Remote Sensing of Environment最新文献

{"title":"Millimeter ground deformation retrieving from high-resolution PAZ SAR images with a combined correction of unwrapping and long-wavelength errors: A case study in Alcoy, Spain","authors":"Xiaojie Liu ,&nbsp;Roberto Tomás ,&nbsp;Chaoying Zhao ,&nbsp;Juan M. Lopez-Sanchez ,&nbsp;Zhangfeng Ma","doi":"10.1016/j.rse.2025.114876","DOIUrl":"10.1016/j.rse.2025.114876","url":null,"abstract":"<div><div>PAZ mission is an X-band synthetic aperture radar (SAR) satellite launched by Spain in February 2018, capable of routinely acquiring images with high spatio-temporal resolution. In this paper, we explore the potential of spotlight-mode (HS) PAZ images for monitoring small-scale ground deformation with millimeter-level accuracy, utilizing 21 HS PAZ images acquired over the Alcoy basin (SE Spain) between September 2019 and February 2021. Phase unwrapping and long-wavelength atmospheric delays significantly impede high-accuracy estimation of small-scale ground deformation in the study area. To address these issues, we first propose an approach for correcting phase unwrapping errors in interferograms by incorporating constraints from both spatial and temporal domains. Subsequently, we propose a block-based correction algorithm based on principal component analysis (PCA) to mitigate long-wavelength errors in the interferograms. Our results demonstrate that the proposed method can effectively eliminate long-wavelength errors in interferograms after both traditional phase-based method and GACOS corrections, reducing the standard deviation of the interferograms by up to a maximum of 68.4 %, and a value of 37.7 % on average. External validations from GPS and topographic measurements confirm that the deformation time series derived from the proposed method show an accuracy higher than 3 mm. This means millimeter-level precision is achieved in small-scale ground deformation measurements using PAZ SAR images. The analysis of the ground deformation derived from PAZ images reveals 41 active deformation areas in the Alcoy basin between September 2019 and February 2021, each that exhibits a deformation rate exceeding 5 mm/year. Through independent component analysis (ICA) and k-means clustering, we identify three distinct temporal evolution patterns corresponding to landslide activities, land subsidence, and land settlements. This study serves as a methodological blueprint for high-accuracy ground deformation estimation using high-resolution PAZ imagery, offering valuable complementary data to conventional medium-resolution SAR systems (e.g., Sentinel-1) in ground deformation monitoring applications.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114876"},"PeriodicalIF":11.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322723","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":"Large-scale mapping of water bodies across sensors using unsupervised deep learning","authors":"Ji Zhao ,&nbsp;Pu Xiao ,&nbsp;Yuting Dong ,&nbsp;Christian Geiß ,&nbsp;Yanfei Zhong ,&nbsp;Hannes Taubenböck","doi":"10.1016/j.rse.2025.114877","DOIUrl":"10.1016/j.rse.2025.114877","url":null,"abstract":"<div><div>Rapid and accurate monitoring of surface water is critical for water resource management, environmental protection, sustainable urban development, among other issues. Landsat and Sentinel data are publicly available optical data with high spatial and temporal resolution, providing the possibility for large-scale surface water mapping. However, traditional threshold-based or supervised classification-based surface water mapping methods often require adjusting thresholds or training samples for different areas or different sensors, which may hinder the generalization performance of the method in large-scale water body mapping. To address these difficulties, we propose an unsupervised cross-sensor deep learning water bodies mapping framework (UUCP) for unlabeled large-scale optical remote sensing images. The UUCP framework adopts an unsupervised multi-segment thresholding strategy to achieve the transition from label-free learning to noisy label learning. It learns robust multi-scale features of water bodies by the developed channel attention multi-scale surface water extraction network and training strategies under noisy labels. The proposed algorithm's effectiveness was evaluated using Sentinel-2 and Landsat-8 images from Guangzhou and Wuhan in China, and nine regions in France. The results show that our proposed method performs well in the overall performance of water extraction and is applicable to different sensors, with Kappa values reaching an average of 0.8859 and 0.8084 on Sentinel-2 and Landsat-8, respectively. More importantly, in cross-sensor experiments (the model trained on Landsat-8 data directly predicts Sentinel-2 dataset), the UUCP algorithm has excellent performance and is superior to other traditional water extraction algorithms. Overall, UUCP has excellent generalization ability and provides a new perspective for large-scale surface water mapping.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114877"},"PeriodicalIF":11.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322724","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":"Bridging the gap between laboratory VNIR-SWIR spectra and Landsat-8 bare soil composite image for soil organic carbon prediction","authors":"Yongsheng Hong ,&nbsp;Yiyun Chen ,&nbsp;Songchao Chen ,&nbsp;Yanyu Wang ,&nbsp;Wenyou Hu ,&nbsp;Su Ye ,&nbsp;Xiaodong Song ,&nbsp;Feng Liu ,&nbsp;Yongcun Zhao ,&nbsp;José A.M. Demattê ,&nbsp;Liangsheng Shi ,&nbsp;Huanfeng Shen ,&nbsp;Zhou Shi ,&nbsp;Ganlin Zhang ,&nbsp;Yaolin Liu","doi":"10.1016/j.rse.2025.114874","DOIUrl":"10.1016/j.rse.2025.114874","url":null,"abstract":"<div><div>International interest is focusing on how to better manage soil organic carbon (SOC) to increase resilience to climate change and reinforce food security. Remote sensing imagery and visible, near-infrared, and short-wave infrared (VNIR-SWIR) spectroscopy combined with advanced modeling algorithms can monitor SOC in an efficient, low-cost, and environmentally friendly manner. However, the limited bands (multi-spectral imagery) as well as the confounding effects such as soil moisture and surface roughness from the satellite may jeopardize the spectral prediction of SOC. We proposed a framework to integrate laboratory spectra with Landsat-8 multispectral data collected in natural real-environments to implement the satellite hyperspectral simulation for SOC prediction and mapping. A soil spectral library (SSL) with 873 samples containing SOC and VNIR-SWIR reflectance was developed in Northeast China. Two pixelwise temporal mosaicking approaches (i.e., averaged bare soil conditions [Median] and dry soil conditions to exclude anomalous values [R90]) were compared for creating bare soil composite images. Fractional-order derivative was explored for spectral preprocessing. Results indicated Median approach outperformed R90 regarding both spectral correlation and the validation performance for SOC. Raw reflectance after hyperspectral image simulation with 201 bands developed by random forest algorithm improved the validation <em>R</em>² from 0.38 and 0.29 to 0.49 and 0.32 for Median and R90 approaches, respectively, as compared to raw reflectance with six bands. Laboratory spectra yielded higher predicted accuracy for SOC than Median-simulated, followed by R90-simulated hyperspectral images. The convolutional neural network models developed with 0.75-order, 0.75-order, and 1.25-order one-dimensional spectra had the optimal validated performances for laboratory, Median-simulated, and R90-simulated hyperspectral data, respectively, with the corresponding validation <em>R</em>² values of 0.79, 0.68, and 0.54. Our study highlights that legacy SSLs show huge potential for integrating with current and forthcoming satellite multispectral images to maximize the predictive performance for SOC monitoring and mapping.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114874"},"PeriodicalIF":11.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321468","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":"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}