Remote Sensing of Environment最新文献

{"title":"Distinct contribution of the blue spectral region and far-red solar-induced fluorescence to needle nitrogen and phosphorus assessment in coniferous nutrient trials with hyperspectral imagery","authors":"Peiye Li ,&nbsp;Tomas Poblete ,&nbsp;Alberto Hornero ,&nbsp;Jagannath Aryal ,&nbsp;Pablo J. Zarco-Tejada","doi":"10.1016/j.rse.2025.114915","DOIUrl":"10.1016/j.rse.2025.114915","url":null,"abstract":"<div><div>Accurate monitoring of plant nutrient status, especially nitrogen (N) and phosphorus (P) content, via remote sensing can facilitate precision forestry, with environmental and management benefits. In previous studies, plant traits derived from hyperspectral data via radiative transfer models (RTMs) and solar-induced chlorophyll fluorescence (SIF) effectively explained the observed variability in leaf N concentrations in crops. However, their contribution to leaf P concentration is unknown. Furthermore, such an approach might not be transferrable to coniferous stands, which are structurally complex and heterogeneous. We evaluated the potential of using physiological plant traits derived from airborne hyperspectral imagery to explain the observed variability in needle N and P concentrations in <em>Pinus radiata D. Don</em> (radiata pine) with four datasets collected over three years in established nutrient trials. RTM-derived data on pigment content in needles, including chlorophyll <em>a</em> + <em>b</em> (C_ab), carotenoid (C_ar), and anthocyanin contents (A_nth), as well as SIF quantified at the O₂A absorption band (SIF₇₆₀), explained variability in N (R² = 0.67–0.97 and NRMSE = 0.07–0.30) and P concentrations (R² = 0.60–0.95 and NRMSE = 0.09–0.27) in needles. Although C_ab was the most important predictor of needle N concentration (ranking C_ab &gt; A_nth &gt; SIF₇₆₀ &gt; C_ar), SIF₇₆₀ contributed the most to explain the variability of needle P concentration (SIF₇₆₀ &gt; A_nth &gt; C_ab &gt; C_ar). Moreover, the blue spectral region was essential for assessing P but not for explaining N variability in needles. Among all reflectance-based indices and inverted traits evaluated, the blue indices best explained the variability in needle P concentration, followed by C_ab, C_ar, and A_nth. The study revealed the distinct contribution of far-red SIF vs. the blue spectral region for needle P compared to needle N, describing new insights for the physiological assessment of nutrient levels in forest stands using hyperspectral imagery.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114915"},"PeriodicalIF":11.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645503","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":"Methodological considerations for studying spectral-plant diversity relationships","authors":"Christine I.B. Wallis ,&nbsp;Anna L. Crofts ,&nbsp;Robert Jackisch ,&nbsp;Shan Kothari ,&nbsp;Guillaume Tougas ,&nbsp;J. Pablo Arroyo-Mora ,&nbsp;Paul Hacker ,&nbsp;Nicholas Coops ,&nbsp;Margaret Kalacska ,&nbsp;Etienne Laliberté ,&nbsp;Mark Vellend","doi":"10.1016/j.rse.2025.114907","DOIUrl":"10.1016/j.rse.2025.114907","url":null,"abstract":"<div><div>The Spectral Variation Hypothesis (SVH) posits that higher spectral diversity indicates higher biodiversity, which would allow imaging spectroscopy to be used in biodiversity assessment and monitoring. However, its applicability varies due to ecological and methodological factors. Key methodological factors impacting spectral diversity metrics include spatial resolution, shadow removal, and spectral transformations. This study investigates how these methodological considerations affect the application of the SVH across ecosystems and sites. Using field and hyperspectral data from forest and open (e.g., wetland, grassland, savannah) ecosystems from five sites of the Canadian Airborne Biodiversity Observatory (CABO), we analyzed three variance-based spectral diversity metrics across and within vegetation sites, examining the effects of illumination corrections, spatial resolution, and shadow filtering on the spectral-plant functional diversity relationship. Our findings highlight that the relationship between spectral diversity metrics and functional diversity are strongly influenced by methods, especially spectral transformations. These illumination corrections notably impacted the spectral regions of importance and the resulting relationships to plant functional diversity. Depending on methodological choices, we observed correlations that varied not only in strength but also direction: in open vegetation we saw negative correlations when using brightness normalization, and positive correlations when using continuum removal. Shadow removal and spatial resolution were important but had less impact on the correlations. By systematically analyzing these methodological aspects, our study not only aims to guide researchers through potential challenges in SVH studies but also highlights the inherent sensitivity of spectral-functional diversity relationships to methodological choices. The variability and context-dependence of these relationships across and within sites emphasize the need for adaptable, site-specific approaches, presenting a key challenge in developing robust methods to enhance biodiversity monitoring and conservation strategies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114907"},"PeriodicalIF":11.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645504","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":"Correction and validation of Sentinel-1 IW radial velocity products using drifter and HF radar across the entire ocean environment","authors":"Lihua Wang ,&nbsp;Benhua Tan ,&nbsp;Xiaoqing Chu ,&nbsp;Hongmei Wang ,&nbsp;Yunxuan Zhou ,&nbsp;Weiwei Sun","doi":"10.1016/j.rse.2025.114909","DOIUrl":"10.1016/j.rse.2025.114909","url":null,"abstract":"<div><div>Since Sentinel-1 synthetic aperture radar (SAR) was launched in 2014, Interferometric Wide swath (IW) mode Level-2 radial velocity (RVL) products have been widely used to map fine-scale ocean surface current (OSC) in coastal zones. However, RVL product applications are restricted by non-geophysical and Wind-wave Induced Artifact Surface Velocity (WASV) errors. Previous studies have focused on improving the current retrieval accuracy in coastal zones, while neglecting open ocean regions and insufficient uncertainty analysis. To address these issues, a non-geophysical correction scheme suitable for both coastal and open sea is proposed by considering land coverage within SAR scenes. Corrected RVL products are validated using 1282 drifters and 78,054 HF radar points collected from the U.S. East Coast, West Coast, and Hawaiian Islands, showing overall accuracy improvements exceeding 60 %. To investigate the impact of WASV correction under different sea states (e.g. pure wind wave, wind wave dominant mix sea, swell dominant mix sea, and pure swell), a total of 127,534 matching points collected from January 2018 to May 2019 are used to assess the performance of four correction schemes. These include CDOP, KaDOP with wind and swell inputs, KaDOP with wind and wind-sea inputs, and CDOP-Y_n. A comprehensive comparison with HF radar current reveals that CDOP performs poorly in pure wind wave sea (RMSE up to 0.34 m/s), while incorporating sea state parameters enhances the retrieval accuracy. KaDOP and CDOP-Y_n yield comparable performance, while KaDOP performs better in pure wind or wind wave dominant mix sea, achieving RMSE of 0.21 m/s and a correlation coefficient (r) of 0.62. The correlation between SAR-derived and in-situ currents also varies with incidence angle, satellite track, and polarization. Overall, these results provide reliable OSC data for mesoscale and sub-mesoscale ocean dynamics research.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114909"},"PeriodicalIF":11.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634005","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-temporal high-resolution urban land-use mapping and change analysis based on a deep geospatial-temporal adaptation network","authors":"Sunan Shi ,&nbsp;Yinhe Liu ,&nbsp;Deren Li ,&nbsp;Yanfei Zhong","doi":"10.1016/j.rse.2025.114912","DOIUrl":"10.1016/j.rse.2025.114912","url":null,"abstract":"<div><div>Automated mapping and change analysis of urban land use are crucial tasks for examining the patterns of urban development and effectively directing the sustainable management of urban land resources. High-resolution (HR) remote sensing imagery offers abundant spatial details and clear urban structures. However, the existing change detection methods require high-quality paired samples and are based on the assumption that the training and test data are independent and identically distributed, and thus lack the flexibility to generalize the trained model to new temporal images. In response to the challenge, a multi-temporal urban scene classification and change detection (MtUS-CCD) framework is proposed to realize urban land-use mapping and change analysis, with the real geographic boundaries provided by OpenStreetMap (OSM) road networks. The key model of the proposed MtUS-CCD framework is the deep geospatial-temporal <strong>A</strong>daptation <strong>N</strong>etwork based on partial self-tra<strong>I</strong>ning and geospatial-<strong>T</strong>emporal <strong>A</strong>lignment (ANITA). The ANITA model employs a geospatial-temporal alignment (GTA) strategy to align the geographical locations of multi-temporal images, acquiring deep features that are invariant to temporal domain shifts. Label migration and self-training classification (STC) are also performed to enhance the model's discriminative capacity for cross-temporal urban scene classification in images obtained from new time phases. To relieve the significant scale differences and high shape variability among urban parcels, the ANITA model leverages the area-weighted voting (AWV) strategy to achieve land-use mapping based on the multi-temporal comprehensive OSM road network data. Subsequently, post-classification comparison (PCC) enables the acquisition of the land-use change directions. The experimental results obtained on tri-temporal datasets from China demonstrate that the MtUS-CCD framework shows a significant improvement in cross-temporal urban scene classification and change detection tasks conducted in different regions. Furthermore, this framework shows robust effectiveness and generalization in a large-scale application for the whole of the city of Wuhan in China. Through comparative analysis with policy planning, it is demonstrated that the urban development patterns inferred by this framework are accurate and reliable, providing strong support for the realization of sustainable development goals.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114912"},"PeriodicalIF":11.1,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634004","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":"CloudRuler: Rule-based transformer for cloud removal in Landsat images","authors":"Jun Li ,&nbsp;Yihui Wang ,&nbsp;Qinghong Sheng ,&nbsp;Zhaocong Wu ,&nbsp;Bo Wang ,&nbsp;Xiao Ling ,&nbsp;Xiang Liu ,&nbsp;Yang Du ,&nbsp;Fan Gao ,&nbsp;Gustau Camps-Valls ,&nbsp;Matthieu Molinier","doi":"10.1016/j.rse.2025.114913","DOIUrl":"10.1016/j.rse.2025.114913","url":null,"abstract":"<div><div>Clouds are a key factor influencing transmission of the radiance signal in optical remote sensing images. For mapping or monitoring the Earth's surface, it is inevitable to mask or remove clouds before applying optical remote sensing images. Nowadays, deep learning (DL) based thin cloud removal methods far outperform traditional methods. Yet these DL-based methods often overlook position information or the physical cloud model in thermal bands. Moreover, most existing cloud physical models for cloud removal overlook the down-transmittance of the cloud in optical bands and do not account for the radiance of thermal bands. This work proposes a novel transformer network, CloudRuler, coupled with three rules in remote sensing domain for cloud removal. The proposed CloudRuler can distinguish the semantic meanings between similar features in different pixel positions by utilizing the Half-Spherical Coordinate System, aggregating features from local neighborhood windows with remote sensing mosaicking, and solving the parameters of the cloud physical model without limitations. Experimental results on 20 paired Landsat 8 and 9 images demonstrate that CloudRuler outperforms seven baseline methods, based on GAN, CNN, and transformer, both visually and quantitatively. Ablation experiments demonstrate that the proposed rule-based modules are highly effective in improving CloudRuler's performance for thin cloud removal. This work demonstrates that the joint use of Landsat 8 and 9 images for cloud removal is effective, producing more reliable data for downstream applications than methods that utilize only one satellite with a longer revisit period. For future research of the field, the code and dataset for reproducing the reported results are available on: <span><span>https://github.com/Neooolee/CloudRuler</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114913"},"PeriodicalIF":11.1,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144630391","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":"Landslide mapping from post-event single-temporal polarimetric SAR image by a deep learning method exploiting a morphological model","authors":"Rubing Liang ,&nbsp;Keren Dai ,&nbsp;Juan M. Lopez-Sanchez ,&nbsp;Yakun Han ,&nbsp;Xianlin Shi ,&nbsp;Qiang Xu","doi":"10.1016/j.rse.2025.114904","DOIUrl":"10.1016/j.rse.2025.114904","url":null,"abstract":"<div><div>Accurate and timely mapping of landslides after the event (e.g., earthquake) is crucial for effective rescue operations and comprehensive disaster assessment. While optical images are often obstructed by clouds and fog, synthetic aperture radar (SAR) can identify landslides independently of weather conditions. In this study, we propose a deep learning method which exploits a morphological model (DLM) to achieve accurate landslide identification using only a post-event single-temporal polarimetric SAR image. The SAR scattering mechanisms and polarimetric characteristics of various ground objects are thoroughly analyzed to select optimal polarimetric parameters for deep learning. To accurately map landslide shapes and extract boundaries, we introduce a Majority Voting mechanism and a morphological optimization model. We have used one quad-pol ALOS-2 image for landslide mapping and achieved an overall accuracy of 95.24 % with the proposed method. Additionally, considering the limited availability of quad-pol SAR data, we have employed dual-pol ALOS-2 and Sentinel-1 data to assess the method's usability with dual-pol data. The dual-pol ALOS-2 image achieved an overall accuracy of 89.78 %, while Sentinel-1 image effectively captured the general landslide shape with an overall accuracy of 76.32 %. This demonstrates the high applicability of the proposed method for landslide mapping using a single post-event polarimetric SAR image, enhancing the timeliness of SAR-based landslide mapping and improving emergency response and post-disaster rescue capabilities.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114904"},"PeriodicalIF":11.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614594","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 new generation aerosol optical depth dataset based on AVHRR data over China from 1981 to 2000","authors":"Yahui Che ,&nbsp;Jie Guang ,&nbsp;Yong Xue ,&nbsp;Gerrit de Leeuw ,&nbsp;Lu She ,&nbsp;Linlu Mei ,&nbsp;Xingwei He ,&nbsp;Ling Sun ,&nbsp;Zhengqiang Li","doi":"10.1016/j.rse.2025.114899","DOIUrl":"10.1016/j.rse.2025.114899","url":null,"abstract":"<div><div>The Advanced Very High Resolution Radiometer (AVHRR) series onboard the National Oceanic and Atmospheric Administration (NOAA) and the EUMETSAT Meteorological Operational Satellite (Metop) polar-orbiting satellites have provided continuous Earth observation data since 1979, which facilitates the development of long-term global climate data records. In this paper, a new version of the algorithm for the retrieval of the Aerosol Optical Depth (AOD) over Land (ADL v2.0) using AVHRR data is proposed with improved accuracy, in particular for high AOD values. The surface reflectance estimation scheme is based on a regression model established using simulated AVHRR reflectances spectrally transferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09/MYD09 product. To address limitations in retrieving high AOD, the surface reflectance is determined using the maximum Normalized Difference Vegetation Index (NDVI) during a certain period of time. To this end, a dynamic NDVI search window is proposed to identify the NDVI that is least affected by aerosols. ADL v2.0 has been applied to provide an AOD dataset covering Mainland China (70^o-140°E, 15^o-60^oN) for the years from 1981 to 2000. This dataset has been evaluated by comparing with AOD data available from the application of the broadband extinction method (BEM) to ground-based solar radiation measurements and from the AVHRR Deep Blue (DB) AOD dataset. The AOD variations retrieved using the BEM data at seven stations (two in North China, two in Northeast China, one in East China, one in Central China, and one in the southwest mountainous region) are well reproduced by the ADL v2.0 algorithm. The comparison with the AVHRR DB AOD dataset shows good agreement with ADL v2.0 retrieval results even though with less valid retrievals for high AOD in Eastern China, Sichuan, and the Guanzhong Basin, as well as over North India.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114899"},"PeriodicalIF":11.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144630390","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":"Estimating strong point CO2 emissions by combining spaceborne IPDA lidar and HSRL","authors":"Chonghui Cheng ,&nbsp;Dong Liu ,&nbsp;Shuaibo Wang ,&nbsp;Xingying Zhang ,&nbsp;Lu Zhang ,&nbsp;Weibiao Chen ,&nbsp;Jiqiao Liu ,&nbsp;Xueping Wan ,&nbsp;Wentai Chen ,&nbsp;Xiaolong Chen ,&nbsp;Jingxin Zhang ,&nbsp;Jiesong Deng ,&nbsp;Wentao Xu ,&nbsp;Lan Wu ,&nbsp;Chong Liu ,&nbsp;Zhen Xiang","doi":"10.1016/j.rse.2025.114898","DOIUrl":"10.1016/j.rse.2025.114898","url":null,"abstract":"<div><div>Anthropogenic CO₂ emissions, particularly from strong point sources like power plants, play a crucial role in the increase of atmospheric CO₂ through a complex interaction with the natural carbon sinks. China successfully launched the Atmospheric Environment Monitoring Satellite (AEMS) loaded with integrated path differential absorption (IPDA) lidar and high-spectral-resolution lidar (HSRL) on April 16, 2022. This satellite is capable of simultaneously detecting atmospheric CO₂ and aerosols. Using AEMS data, we developed a point-source emission retrieval algorithm based on a modified three-dimensional Gaussian plume model and applied it to 12 satellite overpasses of major power plants. Compared with emissions reported by the U.S. Environmental Protection Agency (EPA), our retrievals exhibit an average relative deviation of 6.23 % in the validation cases, which represents a 31.63 % reduction in error compared to the traditional two-dimensional model-based method. In all cases, the estimated emissions exhibit strong agreement with EPA data (<em>R</em> = 0.84) and a low mean absolute error (MAE) of 6.1 kt/day. The analysis indicates that the uncertainty of the emission inversion results ranges from about 12 % to 21 %, with an average of 17.1 %. These results demonstrate the ability of the IPDA–HSRL synergy to accurately quantify point source CO₂ emissions, and can supplement and verify existing bottom-up inventory methods.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114898"},"PeriodicalIF":11.1,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144611636","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":"An optimal sequential physical retrieval system for retrieving high-accuracy diurnal atmospheric gases from FY-4B/GIIRS: Theory, algorithm and evaluation","authors":"Zhenxing Liang ,&nbsp;Dasa Gu ,&nbsp;Xin Li ,&nbsp;Zijie Xu ,&nbsp;Xiangyunong Cao ,&nbsp;Heming Bai ,&nbsp;Rui Li ,&nbsp;Chengxing Zhai ,&nbsp;Hui Su ,&nbsp;Alexis K.H. Lau","doi":"10.1016/j.rse.2025.114901","DOIUrl":"10.1016/j.rse.2025.114901","url":null,"abstract":"<div><div>The Geostationary Interferometric Infrared Sounder (GIIRS) onboard the FY-4B satellite is the world's first and currently the only operational hyperspectral thermal infrared sounder in geostationary orbit, with the unique advantage of continuously scanning the atmosphere over East Asia on an hourly basis during both daytime and nighttime. Compared to previously established low-Earth orbit satellite sounders, developing and applying Level 2 atmospheric products from FY-4B/GIIRS are still in the exploratory stage. In this study, we present an optimal sequential physical retrieval system (OSPRS) for retrieving high-accuracy atmospheric strong absorbers, including water (H₂O), ozone (O₃) and carbon monoxide (CO) from FY-4B/GIIRS. OSPRS first selects a subset of sensitive spectral channels for each variable based on column- and pressure-related sensitivity. It then determines the optimal retrieval sequence consisting of multiple retrieval steps, aiming to reduce the nonlinearity of each inversion problem and the influence of interfering variables on the primary retrieval targets. Finally, OSPRS employs the optimal estimation method as the retrieval operator to perform the retrieval at each step, outputting the profiles of the primary retrieval targets and critical scientific diagnostic information. We confirm the improved accuracy of OSPRS through Observing System Simulation Experiments (OSSE). We compare OSPRS with existing products and evaluate them based on high-quality in situ data from the Integrated Global Radiosonde Archive H₂O, the ground-based Pandonia Global Network O₃, and solar absorption Fourier transform infrared CO measurements. The results show that the mean absolute error, linear fitting slope, and correlation coefficient between OSPRS's H₂O, O₃ and CO and in-situ or ground-based measurements are superior to those of existing products. This study is dedicated to providing the community with high-quality atmospheric products retrieved from FY-4B/GIIRS and promoting the research and application of GIIRS in numerical weather forecasting, atmospheric environment, and other related fields.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114901"},"PeriodicalIF":11.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588716","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":"Spatio-temporal dynamics of L-band zeroth-order vegetation scattering albedo from SMAP observations in tropical forests","authors":"Yuqing Liu ,&nbsp;Xiaojun Li ,&nbsp;Philippe Ciais ,&nbsp;Frédéric Frappart ,&nbsp;Xiangzhuo Liu ,&nbsp;Eric G. Cosio ,&nbsp;Yi Zheng ,&nbsp;Zanpin Xing ,&nbsp;Huan Wang ,&nbsp;Lei Fan ,&nbsp;Mario Julian Chaubell ,&nbsp;Jean-Pierre Wigneron","doi":"10.1016/j.rse.2025.114890","DOIUrl":"10.1016/j.rse.2025.114890","url":null,"abstract":"<div><div>The effective scattering albedo (ω) is a key parameter in the zero-order radiative transfer equation (known as the τ-ω model) for passive microwave retrieval of soil moisture (SM) and vegetation optical depth (VOD), quantifying the scattering energy loss as microwave radiation passes through the vegetation canopy. The scattering effects of vegetation are influenced by time-dependent factors such as plant geometry, vegetation water content, and canopy structure, suggesting that ω may vary over time. However, in the current τ-ω model-based retrieval algorithms used by orbiting L-band sensors, namely the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP), ω is generally assumed to be time-invariant and assigned a fixed value according to land cover types. In this study, we aim to analyze and understand the spatio-temporal dynamics of ω, its relationship with vegetation water stress and the driving factors behind microwave scattering characteristics over tropical forests. By assuming a vegetation transmittance of zero for rigorously selected dense forest areas in the tropics, we calculated ω from the SMAP L-band radiometer observations during 2018–2023. Regarding the spatial distribution of ω, we observed distinct spatial dynamics within the same land cover type. The lowest ω values were typically found in the northeastern Amazon. Additionally, ω exhibited clear temporal dynamics, displaying a unimodal pattern in the Amazon and a bimodal pattern in the Congo. Clear polarization dependence of ω was observed, with values consistently higher at Horizontal (H-) polarization compared to Vertical (V-) polarization. Despite this, the seasonal patterns of ω are similar at both H- and V-polarizations. The seasonal variation of ω was found to be asynchronous with soil water availability indicated by root zone soil moisture (RZSM) across different regions. The shortest time lags (0–30 days) between ω and RZSM were observed in the densely vegetated northeastern Amazon, while the longest occurred in the northeastern Congo. A machine-learning based interpretation of the spatial variability of ω and time lag indicates that the values of ω are strongly and inversely related to canopy height, while the time lag is mainly associated with precipitation and soil water content. Our results deepen the understanding of the spatio-temporal dynamics of ω and could contribute to the improvement of SM and VOD retrieval algorithms, thereby enhancing the utility of these variables as indicators for monitoring vegetation carbon dynamics and phenology in dense tropical forests.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114890"},"PeriodicalIF":11.1,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572409","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}