Remote Sensing of Environment最新文献

{"title":"Probabilistic mapping of high-intensity forest fire potential via time series machine learning and remote sensing-informed fire spread simulations","authors":"Rui Chen ,&nbsp;Yiru Zhang ,&nbsp;Yanxi Li ,&nbsp;Marta Yebra ,&nbsp;Chunquan Fan ,&nbsp;Hongguo Zhang ,&nbsp;Binbin He","doi":"10.1016/j.rse.2026.115233","DOIUrl":"10.1016/j.rse.2026.115233","url":null,"abstract":"<div><div>High-intensity forest fires have significant destructive impacts on ecosystems and society, and are an increasing concern worldwide. Accurate probabilistic risk assessment of these fires can effectively enhance the ability to guide wildfire management, particularly for large and extreme fires. However, forecasting large-scale fire behavior characteristics remains challenging, limiting the effectiveness of spatial estimations of high-intensity forest fire potential (HIFFP). This study aims to integrate fire spread simulations and machine learning (ML) algorithms to enhance HIFFP estimations through multi-step time-series forecasting on fire rate of spread and fireline intensity at regional scales. We first established a high-intensity forest fire dataset based on remote sensing-informed fire spread simulations from the Weather Research and Forecasting coupled fire-spread model (WRF-SFIRE), incorporating explanatory variables on fuel, weather, climate, and topography. Then, the knowledge-guided framework (multi-step time series-based ML, MTS-ML) was designed to estimate HIFFP within different hours after fires occur, integrating with Bayesian Network (BN), Random Forest (RF), and copula models. Results indicate that MTS-ML improved HIFFP modeling compared with ML-based methods, achieving AUC (the area under the receiver operating characteristic curve) &gt; 0.95 (with ∼0.04 increments), F1 score &gt; 0.85 (with ∼0.08 increments), and MAE &lt; 0.15. Topographic index, foliage fuel load, and wind speed are identified as primary contributors to HIFFP. Probabilistic mapping of HIFFP represents wildfire danger, which is closely linked to burn severity and fire-induced carbon emissions. This study presents a novel framework for enhancing regional risk assessment of high-intensity forest fires, providing valuable guidance in wildfire control and management.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115233"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938801","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":"Unveiling the long-term cascading effects of the 2018 Baige landslide and subsequent outburst flood with satellite radar observations","authors":"Bo Chen ,&nbsp;Zhenhong Li ,&nbsp;Chuang Song ,&nbsp;Roberto Tomás ,&nbsp;Chen Yu ,&nbsp;Wu Zhu ,&nbsp;Jianbing Peng","doi":"10.1016/j.rse.2026.115231","DOIUrl":"10.1016/j.rse.2026.115231","url":null,"abstract":"<div><div>Landslide-dammed lakes (LDL) and landslide lake outburst flood (LLOF) can significantly alter the kinematic behavior of upstream and downstream landslides, posing severe threats to human life and infrastructure. However, the long-term impacts of LDL and LLOF on surrounding landslide stability remain poorly understood. In this study, we systematically examine the cascading effects triggered by the 2018 Baige LDL and LLOF on adjacent landslides, based on time series interferometric synthetic aperture radar (InSAR) analysis of 1437 satellite radar images. Unlike previous studies that focused on individual landslides or localized areas, we developed an automated method to detect the onset of landslide acceleration, leading to the establishment of an inventory of 65 accelerated landslides (ALs) and a quantitative evaluation of their controlling factors. Our results show that approximately 30 % of the flood-affected active landslides changed their deformation mechanisms, which can be categorized into five distinct types. Among the landslides accelerated by the Baige event, 43 % exhibited persistent acceleration, whereas 57 % showed signs of self-recovery. For the latter, deformation velocity typically decayed by 90 % within an average of 9.3 years after the outburst, returning to near pre-event levels. Furthermore, compared to 378 flood-involved but non-ALs, ALs preferentially occur on gentler slopes and in areas with lower vegetation cover. More notably, those ALs generally experienced greater flood depth, higher flow velocity, and stronger flood power. This study is the first to assess the long-lasting cascading effects of LDL and LLOF on creep landslides. These findings advance our understanding of LDL and LLOF-induced landslide mechanisms and offer valuable insights for the long-term risk assessment and geohazard mitigation of landslide-prone regions affected by similar cascading processes.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115231"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938859","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 the upper depth of subsurface water on the Greenland Ice Sheet using multi-frequency passive microwave remote sensing, radiative transfer modeling, and machine learning","authors":"Baptiste Vandecrux ,&nbsp;Ghislain Picard ,&nbsp;Pierre Zeiger ,&nbsp;Marion Leduc-Leballeur ,&nbsp;Andreas Colliander ,&nbsp;Alamgir Hossan ,&nbsp;Andreas Ahlstrøm","doi":"10.1016/j.rse.2025.115197","DOIUrl":"10.1016/j.rse.2025.115197","url":null,"abstract":"<div><div>As the Arctic warms, surface melt extends into the Greenland Ice Sheet's accumulation zone, where much of the water infiltrates into the snowpack. This makes monitoring the subsurface water depth and spatial extent important for accurate ice sheet runoff estimations. Subsurface water can be detected using remotely sensed microwave brightness temperatures (T_B). We use vertically polarized T_B at 1.4 GHz from Soil Moisture and Ocean Salinity satellite (SMOS) and at 6.9, 10.7, and 18.7 GHz from the Advanced Microwave Scanning Radiometers (AMSR-E/2) to estimate the upper depth of liquid water (UDLW) on the ice sheet accumulation area. We build a catalogue of simulated UDLW and T_B: realistic UDLW are modeled by the Geological Survey of Denmark and Greenland (GEUS) snow model, forced by the Copernicus Arctic Regional Reanalysis (CARRA), and the corresponding T_B are calculated by the Snow Microwave Radiative Transfer (SMRT) model at 19 sites. We train on this catalogue an ensemble of cross-validated Random Forest (RF) models to predict UDLW and its uncertainty from T_B at four frequencies. On hold-out modeled data and for water within 5 m of the surface, the RF ensemble achieves a median RMSE of 0.68 m and mean error of −0.09 m. Our retrieval, when applied to observed T_B, matches within 2 m UDLW inferred from subsurface temperature profiles down to 4–6 m depth. Performances decrease beyond 5 m depth and for low liquid water amounts. Our retrieval produces daily UDLW maps over the ice sheet's accumulation area during 2010–2023 which reveal the seasonal evolution of UDLW, deliver the first quantitative estimates of subsurface liquid water depth on the ice sheet and offer new insights into meltwater infiltration and storage processes.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115197"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938861","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 large-scale framework for deriving tidal flat topography from SWOT data","authors":"Hao Xu ,&nbsp;Nan Xu ,&nbsp;Wenyu Li ,&nbsp;Kai Tan ,&nbsp;Chunpeng Chen ,&nbsp;Huan Li ,&nbsp;Lucheng Zhan ,&nbsp;Pei Xin ,&nbsp;Jiaqi Yao ,&nbsp;Peng Li ,&nbsp;Zhen Zhang ,&nbsp;Haipeng Zhao ,&nbsp;Bolin Fu ,&nbsp;Yifei Zhao ,&nbsp;Yufeng Li ,&nbsp;Qi Wang ,&nbsp;Fan Zhao ,&nbsp;Xiaojuan Liu ,&nbsp;Zhongwen Hu ,&nbsp;Guofeng Wu ,&nbsp;Qingquan Li","doi":"10.1016/j.rse.2026.115237","DOIUrl":"10.1016/j.rse.2026.115237","url":null,"abstract":"<div><div>Tidal flat topography is a fundamental attribute affecting inundation dynamics, sediment transport, and ecosystem functioning, yet accurate and spatially consistent large-scale monitoring remains challenging. Here, we leveraged satellite altimetry from the Surface Water and Ocean Topography (SWOT) mission to develop a novel, large-scale framework for deriving tidal flat topography from SWOT data, and demonstrated its capability by generating a high-accuracy, national-scale elevation dataset for China. By combining a percentile-based aggregation of multi-temporal water-surface elevation observations with a tide-constrained, adaptive best-quantile (best-q) reconstruction strategy, followed by linear interpolation for gap filling, we improved both vertical accuracy and spatial completeness. Validation against airborne LiDAR, GNSS-RTK surveys, and ICESat-2 photon data demonstrates robust performance across diverse coastal settings, achieving RMSE = 0.34–0.47 m and R² = 0.81–0.88 at a horizontal resolution of 100 m. Compared with existing large-scale digital elevation models (DEMs), the SWOT-derived topography not only improves vertical accuracy by over 80% but also providing substantially more complete spatial coverage of tidal flat elevations. Spatial analyses reveal pronounced latitudinal gradients, with higher tidal flats concentrated in low-latitude regions and extensive low-lying flats dominating northern estuarine and deltaic systems. This study establishes a scalable framework for tidal-flat elevation retrieval and provides a foundational dataset to support coastal monitoring and sustainable management.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115237"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145947722","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":"Annotation-free cloud masking for PlanetScope images in the Arctic via cross-platform ability transfer using deep learning and foundation models","authors":"Zhili Li ,&nbsp;Yiqun Xie ,&nbsp;Sergii Skakun ,&nbsp;Xiaowei Jia ,&nbsp;Gengchen Mai ,&nbsp;William Lu ,&nbsp;Matthew Tong ,&nbsp;Zhihao Wang","doi":"10.1016/j.rse.2025.115138","DOIUrl":"10.1016/j.rse.2025.115138","url":null,"abstract":"<div><div>Cloud masking is an essential task for satellite-based Earth monitoring, and the quality of cloud masks can directly impact the solutions of the downstream Earth monitoring tasks. While significant progress has been made especially for data with desired bands (e.g., thermal bands in Landsat-8), the masking quality on small satellites with higher resolution but fewer spectral bands is still unreliable at high latitudes, where confusion with snow and ice makes the task significantly more challenging. We propose a novel learning-enabled cross-platform ability transfer paradigm that offers a scalable and effective solution to tackle this challenge through a case study using PlanetScope images in the Arctic. A unique characteristic of the new paradigm is that it does not require manual annotations to be collected for PlanetScope images, which is often the bottleneck and the most time-consuming part of machine learning-based cloud masking, especially given the similarity between clouds and snow/ice. To realize this, our approach first designs and creates a new training dataset, Co-Clouds, which contains around 45,000 coincident pairs of PlanetScope and Landsat-8 image patches collected within a nearly simultaneous temporal window. This coincident dataset offers a way to generate large volumes of training data and builds a bridge to transfer Landsat-8’s stronger cloud masking skills in the Arctic to PlanetScope images via data-driven learning. We also show the feasibility of the ability transfer from spectral signatures (e.g., thermal bands) to spatial signatures (e.g., textures). Using our Co-Clouds dataset, we train several deep learning models including both regular-size deep learning models and large foundation models. To validate the quality of the masks, we further create a manually labeled cloud mask dataset for PlanetScope images in the Arctic. Both the quantitative and qualitative results show significant improvements over the current operational cloud masks by PlanetScope. For example, the large foundation models such as SegFormer achieve approximately 20 % higher overall accuracy and 28 % higher producer’s accuracy than the operational cloud masks, while maintaining comparable or better user’s accuracy exceeding 90 %. The new approach is also very easy to implement and extend to other platforms, opening new opportunities for broadcasting advanced skills from one platform to others.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115138"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689116","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 the performance of different hyperspectral satellite imaging spectroscopy in mapping methane point-source emissions","authors":"Fei Li ,&nbsp;Keer Lin ,&nbsp;Yingqi Yan ,&nbsp;Shengxi Bai ,&nbsp;Qidan Huang ,&nbsp;Chenxi Feng ,&nbsp;Shiwei Sun ,&nbsp;Shaohua Zhao ,&nbsp;Wei Zhou ,&nbsp;Chunyan Zhou ,&nbsp;Jun Lin ,&nbsp;Xinwei Zhang ,&nbsp;Yongguang Zhang","doi":"10.1016/j.rse.2025.115224","DOIUrl":"10.1016/j.rse.2025.115224","url":null,"abstract":"<div><div>Accurate detection and quantification of methane point sources are critical for climate change mitigation. Recent advances in spaceborne hyperspectral imaging spectrometers offer new opportunities for global monitoring, yet systematic evaluation across missions remains limited. Here, we assess the spectral and radiometric performance of six operational hyperspectral satellites for methane plume detection. Results show that EMIT, EnMAP, and the GF5 series provide superior capabilities. EMIT achieved the best retrieval precision (<em>σ</em> = 18 ppb), followed by GF5–02-AHSI (<em>σ</em> = 31 ppb), EnMAP (<em>σ</em> = 33 ppb), and GF5-01 A-AHSI (<em>σ</em> = 47 ppb), outperforming PRISMA (<em>σ</em> = 84 ppb) and ZY1-02E-AHSI (<em>σ</em> = 71 ppb). These differences arise from finer spectral sampling distances (SSD: 7.4–8.4 nm), higher signal-to-noise ratios (SNR: 170–250), and more stable central wavelength shifts (0.3–1.3 nm) within the 2300 nm methane absorption region, which collectively enhance plume discrimination against surface backgrounds. In contrast, PRISMA and ZY1-02E-AHSI exhibit lower sensitivity due to larger wavelength shifts (up to 2.8 nm) and lower SNRs (140–150). Case studies illustrate successful detection of methane plumes from diverse sources, including oil and gas (O&amp;G) infrastructure, coal mines, and a landfill site, highlighting both straightforward and challenging retrieval scenarios. This cross-sensor comparison underscores the importance of spectral fidelity and radiometric performance for methane monitoring. The findings provide a quantitative basis for prioritizing existing assets and guiding the design of future missions, emphasizing that high SNR and stable spectral calibration are key for advancing global detection of point-source methane emissions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115224"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894482","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":"Estimation of sea surface foam coverage and effective foam layer thickness from satellite microwave measurements","authors":"Xuchen Jin ,&nbsp;Xianqiang He ,&nbsp;Palanisamy Shanmugam ,&nbsp;Yan Bai ,&nbsp;Jianyun Ying ,&nbsp;Qiankun Zhu ,&nbsp;Yaqi Zhao ,&nbsp;Delu Pan","doi":"10.1016/j.rse.2025.115176","DOIUrl":"10.1016/j.rse.2025.115176","url":null,"abstract":"<div><div>Sea surface foam commonly represents the accumulation of bubbles on the sea surface caused by breaking waves and plays a critical role in the air-sea interaction process and climate research. Both foam coverage and foam layer thickness have a profound effect on sea surface emissivity for moderate to high wind speeds due to the high emissivity of foam at microwave bands. However, there is a lack of models to estimate sea foam layer thickness due to the limited experimental data and constrained empirical formulations. To address this limitation, we propose a dual-channel (L- and Ka-bands) method that utilizes passive microwave satellite measurements to estimate sea surface foam coverage and effective foam layer thickness over the global ocean. This method uses microwave measurements from the Aquarius/SAC-D and Soil Moisture Active Passive (SMAP) missions, together with observations from the WindSat instrument, to convert the top-of-atmosphere (TOA) brightness temperature (TB) to sea surface TBs and then isolate the foam-induced emission component. Because sea surface emissivity is sensitive to foam distributions, a foam emissivity model was developed and implemented to derive the foam layer parameters (coverage and thickness) as functions of wind speed. The retrieved foam coverage shows strong agreement with independent observations, with root mean square differences (RMSDs) of 0.87 % against field measurements and 0.38 % against satellite data. In addition, the retrieved effective foam thickness also shows close consistency with hydrodynamic model expectations. The global distributions of foam coverage and effective layer thickness were further retrieved and analyzed in this study, which demonstrated high potential of the proposed models compared to the previous methods and suggested that the combined use of L- band and Ka-band measurements from a single platform could improve the accuracy of foam coverage and effective layer thickness over the global ocean.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115176"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732130","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 novel SIF framework for decoupling hierarchical water stress impacts on winter wheat photosynthesis","authors":"Kaiqi Du ,&nbsp;Guilong Xiao ,&nbsp;Xia Jing ,&nbsp;Jie Zhang ,&nbsp;Tingting Zhao ,&nbsp;Xuecao Li ,&nbsp;Yelu Zeng ,&nbsp;Jianxi Huang","doi":"10.1016/j.rse.2025.115216","DOIUrl":"10.1016/j.rse.2025.115216","url":null,"abstract":"<div><div>Drought has emerged as a critical constraint on sustainable agricultural development, particularly in water-scarce agroecosystems where multiscale hydrological stresses—such as rainfall deficits, soil moisture depletion, and groundwater exhaustion—interact to undermine crop productivity stability. However, current remote sensing frameworks lack the capacity to isolate and quantify the independent impacts of groundwater drought on crop photosynthesis and yield, leading to long-standing underestimation of deep-layer hydrological stress, especially in groundwater-dependent regions. To address this gap, we have proposed a dynamic monitoring framework based on solar-induced chlorophyll fluorescence (SIF) to assess the photosynthetic response characteristics of winter wheat across the Huang-Huai-Hai Plain under precipitation, surface moisture, and groundwater anomalies. This framework integrates dynamic time warping (DTW) for interannual phenological alignment and constructs a quantile-based dynamic baseline library that overcomes the limitations of traditional normality-based anomaly metrics. On this basis, we have developed a novel photosynthetic response anomaly index (PRAI) to characterize the spatiotemporal evolution of drought-induced photosynthetic stress. Results reveal that groundwater anomalies induce a significantly lagged crop response (mean lag ≈ +2.1 months, <em>p</em> &lt; 0.01) and exert stronger influence on photosynthetic dynamics than soil surface moisture or rainfall. PRAI exhibits more concentrated and persistent negative anomalies during groundwater drought years, correlating more strongly with yield loss (R² = 0.53) than during meteorological drought years (R² = 0.30). Cross-validation using evapotranspiration (ET) and vegetation optical depth (VOD) further confirms PRAI reliability in capturing physiological stress. The proposed SIF-based dynamic monitoring framework not only deepens the understanding of crop eco-physiological response mechanisms to multiscale water stress, but also provides critical scientific support and methodological innovations for regional scale precision agriculture, crop model optimization, and sustainable water resource management.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115216"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823489","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 tree-level mapping of forest structure including species type with remote sensing data and ground measurements","authors":"J. Kostensalo ,&nbsp;P. Packalen ,&nbsp;M. Kuronen ,&nbsp;L. Mehtätalo ,&nbsp;S. Tuominen ,&nbsp;M. Myllymäki","doi":"10.1016/j.rse.2025.115223","DOIUrl":"10.1016/j.rse.2025.115223","url":null,"abstract":"<div><div>Remote-sensing based tree maps can be used to calculate various diversity indices, but the detection probability of trees depends on size and species. We propose a novel approach combining individual tree detection (ITD) with resampling corrections (+R) which aims to simultaneously correct the size, species, and spatial distribution of trees using scalable algorithms. Using airborne laser scanning, optical data, and ground measurements, we demonstrate the compatibility of ITD+R with two different types of forests and ITD algorithms, as well as its scalability to areas exceeding 3000 km². The tree maps were evaluated using plot-level variables and benchmarked against area-based <span><math><mi>k</mi></math></span> nearest neighbors (<span><math><mi>k</mi></math></span>-NN). The ITD+R improved ITD results for most studied metrics, with the Shannon index being an exception, and even outperformed <span><math><mi>k</mi></math></span>-NN in predicting dominant height in managed stands, though <span><math><mi>k</mi></math></span>-NN still outperformed for stem density and volume. The ITD+R approach was shown to be adaptable to various diversity indices which it has not been specifically trained on, with 254 m² plot-level predictions correlating at <span><math><mi>r</mi></math></span>=0.42–0.91. While ITD trees could be classified with OA=82.0%–86.6% to pine, spruce, and deciduous, further research is needed to account for rare tree species, as low prevalence results in a large number of false detections which cannot be sufficiently addressed with classification alone.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115223"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897654","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":"Space-based assessment of NOx emissions from global oil and gas fields: Bridging the gap in current emission inventories","authors":"Piyushkumar N. Patel ,&nbsp;Ritesh Gautam ,&nbsp;Mark Omara","doi":"10.1016/j.rse.2025.115229","DOIUrl":"10.1016/j.rse.2025.115229","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Identifying air pollutant sources and quantifying related emissions provides crucial information towards improving global air quality and public health. However, emission inventories for oil and gas (OG) activities inadequately represent nitrogen oxide (NO&lt;sub&gt;x&lt;/sub&gt;) emissions—a key precursor to tropospheric ozone and secondary aerosols—with notable discrepancies identified. Satellite remote sensing provides a unique vantage point to map and quantify these emissions consistently on a global scale. Here, we quantify annual NO&lt;sub&gt;x&lt;/sub&gt; emissions from 44 major OG basins distributed globally, utilizing TROPOspheric Monitoring Instrument (TROPOMI) nitrogen dioxide (NO&lt;sub&gt;2&lt;/sub&gt;) observations with the divergence flux method. In addition, we use the spaceborne Visible Infrared Imaging Radiometer Suite (VIIRS) natural gas flaring detections to further constrain satellite-derived NO&lt;sub&gt;x&lt;/sub&gt; emissions. The divergence flux method, which addresses 3D topography corrections and chemical loss of NO&lt;sub&gt;x&lt;/sub&gt; while accounting for wind-induced flux smearing, provides a robust approach for estimating NO&lt;sub&gt;x&lt;/sub&gt; emissions. This top-down approach resolves emissions at the facility and basin scale (0.01° x 0.01°), enabling direct quantification of a major gap in current bottom-up inventories. Our findings reveal significant differences between satellite observations and established inventories, which systematically underestimate OG sector emissions. A direct comparison of our TROPOMI-derived NO&lt;sub&gt;x&lt;/sub&gt; emissions against the existing inventories suggests that EDGARv6.1 underestimates onshore emissions by 61 % and offshore emissions by 26 %. The discrepancy is even more pronounced for the CAMS-GLOB-ANT_v5.3 inventory, which underestimates onshore and offshore emissions by 78 % and 92 %, respectively. These findings hold significance for global emission assessments, demonstrating that current inventories are missing a substantial source of NO&lt;sub&gt;x&lt;/sub&gt; pollution, particularly from OG fields. Furthermore, our approach provides detailed spatial emission maps that enhance the granularity in depicting NO&lt;sub&gt;x&lt;/sub&gt; distribution with error analysis (uncertainties: 32 %-54 %) supporting that inventory discrepancies are statistically significant, representing structural deficiencies rather than measurement error. We further explore the variations in the correlation between NO&lt;sub&gt;x&lt;/sub&gt; emissions with OG production volumes and CH&lt;sub&gt;4&lt;/sub&gt; concentrations across different OG basins in North America. The observed co-locations and correlations provide important insights into co-pollution emission characteristics of methane and NO&lt;sub&gt;x&lt;/sub&gt;, that are respectively a potent greenhouse gas and a reactive air quality pollutant, linked to be originating from oil and gas activity. These findings have important implications for regulatory monitoring and verification, particularly for addressing emissions transparency requirements under internationa","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115229"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881024","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}