Science of Remote Sensing最新文献

{"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}

{"title":"Urban informal settlements interpretation via a novel multi-modal Kolmogorov–Arnold fusion network by exploring hierarchical features from remote sensing and street view images","authors":"Hongyang Niu,&nbsp;Runyu Fan,&nbsp;Jiajun Chen,&nbsp;Zijian Xu,&nbsp;Ruyi Feng","doi":"10.1016/j.srs.2025.100208","DOIUrl":"10.1016/j.srs.2025.100208","url":null,"abstract":"<div><div>Urban informal settlements (UIS) interpretation has important scientific value for achieving urban sustainable development. Recent research on UIS interpretation tasks mainly includes the single-modality method, which uses remote sensing images, and the multi-modality method which uses remote sensing and geospatial data. However, from a single remote sensing perspective, the inter-class similarities, and a regional mixture of complex geo-objects from a bird-eye perspective of UIS areas make UIS interpretation extremely challenging. The current multi-modal methods cannot fully explore the modality-specific features within the modality or ignore the modality-correlation features between different modalities. To address these issues, this study proposed a novel multi-modal Kolmogorov–Arnold fusion network, namely KANFusion, to explore the modality-specific features within the modality and fuse the modality-correlation features between different modalities to boost UIS interpretation using remote sensing and street view images. The proposed KANFusion model employs the Kolmogorov–Arnold Network (KAN) instead of the conventional MLP structure to enhance the model-fitting capability of heterogeneous modality-specific features and uses a novel Multi-level Feature Fusion Module with KAN block (MFF) to fuse the hierarchical modality-specific and modality-fusion features from remote sensing and street view images for better UIS interpretation performance. We conducted extensive experiments on the manually annotated ChinaUIS dataset of eight megacities in China and a public <span><math><mrow><msup><mrow><mi>S</mi></mrow><mrow><mn>2</mn></mrow></msup><mi>U</mi><mi>V</mi></mrow></math></span> dataset and compared the proposed KANFusion with other state-of-the-art methods. The experimental results confirmed the superiority of the proposed KANFusion. This work is available in <span><span>https://github.com/cyg-nhyang/KANFusion</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100208"},"PeriodicalIF":5.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474698","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":"Exploring machine learning trends in poverty mapping: A review and meta-analysis","authors":"Badri Raj Lamichhane ,&nbsp;Mahmud Isnan ,&nbsp;Teerayut Horanont","doi":"10.1016/j.srs.2025.100200","DOIUrl":"10.1016/j.srs.2025.100200","url":null,"abstract":"<div><div>Machine Learning (ML) has rapidly advanced as a transformative tool across numerous fields, offering new avenues for addressing poverty-related challenges. This study provides a comprehensive review and meta-analysis of 215 peer-reviewed articles published on Scopus from 2014 to 2023, underscoring the capacity of ML methods to enhance poverty mapping through satellite data analysis. Our findings highlight the significant role of ML in revealing micro-geographical poverty patterns, enabling more granular and accurate poverty assessments. By aggregating and systematically evaluating findings from the past decade, this meta-analysis uniquely identifies overarching trends and methodological insights in ML-driven poverty mapping, distinguishing itself from previous reviews that primarily synthesize existing literature. The nighttime light index emerged as a robust indicator for poverty estimation, though its predictive power improves significantly when combined with daytime features like land cover and building data. Random Forest consistently demonstrated high interpretability and predictive accuracy as the most widely adopted ML model. Key contributions from regions such as the United States, China, and India illustrate the substantial progress and applicability of ML techniques in poverty mapping. This research seeks to provide policymakers with enhanced analytical tools for nuanced poverty assessment, guiding more effective policy decisions aimed at fostering equitable development on a global scale.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100200"},"PeriodicalIF":5.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471656","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":"High spatiotemporal resolution vegetation FAPAR estimation from Sentinel-2 based on the spectral invariant theory","authors":"Yunzhu Tao ,&nbsp;Naijie Peng ,&nbsp;Wenjie Fan ,&nbsp;Xihan Mu ,&nbsp;Husi Letu ,&nbsp;Run Ma ,&nbsp;Siqi Yang ,&nbsp;Qunchao He ,&nbsp;Dechao Zhai ,&nbsp;Huangzhong Ren","doi":"10.1016/j.srs.2025.100207","DOIUrl":"10.1016/j.srs.2025.100207","url":null,"abstract":"<div><div>The fraction of absorbed photosynthetically active radiation (FAPAR) is a key input parameter that drives photosynthesis in terrestrial ecosystem models. It plays an important role in estimating canopy gross primary production and, consequently, the regional terrestrial carbon sink. The growing focus on regional responses to global climate change has increased the demand for FAPAR with high spatiotemporal resolution across spatial heterogeneous landscapes. However, instantaneous FAPAR values from satellites are insufficient for monitoring FAPAR throughout the day under varying sky conditions, given that cloud disturbances pose a significant challenge to the generation of high spatiotemporal resolution FAPAR. We proposed a FAPAR-Pro model based on spectral invariant theory to address this challenge. This model distinguishes simulations under direct and diffuse radiation to suit clear and cloudy conditions. The FAPAR-Pro model was validated across various vegetation types and sky conditions. The model was also compared with the FAPAR-P model and the SAIL model, where it exhibited robust performance (R² = 0.875, RMSE = 0.065, and bias = −0.004). Consequently, an hourly FAPAR estimation algorithm based on the FAPAR-Pro model (HFP) was developed to derive hourly FAPAR at high spatial resolution. It incorporates daily leaf area index retrieved and reconstructed from Sentinel-2 data, the hourly ratio of diffuse radiation retrieved from Himawari-8, and the leaf single scattering albedo and the soil reflectance derived from Sentinel-2 data using the general spectral vector-leaf (GSV-L) model and the general spectral vector (GSV) model, respectively. The resulting estimations closely matched the hourly ground measurements at Huailai station under diverse sky conditions (R² = 0.828, RMSE = 0.070, and bias = −0.011). Furthermore, a set of spatially continuous FAPAR data at the 20 m resolution was generated at the Saihanba area in China in 2022. By contrast, FAPAR estimations from the Sentinel-2 Toolbox and MODIS were significantly affected by cloudy conditions or coarse resolution. Overall, the proposed HFP algorithm can provide blue-sky FAPAR values at high spatiotemporal resolution regardless of various sky conditions. This advancement offers great potential for ecological models and numerous other applications.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100207"},"PeriodicalIF":5.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437557","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}