Remote Sensing of Environment最新文献

{"title":"Mapping recreational marine traffic from Sentinel-2 imagery using YOLO object detection models","authors":"Janne Mäyrä ,&nbsp;Elina A. Virtanen ,&nbsp;Ari-Pekka Jokinen ,&nbsp;Joni Koskikala ,&nbsp;Sakari Väkevä ,&nbsp;Jenni Attila","doi":"10.1016/j.rse.2025.114791","DOIUrl":"10.1016/j.rse.2025.114791","url":null,"abstract":"<div><div>Identifying where maritime activities take place, and quantifying their potential impact on marine biodiversity, is important for the sustainable management of marine areas, spatial planning and marine conservation. Detection and monitoring of small vessels, such as pleasure crafts, has been challenging due to limited data availability with adequate temporal and spatial resolution. Here, we develop an analysis framework to detect and quantify small vessels, using openly available Sentinel-2 optical imagery, and the YOLO-family object detection models. We also show how existing spatial datasets can be used to improve the quality of the model output.</div><div>We chose five distinct marine areas along the Finnish coast in the northern Baltic Sea and manually annotated 8,768 vessels for training and validating object detection models. The top-performing model, based on F1-score, achieved a test set F1-score of 0.863 and an mAP50 of 0.888. This model was then used to quantify pleasure crafts in the southern part of Finland from May to September 2022, using all available Sentinel-2 images with sufficient image quality. Detected recreational traffic was primarily concentrated on boating lanes and close to marinas, with weekend traffic averaging approximately twice the volume of weekday traffic.</div><div>The developed approach can be used to identify areas with high boat traffic, thus supporting sustainable spatial planning, ecological impact avoidance, and serving as an indicator of recreational popularity of marine areas. Additionally, it provides a basis for assessing the potential impacts of pleasure crafts on marine biodiversity. By leveraging openly available satellite images with frequent revisits, the approach offers broad geographical coverage and high spatial accuracy, making it scalable for estimating boat traffic across large areas, even globally.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"326 ","pages":"Article 114791"},"PeriodicalIF":11.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946702","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 knowledge-augmented deep fusion method for estimating near-surface air temperature","authors":"Fengrui Chen ,&nbsp;Xi Li ,&nbsp;Yiguo Wang","doi":"10.1016/j.rse.2025.114819","DOIUrl":"10.1016/j.rse.2025.114819","url":null,"abstract":"<div><div>Near-surface air temperature (Ta) is a critical meteorological variable, and obtaining its precise spatiotemporal distribution is essential for numerous scientific domains beyond meteorology and hydrology. Despite the promising advancements in Ta mapping using machine learning, these models often suffer from inadequate generalization capabilities due to their heavy reliance on data. A critical limitation is that their “free” learning style fails to deeply uncover the intricate spatiotemporal patterns of Ta. Addressing this problem, we propose a novel knowledge-augmented deep fusion method (KADF), designed to enhance the accuracy of Ta mapping through integrating prior knowledge. KADF integrates three categories of prior knowledge concerning Ta: spatial autocorrelation, temporal autocorrelation, and temporal heterogeneity in the relationship between Ta and predictive variables. This tailored strategy enables the model to more efficiently explore the intricate spatiotemporal relationships between grounded Ta observations and satellite-derived auxiliary variables, culminating in accurate Ta estimates through the deep fusion of these datasets. The efficacy of KADF was thoroughly evaluated over Chinese mainland. The validation results show that KADF accurately mapped the spatiotemporal distribution of daily Ta, with root mean square error (RMSE) values of 1.0 °C for mean Ta (T_mean), 1.22 °C for maximum Ta (T_max), and 1.33 °C for minimum Ta (T_min). Moreover, the integration of prior knowledge regarding Ta significantly enhanced the generalizability of the data-driven mapping model. Compared to the state-of-the-art machine learning-based estimation method, KADF reduced the mean absolute error (MAE) values by 23–31 % and RMSEs by 24–29 %. Furthermore, this method considerably improved the ability to capture spatial and temporal variations in Ta across various environmental conditions. Finally, a 1 km daily Ta dataset for the time frame spanning from 2010 to 2018 was produced. Overall, KADF holds great promise for accurately estimating Ta and can be easily adapted to other regions. The source code of KADF has been made publicly available at <span><span>https://github.com/Henu-frch/KADF</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"326 ","pages":"Article 114819"},"PeriodicalIF":11.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942423","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":"Across-scale thermal infrared anisotropy in forests: Insights from a multi-angular laboratory-based approach","authors":"Jennifer Susan Adams ,&nbsp;Alexander Damm ,&nbsp;Mike Werfeli ,&nbsp;Julian Gröbner ,&nbsp;Kathrin Naegeli","doi":"10.1016/j.rse.2025.114766","DOIUrl":"10.1016/j.rse.2025.114766","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The Land Surface Temperature (LST) is well suited to monitor biosphere–atmosphere interactions in forests, as it depends on water availability and atmospheric/meteorological conditions above and below the canopy. Satellite-based LST has proven integral in observing evapotranspiration, estimating surface heat fluxes and characterising vegetation properties. Since the radiative regime of forests is complex, driven by canopy structure, components radiation properties and their arrangement, forest radiative temperatures are subject to strong angular effects. However, this depends on the scale of observation, where scattering mechanisms from canopy – to satellite – scales influence anisotropy with varying orders of magnitude. Given the heterogeneous and complex nature of forests, multi-angular data collection is particularly difficult, necessitating instrumentation distant enough from the canopy to obtain significant canopy brightness temperature and concurrent observations to exclude turbulence/atmospheric effects. Accordingly, current research and understanding on forest anisotropy at varying scales (from local validation level to satellite footprint) remain insufficient to provide practical solutions for addressing angular effects for upcoming thermal satellite sensors and associated validation schemes. This study presents a novel method founded in the optical remote sensing domain to explore the use of microcanopies that represent forests at different scales in the footprint of a multi-angular goniometer observing system. Both Geometric Optical (GO) and volumetric scattering dominated canopies are constructed to simulate impacts of anisotropy in heterogeneous and homogeneous canopies, and observed using a thermal infrared radiometer. Results show that heterogeneous canopies dominated by GO scattering are subject to much higher magnitudes of anisotropy, reaching maximum temperature differences of 3 °C off-nadir. Magnitudes of anisotropy are higher in sparse forests, where the gap fraction and crown arrangement (inducing sunlit/shaded portions of soil and vegetation) drive larger off-nadir differences. In dense forests, anisotropy is driven by viewing the maximum portion of sunlit vegetation (hotspot), where the soil is mostly obscured. Canopy structural metrics such as the fractional cover and gap fraction were found to have significant correlation with off-nadir differences. In more homogeneous canopies, anisotropy reaches a lower magnitude with temperature differences up to 1 °C, driven largely by volumetric scattering and components radiation properties. Optimal placement of instrumentation at the canopy-scale (more heterogeneous behaviour due to proximity to the canopy and small pixel size) used to validate satellite observations (more homogeneous behaviour due to larger pixel size) was found to be in cases of viewing maximum sunlit vegetation, for dense canopies. Given upcoming high spatial resolution sensors and associated validatio","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"326 ","pages":"Article 114766"},"PeriodicalIF":11.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942314","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":"The impact of map accuracy on area estimation with remotely sensed data within the stratified random sampling design","authors":"Sergii Skakun","doi":"10.1016/j.rse.2025.114805","DOIUrl":"10.1016/j.rse.2025.114805","url":null,"abstract":"<div><div>One of the core applications of satellite-based classification maps is area estimation. Regardless of the algorithms used, maps will always contain errors stemming from imperfect input and training/calibration data, incomplete data coverage, and spectral and/or temporal confusion between land cover and land use classes. Because of omission and commission errors, the <em>pixel-counting area estimator</em> will be a biased estimator for area estimation. Therefore, the remote sensing research and application communities have developed a framework and recommended practices to address this problem. One such approach is a stratified random sampling design, in which classification maps could be used for stratification in the sampling design, and areas are estimated from the sample data, which represent reference data or reference class labels. As such, the quality of the map, i.e., producer's (PA) and user's accuracy (UA), will not affect the bias of the estimator, as the bias depends on the sampling design and the choice of estimator. However, map quality will impact the efficiency of stratification: a more accurate map will require a smaller sample size to reach the target variance of the estimate, or it will yield improved precision if the sample size is fixed. This study aims to provide a quantitative assessment of the impact of map accuracies on area estimation within the stratified random sampling design. The relative bias of the pixel-counting estimator is expressed using class-specific PA and UA, and shown to be <span><math><mfrac><mi>PA</mi><mi>UA</mi></mfrac><mo>−</mo><mn>1</mn></math></span>. Furthermore, for the case of binary classification, elements of the confusion matrix, as well as the sample size, variance of the area estimator, and relative efficiency of stratification (the ratio of the products of variance and sample size for the two sampling approaches) are expressed using PA and UA. Numerical simulations demonstrate how relative efficiency depends on area estimation objectives, target area proportion, and the map's performance metrics (PA and UA). Such dependence is nonlinear, and the impact of those parameters varies. For example, when the target class is minor or rare (i.e., its true proportion is &lt;&lt;0.5), the impact of PA outweighs that of UA. As the target area proportion increases, the impact of accuracies converges, and UA has a greater impact on efficiency than PA. There are multiple values in the PA/UA space, though constrained, to reach the same objectives, e.g., in terms of relative efficiency, sample size, and target variance. Overall, this study offers map producers a criterion that can be used to benchmark algorithm performance for map generation when area estimation is the primary objective of the classification maps.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"326 ","pages":"Article 114805"},"PeriodicalIF":11.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942422","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":"Comparing satellite and BGC-Argo chlorophyll estimation: A phenological study","authors":"Alberto Baudena ,&nbsp;Wilhem Riom ,&nbsp;Vincent Taillandier ,&nbsp;Nicolas Mayot ,&nbsp;Alexandre Mignot ,&nbsp;Fabrizio D’Ortenzio","doi":"10.1016/j.rse.2025.114743","DOIUrl":"10.1016/j.rse.2025.114743","url":null,"abstract":"<div><div>Ocean primary production is a key process that regulates marine ecosystems and the global climate, but its estimation is still affected by multiple uncertainties. Typically, the chlorophyll-a concentration (CHL) is used to characterise this process, as it is considered as a proxy of phytoplankton biomass. To date, the most common observing systems for studying CHL are ocean colour satellites and Biogeochemical-Argo (BGC-Argo) floats. These are complementary systems: satellite observations provide global coverage but are limited to the ocean surface, while BGC-Argo floats provide punctual observations along the whole water column. Quantitative matching of these two observing systems has been obtained only at regional or single-float scales, while at a global scale the relatively low and irregular BGC-Argo coverage results in large uncertainties. Here, we propose a different method, by comparing satellite and BGC-Argo climatological annual time series within seven different bioregions, each characterised by a homogeneous phytoplankton phenology, allowing us to smooth the uncertainties. By comparing the mean values, amplitudes, and shapes of the two time series, we identify regions (a) where they agree (58%–61% of the ocean surface area); (b) regions undersampled by the BGC-Argo float network (particularly in the Arabian Sea and near the Amazon delta); (c) where the discrepancy may stem from satellite or (d) BGC-Argo performance (mainly found at subtropical and high latitudes, respectively). Caution is required when using BGC-Argo and satellite data in regions b–d, and, for each region, we provide suggestions on which system could be affected by the largest uncertainties.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"326 ","pages":"Article 114743"},"PeriodicalIF":11.1,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143940012","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":"Improved assessment of post-fire recovery trajectory of forests in Amazon's protected areas","authors":"Qianhan Wu ,&nbsp;Calvin K.F. Lee ,&nbsp;Jonathan A. Wang ,&nbsp;Yingyi Zhao ,&nbsp;Guangqin Song ,&nbsp;Eduardo Eiji Maeda ,&nbsp;Yanjun Su ,&nbsp;Alfredo Huete ,&nbsp;Alice C. Hughes ,&nbsp;Jin Wu","doi":"10.1016/j.rse.2025.114802","DOIUrl":"10.1016/j.rse.2025.114802","url":null,"abstract":"<div><div>Protected areas (PAs) in Amazon forests are vital in preserving tropical forest ecosystems and mitigating forest degradation. However, the increasing frequency and severity of fires in these regions necessitate a comprehensive understanding of post-fire vegetation recovery trajectories, which is essential to evaluate the effectiveness and resilience of PAs in the face of ongoing climate change. Recovery trajectories under natural conditions remain uncertain, as unregulated human settlements often interfere with or influence the recovery process, skewing the actual recovery rates detected by satellite remote sensing. To tackle this issue, we examined 2990 MODIS-derived fire events in eastern Amazon PAs from 2001 to 2020. We assessed the effectiveness of multi-source Earth observation data and the eXtreme Gradient Boost machine learning model to distinguish burned areas undergoing natural recovery (natural recovery areas) from areas that are permanently converted to other uses (permanently converted areas). We then analyzed greenness recovery rates and canopy structure recovery trajectories across all burned areas, natural recovery areas, and permanently converted areas. Greenness recovery rates were derived from Landsat data, while canopy structure recovery was assessed using GEDI lidar-derived metrics and the space-for-time substitution approach. Our model achieved an overall classification accuracy of 87.90 %, accurately differentiating natural recovery areas (<em>n</em> = 1944) from permanently converted areas (<em>n</em> = 1046). The differing patterns of post-fire greenness recovery rates and structure recovery trajectories highlight the importance of this distinction. In natural recovery areas, significant recovery of structural traits such as relative heights (RHs), canopy cover (CC), and plant area index (PAIs), was observed, returning to their pre-disturbance levels over a 20-year period. Notably, metrics related to understory recovery and plant vertical space use, such as PAI values across the entire vertical strata, exhibited stronger recovery rates than height-related metrics like RHs, highlighting their utility in characterizing complex ecosystem recovery processes. These findings demonstrate the potential and necessity of using multi-source Earth observation data to distinguish different post-fire vegetation recovery processes. This distinction improves our understanding of ecological recovery rates and the successional dynamics of post-fire forests under natural conditions, offering new opportunities to explore their biogeographical distribution, recovery rate variabilities, and impacts on carbon sequestration and ecosystem resilience.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"326 ","pages":"Article 114802"},"PeriodicalIF":11.1,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936488","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":"First estimation and evaluation of hourly biomass burning emissions in north American high latitudes","authors":"Fangjun Li ,&nbsp;Xiaoyang Zhang ,&nbsp;Shobha Kondragunta","doi":"10.1016/j.rse.2025.114814","DOIUrl":"10.1016/j.rse.2025.114814","url":null,"abstract":"<div><div>Smoke from wildfires across North American high latitudes can travel long distances, degrading regional air quality. Hourly fire emissions are a crucial input of air quality models. However, they are unavailable for fires at high latitudes. The Advanced Baseline Imager (ABI) onboard NOAA's Geostationary Operational Environmental Satellites (GOES)-R Series satellites detects fires across North America every 10 min at a nominal resolution of 2 km, offering a good opportunity to estimate hourly fire emissions. At high latitudes, the polar-orbiting Visible Infrared Imaging Radiometer Suite (VIIRS) sensor provides 375 m fire observations up to six times a day for the same locations. In this study, we estimated hourly fire emissions at high latitudes for the first time by blending the high-temporal resolution ABI fire radiative power (FRP) and fine-spatial resolution VIIRS FRP. First, we corrected the parallax issue in ABI active fire data by considering land surface elevation. Next, FRPs from ABI and VIIRS fire pixels were separately aggregated into 3 km grids, and ABI FRP was calibrated against VIIRS FRP to correct for underestimation at large view angles. Then, the calibrated ABI FRP and VIIRS FRP were fused to reconstruct FRP diurnal cycles with the help of FRP diurnal climatologies. Finally, hourly emissions for eleven species were estimated using the reconstructed FRP diurnal cycles for two years (2021 and 2022). The results suggest that fires emitted a total of ∼0.54 Tg and 0.4 Tg PM_2.5 (particulate matter with diameter &lt; 2.5 μm) in 2021 and 2022, respectively, with over 86 % of emissions released from summertime boreal forest fires. Moreover, hourly emissions revealed a general diurnal pattern: emissions were limited in the early morning, peaked around 2:00 PM - 3:00 PM local time, and decreased in the evening. Notably, the diurnal pattern of emissions varied by region and season in timing of peak emission and emission magnitude. Furthermore, we evaluated hourly emissions estimates using carbon monoxide (CO) and nitrogen dioxide (NO₂) observations from the TROPOspheric Monitoring Instrument (TROPOMI) over 29 fresh smoke plumes. Evaluation results show that the hourly ABI-VIIRS CO estimates were significantly correlated with the TROPOMI-based CO estimates (R² = 0.94, <em>P</em> &lt; 0.001) and both were generally comparable, with a difference of 17.5 %. The proposed algorithm in this study has been integrated into the near real-time hourly Regional ABI and VIIRS fire Emissions (RAVE) product and is expected to improve air quality forecasting.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"326 ","pages":"Article 114814"},"PeriodicalIF":11.1,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928184","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":"Improvement of land surface phenology monitoring by fusing VIIRS observations with GOES-16/17 ABI time series","authors":"Shuai Gao ,&nbsp;Xiaoyang Zhang ,&nbsp;Yu Shen ,&nbsp;Khuong H. Tran ,&nbsp;Yongchang Ye ,&nbsp;Yuxia Liu","doi":"10.1016/j.rse.2025.114803","DOIUrl":"10.1016/j.rse.2025.114803","url":null,"abstract":"<div><div>Land Surface Phenology (LSP) has been widely derived from polar-orbiting satellite observations to characterize terrestrial vegetation dynamics. However, the uncertainty of LSP detections over large areas is always a big concern because of cloud contamination in the satellite time series, particularly in persistently cloudy regions. The Advanced Baseline Imager (ABI) onboard Geostationary Operational Environmental Satellite-R (GOES-R) provides a high likelihood of obtaining cloud-free observations throughout the vegetation growing season due to the high temporal resolution of 10 minutes. Therefore, this study investigated LSP detections at 500 m pixels from VIIRS (Visible Infrared Imaging Radiometer), ABI, and fused VIIRS-ABI time series in 2019 over North America between 12°N and 48°N. Specifically, the 3-day composite VIIRS NBAR (Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance) EVI2 (two-band Enhanced Vegetation Index) time series was first generated. Similarly, the 3-day composite ABI EVI2 time series was also produced after performing BRDF-adjustment of 10-min GOES-16/17 ABI surface reflectance. The 3-day VIIRS EVI2 time series was then fused with ABI EVI2 observations to generate the synthetic high spatiotemporal VIIRS-ABI EVI2 time series. Further, LSP was separately detected from the VIIRS, ABI, and fused VIIRS-ABI EVI2 time series. Finally, the three LSP detections were analyzed with the variation of cloud cover and ABI view zenith angle (VZA) and validated using the LSP reference produced from the fusion of Harmonized Landsat 8 and Sentinel-2 (HLS) observations with PhenoCam time series. The results showed that VIIRS-ABI LSP could overcome the limitations in the LSP detections from either VIIRS or ABI alone. The improvement of VIIRS-ABI LSP could be over 15 days relative to ABI LSP in large VZA regions and 5 days relative to VIIRS LSP in regions prone to persist cloud cover. Because of the high complementarity between the polar-orbiting and geostationary satellites, their fusion could significantly improve the generation of global LSP products.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"326 ","pages":"Article 114803"},"PeriodicalIF":11.1,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928183","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":"Kinematic inventory of rock glaciers in the Pyrenees based on InSAR and airborne LiDAR data","authors":"Jesús Guerrero ,&nbsp;Miguel Guerra ,&nbsp;Thiery Yannick ,&nbsp;Gloria Desir ,&nbsp;Bastien Colas","doi":"10.1016/j.rse.2025.114798","DOIUrl":"10.1016/j.rse.2025.114798","url":null,"abstract":"<div><div>Rock glaciers (RGs) are ice and debris landforms shaped by long-term permafrost creep. Their inventory has expanded significantly in the past two decades due to their importance as water resources and indicators of climate change. Previous inventories in the Pyrenees are sparse and lack essential kinematic data, leading to an underestimation of active RGs. This study presents the first kinematic database of Pyrenean RGs, integrating European Ground Motion Service (EGMS) InSAR data with airborne Laser Imaging Detection and Ranging (LiDAR) datasets and seven years of Sentinel-1 high-resolution InSAR data processed by the SqueeSAR algorithm. The analysis focuses on three igneous plutons (Panticosa, Cauterets, and Neouvielle) in the central Pyrenees, comparing surface displacement rates from these techniques to previous geodetic measurements. A total of 733 RGs have been mapped in the Pyrenees, covering an area of 58.9 km². 73 % of the mapped RGs are inactive or relict, showing no ground displacement and being partially vegetated. Only 13 % (96 RGs) remain active, covering less than 10 km², and are primarily north-facing. An additional 14 % (102 RGs) lack InSAR data but they are considered potentially active based on their orientation, altitude, and remarkable morphological features. The existence of active RGs at relatively low altitudes lowers the permafrost boundary between 2100 and 2600 m on north- and south-facing slopes, respectively. Despite the limitations of InSAR to measure displacements along the north-south axis and the fact that most active RGS are oriented to the north, according to the decomposition of the LOS displacement to vertical and horizontal components and LiDAR data, the ongoing subsidence and decline in horizontal movement related to ice degradation suggest a transition of many RGs from active to relict, marking rapid permafrost degradation. Finally, the EGMS has proven inadequate for detecting slow-moving active RGs in the Pyrenees due to temporal decorrelation caused by prolonged snow cover periods, particularly when compared to the combined use of SqueeSAR and airborne LiDAR datasets. The EGMS detected ground information for only 14 out of 89 active RGs, provided inaccurate kinematic data and underestimated the decomposed vertical and horizontal velocities by up to fourfold.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"326 ","pages":"Article 114798"},"PeriodicalIF":11.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143921618","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 deep learning method for generating gap-free FAPAR time series from Landsat data","authors":"Guodong Zhang ,&nbsp;Gaofei Yin ,&nbsp;Wei Zhao ,&nbsp;Meilian Wang ,&nbsp;Aleixandre Verger","doi":"10.1016/j.rse.2025.114783","DOIUrl":"10.1016/j.rse.2025.114783","url":null,"abstract":"<div><div>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a key indicator of photosynthetic activity and primary productivity in terrestrial ecosystems. While moderate-coarse spatial resolution FAPAR products have enabled global vegetation studies, their pixel sizes smooth fine-scale heterogeneity and limit applications needing a detailed spatial characterization. Landsat provides multispectral data at 30 m spatial resolution enabling global and long-term FAPAR estimation at finer spatial detail. But challenges exist in deriving continuous Landsat FAPAR time series due to infrequent clear observations. We propose here a generic two-step method to produce gap-free Landsat FAPAR time series. First, in clear-sky conditions, FAPAR is retrieved from Landsat surface reflectance observations using a random forest (RF) regression model previously trained with the Global land surface satellite (GLASS) V6 FAPAR product. In a second step, a novel Bidirectional Temporal Convolutional Network with Sparrow Search Algorithm and Attention mechanism (SSA-BiTCN-Attention) was used to reconstruct the missing FAPAR values. The temporal information of Landsat clear-sky FAPAR within a five-year window was used for predicting the missing values. The reconstruction model accurately predicted missing FAPAR values across different land cover types with RMSE ranging from 0.08 to 0.12. The validation results showed a good agreement between the estimated Landsat FAPAR and ground measurements from VAlidation of Land European Remote Sensing Instruments (VALERI), ImagineS and Ground Based Observations for Validation (GBOV), with R² values ranging from 0.82 to 0.92, RMSE values from 0.10 to 0.12, and bias values from 0.02 to 0.05. The Landsat retrievals are consistent with GLASS and Moderate-Resolution Imaging Spectroradiometer (MODIS) FAPAR products and improve these two products in terms of accuracy and spatial resolution. As a demonstration case study, we applied our method to generate 30-m, 16-day FAPAR time series from 2013 to 2023 over China. The Landsat gap-free FAPAR product exhibits seamless spatial coverage and temporal continuity across China. This innovative method has the potential to be applied to multiple satellite data and land surface products to generate gap-free high spatio-temporal time series of land surface variables at the global scale, which will contribute in improving environmental modeling, carbon cycling studies, and agricultural applications.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"326 ","pages":"Article 114783"},"PeriodicalIF":11.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917985","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}