Science of Remote Sensing最新文献

{"title":"Vegetation browning as an indicator of drought impact and ecosystem resilience","authors":"Ignacio Fuentes ,&nbsp;Javier Lopatin ,&nbsp;Mauricio Galleguillos ,&nbsp;James McPhee","doi":"10.1016/j.srs.2025.100219","DOIUrl":"10.1016/j.srs.2025.100219","url":null,"abstract":"<div><div>Climate change influences climate variability, increasing the frequency and severity of droughts. These events may trigger vegetation browning, a key indicator of drought propagation and shifts in resilience. While long-term trends often measure browning, rapid vegetation declines require alternative approaches. This study examines drought-induced vegetation browning, resilience, and propagation in central Chile using Moderate Resolution Imaging Spectroradiometer (MODIS) time series of normalised difference vegetation index (NDVI), leaf area index (LAI), and gross primary productivity (GPP). The Continuous Change Detection and Classification (CCDC) algorithm identified negative vegetation changes, filtering out non-browning events to reduce uncertainties. Spatial variations in browning were analysed across latitudinal gradients, topographies, and vegetation types, while shifts in temporal autocorrelation served as a proxy for resilience. Results indicated declines in NDVI across 19% of the study area, GPP in 12%, and LAI in 8%. NDVI responded to drought within six months, with productivity losses lagging by 8.7 months. Recovery was slow, averaging 3.6 years, and only 20%–25% of the affected areas recovered. Variations in browning timing and magnitude were driven by topography, vegetation, and latitude. A decline in vegetation resilience highlights the need for strategies to enhance adaptability to climate change.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100219"},"PeriodicalIF":5.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-scale estimation of photosynthetic capacity in larch forests using UAV hyperspectral data: From leaf to canopy","authors":"Chunyan Wu ,&nbsp;Tingdong Yang ,&nbsp;Min Cheng ,&nbsp;Dongsheng Chen ,&nbsp;Xiaomei Sun ,&nbsp;Shougong Zhang","doi":"10.1016/j.srs.2025.100220","DOIUrl":"10.1016/j.srs.2025.100220","url":null,"abstract":"<div><div>Understanding forest photosynthetic capacity is essential for monitoring carbon dynamics under global change. UAV-based imaging spectroscopy is a powerful tool for assessing canopy leaf traits, but the extension of spectral-trait relationships to the canopy scale remains unclear. This study uses UAV-based hyperspectral imaging data to evaluate the photosynthetic characteristics of larch forests across different climate zones in China. We investigate UAV-derived imaging spectroscopy for mapping canopy-level leaf physiological traits, including chlorophyll content, leaf nitrogen, and photosynthetic capacity (Vc, max and Jmax) across three distinct climate zones. High-resolution UAV imaging spectral data and ground-based leaf trait measurements, including biochemical (chlorophyll, leaf nitrogen), morphological (leaf mass per area, LMA), and physiological traits (Vc, max and Jmax), were collected from 150 tree crowns at all sites. We developed and validated models for estimating physiological traits from canopy spectra using Partial Least Squares Regression (PLSR), focusing on the transferability of leaf-level models to the canopy scale. The results show that UAV-based canopy spectra can effectively estimate canopy-level Vc, max25 (R² = 0.56, RMSE = 9.57 μmol CO₂ m⁻² s⁻¹, nRMSE = 17.7 %) and Jmax25 (R² = 0.38, RMSE = 34.8, nRMSE = 18.6 %). Additionally, other leaf traits across all climate zones were accurately predicted, including leaf mass per area (LMA), leaf water content (LWC), chlorophyll content (Chl), nitrogen content (Narea), and phosphorus content (Parea), with R² values ranging from 0.30 to 0.44 and nRMSE between 18.8 % and 24.4 %. Significant differences in canopy trait variability were observed, with Vc, max25 and Jmax25 values driven by climate variability. The range of Vc, max25 (40.5–70.6 μmol CO₂ m⁻² s⁻¹) and Jmax25 (80.6–120.4 μmol CO₂ m⁻² s⁻¹) was wider at the ES site compared to the FS and TS sites, indicating that species differences have a greater impact on photosynthetic capacity. These models demonstrated good transferability, showing robust performance across forests in different climate zones with only slight differences in predictive accuracy. However, canopy structure significantly influenced spectral-trait relationships, particularly for Vc, max and Jmax. While canopy structure had a moderate impact on accuracy, canopy-scale models performed slightly lower than leaf-level models in some cases. This study offers new insights into UAV-based imaging spectroscopy for mapping canopy leaf physiological traits and emphasizes the need to understand different physiological mechanisms at the canopy scale when expanding spectral-trait relationships.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100220"},"PeriodicalIF":5.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment and improvement of GEDI canopy height estimation in tropical and temperate forests","authors":"Myung-Sik Cho ,&nbsp;David P. Roy ,&nbsp;Herve B. Kashongwe ,&nbsp;Lin Yan ,&nbsp;Meicheng Shen","doi":"10.1016/j.srs.2025.100221","DOIUrl":"10.1016/j.srs.2025.100221","url":null,"abstract":"<div><div>The Global Ecosystem Dynamics Investigation (GEDI) relative height product provides 25 m diameter footprint information that can be used to estimate canopy height but with variably reported accuracy. A methodology is presented to improve the GEDI canopy height accuracy using coincident Airborne Laser Scanner (ALS) data. The GEDI canopy height for each footprint is surface adjusted by adding the residual difference between the GEDI ground elevation estimate and coincident footprint ALS digital terrain model (DTM) value, and then calibrated using site-level additive offsets derived by Theil-Sen regression with coincident footprint ALS canopy height model (CHM) data. The approach is demonstrated at a tropical evergreen lowland forest site in the Democratic Republic of Congo (MNDP), a temperate pine and hardwood forest site in Alabama (TALL), and a temperate mix-species forest site in Maryland (SERC). The GEDI canopy height accuracy is first quantified by comparison with ALS CHM data to provide a baseline. The methodology improved the root mean squared error (RMSE) from 7.5m to 3.4m (MNDP), 5.6m to 3.5m (TALL), and 6.7m to 4.3m (SERC), and improved the relative RMSE from 33.0 % to 14.8 % (MNDP), 27.4 % to 17.3 % (TALL), and 24.9 % to 15.8 % (SERC), for GEDI beam sensitivity ≥0.9, with similar improvements demonstrated for beam sensitivity ≥0.95 and ≥ 0.98. Further accuracy improvements were demonstrated for footprints over homogenous canopies where the simulated impact of GEDI geolocation errors were small, underscoring the need for improved GEDI geolocation. Extrapolation of the methodology to regional or national scale merits further research and is discussed.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100221"},"PeriodicalIF":5.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced simulation of gross and net carbon fluxes in a managed Mediterranean forest by the use of multi-sensor data","authors":"Marta Chiesi ,&nbsp;Nicola Arriga ,&nbsp;Luca Fibbi ,&nbsp;Lorenzo Bottai ,&nbsp;Luigi D'Acqui ,&nbsp;Alessandro Dell’Acqua ,&nbsp;Sara Di Lonardo ,&nbsp;Lorenzo Gardin ,&nbsp;Maurizio Pieri ,&nbsp;Fabio Maselli","doi":"10.1016/j.srs.2025.100216","DOIUrl":"10.1016/j.srs.2025.100216","url":null,"abstract":"<div><div>The current paper presents the last advancements introduced into a modelling strategy capable of simulating gross and net forest carbon (C) fluxes, i.e. gross and net primary and net ecosystem production (GPP, NPP and NEP, respectively). The simulation is performed by combining the outputs of a NDVI driven model, Modified C-Fix, and a bio-geochemical model, BIOME-BGC, taking into account the effects of forest disturbances. The proposed advancements are aimed at improving the model performance in managed Mediterranean forests and concern: i) the calibration of C-Fix GPP sensitivity to water stress; ii) the quantification of the green, woody and soil C pools which regulate the prediction of NPP and NEP. These two issues are addressed by the processing of additional remotely sensed datasets, i.e. low spatial resolution satellite imagery and high spatial resolution airborne laser scanner data. The original and modified model versions are tested in a Mediterranean pine forest which has been the subject of several investigations and where a new eddy covariance flux tower was installed at the end of 2012. This allows the assessment of the GPP and NEP estimates versus daily tower observations of eleven years (2013–2023), while mean stand NPP estimates are evaluated against measurements of current annual increments (CAI) taken in the pine forest. The results obtained support the capability of the proposed modifications to improve the model accounting for the major environmental factors which regulate the three C fluxes. The calibration of C-Fix, in particular, improves the reproduction of the high mean daily GPP observations consequent on the moderate ecosystem sensitivity to water stress (r² increases from 0.87 to 0.91, whilst RMSE and MBE decrease from 1.65 to 1.04 and from −1.37 to −0.56 g C m⁻² day⁻¹, respectively). The quantification of the forest C pools enables the consideration of stand aging, which is decisive for the correct simulation of the relatively low NPP and NEP observations. The assessment of the final CAI estimates, in fact, yields a high accuracy (r² = 0.653, RMSE = 1.38 m³ ha⁻¹ y⁻¹ and MBE = 0.42 m³ ha⁻¹ y⁻¹); the case is similar for the mean daily NEP estimates, which accurately reproduce the flux tower observations (r² = 0.669, RMSE = 0.91 g C m⁻² day⁻¹ and MBE = 0.11 g C m⁻² day⁻¹).</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100216"},"PeriodicalIF":5.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combination of GEOBIA and data-driven approach for grassland habitat mapping in the Alta Murgia National Park","authors":"Cristina Tarantino ,&nbsp;Marica De Lucia ,&nbsp;Luciana Zollo ,&nbsp;Mariagiovanna Dell’Aglio ,&nbsp;Maria Adamo ,&nbsp;Rocco Labadessa","doi":"10.1016/j.srs.2025.100214","DOIUrl":"10.1016/j.srs.2025.100214","url":null,"abstract":"<div><div>This study aims to discriminate semi-natural dry grassland habitats (namely: 6210, 6220, 62A0, according to the Annex I of the European Habitat Directive) in the Alta Murgia National Park, in southern Italy. These Mediterranean habitats are often characterized by small and fragmented patches, therefore, multi-season very high spatial resolution satellite images and data-driven Geographic Object-Based Image Analysis (GEOBIA) approach were considered to obtain grassland habitats mapping by an automatic classification process. Different classifiers such as Support Vector Machine (SVM) and Random Forest (RF) were evaluated, and their performance was compared by varying different input feature configurations such as the number of seasonal images used. Pléiades and Worldview-2 satellite images were considered. A dual nomenclature was adopted to consider the set of vegetation mosaics and transitional stages occurring in the field. RF performed better than SVM. Although the F1-scores of the different habitat classes were not greater than 0.75 and further improvements are needed, the results can be considered a satisfying preliminary attempt to automatically reproduce, at fine scale, the fragmentation of grassland habitats on the large area of Alta Murgia National Park. The mapping can be a useful tool for local authorities involved in the periodic monitoring of habitats in protected areas according to the European Habitat Directive and the fine scale can support focused local decision-making process for the conservation of natural ecosystems.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100214"},"PeriodicalIF":5.7,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-series urban green space mapping and analysis through automatic sample generation and seasonal consistency modification on Sentinel-2 data: A case study of Shanghai, China","authors":"Zhuoqun Chai,&nbsp;Keyao Wen,&nbsp;Hao Fu,&nbsp;Mengxi Liu,&nbsp;Qian Shi","doi":"10.1016/j.srs.2025.100215","DOIUrl":"10.1016/j.srs.2025.100215","url":null,"abstract":"<div><div>Urban green space (UGS) is crucial for the vitality and sustainability of the urban environment. However, the current UGS extraction methods based on satellite images still face the problem of high sample costs and phenological interference, which leads to insufficient efficiency and accuracy in UGS results. In response, this study proposes a robust method for UGS mapping from time-series Sentinel-2 data by automatic sample generation and seasonal consistency modification. Specifically, temporal training samples were selected automatically through anomaly detection and probability filtering. Based on the annual UGS maps obtained by random forest classifier, the seasonal consistency modification approach considering phenological information and category interference is introduced to improve the accuracy of UGS mapping. The UGS maps of Shanghai from 2017 to 2022 extracted by the proposed method demonstrate an overall accuracy of 91.4% and a Kappa coefficient of 81.19%. This indicates that the proposed method can significantly enhance the efficiency and accuracy of extracting time-series UGS maps from Sentinel-2 data. The dynamic results also highlight the spatiotemporal patterns of UGS in Shanghai from 2017 to 2022, offering valuable insights for sustainable urban development.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100215"},"PeriodicalIF":5.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A global Swin-Unet Sentinel-2 surface reflectance-based cloud and cloud shadow detection algorithm for the NASA Harmonized Landsat Sentinel-2 (HLS) dataset","authors":"Haiyan Huang ,&nbsp;David P. Roy ,&nbsp;Hugo De Lemos ,&nbsp;Yuean Qiu ,&nbsp;Hankui K. Zhang","doi":"10.1016/j.srs.2025.100213","DOIUrl":"10.1016/j.srs.2025.100213","url":null,"abstract":"<div><div>The NASA Harmonized Landsat Sentinel-2 (HLS) data provides global coverage atmospherically corrected surface reflectance with a 30m cloud and cloud shadow mask derived using the Fmask algorithm applied to top-of-atmosphere (TOA) reflectance. In this study we demonstrate, as have other researchers, low Sentinel-2 Fmask performance, and present a solution that applies a deep learning Swin-Unet model to the HLS surface reflectance to provide unambiguously improved cloud and cloud shadow detection. The model was trained and assessed using 30m HLS surface reflectance for the 13 Sentinel-2 bands and corresponding CloudSEN12+ annotations, that define cloud, thin cloud, clear, and cloud shadow, and is the largest publicly available expert annotation set. All the CloudSEN12 annotations with coincident HLS Sentinel-2 data were considered. A total of 8672 globally distributed 5 × 5 km data sets were used, 7362 to train the model, 464 for internal model validation, and 846 to independently assess the classification accuracy. The HLS Sentinel-2 Fmask had F1-scores of 0.832 (cloud), 0.546 (cloud shadow), and 0.873 (clear), and the Swin-Unet model had higher performance with F1-scores of 0.891 (cloud and thin cloud combined), 0.710 (cloud shadow), and 0.923 (clear) despite the use of surface and not TOA reflectance. The Swin-Unet thin cloud class had low accuracy (0.604 F1-score) likely due to atmospheric correction issues and thin cloud variability that are discussed. The comprehensively trained model provides a solution for users who wish to improve the HLS Sentinel-2 cloud and cloud shadow masking using the available HLS Sentinel-2 surface reflectance data.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100213"},"PeriodicalIF":5.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CIAO: A machine-learning algorithm for mapping Arctic Ocean Chlorophyll-a from space","authors":"Maria Laura Zoffoli ,&nbsp;Vittorio Brando ,&nbsp;Gianluca Volpe ,&nbsp;Luis González Vilas ,&nbsp;Bede Ffinian Rowe Davies ,&nbsp;Robert Frouin ,&nbsp;Jaime Pitarch ,&nbsp;Simon Oiry ,&nbsp;Jing Tan ,&nbsp;Simone Colella ,&nbsp;Christian Marchese","doi":"10.1016/j.srs.2025.100212","DOIUrl":"10.1016/j.srs.2025.100212","url":null,"abstract":"<div><div>Ocean color (OC) remote sensing at a Pan-Arctic scale, with over 27 years of continuous daily data, provides critical insights into long-term trends and seasonal variability in phytoplankton abundance, indexed by Chlorophyll-a concentration (Chl-a). However, existing satellite algorithms for retrieving Chl-a in the Arctic Ocean (AO) exhibit significant limitations, including high uncertainties and inconsistent accuracy across different regions, which propagate errors in primary production estimates and biogeochemical models. In this study, we quantified the uncertainties of seven existing algorithms using harmonized, merged multi-sensor satellite remote sensing reflectance (Rrs) data from the ESA Climate Change Initiative (CCI) spanning 1998–2023. The existing algorithms exhibited varying performance, with Mean Absolute Differences (MAD) ranging from 0.8 to 4.2 mg m⁻³. To improve these results, we developed CIAO (<strong>C</strong>hlorophyll In the <strong>A</strong>rctic <strong>O</strong>cean), a machine learning-based algorithm specifically designed for AO waters and trained with satellite Rrs data. The CIAO algorithm uses Rrs at four spectral bands (443, 490, 510 and 560 nm) and Day-Of-Year (DOY) to account for seasonal variations in bio-optical relationships. CIAO significantly outperformed seven existing algorithms, achieving a MAD of 0.5 mg m⁻³, thereby improving Chl-a retrievals by at least 30%, compared to the best-performing existing algorithm. Furthermore, CIAO produced consistent spatial patterns without artifacts and provided more reliable Chl-a estimates in coastal waters, where other algorithms tend to overestimate. This enhanced the accuracy of seasonal variability tracking at a Pan-Arctic scale. By strengthening the precision of satellite-derived Chl-a estimates, CIAO contributes to more accurate ecological assessments and robust climate projections for the rapidly changing AO.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100212"},"PeriodicalIF":5.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the robustness of Bayesian flood mapping with Sentinel-1 data: A multi-event validation study","authors":"Florian Roth ,&nbsp;Mark Edwin Tupas ,&nbsp;Claudio Navacchi ,&nbsp;Jie Zhao ,&nbsp;Wolfgang Wagner ,&nbsp;Bernhard Bauer-Marschallinger","doi":"10.1016/j.srs.2025.100210","DOIUrl":"10.1016/j.srs.2025.100210","url":null,"abstract":"<div><div>The impact of recent extreme flood events has once again highlighted the importance of accurate near-real-time flood information. Consequently, a number of operational services have been established that primarily use Synthetic Aperture Radar (SAR) data to map flood extent. Among them is the Global Flood Monitoring (GFM) service that is part of the Copernicus Emergency Management Service (CEMS). Using the systematic monitoring capabilities of Sentinel-1, it is the first service to deliver flood maps fully automatic on a global scale. To automatically and reliably monitor flood extent worldwide, the strengths and weaknesses of flood mapping methods need to be known under various and sometimes challenging conditions. To examine the performance of the TU Wien Bayesian flood mapping algorithm, which is one of the scientific flood algorithms used operationally in the CEMS GFM service, we designed this validation study in which we compare our results with all compatible Sentinel-1-based flood events of the CEMS on-demand mapping (ODM) service between January 2021 and January 2022. In total, the study investigates 18 events from five continents. In addition to computing common accuracy metrics, eight representative events were analysed in detail to understand the reasons for the differences found, identify potential improvements for the method, and gain generic insights for radar-based flood mapping. Most differences are caused by the use of the VH polarization in some of the ODM reference maps, while the GFM service so far relies exclusively on VV polarization due to computational costs. The impact of using two polarizations can be seen in particular over vegetation or in case of windy conditions. Furthermore, while the post-processing strategy applied in the TU Wien algorithm helps to prevent speckle impact, it also smooths out important details in small-scale flood events. Nonetheless, the automatic TU Wien algorithm achieved a Critical Success Index (CSI) of over 70% against the semi-automatic reference in 10 of 18 flood events. It exceeds this mark for all large-scale events and in cases without vegetation close to the flooded surfaces. Overall, the median User’s Accuracy (UA) is 84.0 %, the Producer’s Accuracy (PA) is 72.9% and the Overall Accuracy (OA) is 85.3%. The results demonstrate that the GFM service would benefit for using both VV and VH polarization and relaxing filters applied in the SAR processing workflow.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100210"},"PeriodicalIF":5.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New joint estimation method for emissivity and temperature distribution based on a Kriged Marginalized Particle Filter: Application to simulated infrared thermal image sequences","authors":"Thibaud Toullier ,&nbsp;Jean Dumoulin ,&nbsp;Laurent Mevel","doi":"10.1016/j.srs.2025.100209","DOIUrl":"10.1016/j.srs.2025.100209","url":null,"abstract":"<div><div>This paper addresses the challenge of simultaneously estimating temperature and emissivity for infrared thermography in natural environment, aiming for near real-time performance. Existing methods, mainly in satellite observation field, rely on restrictive physical assumptions unsuitable for ground-based application context (Structures and Infrastructures monitoring). Other generic methods are nonetheless computationally intensive, making them impractical for real-time use. Our objective is to provide a method with effective real-time calculation performance while still giving results comparable to those reference methods under the same hypotheses, finally achieving both good accuracy and performance. The proposed method is based on a dynamical state-space modeling for the temperature, where the state vector is assumed to be split into a dynamic component for the temperature and a stationary component representing the emissivity. Then the dynamical component is estimated by a Kalman filter approach, whereas the parameterized model and the emissivity component are estimated through a particle filtering framework resulting in a bank of Kalman filters, also called marginalized particle filter. A spatial assumption of homogeneity for the temperature yields to the addition of a Kriging step to the Marginalized Particle Filter to overcome the ill-posed nature of the problem and to compute the necessary physical estimates in a reasonable amount of time while providing fair results compared to reference methods from the literature.</div><div>A comparison with two state-of-the-art methods, MCMC and CMA-ES, is presented. The results indicate that the proposed method estimates the true value within a maximum deviation of <span><math><mrow><mn>3</mn><mspace></mspace><mtext>K</mtext></mrow></math></span>, similar to CMA-ES, while MCMC achieves a more accurate estimate with a maximum deviation of <span><math><mrow><mn>0</mn><mo>.</mo><mn>5</mn><mspace></mspace><mtext>K</mtext></mrow></math></span>. However, the computational efficiency of the proposed method is significantly improved, reducing the processing time by seven orders of magnitude compared to MCMC and three orders of magnitude compared to CMA-ES. This remarkable efficiency highlights the method’s feasibility for real-time monitoring of temperature and emissivity.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100209"},"PeriodicalIF":5.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}