Remote Sensing of Environment最新文献

{"title":"Spatial patterns of leaf angle distribution covary with canopy fluorescence yield, reflectance indices, and leaf chlorophyll content, in a mixed temperate forest","authors":"Andrew D. Jablonski, Rong Li, Jongmin Kim, Manuel Lerdau, Carmen Petras, Xi Yang","doi":"10.1016/j.rse.2025.114996","DOIUrl":"10.1016/j.rse.2025.114996","url":null,"abstract":"<div><div>Plant canopies are integrated units that coordinate their functional (e.g., foliar biochemistry) and structural properties. This coordination affects remote sensing observations of canopy reflectance and solar-induced chlorophyll fluorescence (SIF). One key canopy structural property is leaf angle. Despite the fact that radiative transfer models have shown the crucial role of leaf angle in modulating remote sensing signals, methodological and technological barriers have prevented detailed investigations of how leaf angle covaries with canopy function and remote sensing observations. In this study, we employ a novel uncrewed aerial system (UAS) called FluoSpecAir to study the spatial patterns in far-red (FR) SIF (SIF<sub>obs,FR</sub>), near-infrared reflectance and radiance of vegetation (NIR<sub>V</sub> and NIR<sub>V</sub>R), normalized difference vegetation index (NDVI), and chlorophyll:carotenoid index (CCI), across individual tree canopies during two separate time periods. Additionally, we collected 3D scans of individual tree canopies using terrestrial laser scanning (TLS) and estimated foliar pigment content from leaf reflectance spectra. We used the 3D scans to calculate the leaf angle distribution (LAD) and leaf area voxel density (LAVD) of each canopy. We modeled LAD using a beta distribution, which is parameterized by μ and <em>ν</em>, and the leaf inclination distribution function (LIDF), which is parameterized by LIDFa and LIDFb. We found that <em>ν</em> and μ, which are inversely related to the variance in leaf angle, covaried with spatial patterns in peak growing season canopy CCI, NDVI, SIF<sub>obs,FR</sub>, and <span><math><mfrac><msub><mi>SIF</mi><mrow><mi>obs</mi><mo>,</mo><mi>FR</mi></mrow></msub><mrow><msub><mi>NIR</mi><mi>V</mi></msub><mi>R</mi></mrow></mfrac></math></span>, and leaf chlorophyll content. Canopies with greater variation in LAD, thus lower <em>ν</em> and μ, have larger values of NDVI, CCI, SIF<sub>obs,FR</sub>, <span><math><mfrac><msub><mi>SIF</mi><mrow><mi>obs</mi><mo>,</mo><mi>FR</mi></mrow></msub><mrow><msub><mi>NIR</mi><mi>V</mi></msub><mi>R</mi></mrow></mfrac></math></span>, and leaf chlorophyll content, while LAVD is not correlated with these remote sensing metrics. We found positive correlations between leaf chlorophyll content and canopy NDVI, SIF<sub>obs,FR</sub>, and <span><math><mfrac><msub><mi>SIF</mi><mrow><mi>obs</mi><mo>,</mo><mi>FR</mi></mrow></msub><mrow><msub><mi>NIR</mi><mi>V</mi></msub><mi>R</mi></mrow></mfrac></math></span>, as well. Together, our results show that across our study site during the peak growing season, spatial variability in remote sensing variables is driven by the coordination between LAD and leaf chlorophyll content. These findings provide important context for how we interpret landscape level variability in SIF and <span><math><mfrac><msub><mi>SIF</mi><mrow><mi>obs</mi><mo>,</mo><mi>FR</mi></mrow></msub><mrow><msub><mi>NIR</mi><mi>V</mi></msub><mi>R</mi></mrow><","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114996"},"PeriodicalIF":11.4,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144906082","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":"High-resolution global soil salinity and sodicity mapping (1980–2024): Box-Cox-based sample optimization, multi-source remote sensing features, and uncertainty quantification","authors":"Tiantian Wang ,&nbsp;Jinwei Dong ,&nbsp;Binyan Lyu ,&nbsp;Xuan Gao ,&nbsp;Nan Wang ,&nbsp;Zhou Shi","doi":"10.1016/j.rse.2025.114991","DOIUrl":"10.1016/j.rse.2025.114991","url":null,"abstract":"<div><div>Global soil salinization and sodification threaten food security by causing excessive salt accumulation in soils, degrading productivity. However, their spatiotemporal patterns have not been well documented due to the lack of high-accuracy estimates of soil salinity and sodicity with a long-term perspective. Here we propose a novel framework combining the Box-Cox transformation that addresses the skewed distribution of samples and more critical predictors as well as remote sensing indices, to predict global soil salinity (electrical conductivity of the saturated soil extract, ECe) and sodicity (exchangeable sodium percentage, ESP) from 1980 to 2024 with a 1 km × 1 km resolution using random forest. Model accuracy is higher than previous studies, with root mean square error (RMSE) of ECe and ESP as 2.24 and 6.04 respectively, and an <em>R</em>² of 0.65, and 0.60 respectively. The implementation of the Box-Cox transformation significantly improved the model performance (<em>R</em>²) by approximately 100 % (from 0.35 to 0.79 for ECe and 0.59 to 0.85 for ESP), while the additional predictors further enhanced the performance (<em>R</em>² increased by 15 %), ranking in the top 30 % of the feature importance list. Results revealed that global multiple-year average salinization and sodification are primarily concentrated in arid regions characterized by low precipitation and high temperatures. We also found a significant increasing trend of soil salinization in 20 % of global land and of sodification in 48 % of global land from 1980 to 2024, both most pronounced near the equator, as well as in central and eastern North America, Europe, southeastern China, and Mongolia. This study provides updated long-term soil salinity and sodicity maps with improved accuracies, offering critical insights for sustainable land management under climate change, serving as an essential resource for addressing food security and land degradation challenges worldwide.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114991"},"PeriodicalIF":11.4,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902909","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":"Satellite observations of water transparency from VIIRS in global aquatic ecosystems","authors":"Jianwei Wei ,&nbsp;Menghua Wang ,&nbsp;Lide Jiang ,&nbsp;Zhongping Lee ,&nbsp;Richard Kirby ,&nbsp;Karlis Mikelsons ,&nbsp;Gong Lin","doi":"10.1016/j.rse.2025.114981","DOIUrl":"10.1016/j.rse.2025.114981","url":null,"abstract":"<div><div>This study presents a new satellite ocean color data record of Secchi depth (<em>Z</em>_<em>SD</em>) observations from the Visible Infrared Imaging Radiometer Suite (VIIRS). As part of the NOAA enterprise satellite data processing, the decade-long <em>Z</em>_<em>SD</em> data are derived from the visible and near-infrared reflectance measurements over oceanic, coastal, and inland waters. Based on in situ data, the model is excellent in generating low-uncertainty <em>Z</em>_<em>SD</em> data with an absolute percentage difference (APD) of 15%–29%. The satellite and in situ matchups confirm reliable satellite retrievals with APD = 19%–26% over the <em>Z</em>_<em>SD</em> range of 0.1–60 m. Although the product uncertainties are dependent on optical water types, assessments show that the satellite <em>Z</em>_<em>SD</em> estimations are very reliable, especially where <em>Z</em>_<em>SD</em> ≥ 1 m. This new satellite product has enabled the ability to access Level-2 daily <em>Z</em>_<em>SD</em> imagery and information as well as Level-3 data aggregated on daily to monthly scales. Our examination indicates that the ocean transparent windows are situated at 443 and 486 nm in the vast open oceans and 551 nm for most coastal waters. They shift to the red band at 671 nm for extremely turbid environments, such as large river estuaries. From a global perspective, the <em>Z</em>_<em>SD</em> data extend from less than half a meter in nearshore environments to &gt;70 m in the South Pacific Gyre, while demonstrating a strong dependency on the optical water types. Short-term fluctuations over time are registered in the satellite <em>Z</em>_<em>SD</em> daily and monthly data from almost every aquatic environment. Trend analyses reveal significant increases in water transparency over many regions, especially the open ocean. We stress the necessity of normalizing satellite <em>Z</em>_<em>SD</em> estimations to eliminate the uncertainties induced by different solar-zenith angles. The present satellite products can be further improved by accounting for the limitations imposed by the multispectral reflectance data with hyperspectral ocean color spectra.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114981"},"PeriodicalIF":11.4,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902943","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 “Investigating the anisotropy of nighttime light using an unmanned aerial vehicle combined with scaled experiments” [Remote Sensing of Environment 329 (2025) 114960]","authors":"Ji Wu,&nbsp;Xi Li,&nbsp;Deren Li","doi":"10.1016/j.rse.2025.114992","DOIUrl":"10.1016/j.rse.2025.114992","url":null,"abstract":"","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114992"},"PeriodicalIF":11.4,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900843","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":"Exploring the ecological potential of SDGSAT-1 MII and TIS data: Methods, applications, and comparisons","authors":"Hanqiu Xu ,&nbsp;Guifen Su ,&nbsp;Guojin He ,&nbsp;Mengmeng Wang ,&nbsp;Yafen Bai ,&nbsp;Jiahui Chen ,&nbsp;Mengjie Ren ,&nbsp;Tengfei Long","doi":"10.1016/j.rse.2025.114976","DOIUrl":"10.1016/j.rse.2025.114976","url":null,"abstract":"<div><div>As global urbanization accelerates and ecological challenges intensify, effective monitoring and assessing ecological conditions have become critical for sustainable development. Remote sensing technologies play an increasingly crucial role in this context. The Sustainable Development Goals Science Satellite 1 (SDGSAT-1), a next-generation remote sensing satellite, provides 10-m spatial resolution and multispectral imaging capabilities, offering new opportunities for ecological monitoring. This study explores the ecological potential of SDGSAT-1 data, focusing on the comprehensive assessment of urban heat islands (UHI), urban vegetation coverage, and regional ecological conditions. This is achieved through a detailed comparison with the widely-used Landsat-8/9 data. The study develops several methodologies for cloud detection, atmospheric correction, and land dryness retrieval. Validation shows that the cloud removal effect achieved by the proposed SDGSAT Cloud Mask (SCM) algorithm is comparable to, or slightly better than, those of the CFMask algorithm for Landsat-9 and the machine learning-based S2cloudless algorithm for Sentinel-2A, with F1 scores greater than 0.92. The results show that the monitoring of regional ecological conditions by SDGSAT-1 is very similar to that of Landsat-8/9, with differences generally under 5 %. Because SDGSAT-1's multispectral and thermal infrared imagery has higher spatial resolution than Landsat-8/9, it can detect 5.6 % more vegetation area and 2.6 times larger high-temperature areas within urban environments than Landsat data. SDGSAT-1's finer resolution enables more detailed ecological assessments, supporting urban sustainability applications. However, due to the lack of shortwave infrared bands in the SDGSAT-1 imagery, it is less effective than Landsat-8/9 in interpreting land surface dryness and moisture content.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114976"},"PeriodicalIF":11.4,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896143","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":"100 m PM2.5 mapping from SDGSAT-1 TOA reflectance: Model development and -evaluation","authors":"Kaixu Bai ,&nbsp;Zhe Zheng ,&nbsp;Songyun Qiu ,&nbsp;Ke Li ,&nbsp;Liuqing Shao ,&nbsp;Chaoshun Liu ,&nbsp;Ni-Bin Chang","doi":"10.1016/j.rse.2025.114977","DOIUrl":"10.1016/j.rse.2025.114977","url":null,"abstract":"<div><div>Satellite-based fine-resolution (∼100 m) PM_2.5 mapping remains challenging because broadband multispectral imagery struggles to decouple land and atmospheric signals, limiting accurate local emission source detection in regions with sparse ground monitoring networks. Here, we introduce LAD-GAT, a novel deep learning framework for 100 m-resolution PM_2.5 estimation from SDGSAT-1––the first science satellite mission dedicated to the UN Sustainable Development Goals. Specifically, LAD-GAT builds a high-dimensional scene-attribute graph by combining PM_2.5-relevant geographical features, meteorological dynamics, top-of-the-atmosphere (TOA) reflectance, and estimated surface reflectance (SR). A specialized land-atmosphere decoupling (LAD) module is introduced to separate latent aerosol signals from ground surface contributions, and a graph attention network (GAT) models nonlinear associations between in-situ PM_2.5 observations and the input graph structure. In 10-fold cross-validation, LAD-GAT achieved RMSE = 5.042 μg m⁻³ (R² = 0.875) using SDGSAT-1 TOA reflectance, and RMSE = 9.428 μg m⁻³ (R² = 0.862) with Sentinel-2 TOA reflectance. Incorporating daily SR yielded an 8.68 % accuracy gain over TOA reflectance alone and outperformed multi-day composites by 7.27 %, highlighting the benefit of accounting for SR dynamics in fine-scale PM_2.5 mapping. Overall, leveraging the proposed novel LAD-GAT method, SDGSAT-derived PM_2.5 estimates rival those from Sentinel-2, providing fine-scale data to better support SDG 11.6.2 monitoring and targeted air-quality interventions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114977"},"PeriodicalIF":11.4,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902941","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":"Simulation framework for solar-induced fluorescence retrieval and application to DESIS and HyPlant","authors":"Miguel Pato ,&nbsp;Kevin Alonso ,&nbsp;Jim Buffat ,&nbsp;Stefan Auer ,&nbsp;Emiliano Carmona ,&nbsp;Stefan Maier ,&nbsp;Rupert Müller ,&nbsp;Patrick Rademske ,&nbsp;Uwe Rascher ,&nbsp;Hanno Scharr","doi":"10.1016/j.rse.2025.114944","DOIUrl":"10.1016/j.rse.2025.114944","url":null,"abstract":"<div><div>Fluorescence light emitted by chlorophyll in plants is a direct probe of the photosynthetic process and can be used to continuously monitor vegetation status. Retrieving solar-induced fluorescence (SIF) using a machine learning (ML) approach promises to take full advantage of airborne and satellite-based instruments to map expected vegetation function over wide areas on a regular basis. This work takes a first step towards developing a ML-based SIF retrieval method. A general-purpose framework for the simulation of at-sensor radiances is introduced and applied to the case of SIF retrieval in the oxygen absorption band O<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>-A with the spaceborne DESIS and airborne HyPlant spectrometers. The sensor characteristics are modelled carefully based on calibration and in-flight data and can be extended to other instruments including the upcoming FLEX mission. A comprehensive dataset of simulated at-sensor radiance spectra is then assembled encompassing the most important atmosphere, geometry, surface and sensor properties. The simulated dataset is employed to train emulators capable of generating at-sensor radiances with sub-percent errors in tens of <span><math><mrow><mi>μ</mi><mi>s</mi></mrow></math></span>, opening the way for their routine use in SIF retrieval. The simulated spectra are shown to closely reproduce real data acquired by DESIS and HyPlant and can ultimately be used to develop a robust ML-based SIF retrieval scheme for these and other remote sensing spectrometers. Finally, the SIF retrieval performance of the 3FLD method is quantitatively assessed for different on- and off-band configurations in order to identify the best band combinations. This highlights how our simulation framework enables the optimization of SIF retrieval methods to achieve the best possible performance for a given instrument.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114944"},"PeriodicalIF":11.4,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896142","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":"Multi-city local climate zone mapping and its quantitative applications on measuring surface urban heat Island in China","authors":"Jia Hu ,&nbsp;Yuyu Zhou ,&nbsp;Yingbao Yang ,&nbsp;Zhengyuan Zhu ,&nbsp;Jun Yang ,&nbsp;Xiangjin Meng ,&nbsp;Feilin Lai","doi":"10.1016/j.rse.2025.114965","DOIUrl":"10.1016/j.rse.2025.114965","url":null,"abstract":"<div><div>Local climate zone (LCZ) provides a detailed classification system for building types in urban areas and offers a unified standard for block-scale surface urban heat island (SUHI) studies. However, LCZ mapping methods with high classification accuracy for global applicability, and multi-city comparison of SUHI based on LCZ are still needed. In this study, we developed a transferable LCZ mapping framework for 30 China cities at 120 m by using multi-source remote sensing and GIS data, and random forest model. The gap-filled LST for 21 cities among 30 cities with diverse urbanized levels and climate conditions were generated, utilizing Landsat 8 data, LST retrieval algorithm and gap-filling method. Spatial patterns of SUHI intensity across climate zones and cities of different sizes were explored, and the impacts of urban morphology on SUHI in built-up LCZs were analyzed using the boosted regression trees model. Results showed that the proposed LCZ mapping framework achieved high accuracy in China cities, with overall accuracy from 0.86 to 0.93. Its robustness and transferability were further demonstrated in three cities in the United States with overall accuracy of 0.91 to 0.93. LST gap-filling method also performed well, with R from 0.71 to 0.91 and RMSE from 1.86 °C to 3.59 °C, respectively. Our multi-city assessment revealed consistent patterns of SUHI in LCZs across climate zones: compact mid-rise (LCZ 2) and large low-rise (LCZ 8) had the highest SUHI intensity, while sparsely built (LCZ 9) and open low-rise (LCZ 6) had the lowest values. Moreover, LCZ 2 tended to have higher SUHI intensity in colder climate regions, while LCZ 8 exhibited higher values in warmer climate regions. City size also influenced SUHI effect in built-up LCZs, with large cities exhibiting SUHI intensity up to 1 °C higher than small cities. Additionally, vegetation exhibited the largest of relative importance (20 % to 68 %) which impacted SUHI intensity in built-up LCZs, with a higher value in cold cities compared to warm cities. Impervious or building surface fraction also accounted for 13 % to 45 % of the SUHI contribution across LCZs, with relative importance about 3 % to 10 % greater in warmer and larger cities. The findings of this study can be useful in developing urban planning policies for intra-city SUHI mitigation, and our transferable LCZ mapping framework can be applied to other global cities for SUHI studies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114965"},"PeriodicalIF":11.4,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890614","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 more precise retrieval of sun-induced chlorophyll fluorescence from satellite data using artificial neural networks","authors":"Dong Li ,&nbsp;Jing M. Chen ,&nbsp;Gregory Duveiller ,&nbsp;Christian Frankenberg ,&nbsp;Philipp Köhler ,&nbsp;Kang Yu","doi":"10.1016/j.rse.2025.114987","DOIUrl":"10.1016/j.rse.2025.114987","url":null,"abstract":"<div><div>In recent years, sun-induced chlorophyll fluorescence (SIF) retrieved from satellite platforms has been demonstrated to be a good proxy of gross primary production (GPP). However, existing data-driven methods based on singular value decomposition (SVD) commonly lead to high retrieval noise for single SIF observations, given the fact that SIF often has a low signal-to-noise ratio. Spatial and/or temporal aggregation has typically been used to reduce these noises. Such aggregation may diminish the effective spatial and/or temporal resolutions of current SIF products but potentially exacerbate uncertainty in interpreting SIF data. To address this issue, this study proposes a more precise data-driven method for retrieving SIF using an artificial neural network (ANN), which was trained using spatially aggregated SIF as the response variable and radiance in the spectral region of 743–758 nm as the explanatory variable. The feasibility of the ANN-based SIF retrieval method was first demonstrated using model simulations based on SCOPE and MODTRAN. Then, the ANN models were trained using OCO-2/3 SIF and TROPOMI radiance after careful matching of the overpass time and “sun-target-viewing” geometry. OCO-2/3 SIF, retrieved using high-spectral-resolution data within a narrow spectral window, is considered accurate and OCO-2 SIF was further validated by airborne SIF measurements. The resulting ANN model led to a high retrieval accuracy for SIF with an R² of 0.85 and an RMSE of 0.217 mW∙m⁻²∙nm⁻¹∙sr⁻¹. Finally, the ANN-based method was adopted to produce the global TROPOMI SIF from May 2018 to December 2024. Assuming that the RMSE is representative of the average retrieval noises of single ANN-based SIF, the retrieval noises of ANN-based TROPOMI SIF were approximately half of the reported noises of SVD-based TROPOMI SIF. The advantage of the low retrieval noise of ANN-based SIF was proven in interpreting the seasonal patterns of SIF and estimating GPP compared with SVD-based SIF. This study provides a new insight into SIF retrieval, and the resulting ANN-based SIF product would contribute to better global carbon cycle observations.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114987"},"PeriodicalIF":11.4,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144892271","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":"RSPECT: A PROSPECT-based model incorporating the real structure of rice leaves","authors":"Shuang Xiang ,&nbsp;Shikuan Wang ,&nbsp;Zhongyu Jin ,&nbsp;Yi Xiao ,&nbsp;Meihan Liu ,&nbsp;Hao Yang ,&nbsp;Shuai Feng ,&nbsp;Ziyi Feng ,&nbsp;Tan Liu ,&nbsp;Fenghua Yu ,&nbsp;Tongyu Xu","doi":"10.1016/j.rse.2025.114962","DOIUrl":"10.1016/j.rse.2025.114962","url":null,"abstract":"<div><div>Radiative transfer models (RTMs) describe how light is absorbed, scattered, and transmitted within leaves by simulating mechanistic light propagation processes. The PROSPECT model is based on measurable parameters (the leaf biochemical content) and a non-measurable parameter (the leaf anatomical structure represented by the leaf structure parameter (N)). The effect of N on the optical properties of leaves has been investigated through a number of local and global sensitivity analyses. Other studies have directly evaluated the effect of the leaf anatomical structure on spectral reflectance, particularly in the near infrared region. However, the relationship between N and the anatomical structure is unclear. In this study, we leveraged eLeaf, a ray tracing-based 3D rice leaf simulator, to establish relationships between leaf anatomical features and spectral properties, enabling us to replace N in the PROSPECT-4 model with measurable leaf anatomical parameters and develop the RSPECT model. The leaf thickness at minor vein, leaf thickness at bulliform cells, mesophyll thickness at minor vein, and distance between two minor veins could be used to predict N effectively. The RSPECT model achieved spectral simulation accuracy comparable to PROSPECT-4 and was more suitable for parameter inversion of the physical and chemical properties of rice leaves, with relative root mean square errors of 7.4 % for chlorophyll content, 5.6 % for equivalent water thickness, and 7.5 % for dry matter content. In conclusion, RSPECT improves radiative transfer modeling by integrating measurable anatomical features and provides a framework for extending this approach to other vegetation types.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114962"},"PeriodicalIF":11.4,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144892286","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}