Remote Sensing of Environment最新文献

{"title":"Relevance of earth observations of essential climate variables in wildfire adaptation","authors":"S.P. Seitzinger ,&nbsp;E. Chuvieco ,&nbsp;F. Di Giuseppe ,&nbsp;A. Bombelli ,&nbsp;C. Cagnazzo ,&nbsp;S. Harris ,&nbsp;N. Tapper","doi":"10.1016/j.rse.2025.115082","DOIUrl":"10.1016/j.rse.2025.115082","url":null,"abstract":"<div><div>Wildfire extent and intensity are changing globally, driven by climate and socio-economic changes. Data across local to global scales are needed to support wildfire adaptation, yet comprehensive analysis of the main variables controlling multiple dimensions of wildfire risk including availability of global data for those variables is lacking. This review examines key variables influencing wildfire risk including fire danger (ignition and propagation), exposure and social and ecosystem vulnerability - the latter two often missing from fire risk analyses. We propose spatial and temporal requirements for these variables, a major gap in the literature, to support adaptation to future conditions. We assess which variables are monitored by national observation networks and evaluate the extent to which the Global Climate Observing System (GCOS) Essential Climate Variables (ECVs), traditionally used for climate system modeling, meet wildfire adaptation needs.</div><div>We identified 30 key wildfire risk variables, 25 of which are represented by GCOS ECV products within 18 ECVs. Cross-product analysis showed most are relevant to multiple wildfire risk components (ignition, propagation, exposure, societal and ecological vulnerability) and that many relevant to fire danger (ignition and propagation) also inform exposure and vulnerability. Currently, 20 key variables are operationally monitored by space agency satellites, with global coverage at estimated temporal and/or spatial resolution available for at least half of variables in each risk component, except exposure. Future research directions and global observation needs to strengthen wildfire adaptation challenges are identified. This work supports UNFCCC and Paris Agreement calls to identify observations needs for climate adaptation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115082"},"PeriodicalIF":11.4,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting plant physiological changes under stress using sun-induced chlorophyll fluorescence from mid-spectral resolution imagery","authors":"Wonseok Choi ,&nbsp;Youngryel Ryu ,&nbsp;Hyungsuk Kimm ,&nbsp;Anirudh Belwalkar ,&nbsp;Tomas Poblete ,&nbsp;Insu Yeon ,&nbsp;Jae-Hyun Ryu ,&nbsp;Kyung-Do Lee ,&nbsp;Pablo J. Zarco-Tejada","doi":"10.1016/j.rse.2025.115085","DOIUrl":"10.1016/j.rse.2025.115085","url":null,"abstract":"<div><div>Sun-induced chlorophyll fluorescence (SIF) serves as a valuable indicator for remote sensing of plant physiology. Recent studies have shown that far-red SIF at 761 nm (hereafter SIF) from mid spectral resolution (SR) imagers (3–7 nm full width half maximum (FWHM)) derived reliable fluorescence emission in relative levels. Such mid SR based SIF (SIF_MS; the subscript MS refers to mid SR basis) has been extensively utilized for assessing fluorescence variability in precision agriculture and plant stress detection across various platforms. However, the uncertainties regarding the ability of SIF_MS to detect physiological SIF yield (ΦF) remain inadequately investigated. Here, we evaluated various retrieval techniques (i.e., Fraunhofer line depth (FLD), its variant methods, and spectral fitting method (SFM)) under theoretical condition (SCOPE simulation), stress condition (DCMU experiment at field level and rice brown spot disease detection at drone level), and at airborne level employing mid SR (3–7 nm FWHM) and sub-nanometer SR (&lt; 0.2 nm FWHM) sensors. Under theoretical conditions, the SIF_MS derived from aFLD, 3FLD and SFM showed strong linear relationships with the simulated fluorescence at 761 nm (R² &gt; 0.9; RMSE &lt;0.5 mW m⁻² nm⁻¹ sr⁻¹). With data collected under stress conditions, however, only the SFM method captured the stress-induced physiological changes in the field-ΦF_MS (<em>p</em>-value &lt;0.05) and from the drone-ΦF_MS (p-value &lt;0.05; R² = 0.51). SFM also produced a robust and clear map of airborne-SIF_MS (SIF_MS = 1.03 x SIF_SN + 0.48 where the subscript SN refers to sub-nanometer SR). Conversely, 3FLD and aFLD methods yielded a limited ability to adequately detect the physiological changes in field-ΦF_MS (<em>p</em>-value&gt;0.05) and drone-ΦF_MS (p-value &lt;0.05; R² ≤ 0.15) and exhibited a noticeable stripe noise in the airborne-SIF_MS map. Our finding demonstrated that mid SR imagery with SFM has the potential to track crop ΦF changes induced by stresses.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115085"},"PeriodicalIF":11.4,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel retrieval of global dust optical depth and effective diameter based on MODIS thermal infrared observations","authors":"Jianyu Zheng ,&nbsp;Hongbin Yu ,&nbsp;Yaping Zhou ,&nbsp;Yingxi Shi ,&nbsp;Zhibo Zhang ,&nbsp;Claudia Di Biagio ,&nbsp;Paola Formenti ,&nbsp;Alexander Smirnov","doi":"10.1016/j.rse.2025.115083","DOIUrl":"10.1016/j.rse.2025.115083","url":null,"abstract":"<div><div>Airborne mineral dust significantly influences Earth's climate through perturbing Earth's radiation budget, modulating cloud formation and microphysical properties, and fertilizing the biosphere. Recent field campaigns have revealed substantially more coarse-mode dust particles in the atmosphere than previously recognized, yet current satellite retrievals and climate models inadequately represent these particles. This study presents a novel retrieval algorithm for dust aerosol optical depth at 10 μm (AOD_10μm) and effective diameter (Deff) using Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) observations over global land and ocean. Building upon the previous synergistic approach for MODIS and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), we improve the retrieval from CALIOP-track-limited coverage to full-swath MODIS observations at 10-km resolution over both ocean and land surfaces. The retrieval improvements include: (1) application of climatological CALIOP dust vertical profiles (2007–2017) to constrain dust vertical distribution for off-CALIOP-track pixels; (2) the improved optimization method to effectively handle non-monotonic cost functions arising from temperature inversions within the Saharan Air Layer; and (3) extension to land surfaces through incorporation of MODIS-retrieved surface emissivity and ERA5 reanalysis data. Validation against coarse-mode AOD from global AERONET (<em>N</em> = 4703) and MAN (<em>N</em> = 1673) observations yields <em>R</em> = 0.82 and 0.85 for AOD_10μm, with retrieval uncertainty characterized as ε = 15 % × AOD + 0.04. The retrieved Deff demonstrates excellent agreement with in-situ measurements collected from 24 field campaigns around the globe (<em>R</em> = 0.84, MBE = 0.23 μm, RMSE = 0.73 μm), capturing the particle size variation from near-source regions (Deff = 7–8 μm) to long-range transport (Deff = 3–5 μm). Case studies of dust events over the Namibian coast and trans-Atlantic corridor demonstrate the retrieval's capability to resolve episodic dust properties and size-dependent deposition during transport. This improved retrieval addresses the critical observational gap for coarse and super-coarse dust particles (D &gt; 5 μm), providing essential constraints for dust life cycle studies and climate model evaluation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115083"},"PeriodicalIF":11.4,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145306303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global uncertainty assessment of vegetation indices from NASA's Harmonized Landsat and Sentinel-2 Project","authors":"Qiang Zhou ,&nbsp;Christopher S.R. Neigh ,&nbsp;Junchang Ju ,&nbsp;Margaret Wooten ,&nbsp;Zhe Zhu ,&nbsp;Tomoaki Miura ,&nbsp;Petya K.E. Campbell ,&nbsp;Madhu K. Sridhar ,&nbsp;Bradley W. Baker ,&nbsp;Rodrigo V. Leite","doi":"10.1016/j.rse.2025.115084","DOIUrl":"10.1016/j.rse.2025.115084","url":null,"abstract":"&lt;div&gt;&lt;div&gt;NASA's Harmonized Landsat and Sentinel-2 (HLS) project recently started to produce in forward production a total of nine Vegetation Index (VI) products from the HLS version 2.0 Landsat 8–9 30 m (L30) and Sentinel-2 30 m (S30) surface reflectance data. The HLS version 2.0 dataset provides revisit observations every 1.6 days globally and every 2.2 days in the tropics (the least frequently covered latitudes), when data from four satellites (Landsat 8–9 and Sentinel-2 A/B) are available. HLS-derived VIs can provide a valuable resource for studying vegetation dynamics, including crop growth, forest loss, and disturbance severity and recovery among others. To characterize the suitability of these VIs for scientific applications, we assessed the between-sensor uncertainties for the nine HLS VI products and 12 additional ones, using VIs derived from HLS V2.0 (L30 and S30) surface reflectance for the years 2021 and 2022. A random sample of over 136 million cloud-free observations from 545 same-day L30 and S30 image pairs were selected to represent different landscapes globally in subarctic, temperate, and tropical climates. First, we evaluated between-sensor consistency for each VI derived from L30 and S30 and found high consistency (R&lt;sup&gt;2&lt;/sup&gt; &gt; 0.94) for most VIs, except for chlorophyll vegetation index (CVI, R&lt;sup&gt;2&lt;/sup&gt; = 0.5). Second, we quantified the impact of potential factors on VI uncertainties using the mean absolute difference (MAD) between L30 and S30. Large view azimuth angle differences (VAD) between observation pairs (&gt; ∼ 125°) increased MAD by ≤0.01 in most VIs. The impact on the Root Mean Square Error Interquartile Range (RMSEIQR) for these VIs varied from a decrease of 0.029 to an increase of 0.017. High solar zenith angle (SZ) (&gt; ∼ 60°), prevalent during winter, also increased MAD by &lt;0.07 and RMSEIQR by &lt;0.2 for most VIs. Furthermore, one of the largest discrepancies was found in the area of terrain shadow, with a relative difference of over 20 %. The findings showed the importance of continuing HLS algorithm refinement. Finally, we analyzed VI uncertainties across VI values and for the qualitative aerosol optical depth characterization at three levels. Using VIs derived from low-level aerosols as a baseline, we assessed the impact of aerosol levels. VIs derived from moderate-level aerosol conditions closely aligned with the baseline. However, high aerosol levels introduced evident discrepancies, highlighting increased uncertainty in VIs under these conditions. Notably, even for low-level aerosol observations, uncertainties increased at VI tail values. For robust application of HLS V2.0 VIs in scientific studies, we recommend VI value ranges associated with low uncertainty. Additionally, we reported standard deviations of discrepancies, stratified by aerosol level and VI value, enabling users to account for uncertainties in their analyses, especially for VIs derived from high aerosol levels or beyond recom","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115084"},"PeriodicalIF":11.4,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145306306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The next Landsat: Mission turning point?","authors":"David P. Roy ,&nbsp;Michael A. Wulder ,&nbsp;Noel Gorelick ,&nbsp;Matthew Hansen ,&nbsp;Sean Healey ,&nbsp;Patrick Hostert ,&nbsp;Justin Huntington ,&nbsp;Volker C. Radeloff ,&nbsp;Ted Scambos ,&nbsp;Crystal Schaaf ,&nbsp;Curtis E. Woodcock ,&nbsp;Zhe Zhu","doi":"10.1016/j.rse.2025.115087","DOIUrl":"10.1016/j.rse.2025.115087","url":null,"abstract":"<div><div>For over fifty years, the Landsat satellite series has provided continuous and comprehensive data for monitoring changes on the Earth's terrestrial surface. Eight successive missions, carrying progressively more sophisticated sensors, with improved radiometric, geometric, and spatial characteristics, have provided an unbroken series of optical and thermal imagery, unparalleled globally. With limited lifetimes for each Landsat satellite, planning of each mission typically overlaps to ensure continuity. Commencing in 2021, planning of a Landsat-9 successor gathered user needs from across the Earth Observation (EO) community, resulting in the Landsat Next (LNext) mission design of three sun-synchronous satellites to acquire reflective and thermal wavelength observations with two to three times the temporal, spatial, and spectral resolution of previous missions. Proposed 2026 U.S. budgets have significantly reduced NASA Earth Science funding. Alternate architectures are now being investigated for Landsat Next that would only meet Landsat-9 design requirements. While this would provide observation continuity, this implies a revised Landsat Next program launched in the early 2030s with nearly 30 year old capabilities, that may acquire data with lower radiometric quality than the current on-orbit Landsat-8 and 9 missions, and that will not support the new capabilities advocated for by the EO user community. This correspondence serves to raise community awareness that the decision is pending, and outlines the observation requirements originally envisioned for LNext and how they were derived to provide context for evaluating the restructured and descoped capability now being considered.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115087"},"PeriodicalIF":11.4,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145306307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monthly monitoring of urban development and renewal at the block-level in China using Sentinel-2 time series","authors":"Haixu He ,&nbsp;Jining Yan ,&nbsp;Lirong Liu ,&nbsp;Xu Long ,&nbsp;Runyu Fan ,&nbsp;Zhongchang Sun","doi":"10.1016/j.rse.2025.115070","DOIUrl":"10.1016/j.rse.2025.115070","url":null,"abstract":"<div><div>Urban renewal has been elevated to a national strategy in China, leading to rapid development and transformation of street blocks. However, monitoring construction events at high temporal resolution remains challenging due to the limitations of existing methods, which often struggle with noise interference and lack continuous monitoring capabilities. To address this, we propose Semantic Similarity Contrast-based Street Block Monitoring (SSC-SB), a method that leverages Sentinel-2 time series imagery for automated, high-frequency detection of street block development and renewal. By extracting deep semantic features with a pretrained encoder, SSC-SB analyzes similarity curves to identify development and demolition construction events. Applied to the Middle Yangtze River Basin (MYRB) urban agglomeration shows that SSC-SB achieves 90.4% spatial domain accuracy, with construction start and end date detection accuracies of 68.8% and 54.9%, respectively. Results indicate an increasing emphasis on urban renewal, as demolished street blocks outnumbered new developments for the first time in 2023, with Hunan Province leading in renewal efforts, where renewal blocks accounted for 41.5% of all changed street blocks, reflecting a balanced focus on expansion and infrastructure renewal. Transfer experiments in Xi’an further demonstrate that SSC-SB retains up to 80% of the performance of a locally trained model when applied across regions without fine-tuning, indicating a decent level of generalizability. By providing fine-grained, continuous monitoring, SSC-SB presents a scalable solution for tracking urban transformation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115070"},"PeriodicalIF":11.4,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145295217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aerosol-cloud layer detection algorithm of the DQ-1/ACDL","authors":"Feiyue Mao ,&nbsp;Weiwei Xu ,&nbsp;Zengxin Pan ,&nbsp;Lin Zang ,&nbsp;Ge Han ,&nbsp;Linxin Dai ,&nbsp;Xiuqing Hu ,&nbsp;Weibiao Chen ,&nbsp;Wei Gong","doi":"10.1016/j.rse.2025.115068","DOIUrl":"10.1016/j.rse.2025.115068","url":null,"abstract":"<div><div>Satellite lidar plays a unique role in observing the global vertical distribution of aerosols and clouds. CALIPSO (Apr 2006–Aug 2023) pioneered such observations, and China's Aerosol and Carbon Detection Lidar (ACDL) on board the DQ-1 satellite (Apr 2022-) continues this mission. Consequently, it is crucial to develop aerosol and cloud products of ACDL. Particularly, detecting the vertical and horizontal extent of aerosol and cloud layers is one of the most challenging tasks. In this study, we developed an ACDL layer detection algorithm based on the Two-Dimensional Multiscale Hypothesis Testing (2D-MHT) methodology. Notably, we proposed an approach for the uncertainty estimation in lidar return signals from the background atmosphere, enabling successful layer detection for ACDL. The results demonstrate that our algorithm not only accurately identifies layers within ACDL measurements, but also provides the probability that a specific signal bin belongs to a layer. This probability enables users to customize layer definitions, a feature not available in other lidar products that typically rely on threshold-based methods. Furthermore, the ACDL layer products offer higher horizontal resolution and detect 53.0 % more layers globally compared to the CALIPSO V4.51 merged layer product in June 2022. These findings underscore the significant potential of our algorithm and ACDL layer products for advancing atmospheric and climate research.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115068"},"PeriodicalIF":11.4,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145288904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emerging remote sensing techniques for hydrological applications","authors":"Jiangyuan Zeng, Di Long, Yongqiang Zhang, Dongryeol Ryu, Jean-Pierre Wigneron, Qi Huang","doi":"10.1016/j.rse.2025.115060","DOIUrl":"https://doi.org/10.1016/j.rse.2025.115060","url":null,"abstract":"In light of the rapid advancements in hydrological science research facilitated by cutting-edge remote sensing technologies, such as synthetic aperture radar (SAR), hyperspectral imaging, and Light Detection and Ranging (LiDAR), we have curated a special issue in <em>Remote Sensing of Environment</em> entitled “Emerging remote sensing techniques for hydrological applications”, spanning from October 2022 to April 2024. This special issue comprises 31 publications that highlight methodologies leveraging multi-sensor satellite platforms, unmanned aerial vehicles (UAVs), and advanced physical models and machine learning approaches to improve the monitoring and modeling of key hydrological flux and state variables. These remote sensing retrievals (e.g., river discharge and soil moisture) have been applied to various operational hydrological applications such as real-time flood monitoring and drought risk assessment. To provide a systematic overview, we categorize these publications based upon hydrological themes and the number of publications, covering topics such as water body, soil moisture, river discharge, water level, drought, water storage, and other related areas. Finally, we provide an outlook that envisages how the emerging trends (e.g., multi-sensor integration and machine learning-driven approaches) identified from the published studies will evolve and shape future research directions in hydrological remote sensing.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"215 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145288903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing nighttime cloud detection for moderate resolution imagers using a transformer based deep learning network","authors":"Yuhao Wu ,&nbsp;Bin Li ,&nbsp;Jun Li ,&nbsp;Yonglou Liang ,&nbsp;Naiqiang Zhang ,&nbsp;Anlai Sun","doi":"10.1016/j.rse.2025.115067","DOIUrl":"10.1016/j.rse.2025.115067","url":null,"abstract":"<div><div>Accurate cloud detection is essential for the quantitative applications of satellite imager observations, but nighttime cloud detection has challenges due to limited spectral bands, for example, physical methods using only infrared (IR) bands without using spatial textures as input for cloud detection often result in high uncertainties, especially in some situations such as cryosphere surface. Although numerous segmentation-style deep learning cloud detection algorithms have proposed in previous studies, they are inadequate for nighttime due to the difficulty in acquiring two-dimensional truth data for training and validation. To overcome these challenges, the Transformer based Nighttime Cloud Detection (TNCD) framework, which integrates spatial features and utilizes an advanced Transformer architecture with relative position encoding, layer scaling, and channel attention mechanisms, is proposed and investigated for nighttime cloud detection. The model was trained on labels derived from CALIOP data, utilizing a dataset comprising nearly one hundred million segments from MODIS. Independent validation indicates that TNCD achieves robust and consistent performance across various scenarios, with an overall accuracy (OA) of 93.26 % and over 90 % in cryosphere regions. The proposed algorithm avoids the pattern noise appeared in the traditional physical methodology due to the utilization of auxiliary data at coarser resolutions, it also mitigates the negative impact of stripes in IR images for cloud detection. Moreover, TNCD shows high transferable practicability across sensors, with over 90 % OA for MERSI. More importantly, our research underscores the importance of water vapor absorption bands for nighttime cloud detection over the cryosphere. TNCD's high accuracy and robustness provide unique methodology that could be used operationally for nighttime cloud detection.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115067"},"PeriodicalIF":11.4,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145283136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of canopy fAPAR using optical reflectance and airborne LiDAR data","authors":"Dalei Han ,&nbsp;Jing Liu ,&nbsp;Shan Xu ,&nbsp;Tiangang Yin ,&nbsp;Siya Liu ,&nbsp;Runfei Zhang ,&nbsp;Peiqi Yang","doi":"10.1016/j.rse.2025.115065","DOIUrl":"10.1016/j.rse.2025.115065","url":null,"abstract":"<div><div>The fraction of absorbed photosynthetically active radiation (fAPAR) of vegetation canopies is a crucial variable for understanding the ecosystem carbon cycle and assessing vegetation responses to climate change. Light absorption of the vegetation canopy is mainly determined by canopy structure and leaf optical properties. Traditional remote sensing methods typically estimate fAPAR from reflectance signals using radiative transfer models or empirical relationships with vegetation indices (VIs) and fAPAR. However, reflectance-based estimates often show moderate accuracy due to the complex relationship between reflected and absorbed fluxes. Airborne LiDAR provides direct information on canopy structural attributes relevant to radiation interception, such as fractional vegetation cover (fCover), which has been used to estimate fAPAR. However, the shortcomings of LiDAR in capturing the role of leaf optical properties introduce some uncertainty in fAPAR estimation. Combining reflectance with LiDAR data offers a promising pathway for improving fAPAR estimation. In this study, we adapted a physically-based model (<span><math><msub><mtext>fAPAR</mtext><mi>RL</mi></msub></math></span>) to integrate reflectance and LiDAR observations for fAPAR estimation. This model is grounded in spectral invariant theory and represents fAPAR as a function of visible and near-infrared reflectance and a LiDAR-derived canopy structural parameter. The model was evaluated against both VI- and LiDAR-based methods using NEON field datasets and synthetic datasets generated by the one-dimensional SCOPE and three-dimensional LESS radiative transfer models. Across these datasets, the combination of LiDAR and reflectance through the <span><math><msub><mtext>fAPAR</mtext><mi>RL</mi></msub></math></span> model consistently outperformed VI- and LiDAR-based approaches, with respective maximum improvements in R² of 0.47 and 0.09. Sensitivity analyses on the simulated datasets further indicated that <span><math><msub><mtext>fAPAR</mtext><mi>RL</mi></msub></math></span> exhibited higher robustness to variations in chlorophyll content and leaf area index (LAI) than other conventional methods. The proposed <span><math><msub><mtext>fAPAR</mtext><mi>RL</mi></msub></math></span> model effectively integrates reflectance and LiDAR data through a physically-based scheme, offering improved accuracy and robustness for large-scale fAPAR estimation and ecosystem monitoring.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115065"},"PeriodicalIF":11.4,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}