Remote Sensing of Environment最新文献

{"title":"Faster, better, and more accurate mapping of burned areas using Sentinel-2 multispectral images","authors":"Peng Liu ,&nbsp;Yongxue Liu ,&nbsp;Xiaoxiao Guo ,&nbsp;Yuchen Liu ,&nbsp;Wanjing Zhao ,&nbsp;Wenxuan Xu","doi":"10.1016/j.rse.2025.115137","DOIUrl":"10.1016/j.rse.2025.115137","url":null,"abstract":"<div><div>Faster, better, and more accurate burned area (BA) mapping is crucial for assessing the environmental and socio-economic impacts of wildfires. However, the diverse background disturbance, spectral variability, and extensive distribution of BAs pose significant challenges to their detection using Sentinel-2 multispectral imagery over large areas. Here, we developed a novel Moderate Spatial Resolution Burned Area Change Detection (MSR-BACD) framework, addressing these challenges through dataset development, model innovation, and inference optimization. Specifically: (<em>1</em>) we constructed a globally comprehensive MSR-BACD dataset, incorporating more than one million positive and negative samples (256 × 256 pixels) across diverse land cover backgrounds and cloud conditions to enhance model generalization; (<em>2</em>) we developed a specialized deep learning (DL) foundation model (custom Swin Transformer) leveraging dual-temporal (pre-and-post-fire) imagery to achieve precise BA delineation; and (<em>3</em>) we designed a candidate-based inference mode, collectively using the computational power and petabyte-scale RS data of the Google Earth Engine to generate BA candidates and the DL foundation model deployed on local GPU servers to filter out erroneous detections and delineate the BA extent, to improve detection efficiency significantly. Experimental results demonstrate that the MSR-BACD framework achieved a mean Intersection over Union (IoU) of 90.50 % in closed-set scenarios and outperformed existing moderate-resolution BA products in open-set evaluations in Portugal, increasing the Dice coefficient by 19.90 % while reducing computational costs by 95.62 % compared to traditional scene-by-scene exhaustive inference. These advancements highlight the MSR-BACD framework as a robust and efficient tool for regional-scale BA detection, contributing to wildfire science and application progress.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115137"},"PeriodicalIF":11.4,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145485706","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":"Forest aboveground biomass in the southwestern U.S. from MISR and GEDI: Assessment with NASA Carbon Monitoring System data","authors":"Mark J. Chopping ,&nbsp;Zhuosen Wang ,&nbsp;Crystal B. Schaaf ,&nbsp;Michael Bull","doi":"10.1016/j.rse.2025.115117","DOIUrl":"10.1016/j.rse.2025.115117","url":null,"abstract":"<div><div>Forest aboveground biomass (AGB) density mapping initiatives generally use one of three remote sensing approaches: lidar, radar, or near-nadir multispectral imaging leveraging machine learning methods, or a combination thereof. However, the active instrument record is limited and near-nadir multispectral imaging data are relatively insensitive to canopy physical structure. Multiangle imaging enables annual wall-to-wall mapping with a global record that extends back to 2000 as these data are highly sensitive to forest AGB. This paper describes work to validate estimates in a published annual, wall-to-wall record of forest AGB on a 250 m grid, derived using 672 nm imagery from the NASA, Jet Propulsion Laboratory's Multiangle Imaging Spectro-Radiometer (MISR) for 2000–2021, covering the southwestern United States. Estimates in the published MISR-derived annual forest AGB map series for the southwestern United States and the Global Ecosystem Dynamics Investigation (GEDI) L4B Gridded 1 km AGB product were both found to be highly consistent with NASA Carbon Monitoring System (CMS) airborne lidar survey (ALS) AGB data. MISR and GEDI v.2 (v.2.1) estimates yielded similar coefficients of determination (∼0.7) and Root Mean Square Error (RMSE) (∼60 Mg ha⁻¹) for all ALS data used. For the large CMS Sonoma County Improved AGB dataset, MISR and GEDI v.2 (v.2.1) estimates yielded <em>R</em>² = 0.88, 0.88 (0.91); RMSE = 58, 40 (37) Mg ha⁻¹. Estimates from MISR thus have an accuracy similar to that of the GEDI L4B gridded AGB product, with some limitations (e.g., topographic shading, tall, dense canopies). However the published MISR maps are on a 250 m grid, wall-to-wall, and cover the period 2000–2021. These results suggest MISR is able to provide a means to investigate trajectories of forest AGB change in the southwestern U.S. from 2000 onwards—over a substantial period of accelerating environmental and human- and climate-driven change– with reasonable precision.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115117"},"PeriodicalIF":11.4,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145485861","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":"Snow effects on altimeter waveforms over sea ice in the Weddell Sea — Part I: Radar waveform decomposition","authors":"Lu Zhou ,&nbsp;Henriette Skourup ,&nbsp;Julienne Stroeve ,&nbsp;Sahra Kacimi ,&nbsp;Stefanie Arndt ,&nbsp;Weixin Zhu ,&nbsp;Alek Petty ,&nbsp;Lanqing Huang ,&nbsp;Shiming Xu","doi":"10.1016/j.rse.2025.115112","DOIUrl":"10.1016/j.rse.2025.115112","url":null,"abstract":"<div><div>Snow on sea ice strongly modulates growth, albedo, and air–sea exchange, but it also drives major uncertainties in altimeter-based thickness retrievals. This is critical for Ku-band radar altimeters (e.g., CryoSat-2, CS-2), whose waveforms integrate backscatter from snow-covered sea ice. Contrary to the common assumption that returns originate near the snow–ice interface, complex snow properties (roughness, layering, wetness, ice lenses) can shift effective scattering upward into the snowpack. We analyze Ku-band CS-2 satellite and Ka-band KAREN airborne waveforms over the Weddell Sea to partition contributions from the snow surface, snow volume, and ice surface. Using a physics-based Forward Backscatter Emulation Model (FBEM) and a CNN trained on simulated waveforms, we retrieve geophysical parameters and assess sensitivity to snow conditions. Under typical Antarctic conditions, snow-volume scattering contributes as much as, or more than, the snow–ice interface to CS-2 returns, while Ka-band is dominated by surface/near-surface snow scattering with minimal penetration to the ice surface. Wet snow further amplifies upper-layer backscatter. Sensitivity tests identify volume scattering and ice-surface roughness as primary controls on waveform shape. These results argue for explicit snow-volume terms in waveform models and support dual-frequency strategies relevant to ESA’s upcoming CRISTAL mission. Part I (this study) treats waveform decomposition; Part II evaluates retracking for improved thickness retrievals.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115112"},"PeriodicalIF":11.4,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462066","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 intensity, footprint, and capacity of surface urban heat islands using a direction-enhanced adaptive synchronous extraction (DEASE) method","authors":"Qiquan Yang ,&nbsp;Richen Ye ,&nbsp;TC Chakraborty ,&nbsp;Ting Hu ,&nbsp;Yue Liu","doi":"10.1016/j.rse.2025.115118","DOIUrl":"10.1016/j.rse.2025.115118","url":null,"abstract":"<div><div>The surface urban heat island (SUHI) effect, assessed through remotely sensed land surface temperature (LST), remains a focal point in urban climate research. Conventional indicators like SUHI intensity (SUHII) and footprint (SUHIF) capture peak values and spatial extent but fail to account for the cumulative thermal load—a critical dimension reflecting the total heat exposure imposed by spatially continuous warming, which directly limits a holistic assessment of ecological and societal impacts of the SUHI effect. Therefore, this study introduces an indicator termed SUHI capacity (SUHIC), designed to quantify the aggregated SUHI effect by integrating the magnitude of the warming signal across all affected areas, thereby enabling a more comprehensive evaluation of urban thermal environments. Furthermore, a direction-enhanced adaptive synchronous extraction (DEASE) method is proposed for the quantification of SUHIC. This method can dynamically identify the optimal background reference area based on the urban-rural LST gradients in various directions within the city, without relying on predefined mathematical models as previously. The results from 102 European cities first confirm that the directional variations in urban-rural LST gradients, and the DEASE method can effectively capture these distinctions for the simultaneous estimation of SUHII, SUHIF, and SUHIC. Secondly, the spatial patterns of absolute SUHIC values show strong associations with those of SUHIF (R² &gt; 0.86), while its relative values (normalized by the area of urban) align more closely with SUHII (R² &gt; 0.64). More importantly, SUHIC can serve as a crucial reference for assessing the urban thermal signal when SUHII and SUHIF diverge. The proposed method and framework contribute to standardizing the quantification of the SUHI effect.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115118"},"PeriodicalIF":11.4,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462067","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":"AnytimeFormer: Fusing irregular and asynchronous SAR-optical time series to reconstruct reflectance at any given time","authors":"Kai Tang ,&nbsp;Xuehong Chen ,&nbsp;Tianyu Liu ,&nbsp;Anqi Li ,&nbsp;Yao Tang ,&nbsp;Peng Yang ,&nbsp;Jin Chen","doi":"10.1016/j.rse.2025.115120","DOIUrl":"10.1016/j.rse.2025.115120","url":null,"abstract":"<div><div>High-quality and temporally continuous surface reflectance data are essential for remote sensing applications, but cloud contamination introduces severe spatiotemporal gaps in optical satellite image time series (SITS). Recent multi-modal approaches, integrating optical and synthetic aperture radar (SAR) data with deep learning models, have advanced gap-free SITS reconstruction. However, several challenges remain. These include the reliance on external preprocessing to generate regular optical composite time series (e.g., 8-day intervals) and to temporally align SAR with optical data; insufficient modeling of the relationship between observation time series and their corresponding timestamps limits output flexibility, especially when generating predictions at any or user-specified time points; and reduced training and inference efficiency due to the incorporation of an additional modality (SAR). Collectively, these limitations hinder the practical applicability of current methods. To address the above challenges, this study proposes AnytimeFormer, a Transformer-based model for optical SITS reconstruction. First, AnytimeFormer accepts Sentinel-2 and Sentinel-1 observations along with their corresponding timestamps as input, and adaptively aligns temporally asynchronous multi-modal time series through a time-align attention module. Second, a low-rank fusion module is introduced to capture multi-modal information while preserving a lightweight architecture. Finally, a time-aware decoder enables the reconstruction of reflectance data at any user-specific time point. Extensive experiments conducted across eight globally distributed study sites, encompassing diverse land cover types and a range of cloud conditions, demonstrate the superior performance of AnytimeFormer (e.g., average RMSE: 0.03, R²: 0.95 at 80 % missing ratio) over benchmarks (HANTS, U-TILISE, STORI, RESTORE-DiT, et al.) under scenarios with different missing ratios and temporal pattern complexity. In terms of efficiency, compared to the suboptimal STORI, it reduces training time tenfold. Furthermore, the comparison with the Planet Fusion product shows a high consistency in temporal trends between the two and demonstrates AnytimeFormer's flexibility in reconstructing user-specified intervals. All the experiment results demonstrate that AnytimeFormer is a promising and practical solution for gap-free optical SITS reconstruction. Code and dataset available at <span><span>https://github.com/tangkai-RS/AnytimeFormer</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115120"},"PeriodicalIF":11.4,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455387","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":"Divergent remote sensing signals in long-term post-fire Eucalyptus recovery: Structural vs. functional traits","authors":"Feng Qiu ,&nbsp;Jinlong Zang ,&nbsp;Yongguang Zhang","doi":"10.1016/j.rse.2025.115121","DOIUrl":"10.1016/j.rse.2025.115121","url":null,"abstract":"<div><div>Climate-driven increases in wildfire frequency threaten forest resilience in fire-prone ecosystems. Although short-term studies indicate rapid recovery of eucalypt forests, such assessments may be overly optimistic, given the uncertain long-term alignment of structural and functional trajectories. This study integrated multi-source remote sensing data (2000−2020) with eddy covariance flux measurements to quantify post-fire recovery dynamics of eucalypt forests in southeastern Australia. We analyzed key structural metrics, including fraction of tree cover (FTC), the Normalized Difference Vegetation Index (NDVI), and leaf area index (LAI), as well as functional traits, including gross primary productivity (GPP), solar-induced fluorescence (SIF), evapotranspiration (ET), fuel moisture content (FMC), and vegetation optical depth (VOD). On average, all these traits recovered to pre-fire levels within 5.5 to 7.5 years, though some areas required over a decade or more. Recovery trajectories diverged notably both between the structural and functional categories and among individual traits within each category. These recovery rates varied across forest groups, modulated by disturbance severity, understory compositions, and fire frequency. Notably, functional traits such as GPP and SIF recovered rapidly, surpassing even the fastest structural metrics. At the Wallaby Creek flux site (burned in 2009 and rebuilt), GPP and SIF exhibited a distinct “peak-decline-stabilize” pattern, surpassing pre-fire levels within three years before declining as LAI recovered. In contrast, structural metrics recovered gradually, with LAI lagging behind FTC and NDVI. Our findings highlight the importance of integrating multi-dimensional data to comprehensively assess forest recovery, offering critical insights for ecosystem management and carbon cycle modeling in fire-prone regions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115121"},"PeriodicalIF":11.4,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455571","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":"Past, current and future solar radiation trends in Europe: Multi-source assessment of the role of clouds and aerosols","authors":"Leandro C. Segado-Moreno ,&nbsp;José A. Ruiz-Arias ,&nbsp;Juan Pedro Montávez ,&nbsp;Juraj Betak","doi":"10.1016/j.rse.2025.115122","DOIUrl":"10.1016/j.rse.2025.115122","url":null,"abstract":"<div><div>The increase of surface solar radiation (SSR) observed during the last decades in Europe has raised concerns for its implications for the climate system. Here, we evaluate the past and projected SSR trends in Europe from 1994 to 2054 based on a comprehensive set of ground site observations, five historical gridded datasets and a remarkable ensemble of 30 CMIP6 climate models with projections in four different forcing scenarios. Together, they provide a seamless unprecedented characterisation of the SSR trend in Europe across time and space. The regional SSR trend observed in the period 1994–2023 is 3.1 W/m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>/decade. All gridded datasets, except the ERA5 reanalysis, deviate from this value by <span><math><mo>±</mo></math></span>0.3 W/m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>/decade, or less. Assuming that clouds and aerosols are the only boosters of the SSR trend, it is found that <span><math><mo>≈</mo></math></span>20% of the trend is explained by aerosol direct effect (ADE). It is shown that the first aerosol indirect effect (AIE) alone cannot explain the SSR trend related to cloud changes, thus leaving a significant role for changes in cloud coverage. It is estimated that the total SSR trend is approximately explained in 1/5 by ADE, 2/5 by first AIE and 2/5 by cloud coverage changes. However, this fractions are likely affected by biases in aerosol optical depth. The results suggest that the CMIP6 ensemble overestimates (underestimates) the clear (cloudy) SSR trend during 1994–2014. The median SSR trend projected by the CMIP6 models is, on average, <span><math><mo>≈</mo></math></span>85% smaller than the trend observed during 1994–2023 with small differences between forcings scenarios.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115122"},"PeriodicalIF":11.4,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455574","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":"Streetlight density and quantity estimation using glimmer images from SDGSAT-1","authors":"Ziqi Yin ,&nbsp;Mingquan Wu ,&nbsp;Zheng Niu ,&nbsp;Li Wang ,&nbsp;Changyong Dou","doi":"10.1016/j.rse.2025.115119","DOIUrl":"10.1016/j.rse.2025.115119","url":null,"abstract":"<div><div>Streetlights are essential infrastructure in cities. They enhance traffic and public safety at night, but they also entail significant energy consumption. The study of the density, quantity and distribution of streetlights is essential for sustainable urban development. The Sustainable Development Global Scientific Satellite-1 has enriched night-time light data. This study is based on the Crowded Scene Recognition Network (CSRNet) and proposes a novel Streetlight Counting Network (SLCNet) by introducing a deeper front-end network and adding a spatial attention mechanism. This model can effectively estimate the density distribution of streetlights within a region and achieve automatic counting of large-scale streetlight numbers. Experimental results show that SLCNet achieves a mean absolute error of 135.35, a root mean square error of 231.37, and a normalised absolute error of 0.22, representing reductions of 5.60 %, 8.08 %, and 12.00 % respectively outperforming the original CSRNet model. The visualisation results also show that SLCNet predicts the distribution of streetlight density more accurately than CSRNet. Comprehensive ablation experiments further validate the effectiveness of each improvement method. Compared with the high-resolution yet costly Jilin-1 night-time images, SLCNet maintains comparable accuracy while enabling large-scale streetlight density and quantity estimation with publicly accessible SDGSAT-1 images. This study demonstrates the application potential of SDGSAT-1 in urban night-time lighting monitoring, providing new technical pathways and research frameworks for urban lighting management, sustainable development assessment, and smart city construction.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115119"},"PeriodicalIF":11.4,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462068","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":"Reducing bias in cropland soil organic carbon and clay predictions using Sentinel-2 composites and data balancing","authors":"Tom Broeg ,&nbsp;Axel Don ,&nbsp;Thomas Scholten ,&nbsp;Stefan Erasmi","doi":"10.1016/j.rse.2025.115109","DOIUrl":"10.1016/j.rse.2025.115109","url":null,"abstract":"<div><div>Accurate maps of cropland soil organic carbon stocks (SOCS) and clay content are essential for climate-smart agriculture. Soil reflectance composites (SRC), derived from multispectral bare soil observations, offer a scalable approach to high-resolution soil mapping. While studies often focus on maximizing model performance, challenges remain regarding (1) the bias introduced by masking and excluding soil samples during SRC generation and (2) the accurate representation of the full range and distribution of soil properties in the resulting maps. Evaluating different SRC parameters, we found that commonly used indices such as the Normalized Burn Ratio 2 (NBR2) and the Normalized Difference Vegetation Index (NDVI) were significantly correlated with clay content and SOCS, respectively. These dependencies can lead to the systematic exclusion of high SOCS (&gt;80 Mg ha⁻¹) and clay (&gt;30 mass%) samples during SRC generation, introducing bias in the resulting maps. Models trained solely on SRC bands failed to capture the full range of the training data, limiting the applicability of the soil property maps. While the inclusion of additional remote sensing features, such as spectral-temporal metrics and indices, significantly improved the prediction accuracy, the representation of the imbalanced samples remained challenging. We demonstrated that a combined framework of spatial data augmentation and majority undersampling was effective in improving the range and concordance correlation coefficient (CCC) of the predictions (SOCS = 0.82; Clay = 0.9). Our findings emphasize the importance of (1) evaluating excluded samples to identify potential SRC-induced bias, and (2) optimizing model predictions reflecting the observed data range to improve the reliability and usability of the resulting soil maps.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115109"},"PeriodicalIF":11.4,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145448021","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 relationship between spectral and integrated sun-induced chlorophyll fluorescence and its implication for photosynthesis estimation using fluorescence observations","authors":"Ji Li ,&nbsp;Lianhong Gu ,&nbsp;Jing M. Chen ,&nbsp;Yongguang Zhang","doi":"10.1016/j.rse.2025.115113","DOIUrl":"10.1016/j.rse.2025.115113","url":null,"abstract":"<div><div>Chlorophyll fluorescence (ChlF) is tightly linked to photosynthetic electron transport and informs gross primary productivity (GPP) across scales. Sun-induced ChlF (SIF) is typically retrieved at specific wavelengths (e.g., Fraunhofer lines, oxygen absorption bands) within a narrow field of view (solid angle), and given in power units. This spectral SIF radiance, denoted SIF_λ, is not directly photosynthetically relevant, rather, it is the integrated radiant exitance, SIF_int, expressed in molar units and integrated over 660–800 nm and the full angular domain, that corresponds mechanistically to photosynthesis. It is generally assumed that the SIF_λ is proportional to SIF_int and that this proportionality is spatially and temporally invariant. Here we tested this assumption with spectrally resolved SIF measured in three crop and six tree species at the leaf level. We found that while SIF_λ is significantly related to SIF_int at individual Fraunhofer lines, this relationship varies with wavelength and leaf chlorophyll content (LCC). We therefore developed a model to predict SIF_int from SIF_λ using wavelength and LCC as inputs. The model performed well across the ChlF emission band, particularly in the far-red region, enabling accurate conversion from observed SIF_λ to mechanistically relevant SIF_int. As an exploratory extension, the model was applied at the canopy scale for C3 and C4 crops with the Mechanistic Light Response model, yielding encouraging agreement between modeled and observed GPP. The core contribution of this work is establishing the SIF_λ–SIF_int relationship and a transfer model at the leaf scale, while the canopy application serves as an exploratory extension illustrating its scaling potential. Together, these findings provide a more mechanistically consistent basis for SIF-based GPP estimation and strengthen the application of fluorescence observations in carbon cycle research.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115113"},"PeriodicalIF":11.4,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145448022","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}