Remote Sensing of Environment最新文献

{"title":"Accurately detecting nocturnal cloud over land using next-generation geostationary satellite imagery: A case study using advanced Himawari imager data for Australia","authors":"Yi Qin,&nbsp;Tim R. McVicar,&nbsp;Randall J. Donohue","doi":"10.1016/j.rse.2025.114861","DOIUrl":"10.1016/j.rse.2025.114861","url":null,"abstract":"<div><div>Cloud detection is a requisite step of almost all terrestrial applications using optical remote sensing imagery, as many applications are sensitive to cloud contamination. In this research, a new algorithm has been developed to detect nocturnal cloud (from sunset to sunrise) in Himawari-8/9 AHI (Advanced Himawari Imager) imagery over land, with simultaneous aims of maximising accuracy, simplicity and efficiency. The algorithm consists of two cloud detection methods: (i) proxy emissivity temporal variation, measured by pixel wise standard deviation within an hour; and (ii) monthly clear surface proxy emissivity database which is updated daily. Results from the two components are combined based on their respective confidence. A validation was conducted against 6 years of CALIPSO LiDAR data over the Australian continent, showing an overall accuracy of 96 %. The algorithm requires no ancillary data. It is also computationally efficient and so is suitable for near real-time (i.e., within 2 h) operation and can be readily adopted to similar operational geostationary sensors.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114861"},"PeriodicalIF":11.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314405","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":"RESTORE-DiT: Reliable satellite image time series reconstruction by multimodal sequential diffusion transformer","authors":"Qidi Shu,&nbsp;Xiaolin Zhu,&nbsp;Shuai Xu,&nbsp;Yan Wang,&nbsp;Denghong Liu","doi":"10.1016/j.rse.2025.114872","DOIUrl":"10.1016/j.rse.2025.114872","url":null,"abstract":"<div><div>Repetitive optical observations from satellites are crucial for monitoring earth surface dynamics over time. However, optical satellite image time series is severely affected by frequent data gaps due to clouds and shadows. While synthetic aperture radar (SAR) provides cloud-penetrating capabilities to complement missing optical data, recent advancements in time series reconstruction have shifted focus from incorporating single SAR image to exploiting SAR time series. However, current methods still struggle for challenging scenarios like highly dynamic surface, persistent data gaps, and exhibit poor resilience to inaccurate cloud masks. In this research, we approach the time series reconstruction problem from the perspective of conditional generation. We propose a multimodal diffusion framework termed RESTORE-DiT, which firstly promotes the sequence-level optical-SAR fusion through a diffusion framework. Specifically, date-matched SAR time series provide under-cloud surface dynamics to guide the denoising process of cloudy areas, and date information is embedded to account for irregular observation intervals and periodic patterns. Extensive experiments on three regions have shown the proposed method achieves state-of-the-art performance. RESTORE-DiT outperforms comparison methods by 2.87 dB in PSNR and a 27.2 % reduction in RMSE on France site. SAR and date information together increase PSNR by 2.41 dB. The reconstructed optical image time series is verified to accurately reflect the crop growth condition and support for long-term vegetation observations. In addition, RESTORE-DiT can be easily extended to other conditional reconstruction or prediction tasks for arbitrary time series image data, thus facilitating spatiotemporal analysis research. The codes will be public available at: <span><span>https://github.com/SQD1/RESTORE-DiT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114872"},"PeriodicalIF":11.1,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297247","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":"Assessing discrepancies in global aerosol trends from satellites, models and reanalyses","authors":"Ruben Urraca ,&nbsp;Fabrizio Cappucci ,&nbsp;Christian Lanconelli ,&nbsp;Nadine Gobron","doi":"10.1016/j.rse.2025.114827","DOIUrl":"10.1016/j.rse.2025.114827","url":null,"abstract":"<div><div>Aerosols, which offset a third of the greenhouse gas forcing, remain the primary source of uncertainty in climate monitoring. Satellite products, models, or reanalyses provide time series of Aerosol Optical Depth (<span><math><mrow><mi>A</mi><mi>O</mi><mi>D</mi></mrow></math></span>), each with distinct strengths and weaknesses. This study evaluates the temporal stability of these datasets from 2003 to 2022 using spatially representative long-term AERONET measurements as a reference.</div><div><span><math><mrow><mi>A</mi><mi>O</mi><mi>D</mi></mrow></math></span> has decreased globally since 2015, driven by anthropogenic emissions reduction in Europe, the US, and particularly in East Asia. Aerosols continue to rise in India due to growing anthropogenic emissions, and in South America due to a shift from declining to increasing organic matter aerosol trends. While all products capture these regional trends, they diverge in terms of magnitude, seasonal variability, and temporal patterns. Only CAMS EAC4 reanalysis reproduces the <span><math><mrow><mi>A</mi><mi>O</mi><mi>D</mi></mrow></math></span> decrease observed at AERONET stations in Europe and the US with a trend in the bias below 5%/decade. The other products evaluated show drifts above 10%/decade, underestimating the <span><math><mrow><mi>A</mi><mi>O</mi><mi>D</mi></mrow></math></span> decrease in these regions. MERRA-2 and the C3S multi-satellite products exhibit spurious jumps likely caused by methodological changes. Satellite-based (MODIS and MISR) <span><math><mrow><mi>A</mi><mi>O</mi><mi>D</mi></mrow></math></span> trends have a positive drift compared to AERONET, potentially due to the use of static aerosol composition datasets. The low satellite revisit time and a calibration drift could also contribute to the MISR spurious trend. An analysis of the reduced temporal sampling of satellites revealed that the impact at the station level is small, suggesting that the drifts found can be attributed to the products. Our results suggest that the aerosol Essential Climate Variable could be better served by reanalysis rather than by direct satellite observations.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114827"},"PeriodicalIF":11.1,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288994","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 cloud-regulated land surface warming model to reconstruct daytime surface temperatures under cloudy conditions","authors":"Fei Xu ,&nbsp;Xiaolin Zhu","doi":"10.1016/j.rse.2025.114873","DOIUrl":"10.1016/j.rse.2025.114873","url":null,"abstract":"<div><div>Daytime land surface temperature (D-LST) plays a pivotal role in regulating net ecosystem exchanges and is characterized by rapid fluctuations. Thermal infrared satellite remote sensing (TIRS) is widely used to acquire D-LST data owing to its global coverage and high-frequency observations. However, the presence of cloud cover impedes the TIRS technique by obstructing ground thermal emissions. A prevalent solution to this challenge involves correcting clear-sky surface temperatures using the cloud effect which is derived from surface energy balance (SEB) models representing distinct weather conditions. Yet, conventional methods might encounter substantial uncertainties primarily due to the oversimplified SEB modeling, which exacerbates the temperature estimation errors caused by the biases in their employed data products. This study introduces a novel SEB model termed ‘C-SWARM’, designed to reconstruct D-LST under cloudy conditions. The C-SWARM model characterizes D-LST as the result of a cloud-moderated surface warming process, with coefficients indicating the complementary mechanism for solar heating and atmospheric insulation driving surface warming throughout the day. The new model was implemented to fill missing data caused by cloud cover in the LST product of NOAA's Geostationary Operational Environmental Satellite (GOES-R), demonstrating a mean absolute error of 2.57 K and accuracy improvements of 0.38 to 1.89 K over benchmark methods at 49 flux tower sites across the contiguous United States. The explicit physical mechanisms make the C-SWARM model a generalized solution for all-weather remote sensing across spatial and temporal scales.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114873"},"PeriodicalIF":11.1,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288941","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":"Hedgerow mapping with high resolution satellite imagery to support policy initiatives at national level","authors":"Javier Muro ,&nbsp;Lukas Blickensdörfer ,&nbsp;Axel Don ,&nbsp;Anna Köber ,&nbsp;Sarah Asam ,&nbsp;Marcel Schwieder ,&nbsp;Stefan Erasmi","doi":"10.1016/j.rse.2025.114870","DOIUrl":"10.1016/j.rse.2025.114870","url":null,"abstract":"<div><div>Hedgerows provide habitat and food for a wide range of species and play a crucial role for biodiversity in agricultural landscapes. In addition, hedgerows render an important carbon stock, above and below ground, and protect agricultural soils from erosion. However, comprehensive, standardized and area wide information regarding the distribution of hedgerows is often lacking, which makes it hard to incorporate them in nature conservation plans and national carbon balance models. We evaluate the potential of high-resolution PlanetScope multitemporal satellite data and semantic segmentation approaches to map the distribution of hedgerows across the entire agricultural landscape in Germany. Based on a comprehensive set of independent reference data from the federal state of Schleswig-Holstein, we evaluate the performance of different loss functions and different combinations of spectral and temporal input feature sets. We assess the transferability of the final model using independent test data from three additional German Federal states. Additionally, we compare our results against the Copernicus Land Monitoring Service High Resolution Layer Small Woody Features, and a recently published biomass map of trees outside forests. All loss functions tested offered similar performance, but the binary-cross entropy function allowed for overcoming sensor artifacts to some extent. Visible and near-infrared imagery from all four monthly mosaics (April, June, August and October) of PlanetScope data was found to yield better results (F1-score 0.65) than different combinations of months and only red-green-blue inputs. We estimate a total surface of 4081 (± 1425) km² of hedgerows across Germany, which represent 2.3 % of the agricultural land in Germany. By combining our results with a digital landscape model, we reveal heterogenous estimates of hedgerow height across municipalities. Our findings highlight that semantic segmentation approaches are well-suited for area-wide hedgerow mapping, especially in combination with multitemporal high-resolution satellite data. Furthermore, we underscore the relevance of using application-specific models over post-processing existing products, and provide for the first time a spatially explicit and comprehensive overview of the distribution of hedgerows and their structure across agricultural landscapes in Germany. Our methodology and product can be incorporated into landscape biodiversity models, carbon balance estimations and soil protection policies at national, regional and local scale.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114870"},"PeriodicalIF":11.1,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288657","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":"Modeling diurnal gross primary production in East Asia using Himawari-8/9 geostationary satellite data","authors":"Yuhei Yamamoto ,&nbsp;Kazuhito Ichii ,&nbsp;Wei Yang ,&nbsp;Yui Shikakura ,&nbsp;Youngryel Ryu ,&nbsp;Minseok Kang ,&nbsp;Shohei Murayama ,&nbsp;Su-Jin Kim ,&nbsp;Yuta Takao ,&nbsp;Masahito Ueyama ,&nbsp;Tomoko Kawaguchi Akitsu ,&nbsp;Hiroki Iwata ,&nbsp;Hojin Lee ,&nbsp;Junghwa Chun ,&nbsp;Atsushi Higuchi ,&nbsp;Takashi Hirano ,&nbsp;AReum Kim ,&nbsp;Hyun Seok Kim ,&nbsp;Kenzo Kitamura ,&nbsp;Yuji Kominami ,&nbsp;Yukio Yasuda","doi":"10.1016/j.rse.2025.114866","DOIUrl":"10.1016/j.rse.2025.114866","url":null,"abstract":"<div><div>Gross primary production (GPP) is a key indicator of plant growth and ecosystem health, and accurately capturing its diurnal variation is crucial for understanding vegetation responses to extreme heat and drought. However, the applicability of satellite-based semi-empirical models to diurnal GPP estimation remains limited. This study refined diurnal GPP estimation in humid temperate climates by leveraging Himawari-8/9 geostationary satellite data to incorporate direct/diffuse radiation and the nonlinear GPP response to diurnal variations in absorbed photosynthetically active radiation (APAR). The eddy covariance-light use efficiency (EC-LUE) model was employed by adopting three approaches: the direct/diffuse (DD) setting to consider the direct/diffuse components of APAR, DD with nonlinear relationship (DD-NL) setting to additionally consider the nonlinear GPP-APAR relationship, and the baseline setting. The model was calibrated and validated using the eddy-covariance tower observations from 18 sites across Japan and South Korea. The DD-NL setting improved accuracy by correcting the baseline's overestimation of GPP under high APAR and underestimation under low APAR. Particularly for forest sites, the DD-NL setting reduced midday overestimations by 12–30 % on clear-sky days and morning/afternoon underestimations by 25–40 % on cloudy days. In the baseline setting, low-APAR biases progressively accumulated across daily to annual timescales, whereas the DD-NL setting reduced them to −0.10 g C m⁻² day⁻¹ and -51 g C m⁻² year⁻¹ (−2.7 % of the site's average GPP). The DD setting had minimal impact in densely vegetated sites. Our findings show that the DD-NL setting in LUE models enhances geostationary satellite-based GPP estimates across diurnal to annual timescales, supporting ecosystem monitoring during extreme events and long-term carbon assessments.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114866"},"PeriodicalIF":11.1,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288995","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":"Thermal sentinel: Low-earth orbit infrared intelligent system for flying civil aircraft safety","authors":"Liyuan Li ,&nbsp;Xiaoxuan Zhou ,&nbsp;Wencong Zhang ,&nbsp;Yifan Zhong ,&nbsp;Long Gao ,&nbsp;Jianing Yu ,&nbsp;Xiaoyan Li ,&nbsp;Fansheng Chen","doi":"10.1016/j.rse.2025.114826","DOIUrl":"10.1016/j.rse.2025.114826","url":null,"abstract":"<div><div>The surveillance and detection of civil aircraft over a wide area has long been a technical challenge, with no available datasets and complete detection methods yet. The first global space-based three-channels thermal infrared flying civil aircraft dataset (TIFAD.v1) is established by this paper, covering 17 months, six continents, with 21,004 aircraft and 1252 contrail aircraft, integrating ADS-B civil aviation data. TIFAD.v1 is a fine-grained dataset of small flying targets with aircraft type, calibrated altitude, ground speed, and track. We investigates the radiative feature of high-altitude flying targets and finds that their thermal radiation peaks are primarily distributed in the 10.3–12.5 μm band. Additionally, a significant altitude-dependent difference in transmittance is observed at 11.72 μm, which helps suppress background interference and enhances the reliability of target detection. And an innovative detection method for wide-area flying target is developed, combined YOLOv11n-based deep learning algorithms with in-situ radiative characteristics including the top of atmosphere radiance, temperature contrast, SCR to enhance detection accuracy. This technology provides an effective and robust new approach for all-weather detection of maneuverable flight targets on a global scale, demonstrating the significant potential of intelligent technology in the field of thermal infrared remote sensing applications. Moreover the in situ data allows for quantitative measurement of the radiative feature of flight targets, providing essential data for research on infrared and atmospheric transmittance.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114826"},"PeriodicalIF":11.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271885","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":"Comparison of snowmelt timing estimates from Sentinel-1 SAR and surface observations in British Columbia, Canada","authors":"Sara E. Darychuk ,&nbsp;Joseph M. Shea ,&nbsp;Chris Derksen","doi":"10.1016/j.rse.2025.114863","DOIUrl":"10.1016/j.rse.2025.114863","url":null,"abstract":"<div><div>Snowmelt provides critical water resources that impact ecosystem health and hazard frequency; however, the timing of melt is difficult to infer across large spatial scales. While Synthetic Aperture Radar (SAR) has been used to detect snowmelt onset, the accuracy of different methodological approaches requires evaluation. We use Sentinel-1 SAR observations to estimate snowmelt timing at automated snow water equivalent (SWE) stations (<em>n</em> = 52) across British Columbia between 2018 and 2021. The timing of backscatter minima are compared to snowmelt onset estimates derived from continuous SWE and surface air temperature records. First, we develop a manual selection method, which requires SWE and air temperature records, to determine the feasibility of SAR for estimating snowmelt onset. Using this approach, we demonstrate that images with identical viewing geometries (i.e., those from the same orbital track) should be utilized for snowmelt onset estimation with SAR, and that including the timing of local minima, a minimum value over an interval, as potential dates of snowmelt initiation increases accuracy compared to the use of absolute minima alone. Accuracy further increases when snowmelt analysis is constrained to specific timeframes informed by region and topography, with error reduced by greater than 50 % in some study years. After demonstrating the potential accuracy of SAR with manually selected estimates (RMSE = 5.3 d), we develop an automated method that can produce SAR estimates of melt onset that are independent of surface observations. While accuracy was reduced from automated approaches, the mean date of absolute backscatter minima in track-separated SAR time series (both cross and co-polarized images) provided the most accurate automated estimates of snowmelt (RMSE = 11 d). We observed no spatially coherent bias in SAR error which suggests that SAR observations can be used to detect snowmelt onset across large regions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114863"},"PeriodicalIF":11.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271886","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":"Coupled retrieval of turbulent heat fluxes and gross primary productivity via the assimilation of land surface temperature data from geostationary satellites","authors":"Zongbin Xu ,&nbsp;Tongren Xu ,&nbsp;Xinlei He ,&nbsp;Jingfeng Xiao ,&nbsp;Sayed M. Bateni ,&nbsp;Changhyun Jun ,&nbsp;Gangqiang Zhang ,&nbsp;Wenting Ming ,&nbsp;Shaomin Liu","doi":"10.1016/j.rse.2025.114862","DOIUrl":"10.1016/j.rse.2025.114862","url":null,"abstract":"<div><div>Compared with those provided by polar-orbiting satellites/sensors (e.g., Landsat/MODIS), new-generation geostationary satellites, such as Himawari-8 and Geostationary Operational Environmental Satellite-R series (GOES-R), offer temporally continuous and much more frequent observations of the land surface over the course of the diurnal cycle. In this study, Himawari-8 land surface temperature (LST) data and GLASS leaf area index (LAI) were assimilated into a coupled two-source surface energy balance–vegetation dynamics model (TSEB-VDM) via the variational data assimilation (VDA) method to retrieve the regional sensible heat flux (<em>H</em>), latent heat flux (LE), and gross primary productivity (GPP) (hereafter referred to as the VDA_Himawari-8 scheme). Regional <em>H</em>, LE, and GPP values were estimated across the Heihe River Basin (HRB) in northwestern China, with a spatial resolution of 0.02° × 0.02°. Moreover, LST data from the MODIS, which is an instrument onboard polar-orbiting satellites (i.e., Terra and Aqua), were assimilated into the TSEB-VDM model (hereafter referred to as the VDA_MODIS scheme) for comparison with the VDA_Himawari-8 scheme. Four unknown parameters of the TSEB-VDM model, i.e., the neutral bulk heat transfer coefficient (<em>C</em>_<em>HN</em>), the evaporative fractions of the soil and canopy (EF_S and EF_C, respectively), and the specific leaf area, were optimized via the VDA approach. The estimated <em>H</em> and LE values were validated against ground measurements from the large-aperture scintillator, and the GPP estimates were validated against eddy covariance data at three sites (Arou, Daman, and Sidaoqiao) in the HRB. The results indicated that the assimilation of geostationary satellite-based LST data significantly improved the performance of the VDA model. The three-site-average root mean square errors (RMSEs) of the <em>H</em>, LE, and GPP estimates from the VDA_MODIS scheme were 45.27 W m⁻², 83.77 W m⁻², and 3.30 g C m⁻² d⁻¹, respectively. Compared with the VDA_MODIS scheme, the VDA_Himawari-8 scheme notably enhanced the performance of the VDA framework and decreased the RMSE by 15.0 % for <em>H</em>, 22.5 % for LE, and 38.5 % for the GPP, with an especially notable enhancement in humid (or vegetated) areas. The primary factors contributing to the enhancement in the performance of the VDA framework were the availability of much more frequent LST observations and the diurnal sampling capability of the Himawari-8 satellite.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114862"},"PeriodicalIF":11.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144278622","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":"Smoke absorption retrieval algorithm using critical reflectance method with geostationary satellite over North America","authors":"Roshan Kumar Mishra ,&nbsp;Yingxi Shi ,&nbsp;Zhibo Zhang ,&nbsp;J. Vanderlei Martins ,&nbsp;Lorraine A. Remer ,&nbsp;Robert C. Levy","doi":"10.1016/j.rse.2025.114837","DOIUrl":"10.1016/j.rse.2025.114837","url":null,"abstract":"<div><div>In recent years, increasing wildfire activity in the western United States has led to significant emissions of smoke aerosols, impacting the atmospheric energy balance through their absorption and scattering properties. Single scattering albedo (SSA) is a key parameter that governs these radiative effects, but accurately retrieving SSA from satellites remains challenging due to limitations in sensor resolution, low sensitivity of traditional remote sensing methods, and uncertainties in radiative transfer modeling, particularly from surface reflectance and aerosol characterization. Smoke optical properties evolve rapidly after emission, influenced by fuel type, combustion conditions, and chemical aging. Accurate SSA retrieval near the source thus requires high-temporal-resolution satellite observations. Critical Reflectance (CR) method provides this capability by identifying a unique reflectance value at which top-of-atmosphere (TOA) reflectance becomes insensitive to aerosol loading and primarily reflects aerosol absorption. SSA can be retrieved from this critical reflectance. This study presents a geostationary-based CR method using the Advanced Baseline Imager (ABI) on GOES-R satellites. The approach leverages ABI’s high temporal (5–10 min) and spatial (3 km) resolution, consistent viewing geometry, and wide coverage. A tailored look-up table, based on an AOD-dependent smoke model for North America, links CR to SSA. Case studies show strong agreement with AERONET measurements, with retrieval differences mostly within 0.01—well below AERONET’s ±0.03 uncertainty. The method captures temporal and spatial variations in smoke absorption and demonstrates robustness across daylight hours. This GEO-based CR approach offers an effective tool for high-resolution SSA retrieval, contributing to improved aerosol radiative forcing estimates and climate modeling.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114837"},"PeriodicalIF":11.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271884","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}