Remote Sensing of Environment最新文献

{"title":"Towards resolution enhancement of P-band brightness temperature data using passive-passive downscaling with L-band radiometer data","authors":"Luisa F. White-Murillo ,&nbsp;Jeffrey P. Walker ,&nbsp;Nan Ye ,&nbsp;James Hills ,&nbsp;Xiaoling Wu ,&nbsp;Lixiaozhou Zhou ,&nbsp;Ziwei Xiong ,&nbsp;Liujun Zhu ,&nbsp;Brian Ng ,&nbsp;Mahta Moghaddam ,&nbsp;Simon Yueh","doi":"10.1016/j.rse.2025.114737","DOIUrl":"10.1016/j.rse.2025.114737","url":null,"abstract":"<div><div>Current space-borne L-band radiometers have been routinely providing global soil moisture maps every 2 to 3 days for more than 15 years. However, they can only estimate soil moisture in the top 5 cm of the soil, which limits its usefulness in applications that need deep layer soil moisture. Fortuitously, recent work has shown that there is a potential to retrieve root zone soil moisture (RZSM) when combining P-band radiometer data with L-band radiometer data at the same spatial resolution. Nevertheless, there are antenna size limitations that currently restrict L-band radiometer data to a resolution of 40 km. Accordingly, if the same antenna is used for a joint P-band (750 MHz) and L-band (1.4GHz) satellite mission, the footprint size at P-band will be double that attained at L-band, limiting its usefulness in applications and making joint interpretation difficult. It is therefore essential to downscale the P-band radiometer measurements to at least the same resolution as the L-band measurements. Consequently, this paper has explored three alternative passive-passive P-band downscaling algorithms using the spatial information that exists in the L-band radiometer data, to achieve P-band brightness temperature (Tb) information at the same resolution as the L-band passive measurements. Analysis was conducted using data from three airborne campaigns in south-eastern Australia, with resolutions ranging from 36 km to 200 m under a range of moisture conditions and spatial characteristics. The three alternate downscaling algorithms used in this paper, designated as Algorithm A, B, and C, were not only compared to determine which generated the best performance, but were also analysed according to different land cover types and seasons. The results demonstrated that the Smoothing Filter-based Intensity Modulation (SFIM) method, referred to as Algorithm A, outperformed the others, with a median root mean square error (RMSE) compared to the original observations of 3 K when downscaling to half of the original spatial resolution, increasing to around 8 K when downscaling to 36 times finer than the original resolution.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114737"},"PeriodicalIF":11.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing hourly gross primary productivity mapping over East Asia using Himawari-8 AHI and artificial intelligence: Unveiling the impact of aerosol-induced radiation dynamics","authors":"Sejeong Bae ,&nbsp;Bokyung Son ,&nbsp;Taejun Sung ,&nbsp;Yoojin Kang ,&nbsp;Jungho Im","doi":"10.1016/j.rse.2025.114735","DOIUrl":"10.1016/j.rse.2025.114735","url":null,"abstract":"<div><div>Advancements in geostationary satellites allow monitoring of terrestrial photosynthesis at sub-daily scales, offering unprecedented opportunities for understanding vegetation productivity. However, at short temporal scales, photosynthesis is highly influenced by illumination conditions, particularly diffuse radiation (D_dif). Existing empirical models often overlook D_dif's impact, leading to uncertainties in hourly gross primary productivity (GPP) mapping. We determined that incorporating D_dif effects into GPP modeling improves clear-sky GPP mapping using Himawari-8. We employed a light gradient boosting machine (LGBM) considering D_dif and compared it with other empirical models: parametric, regression, and machine learning. The LGBM outperformed others, achieving an R² of 0.8146 and root mean square error of 2.848 μmol CO₂/m²/s against ground observations from 2020 to 2021. To investigate input variable contributions in LGBM predictions, we performed SHapely Additive exPlanation (SHAP) analysis. Results confirmed that aerosol optical depth (AOD) had a greater impact during morning and evening when D_dif influence increased due to solar path length. Hourly GPP maps over East Asia from 2020 to 2021 using the LGBM demonstrated that diurnal patterns differ by landcover, with variations observed in latitudinal profiles. This underscores the need to examine GPP spatial distribution at high frequency. We confirmed that the spatial distribution of AOD SHAP values varied over time, highlighting temporal dynamics of aerosol effects. Our findings demonstrate the necessity of GPP mapping using geostationary satellites and suggest various impact studies can use our proposed framework. This approach provides a valuable tool for understanding vegetation's rapid response to atmospheric aerosols, contributing to more accurate ecosystem flux modeling.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114735"},"PeriodicalIF":11.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768818","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":"Net primary production in the Labrador Sea between 2014 and 2022 derived from ocean colour remote sensing based on ecological regimes","authors":"E. Devred ,&nbsp;S. Clay ,&nbsp;M. Ringuette ,&nbsp;T. Perry ,&nbsp;M. Amirian ,&nbsp;A. Irwin ,&nbsp;Z. Finkel","doi":"10.1016/j.rse.2025.114713","DOIUrl":"10.1016/j.rse.2025.114713","url":null,"abstract":"<div><div>Phytoplankton play a major role in carbon export and storage in the ocean interior through remineralization of particulate carbon into dissolved inorganic carbon (DIC), and represent the “gain” side of the biological carbon pump. Shifts in phytoplankton community structure and species succession impact primary production, quality of food for zooplankton consumers and the fate of organic matter in marine ecosystems.</div><div>In the Labrador Sea (LS), a sub-arctic environment, the emergence of large blooms of <em>Phaeocystis</em> spp. in spring at the expenses of diatoms may disrupt phytoplankton species succession with drastic consequences on the carbon cycle and the functioning of the marine ecosystem as these small flagellates aggregate in colonies of up to several millimeters, embedded in gelatinous matrices, that modify elemental stoichiometry, sinking rates and transfer of energy to higher trophic levels. In this study, we develop an ecological approach to estimate primary production due to <em>Phaeocystis</em> sp. in the LS from satellite remote sensing data. We used a regionally-tuned primary production model to assign phytoplankton photosynthesis efficiency as a function of oceanographic regimes defined by phytoplankton community structure and biomass, and sea-surface temperature. We found that four oceanographic regimes corresponded to broad phytoplankton taxonomic assemblages and environmental factors in the LS: the diatom-dominated Shelf, the low chlorophyll-a Basin, the mesotrophic Basin regimes and a last oceanographic regime within the Basin, where the flagellated prymnesiophyte <em>Phaeocystis</em> spp. likely dominated the assemblage. Annual primary production in the Labrador Sea varied between 200 and 300 Tg of carbon between 2014 and 2022 in agreement with previous studies. While <em>Phaeocystis</em> spp. contributed about 10 % of the annual production, two unusual blooms that occurred in 2015 and 2022 contributed about 14 and 20 % of total production, respectively. During these two events <em>Phaeocystis</em> sp. contributed 40 % and 60 % to the May production and extended over more than half the Labrador Sea. Spring blooms dominated by <em>Phaeocystis</em> may occur more frequently due to climate change and have the potential to impact the fate of carbon and alter the functioning of the LS ecosystem.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114713"},"PeriodicalIF":11.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of data density and endmember definitions on long-term trends in ground cover fractions across European grasslands","authors":"Katarzyna Ewa Lewińska ,&nbsp;Akpona Okujeni ,&nbsp;Katja Kowalski ,&nbsp;Fabian Lehmann ,&nbsp;Volker C. Radeloff ,&nbsp;Ulf Leser ,&nbsp;Patrick Hostert","doi":"10.1016/j.rse.2025.114736","DOIUrl":"10.1016/j.rse.2025.114736","url":null,"abstract":"<div><div>Long-term monitoring of grasslands is pivotal for ensuring continuity of many environmental services and for supporting food security and environmental modeling. Remote sensing provides an irreplaceable source of information for studying changes in grasslands. Specifically, Spectral Mixture Analysis (SMA) allows for quantification of physically meaningful ground cover fractions of grassland ecosystems (i.e., green vegetation, non-photosynthetic vegetation, and soil), which is crucial for our understanding of change processes and their drivers. However, although popular due to straightforward implementation and low computational cost, ‘classical’ SMA relies on a single endmember definition for each targeted ground cover component, thus offering limited suitability and generalization capability for heterogeneous landscapes. Furthermore, the impact of irregular data density on SMA-based long-term trends in grassland ground cover has also not yet been critically addressed.</div><div>We conducted a systematic assessment of i) the impact of data density on long-term trends in ground cover fractions in grasslands; and ii) the effect of endmember definition used in ‘classical’ SMA on pixel- and map-level trends of grassland ground cover fractions. We performed our study for 13 sites across European grasslands and derived the trends based on the Cumulative Endmember Fractions calculated from monthly composites. We compared three different data density scenarios, i.e., 1984–2021 Landsat data record as is, 1984–2021 Landsat data record with the monthly probability of data after 2014 adjusted to the pre-2014 levels, and the combined 1984–2021 Landsat and 2015–2021 Sentinel-2 datasets. For each site we ran SMA using a selection of site-specific and generalized endmembers, and compared the pixel- and map-level trends. Our results indicated no significant impact of varying data density on the long-term trends from Cumulative Endmember Fractions in European grasslands. Conversely, the use of different endmember definitions led in some regions to significantly different pixel- and map-level long-term trends raising questions about the suitability of the ‘classical’ SMA for complex landscapes and large territories. Therefore, we caution against using the ‘classical’ SMA for remote-sensing-based applications across broader scales or in heterogenous landscapes, particularly for trend analyses, as the results may lead to erroneous conclusions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114736"},"PeriodicalIF":11.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of data and methods on high-resolution imagery-based tree species recognition considering phenology: The case of temperate forests","authors":"Xinlian Liang ,&nbsp;Jianchang Chen ,&nbsp;Weishu Gong ,&nbsp;Eetu Puttonen ,&nbsp;Yunsheng Wang","doi":"10.1016/j.rse.2025.114654","DOIUrl":"10.1016/j.rse.2025.114654","url":null,"abstract":"<div><div>Seasonal phenological transformations alter tree appearances, notably by influencing the size and color of the foliage. It has long been anticipated that such phenology induced characteristics can help address the tree-species recognition problem, a fundamental challenge in forest science. Yet, studies on tree-species recognition using remote sensing and phenological characteristics have been rare, due to the very limited availability of high spatiotemporal resolution observations. Moreover, the interactions between the effectiveness of phenological characteristics, remote sensing data, and the analytical methodologies have not yet been sufficiently explored. The understanding of how to integrate multi-temporal observations and phenological characteristics in tree-species recognition has been lacking. This study aims to identify principles for optimizing species recognition by combining data, methods, and phenological dynamics. This involves understanding the impact factors of various methodologies, and how they interact with phenological characteristics and datasets at different times and/or frequencies. The study was carried out using multi-temporal high-resolution optical images of a temperate forest, which were collected in 2021 during leaf growth and senescence periods between May and October, i.e., three leaf growth (May–August) and three leaf senescence (September–October) periods. The test site comprised 14 different tree classes, including 11 species, 2 genera, and 1 dead tree class. The experimental results showed that, for deep learning approaches, the current main limitations in the tree species recognition lie in sample imbalance as the targeted species number increases. With the state-of-the-art data and methods, distinguishing between species within a same genus is much more challenging than differentiating between species from different genera or families. It is also revealed that the best timing for tree species classification is early autumn (September) or late spring (May) when a single-temporal (one-timepoint) data is applied; all-temporal (six-timepoint) data improves the recognition results in comparison with single-temporal observations; however, the improvements from adding additional timepoints became marginal after two timepoint are used with one from late spring and other from early autumn. Furthermore, prior knowledge of individual crown boundaries, typically obtained through individual tree crown delineation, is essential for efficiently incorporating phenological variations into species recognition.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114654"},"PeriodicalIF":11.1,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic estimation of mangrove canopy height across coastal China: Integrating SDGSAT-1 multispectral data with Sentinel-1/2 time-series imagery","authors":"Mingming Jia ,&nbsp;Rong Zhang ,&nbsp;Chuanpeng Zhao ,&nbsp;Yaming Zhou ,&nbsp;Chunying Ren ,&nbsp;Dehua Mao ,&nbsp;Huiying Li ,&nbsp;Genyun Sun ,&nbsp;Hongsheng Zhang ,&nbsp;Wensen Yu ,&nbsp;Zongming Wang ,&nbsp;Yeqiao Wang","doi":"10.1016/j.rse.2025.114719","DOIUrl":"10.1016/j.rse.2025.114719","url":null,"abstract":"<div><div>Mangrove canopy height (MCH) is a critical indicator used to evaluate blue carbon sequestration and biodiversity conservation. However, mapping MCH is challenging because of the dense tree canopy and fluctuating tide conditions. To solve the issue, this study developed a novel approach to retrieve MCH by training a robust XGBoost regression model using UAV-LiDAR, SDGSAT-1, and time series Sentinel-1 SAR and Sentinel-2 MultiSpectral Instrument imagery. The approach was applied to mangrove forests along China's coast. The study resulted in a 10 m resolution MCH map and so named China's mangrove canopy height (CMCH). The accuracy of CMCH was assessed using in-situ and UAV-LiDAR data, achieving an <em>R</em>² of 0.84 and an RMSE of 1.19 m. Band 6 from SDGSAT-1, the only available 10 m resolution red edge spectral band of current available satellite data, was identified as the most crucial feature for predicting MCH. After analyzing the geographic characteristics of CMCH at species level, we had three innovative and quantitative discoveries. Firstly, the mean height of mangrove forests in China was 6.0 m, significantly lower than the global average of 12.7 m. Secondly, the height of mangrove forests in China was found to decrease with increasing latitude. Thirdly, the exotic <em>S. apetala</em> was identified as the tallest mangrove species in China, with the highest trees in 18.7 m along the coasts of Inner Deep Bay. To the best of our knowledge, this is the first national-scale study to investigate the geographic characteristics of MCH at species level. The resultant CMCH map and species-level findings provide essential information for managing mangrove ecosystems in China. The technical methodology employed has the potential to be expanded globally, thereby enhancing the execution of the UN's Sustainable Development Goals related to coastal and marine ecosystems. Additionally, it can contribute to the safeguarding of nature, fostering the preservation of biodiversity.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114719"},"PeriodicalIF":11.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738650","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":"Changes in GEDI-based measures of forest structure after large California wildfires relative to pre-fire conditions","authors":"Matthew L. Clark ,&nbsp;Christopher R. Hakkenberg ,&nbsp;Tim Bailey ,&nbsp;Patrick Burns ,&nbsp;Scott J. Goetz","doi":"10.1016/j.rse.2025.114718","DOIUrl":"10.1016/j.rse.2025.114718","url":null,"abstract":"<div><div>Forest productivity, biodiversity, and ecosystem services in California and the Western United States are closely tied to fire. However, fire regimes are shifting toward larger, more severe fires driven by factors such as high fuel loads and increased temperature and aridity. While multispectral satellite (e.g., Landsat) burn indices provide valuable insights into fire severity, they primarily capture top-of-canopy greenness, missing important sub-canopy changes in vegetation structure and residual fuels. The Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar mission provides current and consistent, near-global 3D forest structure measurements, enabling detailed assessment of forest changes from disturbances such as wildfire. This study utilized near-coincident, bitemporal pre- and post-fire GEDI on-orbit measurements to analyze structural changes across thirty-four large California wildfires (2019 to 2021). We examined twelve GEDI-based forest structure metrics representing a variety of 3D fuels properties, including forest height, low-stature fuels, biomass, canopy heterogeneity, volume and cover. Our broad goals were to: 1) assess GEDI's ability to detect structural changes in burned areas relative to control areas; and 2) in burned areas, explore relationships between forest structural change and factors such as pre-fire fuel loads, Landsat-based differenced Normalized Burn Ratio (dNBR), topographic slope, wind speed, vapor pressure deficit, evapotranspiration, and time since fire. Results showed significant structural loss in all GEDI structural metrics for burned areas relative to nearby controls. Pre-fire fuel loads measured by GEDI metrics were the strongest predictors of post-fire structural change, with linear models explaining an average of 46 % of variance. Model slopes showed increasing levels of pre-fire fuels were associated with large, significant post-fire decreases in canopy structure – that is, more fuels lead to higher wildfire severity, particularly for conifer forests of the Klamath, Cascades and Sierra Nevada ecoregions. One metric, measuring the proportion of waveform energy below 10 m height, increased significantly after fire in mixed and conifer forests due to canopy opening, which enhanced lidar penetration toward the ground. In contrast, the widely-used dNBR burn severity index was less correlated with GEDI-based forest structural change than pre-fire fuels, particularly in sub-canopy fuels, with models explaining no more than 32 % of the variance (average 19 %). GEDI overcomes key limitations of airborne lidar, including high cost, limited extent, and data latency, enabling scalable and timely assessments of wildfire impacts needed to manage fuels and track forest resilience and recovery. Further, GEDI metrics are physically-based and ecologically interpretable, providing complimentary information to multispectral burn severity indices.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114718"},"PeriodicalIF":11.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bias correction for near-real-time estimation of snow water equivalent using machine learning algorithms: A case study in the Tuolumne River basin, California","authors":"Kehan Yang ,&nbsp;Thomas H. Painter ,&nbsp;Jeffrey S. Deems ,&nbsp;Noah P. Molotch","doi":"10.1016/j.rse.2025.114693","DOIUrl":"10.1016/j.rse.2025.114693","url":null,"abstract":"<div><div>Accurately estimating snow water equivalent (SWE) in near-real-time is important for water resources management and water supply forecasting in snow-dominant regions. However, conventional SWE estimation approaches have large uncertainties in mountainous regions due to complex terrain, snow-vegetation interactions, and other challenging factors. This study develops a SWE bias correction framework (SWE-BCF) that utilizes the Airborne Snow Observatories (ASO) SWE data and machine learning (ML) algorithms to correct biases in a near-real-time SWE estimation linear regression model (LRM). The spatial distribution of LRM SWE residuals, which are estimated using the ASO SWE, is explicitly modeled using multiple ML algorithms and evaluated using a leave-one-out cross-validation (LOOCV) workflow. A wide range of commonly used ML algorithms is examined to model LRM SWE residuals, including Gaussian Process Regression (GPR), Support Vector Machine (SVM), Bayesian Regularized Neural Networks (BRNN), Random Forest (RF), and Gradient Boosting Machine (GBM). The results show that all ML algorithms substantially improve LRM SWE estimation accuracy. While the Kruskal-Wallis test indicates no significant difference (<em>p</em>-value &gt;0.05) among the bias correction models, the RF model outperforms others, with the highest median R² (0.89), the lowest median RMSE (69 mm), MAE (41 mm), and NRMSE (37.4 %), as well as the second-best median PBIAS (−6.6 %) in the LOOCV for correcting SWE bias. Four performance metrics (R², MAE, RMSE, NRMSE) show significant improvements (<em>p</em>-value &lt;0.05) over the original LRM model, highlighting the effectiveness of SWE-BCF in correcting the spatial patterns of SWE. However, the correction in the basin-wide average SWE, as indicated by the PBIAS values, exhibits high variance and does not show significant improvement (<em>p</em>-value &gt;0.05). Among the three land cover types in the Upper Tuolumne River Basin, the alpine area showed the most substantial SWE improvements with the SWE-BCF. The structural adaptability of the SWE-BCF enables its transferability to various geographic locations and SWE datasets, allowing for an extension of coverage and frequency of more accurate SWE estimates. This potential advancement may improve water management decisions which rely on accurate water supply forecasts.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114693"},"PeriodicalIF":11.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738649","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":"Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome","authors":"Kathleen M. Orndahl ,&nbsp;Logan T. Berner ,&nbsp;Matthew J. Macander ,&nbsp;Marie F. Arndal ,&nbsp;Heather D. Alexander ,&nbsp;Elyn R. Humphreys ,&nbsp;Michael M. Loranty ,&nbsp;Sarah M. Ludwig ,&nbsp;Johanna Nyman ,&nbsp;Sari Juutinen ,&nbsp;Mika Aurela ,&nbsp;Juha Mikola ,&nbsp;Michelle C. Mack ,&nbsp;Melissa Rose ,&nbsp;Mathew R. Vankoughnett ,&nbsp;Colleen M. Iversen ,&nbsp;Jitendra Kumar ,&nbsp;Verity G. Salmon ,&nbsp;Dedi Yang ,&nbsp;Paul Grogan ,&nbsp;Scott J. Goetz","doi":"10.1016/j.rse.2025.114717","DOIUrl":"10.1016/j.rse.2025.114717","url":null,"abstract":"<div><div>The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m⁻²) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ∼6000 g m⁻² (mean ≈ 350 g m⁻²), while predicted values ranged from 0 to ∼4000 g m⁻² (mean ≈ 275 g m⁻²), resulting in model validation root-mean-squared-error (RMSE) ≈ 400 g m⁻² and R² ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114717"},"PeriodicalIF":11.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Internal solitary waves in the Banda Sea, a pathway between Indian and Pacific oceans: Satellite observations and physics-AI hybrid forecasting","authors":"Xudong Zhang ,&nbsp;Haoyu Wang ,&nbsp;Xiaofeng Li ,&nbsp;Adi Purwandana ,&nbsp;I Wayan Sumardana Eka Putra","doi":"10.1016/j.rse.2025.114733","DOIUrl":"10.1016/j.rse.2025.114733","url":null,"abstract":"<div><div>Internal solitary waves (ISW) are widespread in global oceans, and satellite/in-situ observations showed that the Banda Sea has frequent ISW activities, characterized by long-wave crests, fast propagation speeds, and large amplitudes exceeding 100 m. In this paper, we conducted a comprehensive ISW study in the Banda Sea to reveal ISW characteristics by collecting 417 synthetic aperture radar and optical images from 2013 to 2019. The constructed dataset comprises 134 pairs of matched satellite images and a total of 12,021 ISW propagation vectors were extracted. Satellite observation reveals that ISWs in the Banda Sea mainly originate from the Ombai Strait and propagate northward, with an average propagation speed of over 2.50 m/s and with seasonal variation of less than 20 %. To forecast ISW propagations, we developed a physics-informed neural network ISW forecast model combining the classic Eikonal Eq. (EE) and the data-driven AI algorithms following a two-step transfer learning scheme. The forecast model employs a three-hidden-layer structure with 512 nodes in each layer. Firstly, the hybrid model includes ISW physics by setting the EE as the loss function. The second step is the data-driven process, which exploits a fully connected neural network and collected ISW dataset to improve EE-based model performance by 61 % with a loss function of the mean squared error. Through the two-step training, the forecast model adopts ISW physics and also benefits from the high accuracy of the data-driven process. We randomly selected 188/118 satellite images from the built dataset to serve as the training/test dataset for the data-driven process. After the second-step training, the root mean square (average) error of the model-predicted ISW propagation time reduced from 2.59 (2.37) h to 1.01 (−0.01) h. Error analysis shows that the data-driven process can efficiently correct the systematic error in the first-step model, which stems from errors in determining the ISW source and the propagation speed distribution map. Using the developed model, we predicted the propagation time of the ISWs and compared these predictions with satellite observations and in-situ observations. The comparison showed a high degree of agreement regarding the ISWs' location and their wave crests' geometry between model predictions and satellite/in-situ observations. Key differences between the proposed model and previous models are discussed.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114733"},"PeriodicalIF":11.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745215","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}