phenology monitoring methods are limited in scalability and consistency, underscoring the need for satellite-based alternatives. In this study, we investigated the feasibility of using time-series data from Sentinel-2 and Sentinel-1 imagery, each analyzed separately and combined with curve-based rules (i.e., threshold- or inflection-point criteria applied to temporal profiles), to retrieve five phenology stages of germination date, flowering date, senescence date, harvesting date, and cycle length for a large number of wheat fields in the Al-Jouf region of Saudi Arabia, a hot desert environment receiving less than 100 mm of annual precipitation that typifies dryland agriculture. To achieve this, we used 85 cloud-free Sentinel-2 and 94 Sentinel-1 images, along with in situ phenology observations. From Sentinel-2 imagery, we derived the normalized difference vegetation index (NDVI), green-red vegetation index (GRVI), and plant senescence reflectance index (PSRI), while volume backscattering (VV-VH), i.e., difference between vertical transmit/vertical receive (VV) and vertical transmit/horizontal receive (VH), was derived from Sentinel-1 imagery. Field-level time-series of each Sentinel-2 and Sentinel-1 metric were extracted and fitted with Gaussian functions. Next, the five phenology stages were retrieved by applying stage-specific curve-based rules to Gaussian-fitted field-level time-series of each metric. The accuracy of retrievals was assessed against in situ observations. Results showed that curve-based rules applied to NDVI, GRVI, and PSRI provided relative root mean square error (rRMSE) values of 2.64%–14.17% and bias magnitudes of 7.34–20.60 days across all stages without requiring rule refinement. Rules applied to VV-VH produced rRMSE values of 4.60%–26.22% and bias magnitudes of 7.11–43.73 days. Targeted refinement of flowering and harvesting rules reduced these errors. Overall, this work demonstrates the effectiveness of applying curve-based rules to each of Sentinel-2 and Sentinel-1 data separately in the dryland setting examined here, providing a foundation for evaluation across the broader range of drylands.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1941508</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1941508</link>
        <title><![CDATA[Diminishing aerosol-related compensation of vegetation productivity across China: asymmetric responses of AGC, LAI, NPP, and GPP from 2002 to 2020]]></title>
        <pubdate>2026-09-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ruige Dong</author><author>Xiangyuan Duan</author><author>Chencheng Yin</author><author>Meng Zhang</author><author>Wenping Jin</author><author>Yuan Zhang</author>
        <description><![CDATA[The mechanisms and influencing factors of changes in vegetation productivity have always been a global concern. Current research focuses on how precipitation, temperature, and human activities affect vegetation productivity. Atmospheric aerosols scatter and absorb solar radiation, which affects the terrestrial radiation balance, surface temperature, and water cycle, thereby indirectly affecting vegetation productivity. However, the statistical responses of vegetation productivity to dynamic changes in atmospheric aerosols, particularly their potential asymmetric characteristics, remain insufficiently understood. Therefore, this paper used the asymmetry index (AI) to analyze the asymmetric response of four vegetation productivity parameters to the dynamic changes of aerosol optical depth (AOD) in China from 2002 to 2020 and discussed the response under different vegetation types and environmental conditions. The results indicate that the asymmetric response of vegetation productivity to AOD is widespread and spatially heterogeneous. Regions in northeastern, eastern, and central China displayed a negative asymmetric response, which demonstrated that the negative impact of high AOD on vegetation productivity surpassed the positive impact of low AOD, resulting in losses outweighing gains. During the study period, the positive asymmetry shifted to a negative asymmetry, as shown by the decreasing AI. Notably, the AI of the four vegetation productivity parameters all peaked around 2008–2013, indicating vegetation productivity had its strongest compensatory effect. Grassland and cropland productivity exhibit pronounced sensitivity to atmospheric aerosol variations, whereas forest ecosystems demonstrate relatively muted responses. In areas with higher precipitation or temperature, the negative asymmetric response of vegetation productivity to AOD gradually increases. In contrast, vegetation productivity gradually shows a positive asymmetric response trend as altitude increases. This study reveals the asymmetric responses of vegetation productivity to aerosol changes and the differentiation patterns of these responses along environmental gradients, offering new insights for research on ecosystem–atmosphere interactions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1698781</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1698781</link>
        <title><![CDATA[Deep learning-based classification of wet direct seeded rice, broadcasted direct seeded rice and transplanted rice using drone imagery for precision agriculture]]></title>
        <pubdate>2026-09-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Amrutha Lakshmi Gubbala</author><author>Santosha Rathod</author><author>Ramesh Dasyam</author><author>Mahender Kumar Rapolu</author><author>Arun Kumar Dasari</author><author>Pundarikakshudu Kurra</author><author>Hanuma Raviteja Madireddy</author><author>Prajwal R. Shashishekhar</author><author>Ravi V. Mural</author><author>Anil Kumar</author><author>Raman Meenakshi Sundaram</author>
        <description><![CDATA[Accurate estimation of crop area using classification techniques applied to drone imagery plays an important role in precision agriculture. Traditional machine learning (ML) approaches have been widely used for agricultural image classification; however, advanced deep learning (DL) models generally provide superior feature extraction and classification capability for complex crop patterns. Differentiating various rice establishment methods is essential for precise area estimation. In this study, advanced deep learning models were employed to classify three types of rice cultivation: (i) Broadcasted Direct Seeded Rice (DSR), (ii) Wet Direct Seeded Rice (Wet DSR), and (iii) Transplanted Rice (TR) using drone imagery. The drone imagery was collected from an experimental field at Praanadhaara Organised Agro Forestry Private Limited, Bapatla District, Andhra Pradesh, India. The images were captured in the visible spectrum (Red, Green, and Blue bands) on three dates, viz., 15 October 2023, 27 October 2023, and 01 December 2023, from an altitude of 40 m and were used to train and test classification models. Classification was performed using six ResNet-50 based hybrid models, namely, ResNet-50+K-Nearest Neighbor (ResNet-50+KNN), ResNet-50+Support Vector Machine (ResNet-50+SVM), ResNet-50+Decision Trees (ResNet-50+DT), ResNet-50+Random Forest (ResNet-50+RF), ResNet-50+Naïve Bayes (ResNet-50+NB), and ResNet-50+Neural Network (ResNet-50+NN), along with two additional DL architectures, namely, You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8). Model performance was evaluated using overall accuracy (OA), precision (P), recall (R), kappa coefficient (K), F1-score (F1), and mean Average Precision (mAP). Among the tested classifiers, the ResNet-50+NN model consistently achieved the highest average overall accuracies of 93.16%, 95.81%, and 93.31% at T1, T2, and T3, respectively, outperforming all other models, whose accuracies ranged from 78.05% to 92.07%, 86.11%–95.24%, and 77.47%–92.09% across the respective time intervals. The ResNet-50+NN model also recorded the highest precision (0.93–0.97), recall (0.92–0.97), F1-score (0.92–0.97), and kappa coefficient (0.89–0.96), demonstrating superior and consistent classification performance across all observation dates. The methodology developed in this work enables precise identification of rice establishment methods, improving crop mapping and monitoring. This identification enhances resource efficiency, optimizes input use, and supports site-specific management, contributing to sustainable precision agriculture.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1860176</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1860176</link>
        <title><![CDATA[Hierarchical spectral ensemble with physics-informed augmentation for global water quality retrieval from hyperspectral remote sensing ]]></title>
        <pubdate>2026-09-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Amirthalakshmi TM</author><author>Hemanth S</author><author>Ganesan PV</author><author>Rahul SG</author><author>Gayathri M</author>
        <description><![CDATA[Retrieving water quality variables from remotely sensed reflectance spectra (Rrs) with global-scale, cross-waterbody applicability remains a grand challenge in environmental remote sensing. Here we introduce Hierarchical Spectral Ensemble (HSE), a machine learning pipeline for concurrent retrieval of four ecologically relevant water quality parameters (chlorophyll-a (Chl-a), total suspended solids (TSS), coloured dissolved organic matter absorption coefficient at 440 nm (aCDOM(440)), and Secchi depth (Zsd)), applied to the GLORIA 2022 global dataset of 7,572 co-located hyperspectral in situ ground truth measurements spanning six continents. HSE leverages a combination of five diverse base learners trained with 10-fold out-of-fold stacking, and integrated with a diversity-constrained non-negative least squares (NNLS) meta-learner, supported by a 291-dimensional feature suite spanning spectral, spatial, and temporal representations. We propose Interpolation-based Spectral Data Augmentation (ISDA), an ecologically inspired, class-balance oversampling methodology applied to the training set after the initial stratified split to prevent information leakage. Evaluated on the held-out global test set, HSE attains R2 = 0.822, 0.704, 0.841, and 0.962 for Chl-a, TSS, aCDOM(440), and Zsd respectively (mean R2 = 0.832; all in original physical units following back-transformation). Stratified analysis on a per-quartile basis reveals negative values of R2 in low-concentration regimes for Chl-a, TSS, and aCDOM(440), illustrating how global metrics can mask failures in retrieval across a majority of the distribution, specifically in the oligotrophic regime. This constitutes the primary known limitation of the method: reliable prediction in low-concentration, low-optical-signal regimes remains elusive and is demonstrated only in high-concentration, eutrophic samples where a measurable optical signal exists.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1880618</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1880618</link>
        <title><![CDATA[Simultaneous retrieval of 13 phytoplankton pigments from Sentinel-3 OLCI using convolutional neural networks and transfer learning]]></title>
        <pubdate>2026-09-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Borja Sánchez-López</author><author>Marco Talone</author><author>Jesus Cerquides</author><author>Annalisa Di Cicco</author><author>Gonzalo Martínez-Fornos</author><author>Petra Slavinec</author>
        <description><![CDATA[Phytoplankton pigment concentrations retrieved from ocean color satellite observations provide key information on marine ecosystem composition and biogeochemical cycling. Although Machine Learning (ML) approaches have been applied to multi-spectral sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), no study has systematically exploited the specific band configuration of the Ocean and Land Colour Instrument (OLCI) onboard Copernicus Sentinel-3 for the simultaneous retrieval of multiple diagnostic pigments. This study presents the first systematic benchmark of five ML architectures: Random Forest (RF), eXtreme Gradient Boosting (XGB), Dense Neural Network (DNN), one-dimensional Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory Network (BiLSTM) for retrieving 13 pigments of interest to the ocean color community from OLCI-equivalent multi-spectral radiometry. Training and evaluation rely on 185 co-located in situ remote-sensing reflectance and High Performance Liquid Chromatography (HPLC) pigment measurements collected across optically diverse European waters (Mediterranean Sea, Black Sea, Atlantic, and English Channel) within the BiOMaP programme. A novel two-phase Deep Learning (DL) training strategy is introduced, in which pigment-specific modules are first trained independently and subsequently concatenated into a joint multi-output model, preventing dominant, easily retrieved pigments from degrading performance for rarer accessory ones. To extend the methodology to operational satellite products, a transfer learning strategy based on fine-tuning is applied and explicitly benchmarked against training from scratch on a global matchup dataset. Additionally, a spectrally reduced version (5 central wavelengths) is developed and validated for compatibility with the Copernicus-GlobColour and European Space Agency’s Ocean Colour Climate Change Initiative datasets, enabling application of the multi-pigment retrieval framework to a 26-year continuous satellite record. Results indicate that CNN consistently outperforms all other architectures across all evaluation metrics, achieving coefficients of determination of 0.70–0.93 and mean absolute percentage errors of 25–55% across the 13 target pigments, close to the approximate 20% average percent differences of published inter-laboratory HPLC measurement round-robin experiments. The fine-tuned model systematically outperforms the from-scratch version across all 13 pigments, with mean absolute percentage errors generally lower than 70% and approximately 20-25% smaller than those obtained when training from scratch, and a coefficient of determination (R2) decreasing by approximately 0.2 relative to the in situ baseline versus a decrease of 0.3 for the from-scratch model. The spectrally reduced model retains comparable in situ performance and similar transfer learning advantages after fine-tuning. These results provide a quantitative demonstration of the value of high-quality regional in situ radiometry as a pre-training foundation for global satellite pigment retrieval. Together, these findings demonstrate the strong potential of OLCI-based CNN models for operational multi-pigment retrieval across current and future satellite missions and for long-term climatological analysis.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1930744</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1930744</link>
        <title><![CDATA[WD-CD: a large-scale high-resolution optical remote sensing benchmark for fine-grained change detection in war scenarios]]></title>
        <pubdate>2026-09-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dongjing Li</author><author>Lei Ma</author><author>Guangjun He</author><author>Yaohui Chu</author><author>Yingnan Guo</author><author>Ruikun Wang</author><author>Ying Liang</author><author>Pengming Feng</author>
        <description><![CDATA[IntroductionRapid detection of war-induced damage is vital for humanitarian response and post-war reconstruction, yet large-scale, well-annotated benchmarks for war-damage change detection are still lacking.MethodsWe present a large-scale, high-resolution bi-temporal optical remote sensing benchmark for war-damage change detection, named WD-CD. WD-CD contains 9,687 bi-temporal image pairs of 512 × 512 pixels collected from five conflict-affected theaters across Europe, Africa, and Asia, covering a total area of 1,055.83 km2. It provides annotations for 16 target categories and up to six change states. We evaluated representative deep learning models, performed cross-dataset comparisons, and conducted intercontinental transfer experiments.ResultsThe benchmark evaluations demonstrated consistent and competitive performance while revealing the challenges of damage-state discrimination. The intercontinental transfer experiments showed that building geometry generalized well across regions, whereas damage morphology and background landscapes were region-specific.DiscussionWD-CD addresses the lack of large-scale, fine-grained benchmarks for war-damage change detection and provides a foundation for evaluating model performance and cross-regional generalization. To support open science and reproducibility, the dataset will be made available upon request for non-commercial academic research.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1981032</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1981032</link>
        <title><![CDATA[Editorial: Remote sensing applications in mountainous regions]]></title>
        <pubdate>2026-09-09T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Divyesh Varade</author><author>Dericks P. Shukla</author><author>Dipankar Mandal</author><author>Narendra Das</author>
        <description></description>
      </item><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>
      </channel>
    </rss>