Remote Sensing of Environment最新文献

{"title":"A review of forward modelling and retrieval approaches for forest soil moisture and vegetation optical depth using L-band radiometry","authors":"Andreas Colliander ,&nbsp;Mike Schwank ,&nbsp;Yiwen Zhou ,&nbsp;Mehmet Kurum ,&nbsp;Cristina Vittucci ,&nbsp;Leung Tsang ,&nbsp;Alex Roy ,&nbsp;Aaron Berg","doi":"10.1016/j.rse.2025.115158","DOIUrl":"10.1016/j.rse.2025.115158","url":null,"abstract":"<div><div>Forests are a critical component of the Earth system, accounting for approximately one-third of global photosynthetic activity and carbon storage. They also provide essential habitats for countless species and vital resources for human activities. Low-frequency (L-band; 1–2 GHz) microwave radiometry enables the measurement of forest soil moisture (SM) and L-band vegetation optical depth (L-VOD), offering valuable insights into processes such as tree growth, water infiltration, soil fertility, fuel moisture, carbon stocks, wildfire vulnerability, and biodiversity dynamics. These measurements also support the study of carbon and water fluxes, tree responses to hydrological stress (e.g., drought), and fuel moisture estimation. However, existing algorithms for retrieving SM and L-VOD were primarily developed for low-biomass vegetation types (e.g., grasslands and croplands), differing structurally from forests. This motivates the present review to evaluate the current retrieval approaches, their performance assessment methods, and available validation resources. The review found that systematic uncertainties persist in forest retrievals, despite the demonstrated sensitivity of L-band brightness temperature (TB) to forest SM and L-VOD. Moreover, the focus on non-forest ecosystems has led to a lack of suitable ground truth and reference data for validating forest SM and L-VOD products, and current validation techniques remain underdeveloped. To fully harness the potential of L-band radiometry in forest monitoring, new retrieval algorithms that account for the unique structural and compositional characteristics of forests are required. Additionally, validation efforts must be enhanced both quantitatively and qualitatively—particularly for L-VOD—to improve confidence in these remote sensing products.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115158"},"PeriodicalIF":11.4,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759818","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":"Highest quality remote sensing reflectance database compiled from 20+ years of MODIS-aqua measurements","authors":"Longteng Zhao,&nbsp;Zhongping Lee,&nbsp;Xiaolong Yu,&nbsp;Tianhao Wang,&nbsp;Daosheng Wang,&nbsp;Shaoling Shang","doi":"10.1016/j.rse.2025.115195","DOIUrl":"10.1016/j.rse.2025.115195","url":null,"abstract":"<div><div>Remote sensing reflectance (<em>R</em>_<em>rs</em>) is a fundamental property in satellite ocean color remote sensing, which is critical for retrieving optical-biogeochemical properties and data-driven atmospheric correction algorithms. In this study, with three criteria applicable to ∼91% of the global ocean, we compiled a database of the highest quality <em>R</em>_<em>rs</em> (HQ_MODISA-<em>R</em>_<em>rs</em>) of oceanic waters based on 20+ years of ocean color measurements by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. While removing a large number of daily “standard” data products, our evaluation showed that the criteria for the highest-quality <em>R</em>_<em>rs</em> (CHQR) improved MODIS <em>R</em>_<em>rs</em> data consistency with benchmark <em>in situ R</em>_<em>rs</em> datasets, such as those from MOBY and AERONET-OC. After applying CHQR, analysis of imagery products in the South Pacific Ocean revealed that the coefficient of variation (CV) of <em>R</em>_<em>rs</em> among pixels reduced from 0.042 (standard quality control) to 0.030, along with enhanced temporal consistency, which indicates that this approach effectively filters abnormal data products. While such a dataset played a key role in the development of the cross-satellite atmospheric correction algorithm (Lee et al., 2024), we here further demonstrate that applications of HQ_MODISA-<em>R</em>_<em>rs</em> have ∼21.0% of oceanic areas between 50°S and 50°N showing reversed long-term trends of <em>R</em>_<em>rs</em> compared to the trend based on the standard <em>R</em>_<em>rs</em> product. We anticipate that this highest-quality <em>R</em>_<em>rs</em> database would not only improve our evaluation and understanding of long-term changes in various <em>R</em>_<em>rs</em>-derivative bio-optical properties of the global ocean, but also help to obtain consistent products among various satellite ocean color missions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115195"},"PeriodicalIF":11.4,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731548","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":"Retrieval of global surface phytoplankton community structure using a minimal set of predictors","authors":"Xueyin Li ,&nbsp;Shuguo Chen ,&nbsp;Junwei Wang ,&nbsp;Qing Zhu","doi":"10.1016/j.rse.2025.115174","DOIUrl":"10.1016/j.rse.2025.115174","url":null,"abstract":"<div><div>Phytoplankton underpin marine food webs and drive essential biogeochemical processes. Their pigment composition serves as a vital indicator of community structure and physiological state. In this study, we developed a multilayer perceptron (MLP)-based model to estimate the concentrations of 26 pigments and infer phytoplankton community structure in global surface waters. By integrating prior knowledge and quantifying through SHapley Additive exPlanations (SHAP), we selected a physically meaningful subset of 10 input features to construct pigment inversion models, including environmental parameters such as sea surface temperature (SST), salinity (SSS), and sea surface height (SSH), as well as remote sensing reflectance (<em>R</em>_<em>rs</em>) at seven spectral bands. For 24 pigments, the model achieved high predictive accuracy and strong generalization performance, with an average coefficient of determination (<em>R</em>^<em>2</em>) of 0.76 and median absolute percentage difference (MAPD) of 12.01 % under random cross-validation, and slightly lower accuracy under temporal cross-validation (<em>R</em>^<em>2</em> = 0.67; MAPD = 14.35 %). Feature analysis revealed that <em>R</em>_<em>rs</em>, particularly spectral regions encompassing absorption peaks and adjacent gradients, dominated pigment predictions. Moreover, the model captured nonlinear thermal responses of phytoplankton to SST, consistent with known ecophysiological patterns, and reflected synergistic interactions between SST and SSS affecting pigment variability. The inferred phytoplankton community structures, estimated <em>via</em> Diagnostic Pigment Analysis (DPA), showed strong agreement with previous studies, validating the model's ecological reliability. Additionally, Hydrolight simulations based on <em>in situ a</em>_<em>ph</em> spectra demonstrated that the model performs reliably under water conditions with suspended particulate matter (SPM) ≤ 1 g m⁻³ and colored dissolved organic matter (CDOM) absorption at 440 nm is ≤0.25 m⁻¹, maintaining MAPD below 25 %. Our research demonstrates that neural networks operate in a physically informed manner rather than as purely data-driven models, enabling them to represent complex interactions between phytoplankton and ecological environment. This underscores the necessity of designing ecologically informed and physically interpretable input features in remote sensing applications, offering valuable guidance for future biogeochemical modeling efforts.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115174"},"PeriodicalIF":11.4,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732128","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":"Long-term forest structure trends in the peninsular Spain from lidar-optical sensors synergies","authors":"M. Tanase ,&nbsp;J.P. Martini ,&nbsp;P. Miranda ,&nbsp;D. Garcia ,&nbsp;V. Wilke ,&nbsp;S. Miguel ,&nbsp;C. Mihai ,&nbsp;J. Diez ,&nbsp;S. Natal ,&nbsp;D. San Martin ,&nbsp;P. Ruiz-Benito","doi":"10.1016/j.rse.2025.115196","DOIUrl":"10.1016/j.rse.2025.115196","url":null,"abstract":"<div><div>Information on forest structure is needed for many management aspects, from carbon stock evaluation to fire hazard prediction. Such information is increasingly available from remote sensing data, including light detection and ranging (lidar) sensors. However, continuous forest monitoring is hindered by the limited temporal availability of lidar acquisitions. This study focused on integrating temporally consistent optical acquisitions with temporally sparse lidar acquisitions to provide long term information on forest structural characteristics across the peninsular Spain. We evaluated different modeling approaches including machine learning and advanced deep learning models to ascertain their limitations for long-term forest monitoring. Subsequently, annual estimates of forest structural attributes were generated from 1985 to 2024. Across all forests, height (H), canopy cover (FCC), and above ground biomass (AGB) increased by 34 %, 29 % and respectively 17.5 % from the first (1985–1989) to the last lustrum (2019–2024). By biome, the increase in H, FCC and AGB was larger over the Mediterranean (35 %, 33 %, and 18 % respectively) when compared to the Atlantic (33 %, 10 %, and 15 % respectively) forests. For the model training year, deep learning models improved estimation accuracy by 27 ± 2.6 % (mean value across regions and variables) when compared to machine learning models. However, when applied to data from other years (temporal inference), model precision was similar except for height in the Mediterranean were deep learning models improved RMSE estimates by 7 %. Overall, the deep learning models presented significant computational drawbacks when applied to large geographic extents and extensive temporal ranges, substantially increasing both training and prediction times without providing a clear improvement in accuracy compared to machine learning across years. Therefore, the long-term forest database was generated using machine learning models. This database provides a spatially explicit understanding of forest structural changes over nearly four decades, offering a valuable resource for forest management, ecological assessments, and climate-related policy-making.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115196"},"PeriodicalIF":11.4,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145752945","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":"Temporal attention multi-resolution fusion of satellite image time-series, applied to Landsat-8/9 and Sentinel-2: all bands, any time, at best spatial resolution","authors":"Julien Michel,&nbsp;Jordi Inglada","doi":"10.1016/j.rse.2025.115159","DOIUrl":"10.1016/j.rse.2025.115159","url":null,"abstract":"<div><div>This paper introduces a general formulation for the fusion of Satellite Image Time Series (SITS) of variable length from several sensors at different spatial resolutions and acquisition times over the same geographical area. In this formulation, all the spectral bands from all the input sensors are predicted at the best input spatial resolution, and at any observed or non-observed acquisition time requested. To address this general problem, an advanced Masked Auto-Encoder training strategy is proposed, utilising two new loss functions: a Linear-Regression Learned Perceptual Image Similarity term to favor high spatial frequency details, and a mask-contrastive term to ignore clouds and other non-informative areas in the input data. This strategy is applied to the training of Temporal Attention Multi-Resolution Fusion of Satellite Image Time-Series (TAMRF-SITS), a novel Deep Learning architecture designed to implement the proposed general formulation. Experiments with joint Landsat-8/9 and Sentinel-2 time-series were conducted on four different tasks from the literature and demonstrate that a single pre-trained TAMRF is on par with or better than existing ad-hoc methods. For instance, TAMRF provides a gain of 0.01 surface reflectance Root Mean Square Error on a Spatio-Temporal Fusion task when compared to competing algorithms, while showing the highest spatial frequency content. Moreover, the proposed method relaxes unrealistic assumptions routinely found in the literature, including: same or similar spectral bands in different sensors, same-day acquisitions, and scale-invariance of the relationship between high and low resolution images. To the best of our knowledge, our method is the first to achieve this range of capabilities with a single model, without making any of these assumptions. The complete source code for training and experiments is available here: <span><span>https://github.com/Evoland-Land-Monitoring-Evolution/tamrfsits</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115159"},"PeriodicalIF":11.4,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731843","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":"Began+: Leveraging bi-temporal SAR-optical data fusion to reconstruct clear-sky satellite imagery under large cloud cover","authors":"Yu Xia ,&nbsp;Wei He ,&nbsp;Liangpei Zhang ,&nbsp;Hongyan Zhang","doi":"10.1016/j.rse.2025.115171","DOIUrl":"10.1016/j.rse.2025.115171","url":null,"abstract":"<div><div>In recent years, optical remote sensing imagery has played an increasingly vital role in Earth observation, but cloud contamination exists as an inevitable degradation. Combining synthetic aperture radar (SAR) and optical data with machine learning offers a promising solution for reconstructing clear-sky satellite imagery. Nevertheless, several challenges persist, including insufficient attention to large cloud cover, difficulties in restoring temporal changes, and limited practicality of deep models. To address these issues, this paper introduces a novel deep learning-based cloud removal framework, termed Began+, which integrates bi-temporal SAR-optical data to deal with cloudy images with high cover ratios. The Began+ framework comprises two primary components: a deep network and a flexible post-processing step, combining the strengths of data-driven models for restoring change information and traditional gap-filling algorithms for mitigating radiance discrepancies. First, a bi-output enhanced generative adversarial network, abbreviated as Began, is designed for image synthesis, featuring an enhanced channel-wise fusion block (ECFB) and a multi-scale depth-wise convolution residual block (MDRB). By applying the dual-tasking optimization and co-learning strategy, the Began model identifies potential change areas from bi-temporal SAR and pre-temporal optical inputs, guiding the synthesis of target optical images. Second, a range of cloud masking and gap-filling techniques can be optionally employed to effectively reduce radiometric discrepancies between the synthesized images and the cloudy data, ultimately yielding high-quality, clear-sky imagery. To meet the big data requirements of deep learning, we constructed two globally distributed cloud removal datasets, named BiS1L8-CR and BiS1S2-CR. Supported by these datasets, extensive experiments demonstrated that the Began+ framework effectively captures bi-temporal change features, reconstructing precise surface information in both Landsat-8 and Sentinel-2 satellite images under large cloud cover. Compared to the latest solutions and algorithms, our proposed Began+ framework exhibits significant advantages from both qualitative and quantitative perspectives in both simulated and real experiments. Furthermore, without strict constraints on input timing, the Began+ framework enables accurate reconstruction of large-scale dual-sensor imagery under high-ratio cloud cover, effectively restoring changing surfaces and improving the quality of unsupervised vegetation extraction.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115171"},"PeriodicalIF":11.4,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732131","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":"2025-12-11","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 UAV lidar-derived shrub structural index for estimating above-ground biomass","authors":"Jiaming Wu ,&nbsp;Yaxin Wang ,&nbsp;Liang Hong ,&nbsp;Bin Sun ,&nbsp;Zhenping He ,&nbsp;Zejiang Li ,&nbsp;Zhijie Ma","doi":"10.1016/j.rse.2025.115189","DOIUrl":"10.1016/j.rse.2025.115189","url":null,"abstract":"<div><div>Precise estimation of shrub above-ground biomass (AGB) in arid regions is crucial for carbon cycle research and ecosystem assessment. Unmanned aerial vehicle (UAV) -borne light detection and ranging (LiDAR) has become a key tool for quantifying three-dimensional vegetation structure and estimating AGB. However, the short stature of arid zone vegetation, combined with sparse and low-quality point clouds acquired by UAV, limits high-accuracy shrub AGB estimation. To address this issue, this study selected <em>Caragana korshinskii</em>, a typical psammophytic shrub in Ordos City, as the research object. By integrating UAV-based multispectral and LiDAR data, a biomass estimation method based on a novel Shrub Structure Index (SSI) was proposed. The SSI workflow reconstructs the three-dimensional shrub structure under sparse point cloud conditions and improves AGB estimation accuracy. This workflow comprises Object-based image analysis (OBIA) classification for individual shrub extraction, Delaunay linear up-sampling, voxel-based partitioning, and dynamic stratification by height percentiles. Experimental results demonstrate that: (1) The individual shrub extraction method utilizing the large-scale mean shift (LSMS) segmentation algorithm and support vector machine (SVM) classification achieved a total quadrat segmentation accuracy of over 90.61 %, an overall classification accuracy of 91.51 % (Kappa = 0.86). (2) In SSI construction, the height-percentile stratification thickness, point-cloud sampling, and voxel edge length together set <em>Caragana korshinskii</em> stratification accuracy and density scale; the 5 % height percentile interval, a sampling size of 100 points, and 0.04 m voxel edge length proved optimal. (3) Comparative experiments showed that the three-dimensional feature integrated SSI significantly outperformed single-feature, two-feature, traditional allometric equation, and random forest (RF) models, with the SSI-based model achieving R², RMSE, MAE, and rRMSE of 0.90, 529.01 g, 432.58 g, and 26.54 %, respectively. These results indicate that SSI more effectively captures shrub spatial structure and improves AGB prediction under sparse UAV-LiDAR conditions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115189"},"PeriodicalIF":11.4,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729070","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":"Edge effects on forest dynamics in China from 2000 to 2020: Evidence from satellite remote sensing","authors":"Jinlong Chen,&nbsp;Zeng Cui,&nbsp;Zhiyao Tang","doi":"10.1016/j.rse.2025.115187","DOIUrl":"10.1016/j.rse.2025.115187","url":null,"abstract":"<div><div>In the 21st century, forest edge-to-interior gradients have exhibited remarkable spatiotemporal variation in China. In this study, we employed a high-resolution framework integrating NDVI, tree cover, canopy height, and forest age to evaluate edge effects on forest growth, structure, and maturity in China from 2000 to 2020. The results revealed that (1) the proportion of forest edges within 60 m declined from 38 % (653,000 km² out of 1,694,000 km²) in 2000 to 33 % (680,000 km² out of 2,033,000 km²) in 2020, accompanied by an increase in interior forests (&gt;300 m) from 22 % (381,000 km²) to 29 % (590,000 km²); (2) NDVI, tree cover, canopy height and forest age increased from the forest edge toward the interior, with the strongest effects occurring within 300 m of the edge and detectable up to 1.2 km into the interior. However, the edge forests demonstrated faster temporal increases in NDVI and tree cover; and (3) these effects varied with the land cover types adjacent to the edge. Specifically, NDVI (0.862) and tree cover (81.17 %) peaked at cropland edges and were lowest at grassland edges (0.793 and 41.24 %), while canopy height ranged from 9.77 m at grassland edges to 12.76 m at waterbody edges. These findings are valuable for advancing forest restoration, mitigating fragmentation, and enhancing natural carbon storage potential in China.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115187"},"PeriodicalIF":11.4,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145703964","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":"Performance assessment of canopy gap fraction retrieval using multiple airborne LiDAR instrument configurations","authors":"Yi Li ,&nbsp;Hao Tang ,&nbsp;Shanshan Wei ,&nbsp;Donghui Xie ,&nbsp;Xihan Mu ,&nbsp;Guangjian Yan","doi":"10.1016/j.rse.2025.115166","DOIUrl":"10.1016/j.rse.2025.115166","url":null,"abstract":"<div><div>Canopy gap fraction (GF) is one of the most important vegetation structure parameters widely used in forest ecology and meteorology. Small-footprint airborne LiDAR has gained great popularity for canopy GF retrieval in the past decade; yet most studies were carried out at individual sites using different commercial-off-the-shelf instruments, often ignoring possible inconsistency due to the use of different instruments or scanning configurations. This study aims to provide an assessment of the performance of canopy GF retrieval from small-footprint airborne LiDAR under different collection scenarios. We utilized small-footprint airborne LiDAR data and field digital hemispherical photo (DHP) measurements from the National Ecological Observatory Network (NEON), encompassing 30 sites across 16 eco-regions in the United States from 2016 to 2022. A total of eight baseline cases together with different instrument configurations were included in this analysis. LiDAR-based canopy GF was first retrieved using established methods and then compared to a total of 4596 DHPs from 383 plots. Results show that all instruments could reach an accuracy better than 10 % RMSE under a proper configuration, however, the difference of using waveforms and point clouds alone could reach an RMSE up to 18 %. Scan angle showed the greatest impact among all sensor related parameters and could lead to a mean bias up to 5 %. Interestingly, waveforms did not always outperform point clouds, likely due either to the varying pulse shape of transmitted waveform or to low digitizer performance. In sum, our results caution the use of airborne LiDAR as the only means for validating large-scale satellite vegetation structure products or monitoring subtle long-term canopy changes. Assessing both instrument and acquisition specifications can help minimize potential bias in canopy GF retrieval over different ecosystems.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115166"},"PeriodicalIF":11.4,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731846","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}