Remote Sensing of Environment最新文献

{"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":"2026-03-01","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":"Integrating classifier transfer and sample transfer strategies for in-season crop mapping based on sample weighting technique","authors":"Yunze Zang ,&nbsp;Junxiong Zhou ,&nbsp;Xuehong Chen ,&nbsp;Tianyu Liu ,&nbsp;Miaogen Shen ,&nbsp;Wei Yang ,&nbsp;Xiufang Zhu ,&nbsp;Fei Zhang ,&nbsp;Jin Chen","doi":"10.1016/j.rse.2025.115208","DOIUrl":"10.1016/j.rse.2025.115208","url":null,"abstract":"<div><div>Timely crop mapping is crucial for field management, policy formulation, phenological monitoring, and yield forecasting. However, acquiring sufficient labeled samples in the current year presents a formidable challenge for in-season mapping. Previously proposed solutions mainly include classifier transfer and sample transfer strategies. The classifier transfer strategy trains classifiers with historical samples associated with historical-year features and then transfers the trained historical sample classifiers (HSC) to classify remote sensing data in the current year; the sample transfer strategy generates trusted samples associated with current-year remote sensing features by predicting labels of the current-year sample based on some prior knowledge (e.g., crop rotation pattern) and then trains trusted sample classifiers (TSC) for current-year classification. However, the performance of the classifier-transfer strategy may degrade when there is large interannual feature variation, while the performance of the sample-transfer strategy depends on the reliability of the generated trusted samples. This study proposes a novel approach that integrates the above two strategies for in-season mapping through a sample weighting technique. Firstly, two sample sets, trusted samples and classified samples associated with current-year features, are generated by crop rotation prediction and HSC, respectively. Subsequently, based on an independent assumption between the rotational prediction errors and the current-year remote sensing features, the optimal weights of these two sample sets are derived based on the Bayesian principle. Finally, an optimal weighted sample classifier (OWSC) is trained using the weighted samples for in-season classification. To illustrate the robustness of the proposed OWSC, we compared it with different methods combined with various classification models across four regions with different interannual feature variation and crop rotation stability. Results demonstrated that OWSC maintained its advantages across various regions and different available lengths of historical crop-type sequences. Owing to its independence from specific classifiers, the proposed sample weighting method can be seamlessly applied to any classification model and thus continues to benefit from advances in classification algorithms. Additionally, sensitivity experiments regarding the uncertainty in trusted samples and historical crop-type sequences showed that OWSC performed stably across different scenarios. Therefore, OWSC provides a promising solution for in-season crop mapping without current-year samples.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115208"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836274","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":"Leaf fall witnessed by night-time light: A first attempt to detect urban leaf fall dates using satellite nighttime light data","authors":"Liming Wang ,&nbsp;Yang Hu ,&nbsp;Xiaoyue Tan ,&nbsp;Zixuan Pei ,&nbsp;Xiaolin Zhu ,&nbsp;Jin Chen","doi":"10.1016/j.rse.2025.115211","DOIUrl":"10.1016/j.rse.2025.115211","url":null,"abstract":"<div><div>Autumn leaf fall is a critical phenological event in temperate deciduous forests, with important ecological and socioeconomic implications. Traditional estimates based on daytime vegetation indices primarily capture foliage color changes rather than the actual timing of leaf fall, and are often affected by mixed-pixel effects that reduce the accuracy. This study proposes a novel workflow for detecting the autumn leaf fall date (LFD) using nighttime light (NTL) data in urban areas, validated through in-situ observations from phenological cameras and city-scale assessments. Results showed that NTL-derived LFDs closely matched in-situ observations across three cities (New York City, Boston, and Beijing), with an RMSE of around 5 days and a bias of 0.77 days. In Beijing, both interannual (2012–2024) and spatial variations (2024) in LFD were delayed by higher preseason temperature and precipitation but advanced by greater strong-wind frequency, consistent with known autumn phenological controls and supporting the reliability of the NTL-based approach. These results demonstrate that NTL data can provide more accurate and interpretable LFD estimates than traditional daytime remote sensing, enabling detailed city-scale mapping. The use of NTL-derived LFD dynamics facilitates a more comprehensive understanding of their spatiotemporal patterns in urban environments and their linkages to climate change and human activities.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115211"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881019","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":"Development of an interference mitigation chlorophyll index for mitigating soil and canopy dependence to improve vegetation chlorophyll content monitoring","authors":"Tao Sun ,&nbsp;Zijun Tang ,&nbsp;Youzhen Xiang ,&nbsp;Junsheng Lu ,&nbsp;Yaohui Cai ,&nbsp;Wangyang Li ,&nbsp;Ruiqi Du ,&nbsp;Xianghui Lu ,&nbsp;Shouyang Liu ,&nbsp;Tianjie Zhao ,&nbsp;Zhijun Li ,&nbsp;Fucang Zhang","doi":"10.1016/j.rse.2025.115202","DOIUrl":"10.1016/j.rse.2025.115202","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Chlorophyll is a fundamental component of photosynthesis and a key indicator in quantitative vegetation remote sensing. However, accurate retrieval of leaf chlorophyll content from top-of-canopy (TOC) reflectance is often hindered by soil background and canopy structural effects. To address these challenges, we developed the Interference-Mitigation Chlorophyll Index (IMCI), derived from the spectral properties of leaves and soils together with canopy radiative transfer processes, to enhance robustness under complex soil and canopy conditions.&lt;/div&gt;&lt;div&gt;The development of IMCI consists of three steps. First, the soil single-scattering component (&lt;span&gt;&lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;/math&gt;&lt;/span&gt;) is removed from TOC reflectance using two spectral features: (a) vegetation reflectance remains equivalent at 598 and 694 nm regardless of leaf color, while soil reflectance differs; and (b) soil reflectance exhibits an approximately linear dependence on wavelength within 590–750 nm. These features enable accurate estimation and correction of &lt;span&gt;&lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;/math&gt;&lt;/span&gt;, yielding soil-adjusted TOC spectra (&lt;span&gt;&lt;math&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;/math&gt;&lt;/span&gt;). Second, a proxy for the vegetation single-scattering component (&lt;span&gt;&lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;/math&gt;&lt;/span&gt;) is constructed from soil-adjusted red-edge difference–ratio reflectance values (&lt;span&gt;&lt;math&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;j&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt;). Exploiting the linearity of the red-edge, we demonstrated that the extrapolated intersection of &lt;span&gt;&lt;math&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;/math&gt;&lt;/span&gt; in the red-edge region converges to a reflectance value approaching 0 and further derived that &lt;span&gt;&lt;math&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;j&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt; can serve as a direct approximation of &lt;span&gt;&lt;math&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;j&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt;, which was adopted as the basic form of IMCI. Furthermore, under the chlorophyll-induced red-edge shift, we established a quantitative relationship between IMCI and red-edge displacement, confirming its sensitivity to chlorophyll content. Theoretical derivations and assumpt","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115202"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785689","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":"2026-03-01","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":"Spatial-X fusion for multi-source satellite imageries","authors":"Jiang He ,&nbsp;Liupeng Lin ,&nbsp;Zhuo Zheng ,&nbsp;Qiangqiang Yuan ,&nbsp;Jie Li ,&nbsp;Liangpei Zhang ,&nbsp;Xiao xiang Zhu","doi":"10.1016/j.rse.2025.115214","DOIUrl":"10.1016/j.rse.2025.115214","url":null,"abstract":"<div><div>Multi-source remote sensing data can highlight different types of information based on user needs, resulting in large volumes of data and significant challenges. Hardware and environmental constraints create mutual dependencies between information types, particularly between spatial data and other types, limiting the development of high-precision applications. Traditional methods are task-specific, leading to many algorithms without a unified solution, which greatly increases the computational and deployment costs of image fusion. In this paper, we summarize four remote sensing fusion tasks, including pan-sharpening, hyperspectral-multispectral fusion, spatio-temporal fusion, and polarimetric SAR fusion. By defining the spectral, temporal, and polarimetric information, as X, we propose the concept of generalized spatial-channel fusion, referred to as Spatial-X fusion. Then, we design an end-to-end network SpaXFus, a generalized spatial-channel fusion framework through a model-driven unfolding approach that exploits spatial-X intrinsic interactions to capture internal dependencies and self-interactions. Comprehensive experimental results demonstrate the superiority of SpaXFus, e.g., SpaXFus can achieve four remote sensing image fusion tasks with superior performance (across all fusion tasks, spectral distortion decreases by 25.48 %, while spatial details improve by 7.5 %) and shows huge improvements across multiple types of downstream applications, including vegetation index generation, fine-grained image classification, change detection, and SAR vegetation extraction.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115214"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001412","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":"GPP-net: a robust high-resolution GPP estimation network for Sentinel-2 using only surface reflectance and photosynthetically active radiation","authors":"Shaoyu Wang ,&nbsp;Youngryel Ryu ,&nbsp;Benjamin Dechant ,&nbsp;Helin Zhang ,&nbsp;Huaize Feng ,&nbsp;Jeongho Lee ,&nbsp;Changhyun Choi","doi":"10.1016/j.rse.2025.115198","DOIUrl":"10.1016/j.rse.2025.115198","url":null,"abstract":"<div><div>High-resolution gross primary productivity (GPP) estimation is crucial for ecological and agricultural applications that require fine spatial details to capture GPP heterogeneity. Satellite-based GPP estimation usually relies on land cover and meteorological data. However, the misclassification of land cover data and coarse resolution of meteorological data greatly increase the uncertainty. Here, we propose a robust high-resolution GPP estimation deep learning (DL) network, named GPP-net, using only satellite surface reflectance (SR) from Sentinel-2 and photosynthetically active radiation (PAR). Specifically, GPP-net is based on a fully 1-D convolutional encoder-decoder network combined with a spectral band importance estimation module. To enhance the generalization of GPP-net, we ran the soil-canopy energy balance radiative transfer (SCOPE) model, and then combined these SCOPE-simulated reflectance data with GPP and PAR data extracted from FLUXNET2015 to pre-train GPP-net. Compared to benchmark models including near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP), partial least squares (PLS) and random forest (RF), GPP-net improved half-hourly and daily GPP retrieval across seven plant functional types (PFTs) including four forest types, cropland, grassland and wetland. Owing to its robust nonlinear feature learning capabilities, GPP-net also facilitated robust GPP estimation across both C3 and C4 vegetation. We found that GPP-net could reliably estimate GPP under drought and heatwave conditions, with minimal improvement from including vapor pressure deficit (VPD) as a predictor. Furthermore, GPP-net demonstrated great robustness to soil effects in GPP mapping, and had strong ability in capturing inter-annual variability of GPP. The pretraining paradigm enabled us to fully leverage historical data, and the DL framework ensured that the model generalization continually improves as new data is integrated. Our model dispenses with land cover data and minimizes the requirements of coarse-resolution meteorological data for high-resolution GPP estimation, which could support future efforts in global high-resolution GPP mapping.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115198"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145771648","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":"2026-03-01","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":"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":"2026-03-01","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}

{"title":"Integrated scan simultaneous trajectory enhancement and mapping (IS2-TEAM) for fine resolution forest inventory using backpack LiDAR","authors":"Chunxi Zhao ,&nbsp;Songlin Fei ,&nbsp;Ayman Habib","doi":"10.1016/j.rse.2025.115212","DOIUrl":"10.1016/j.rse.2025.115212","url":null,"abstract":"<div><div>Advancements in close-range remote sensing offer promises in automated forest inventory, facilitating sustainable forest management. Backpack LiDAR is a mobile mapping system that can be deployed for different applications, including forest inventory. However, under-canopy trajectories derived from backpacks equipped with GNSS units are often unreliable due to signal outages, resulting in degraded mapping results. To overcome this challenge, an Integrated Scan Simultaneous Trajectory Enhancement and Mapping (IS^{<strong>2</strong>}-TEAM) framework is proposed for fine-resolution forest inventory. For multi-beam spinning LiDAR, a single scan refers to points from a full revolution of the laser beam assembly, while an integrated scan combines multiple successive single scans. The framework introduces feature extraction strategies for defining reliable semantic features (tree trunk and ground) from integrated scans to enhance trajectory and mapping results. If available, the framework can incorporate a Digital Terrain Model (DTM) extracted from existing geospatial data to enhance georeferencing accuracy of backpack LiDAR. Finally, extracted features together with enhanced point cloud undergo a machine-learning post-processing strategy to evaluate the IS^{<strong>2</strong>}-TEAM's performance in tree detection and provide tree classification results based on maturity. The proposed approach has been thoroughly evaluated across multiple study sites with different forest types and terrain conditions. Through the experimental results, it has been shown that the IS^{<strong>2</strong>}-TEAM extracts reliable ground and tree trunk features and generates point clouds with alignment quality range of 2–4 cm. Furthermore, DTM-assisted IS²-TEAM significantly improves the georeferencing accuracy of the derived point cloud in forests with varying terrain conditions, achieving an average vertical accuracy improvement of 1 m across all test datasets. Finally, the proposed self-evaluation strategy successfully identifies all mature trees and achieves over 90 % accuracy in tree classification.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115212"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881025","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}