Remote Sensing of Environment最新文献

{"title":"Balancing accuracy and feasibility in diurnal temperature modeling: A comparison of data-driven and physical-based models using geostationary satellite observations","authors":"Nur Fajar Trihantoro,&nbsp;Karin J. Reinke,&nbsp;Simon D. Jones","doi":"10.1016/j.rse.2025.114902","DOIUrl":"10.1016/j.rse.2025.114902","url":null,"abstract":"<div><div>The derivation of Diurnal Temperature Cycle (DTC) models from geostationary satellite data plays a critical role in temperature monitoring of the landscape and thermal anomaly applications such as wildfire detection. This study compares the performance of physical-based and data-driven DTC models on 1,305 study sites across Australia, leveraging Himawari-8 AHI middle-infrared (MIR) band 7 data. The physical-based model, GOT09 (based on Göttsche and Olesen study), achieved the highest accuracy, with a mean validation Root Mean Square Error (RMSE) of 2.41 K, but its practical application was limited by a lower model generation rate (48.77%), especially under high cloud cover conditions. Among data-driven methods, the proposed TRI model (named after the first author) balances accuracy and practical feasibility, achieving a validation RMSE of 3.62 K and a generation rate of 85.07%. The TRI model consistently generated reliable DTCs under various environmental conditions, including high cloud cover, outperforming alternative data-driven models such as RW (from Roberts-Wooster study), XIE (from Xie et al. study), and HAL (from Hally et al. study). Additionally, the TRI model maintained reliability across diverse land cover and climate types, showing only minimal variations in performance. The study further highlights strategies for addressing cloud and data availability challenges, proposing methods such as the use of previous day’s DTC or adjusting training data criteria in cloudy conditions. These approaches ensure a continuous temperature background where continuity of measurements is required, such as for wildfire detection. Overall, the research underscores the importance of balancing accuracy and model generation rates in DTC modeling, particularly for real-time applications. Future work could explore hybrid models and additional factors to further improve performance.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114902"},"PeriodicalIF":11.1,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670487","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 hybrid neural network for mangrove mapping considering tide states using Sentinel-2 imagery","authors":"Longjie Ye ,&nbsp;Qihao Weng","doi":"10.1016/j.rse.2025.114917","DOIUrl":"10.1016/j.rse.2025.114917","url":null,"abstract":"<div><div>Mangroves, as cradles of biodiversity and blue carbon reservoirs, are facing survival challenges due to climate change and anthropogenic disturbance. Precise and rapid mapping of mangrove forests has thus become highly relevant, which can provide essential information to support the conservation practices of such blue carbon resources. Existing machine learning algorithms for mangrove mapping are incapable of delivering precise cartographic solutions under dynamic tidal conditions because of poor transferability. This study developed a generalized approach for large-area mangrove mapping using a hybrid neural network integrated with a vision transformer to effectively capture representative features. To adapt mangrove mapping to the variety of tidal conditions, a vision transformer architecture was developed by encoding the fusion of three Sentinel-2 bands: Green, NIR, and SWIR. The ground truth dataset for the year 2021 was created from the composited Sentinel-2 images after interpreting Google Earth and drone images, which comprised 88,645 training samples (256 × 256 pixels per sample) and 24,969 test samples. We selected 30 coastal counties as test dataset in China to evaluate the effectiveness of the proposed network and produced a 10 m mangrove map that reported a total mangrove area of 28,006.24 ha in China in 2021, yielding an overall accuracy (OA) of 95.91 %. Compared to existing data products, Global Mangrove Watch 3.0, ESA WorldCover V200 and HGMF, our method outperformed the second-best product HGMF in mixed tide regions, by a margin of 9.19 % in OA and by 9.36 % in mean F1 score. Despite fluctuations in tide levels captured by Sentinel-2 imagery, the proposed method consistently yielded robust mangrove mapping results, highlighting effective derivation of tidal information. In comparison with previous mapping methods, the superior efficacy of the proposed network is distinctly discernible in distinguishing and delineating mangrove ecosystems in mixed tide regions, presenting prospects for improved monitoring of mangroves at regional and global scales.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114917"},"PeriodicalIF":11.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662679","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":"Discrepancies between time-based and real depth profiles in ocean lidar due to multiple scattering","authors":"Mingjia Shangguan,&nbsp;Yirui Guo,&nbsp;Zhuoyang Liao,&nbsp;Zhongping Lee","doi":"10.1016/j.rse.2025.114910","DOIUrl":"10.1016/j.rse.2025.114910","url":null,"abstract":"<div><div>Due to its ability to provide day-and-night profiling and high depth resolution, ocean lidar has become an important tool for marine remote sensing. However, a lidar system provides time-based measurements of backscattered photons, where the distance (or depth for vertical profiling) is a product of light speed in water and the time photons pass. When there are significant contributions of multiple scattering in the backscattered signals of ocean lidar, the perceived depth of these measured photons will be deeper than the real depth. Therefore, if the objective of a lidar system is to sense the vertical profile of particles, the present time-based depth profile will not match the real depth profile of particles in the water column. To address this discrepancy, we carried out semi-analytical Monte Carlo simulations for a wide range of water properties (represented by scattering coefficient, <em>b</em>), focusing on Case-1 water, with platforms including spaceborne, airborne, shipborne, and underwater. In the simulation process, it is assumed that the water column is vertically homogeneous, and the influence of sea surface fluctuations is ignored. Based on the simulated data, relationships between the discrepancy and <em>b</em>, as well as the radius of the received footprint on the water surface (<em>r</em>_<em>s</em>), are established. Sensitivity analysis indicates that the discrepancy is more sensitive to <em>b</em> than to <em>r</em>_<em>s</em>. Further, the impact of the absorption coefficient, scattering phase function, rough sea surface, and vertically non-uniform inherent optical properties on this discrepancy is discussed. Our results not only highlight the significance of considering multiple scattering, particularly for airborne and spaceborne platforms, in sensing the vertical profiles of particles but also provide guidance for interpreting backscattered signals in ocean lidar applications.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114910"},"PeriodicalIF":11.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662680","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":"Understanding flood detection models across Sentinel-1 and Sentinel-2 modalities and benchmark datasets","authors":"Enrique Portalés-Julià ,&nbsp;Gonzalo Mateo-García ,&nbsp;Luis Gómez-Chova","doi":"10.1016/j.rse.2025.114882","DOIUrl":"10.1016/j.rse.2025.114882","url":null,"abstract":"<div><div>In recent years, research in flood mapping from remote sensing satellite imagery has predominantly focused on deep learning methods. While new flood segmentation models are increasingly being proposed, most of these works focus on advancing architectures trained on single datasets. Therefore, these studies overlook the intrinsic limitations and biases of the available training and evaluation data. This often leads to poor generalization and overconfident predictions when these models are used in real-world scenarios. To address this gap, the objective of this work is twofold. First, we train and evaluate flood segmentation models on five publicly available datasets including data from Sentinel-1, Sentinel-2, and both SAR and multispectral modalities. Our findings reveal that models achieving high detection accuracy on a single dataset (intra-dataset validation) do not necessarily generalize well to unseen datasets. In contrast, models trained on more diverse samples from multiple datasets demonstrate greater robustness and generalization ability. Furthermore, we present a dual-stream multimodal architecture that can be independently trained and tested on both single-modality and dual-modality datasets. This enables the integration of all the diversity and richness of the available data into a single unified framework. The results emphasize the need for a more comprehensive validation using diverse and well-designed datasets, particularly for multimodal approaches. If not adequately addressed, the shortcomings of current datasets can significantly limit the potential of deep learning-based operational flood mapping approaches.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":""},"PeriodicalIF":11.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144640938","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":"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}