Remote Sensing of Environment最新文献

{"title":"Sensitivity of sun-induced chlorophyll fluorescence (SIF) and hyperspectral reflectance to drought response in soybean genotypes with contrasting affinities for arbuscular mycorrhizal fungi","authors":"Christine Y. Chang ,&nbsp;Jinyoung Y. Barnaby ,&nbsp;Jude E. Maul","doi":"10.1016/j.rse.2025.114722","DOIUrl":"10.1016/j.rse.2025.114722","url":null,"abstract":"<div><div>Increasing frequency and severity of drought events impact global and domestic agricultural productivity. Monitoring drought in agricultural fields with remote sensing can provide faster, lower-cost decision management support for critical field management activities. We evaluated the application of sun-induced chlorophyll fluorescence (SIF) emitted at red (SIF_Red) and far-red (SIF_FarRed) wavelengths in comparison with chlorophyll- and xanthophyll-sensitive reflectance-based remote sensing indices (NDVI, NIR_V, NIR_VP and PRI) for drought stress monitoring at the canopy scale. To do so, we evaluated impacts of drought stress on two soybean varieties with similar phenology but contrasting affinities for arbuscular mycorrhizal fungi (AMF), which can provide host plants with extended access to water and nutrients in exchange for carbohydrates. Drought response physiology of the two genotypes was further explored using leaf level photosynthetic gas exchange, chlorophyll fluorescence, water potential and phenology. We observed distinct responses, with the low-affinity genotype exhibiting lower SIF_Red and more negative midday leaf water potential, as well as reduced growth and development rate compared with the high-affinity genotype. SIF_FarRed and NIR_VP exhibited the strongest correlation with canopy photosynthesis followed by NIR_V. We also observed different timing of drought response parameters associated with different remote sensing signals. Our findings demonstrate the particular sensitivity of SIF to physiological drought responses, conferred here through AMF associations in the soil, and provide insight to the physiological drought responses tracked by different remote sensing signals.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114722"},"PeriodicalIF":11.1,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705771","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":"STARS: A novel gap-filling method for SDGSAT-1 nighttime light imagery using spatiotemporal and spectral synergy","authors":"Congxiao Wang ,&nbsp;Wei Xu ,&nbsp;Zuoqi Chen ,&nbsp;Shaoyang Liu ,&nbsp;Wei Li ,&nbsp;Lingxian Zhang ,&nbsp;Shimin Gao ,&nbsp;Yan Huang ,&nbsp;Jianping Wu ,&nbsp;Bailang Yu","doi":"10.1016/j.rse.2025.114720","DOIUrl":"10.1016/j.rse.2025.114720","url":null,"abstract":"<div><div>The Sustainable Development Goals Satellite 1 (SDGSAT-1), equipped with the Glimmer Imager (GLI), provides high-resolution nighttime light (NTL) data across multiple spectral bands. Thus, it can notably monitor human dynamics and light pollution with enhanced spectral and spatial resolution. However, cloud cover and low-quality observations often contaminate the SDGSAT-1 GLI NTL data, limiting its effectiveness. This challenge is addressed by the development of a novel method, namely the SpatioTemporal And spectRal gap-filling method for Sdgsat-1 (STARS) GLI NTL images, which combines spatiotemporal and spectral information to generate cloud-free NTL images with satisfactory pixel brightness and continuity. STARS is the first method to effectively address gap-filling in multiband NTL data using RGB spectral information, even with irregular time intervals and limited image inputs. Compared with traditional methods such as the temporal gap-filling method and the mean-weighted gap-filling method, the Cloud Removing bY Synergizing spatioTemporAL information (CRYSTAL) method, and the spatial and temporal adaptive reflectance fusion model (STARFM), which do not specifically account for the differences in light source variations in multi-band NTL data, STARS demonstrates superior performance (higher R-squared (R²) and lower root-mean-square error (RMSE)) in simulations across seven global cities, demonstrating its effectiveness in filling cloud-induced gaps in multi-band NTL data. On average, STARS achieves R² values for the gap-filling results compared to the actual values of 0.79, 0.78, and 0.70 in the RGB bands, respectively. The cloud-free images produced by STARS extend the time series of the SDGSAT-1 GLI NTL data, supporting multitemporal quantitative analysis. In cloudy regions like Tianjin, China, STARS effectively captures dynamic changes in NTL before and after the Spring Festival, closely matching human activity patterns from Baidu Maps, both spatially and temporally. Overall, STARS offers an innovative and effective approach for gap-filling multiband NTL data, with potential applications in similar datasets.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114720"},"PeriodicalIF":11.1,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697400","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":"Quantified positive radiative forcing at a greening Canadian boreal-Arctic transition over the last four decades","authors":"Florent Dominé ,&nbsp;Arthur Bayle ,&nbsp;Maria Belke-Brea ,&nbsp;Esther Lévesque ,&nbsp;Ghislain Picard","doi":"10.1016/j.rse.2025.114715","DOIUrl":"10.1016/j.rse.2025.114715","url":null,"abstract":"<div><div>Climate warming in northern and Arctic regions drives vegetation growth and shifts species distribution. In northern Quebec's Boreal-Arctic transition (forest-tundra ecotone), this is seen in the replacement of lichen by shrubs, primarily dwarf birch. These changes impact surface albedo, contributing to climate forcings with broad consequences. This study measures vegetation changes in Tasiapik valley near Umiujaq, Quebec, using a combination of (1) hyperspectral data (347–2400 nm) collected from 62 vegetation assemblages, including lichen, dwarf birch, willow, and spruce, to calculate broadband albedo, and (2) remote sensing data from Landsat satellites over 1984–2023. By combining these data, the proportion of vegetation type for each pixel was determined at the beginning and end of the 40-year period. The areal coverage of six main vegetation types was quantified over the 9.25 km² valley. The most significant change was lichen replacement by dwarf birch with lichen understory, leading to an albedo reduction from 0.233 to 0.168 and a summer shortwave forcing of 11.17 W m⁻². At the valley scale, the spatially-averaged summer forcing was 2.16 W m⁻² when considering all observed vegetation changes. These values, lower than those in previous Norwegian studies, highlight the spatial variability of shortwave forcing due to lichen replacement. We observed that the vegetation change producing the greatest positive radiative forcing also caused the strongest greening. This suggests that Landsat-based greening may be used as a proxy for surface albedo change on an Arctic scale. This unique combination of ground and satellite data allows quantification of a direct, first-order effect of Arctic shrubification.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114715"},"PeriodicalIF":11.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143677933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preface: Advancing deep learning for remote sensing time series data analysis","authors":"Hankui K. Zhang, Gustau Camps-Valls, Shunlin Liang, Devis Tuia, Charlotte Pelletier, Zhe Zhu","doi":"10.1016/j.rse.2025.114711","DOIUrl":"https://doi.org/10.1016/j.rse.2025.114711","url":null,"abstract":"This special issue explores the burgeoning field of deep learning for remote sensing time series analysis. The 20 contributed papers showcase diverse applications, including land cover mapping, change detection, atmospheric and biophysical/biochemical parameter retrieval, and disaster monitoring. The articles demonstrate a variety of approaches to address the challenges of irregular time series, such as data compositing, harmonic modeling, and direct ingestion of irregular data using recurrent and attention-based networks (e.g., LSTMs and Transformers). Several studies highlight the potential of integrating physical models with deep learning to improve model trustworthiness and interpretability. Looking ahead, we identify key future directions: the development of globally representative benchmark datasets with time series labels; the creation of readily available, operational time series products and models; the exploration of multi-modal and foundation models tailored to remote sensing time series; and more sophisticated integration of physical knowledge within deep learning frameworks. This collection highlights current progress and fosters innovation in time-aware deep learning for Earth observation.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"17 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675514","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":"Hydrological proxy derived from InSAR coherence in landslide characterization","authors":"Yuqi Song ,&nbsp;Xie Hu ,&nbsp;Xuguo Shi ,&nbsp;Yifei Cui ,&nbsp;Chao Zhou ,&nbsp;Yueren Xu","doi":"10.1016/j.rse.2025.114712","DOIUrl":"10.1016/j.rse.2025.114712","url":null,"abstract":"<div><div>Quantifying landslide susceptibility saves lives, especially in populous areas exposed to wet climates. However, available hydrological data sets such as precipitation and soil moisture are usually from reanalysis with a few to tens of kilometers' coarse resolution compared to the dimensions of landslides. Here we aim to seek substitutes to characterize hydrological features with finer spacing for landslide susceptibility assessment encompassing the tectonically active California. We synergize remote sensing big data and derivatives including topographic characteristics, vegetation index, hydrological variables, land cover, and geological units in different machine learning architectures. Our results illuminate that the interferometric coherence derived from synthetic aperture radar (SAR) can be an effective hydrological proxy, providing enhanced resolution by three orders of magnitude to tens of meters and presenting satisfactory performance, with recalls &gt;85 % and AUCs &gt;90 % in our landslide susceptibility models. The consequent spatially continuous landslide susceptibility map further demonstrates the effectiveness of high-resolution SAR products in compensating for limitations in traditional hydrological data sets. The map and our inferred relationship with the mélange and the distance to faults improve our ability in landslide hazard mitigation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114712"},"PeriodicalIF":11.1,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675515","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":"Corrigendum to “Non-linear spectral unmixing for monitoring rapidly salinizing coastal landscapes” [Remote Sensing of Environment Volume 319, 15 March 2025, 114642]","authors":"Manan Sarupria, Rodrigo Vargas, Matthew Walter, Jarrod Miller, Pinki Mondal","doi":"10.1016/j.rse.2025.114710","DOIUrl":"https://doi.org/10.1016/j.rse.2025.114710","url":null,"abstract":"The authors regret an error in the second bullet point of the “Highlights” section in the published article. The original statement:","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"183 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661032","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":"Urban thermal anisotropies by local climate zones: An assessment using multi-angle land surface temperatures from ECOSTRESS","authors":"Yue Chang ,&nbsp;Qihao Weng ,&nbsp;James A. Voogt ,&nbsp;Jingfeng Xiao","doi":"10.1016/j.rse.2025.114705","DOIUrl":"10.1016/j.rse.2025.114705","url":null,"abstract":"<div><div>Knowledge of anisotropy-induced spatial and temporal variations of land surface temperature (LST) is crucial for enhancing the quality of remote sensing products, refining land surface process modeling, and optimizing climate models. However, the limited availability of simultaneous multi-angle LST observations from space has hindered the exploration of this topic. NASA's latest ECOSTRESS sensor deployed on the International Space Station (ISS) generates multi-angle LST measurements at a 70-m spatial resolution for different times of day/night, providing a new avenue for investigating urban thermal anisotropy. In this study, we presented an initial examination of the performance of ECOSTRESS LST observations in unraveling the fine-grained urban thermal anisotropy, by taking the City of Phoenix, Arizona, United States, as the study area. We proposed a method to generate a quasi-simultaneous multi-angle ECOSTRESS LST dataset over the course of the diurnal cycle with the assistance of air temperature data from weather stations and hourly LST observations from a geostationary satellite, GOES-R. We then examined the thermal anisotropic patterns and their diurnal and seasonal variations across different Local Climate Zones (LCZs) at a spatial resolution of 200 m. Based on quasi-simultaneous multi-angle ECOSTRESS observations, Vinnikov and Vinnikov-RL models were employed to generate LCZ-scale anisotropy profiles of the study area to quantify and correct the LST directional effect. The results revealed that ECOSTRESS observations manifest unique angular patterns, featuring substantial variations in sensor viewing azimuth angles (VAA) and limited changes in sensor viewing zenith angles (VZA) within a 30° range. The angular effect led to notable variations in the observed LST, with potential deviations at the city scale of up to 10 K during winter and around 5 K during summer, relative to the nadir LST. Furthermore, the LST anisotropy exhibited distinct diurnal and seasonal patterns across LCZs, characterized by prominent variations in the intensity and width of hot/cold spots. LCZ 6, 9, and D typically displayed higher hotspot intensity and width than other LCZs at varying times of day in both summer and winter. In addition, the Vinnikov-RL model had good performance in simulating diurnal LST anisotropy over LCZs. This study reveals the potential of multi-angle ECOSTRESS LST observations in exploring urban thermal anisotropy, and contributes to better utilization of ECOSTRESS LST products. The integration of ECOSTRESS LST data with other satellite derived LST data have important implications for studying urban climate and improving long-term surface climate record, contributing to global climate studies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114705"},"PeriodicalIF":11.1,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling soil salinity patterns in soda saline-alkali regions using Sentinel-2 and SDGSAT-1 thermal infrared data","authors":"Zirui Gao ,&nbsp;Xiaojie Li ,&nbsp;Lijun Zuo ,&nbsp;Bo Zou ,&nbsp;Bin Wang ,&nbsp;Wen J. Wang","doi":"10.1016/j.rse.2025.114708","DOIUrl":"10.1016/j.rse.2025.114708","url":null,"abstract":"<div><div>Soil salinization, a critical form of global soil degradation, threatens agricultural productivity and ecosystem functions. Accurate mapping of soil salinity is essential for sustainable land management and informed decision-making. However, conventional optical or radar satellite sensors are often limited in detecting key salinity spectral signatures due to their insufficient thermal infrared (TIR) coverage. TIR remote sensing offers unique advantages for soil salinity assessment, owing to its sensitivity to the emissivity of saline soils within the TIR spectrum, but its application remains underexplored. This study evaluated the suitability and robustness of SDGSAT-1 TIS data for large-scale soil salinity mapping in the Songnen Plain, China, one of the world's three largest soda saline-alkali soil regions. We compared the performance of soil salinity models integrating SDGSAT-1 TIS data with those using optical (Sentinel-2) and radar (Sentinel-1 and GF-3) data across several machine learning techniques. Our results demonstrated that incorporating SDGSAT-1 TIS data significantly enhanced soil salinity modeling accuracy, consistently outperforming models based solely on Sentinel-2 optical or Sentinel-1/GF-3 radar data. The combination of SDGSAT-1 TIS and Sentinel-2 data, optimized using the Gaussian Process Regression model, achieved the highest accuracy (R² = 0.75, RMSE = 0.65 dS/m). The resulting salinity maps revealed widespread soil salinization across the region, with the majority of the area exhibiting slight to moderate salinity levels, posing substantial challenges to plant growth and ecosystem resilience. This study offers a robust, data-driven validation of TIR's unique sensitivity to soil salinity, emphasizing its potential for integration into large-scale soil salinity mapping frameworks.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114708"},"PeriodicalIF":11.1,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A model based on spectral invariant theory for correcting topographic effects on vegetation canopy reflectance","authors":"Weihua Li ,&nbsp;Guangjian Yan ,&nbsp;Jun Geng ,&nbsp;Yuhan Guo ,&nbsp;Tian Xie ,&nbsp;Xihan Mu ,&nbsp;Donghui Xie ,&nbsp;Jean-Louis Roujean ,&nbsp;Guoqing Zhou ,&nbsp;Jean-Philippe Gastellu-Etchegorry","doi":"10.1016/j.rse.2025.114695","DOIUrl":"10.1016/j.rse.2025.114695","url":null,"abstract":"<div><div>Topography alters both the incident radiation and radiative transfer (RT) processes within the canopy, leading to changes in the canopy bidirectional reflectance factor (BRF). Most traditional semi-physical terrain correction (TC) methods for vegetation canopy BRFs rely on simplifying physically-based analytical RT models. However, these analytical RT models are not comprehensively parameterized for all RT computations, leading to the neglect of crucial processes, such as multiple scattering processes during the derivation of semi-physical TC methods. The spectral invariants theory (<em>p</em>-theory) offers an efficient approach to model canopy BRFs by simplifying RT computations. We extended <em>p</em>-theory to sloping terrain, considering the variation of the terrain-induced incident radiation and RT processes, and developed a canopy BRF TC model, termed the <em>p</em>-C method. The <em>p</em>-C method applies not only to spectral bands with lower multiple scattering within the canopy (e.g., visible bands) but also to near-infrared (NIR) bands, where multiple scattering effects may be more pronounced than in the visible bands within the canopy. We used the three-dimensional RT model DART (Discrete Anisotropic Radiative Transfer) to simulate BRFs of homogeneous, realistic canopies of the RAMI (RAdiation transfer Model Intercomparison) experiment, and BRF images with real DEM (Digital Elevation Model) to evaluate the <em>p</em>-C method and to compare it with traditional empirical and semi-physical TC methods (CC, SCS, SCS+C, DS, PLC-S, and SE). The <em>p</em>-C method reduced the RMSE (root mean square error) by 67 %, 64 %, 64 %, 85 %, 83 %, and 54 % respectively over these methods. Furthermore, when applied to Landsat 8 OLI remote sensing BRF images, the <em>p</em>-C method effectively eliminated terrain texture, as confirmed by visual interpretation and the linear regression between the corrected BRF images and the local solar incidence angle. Currently, the <em>p</em>-C method only considers illuminated slopes, and corrections for shaded slopes need to be studied in the future.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114695"},"PeriodicalIF":11.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608529","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":"Characterizing leaf-scale fluorescence with spectral invariants","authors":"Wendi Lu ,&nbsp;Yelu Zeng ,&nbsp;Nastassia Vilfan ,&nbsp;Jianxi Huang ,&nbsp;Shari Van Wittenberghe ,&nbsp;Yachang He ,&nbsp;Yongyuan Gao ,&nbsp;Laura Verena Junker-Frohn ,&nbsp;Jennifer E. Johnson ,&nbsp;Wei Su ,&nbsp;Qinhuo Liu ,&nbsp;Bastian Siegmann ,&nbsp;Dalei Hao","doi":"10.1016/j.rse.2025.114704","DOIUrl":"10.1016/j.rse.2025.114704","url":null,"abstract":"<div><div>Sun-induced chlorophyll fluorescence (SIF) is increasingly recognized as a non-destructive probe for tracking terrestrial photosynthesis. Emerging developments in spectral invariants theory provide an innovative and efficient approach for representing SIF radiative transfer processes at the canopy scale. However, modeling leaf-scale fluorescence based on the spectral invariants properties (SIP) remains underexplored. In this study, the spectral invariants theory is employed for the first time to model the leaf-scale total, backward and forward fluorescence (leaf-SIP SIF). The leaf-SIP SIF model separates the leaf-scale radiative transfer process into two distinct components: the wavelength-dependent one associated with leaf biochemical properties, and the wavelength-independent component linked to leaf structural characteristics. The leaf structure-related effects are characterized by two spectrally invariant parameters: the photon recollision probability (<em>p</em>) and the scattering asymmetry parameter (<em>q</em>), which are parameterized using the directly measurable leaf dry matter. Evaluation against field measurements shows that the proposed leaf-SIP SIF model has a good performance, with coefficient of determination (<em>R</em>²) of 0.89, 0.89, 0.90 and root mean squared errors (RMSE) of 1.28, 0.69, 0.74 Wm⁻²μm⁻¹sr⁻¹, respectively for the total, backward, and forward fluorescence (660–800 nm). The leaf-SIP SIF model with a more concise formulation demonstrates comparable performance with the widely used Fluspect model. The leaf-SIP SIF model provides a simple and efficient approach for simulating leaf-scale fluorescence, with the potential to be integrated into a unified SIP-based model framework for simulating the radiative transfer processes across the soil-leaf-canopy-atmosphere continuum.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"322 ","pages":"Article 114704"},"PeriodicalIF":11.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608530","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}