Remote Sensing of Environment最新文献

{"title":"On the relationship between shoot Silhouette area to Total needle Area Ratio (STAR) and contour length","authors":"Jan Pisek ,&nbsp;Andres Kuusk ,&nbsp;Oleksandr Borysenko","doi":"10.1016/j.rse.2024.114520","DOIUrl":"10.1016/j.rse.2024.114520","url":null,"abstract":"<div><div>The calculation of the shoot Silhouette area to Total needle Area Ratio (STAR) provides a method for assessing the light interception efficiency of a coniferous shoot. We illustrate the effectiveness of a close-range, blue light 3D scanning system as a new, affordable, and highly efficient technique for estimating STAR values. The distributions of STAR and contour length of shoots for most of the diverse species studied here are similar to those of a recently proposed optical model of a Norway spruce (<em>Picea abies</em>) shoot (Kuusk, A., Borysenko, O., Pisek, J., 2023. Optical model of a conifer shoot. Journal of Quantitative Spectroscopy and Radiative Transfer 310, 108,715). This implies that the conclusions, regarding the radiation scattering in a shoot can be more widely applicable and extended to the species with longer and curved, twisted needles as well.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"317 ","pages":"Article 114520"},"PeriodicalIF":11.1,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684698","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":"Genetic Algorithm for Atmospheric Correction (GAAC) of water bodies impacted by adjacency effects","authors":"Yanqun Pan ,&nbsp;Simon Bélanger","doi":"10.1016/j.rse.2024.114508","DOIUrl":"10.1016/j.rse.2024.114508","url":null,"abstract":"<div><div>Adjacency effect (AE) corrections over inland water surfaces has been a known issue in space-borne optical remote sensing over more than four decades. Here we present a novel algorithm able to simultaneously retrieve the aerosol optical depth, sun glint, AE, water reflectance, and water inherent optical properties (IOPs). The method was evaluated against an <em>in situ</em> data set of remote sensing reflectance (<span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>r</mi><mi>s</mi></mrow></msub></math></span>) collected in <span><math><mo>∼</mo></math></span>100 lakes across Canada. The new algorithm is based on a genetic optimization scheme (GAAC: Genetic Algorithm for Atmospheric Correction), and was here compared to the most popular atmospheric correction algorithms available (ACOLITE, iCOR+SIMEC). The statistical metrics of the <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>r</mi><mi>s</mi></mrow></msub></math></span> retrieval were improved by a factor of almost 2 in all wavelengths, and for all metrics (Bias, Error, Similarity Angle) relative to other algorithms. Demonstrations of GAAC on scenes of Lansdat-8 OLI, and Sentinel-2 MSI sensors demonstrate the algorithm’s robustness when applied to spatially complex small lake (<span><math><mo>∼</mo></math></span>10 km of width) surfaces.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"317 ","pages":"Article 114508"},"PeriodicalIF":11.1,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684697","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":"Two-decade surface ozone (O3) pollution in China: Enhanced fine-scale estimations and environmental health implications","authors":"Zeyu Yang ,&nbsp;Zhanqing Li ,&nbsp;Fan Cheng ,&nbsp;Qiancheng Lv ,&nbsp;Ke Li ,&nbsp;Tao Zhang ,&nbsp;Yuyu Zhou ,&nbsp;Bin Zhao ,&nbsp;Wenhao Xue ,&nbsp;Jing Wei","doi":"10.1016/j.rse.2024.114459","DOIUrl":"10.1016/j.rse.2024.114459","url":null,"abstract":"<div><div>Surface ozone (O₃) has become a primary pollutant affecting urban air quality and public health in mainland China. To address this concern, we developed a nation-wide surface maximum daily average 8-h (MDA8) O₃ concentration dataset for mainland China (ChinaHighO₃) at a 10-km resolution with a start year of 2013, which has been widely employed in a wide range of studies. To meet the increasing demand for its usage, we have made important enhancements, including the development of a more advanced deep-learning model and the incorporation of major source updates, such as 1-km surface downward shortwave radiation and temperature directly from satellite retrievals, as well as a 1-km emission inventory. Additionally, we have extended the temporal coverage dating back to 2000, increased the spatial resolution to 1 km, and most importantly, notably improved the data quality (e.g., sample-based cross-validation coefficient of determination = 0.89, and root-mean-square error = 15.77 μg/m³). Using the substantially improved new product, we have found dynamic and diverse patterns in national surface O₃ levels over the past two decades. Peak-season levels have been relatively stable from 2000 to 2015, followed by a sharp increase, reaching peak values in 2019 and subsequently declining. Additionally, we observed a large relative difference of 12 % in peak-season surface O₃ concentrations between urban and rural regions in mainland China. This disparity has greatly increased since 2015, particularly in the Beijing-Tianjin-Hebei and Pearl River Delta regions. Notably, since 2000, nearly all of the population across mainland China (&gt; 99.7 %) has resided in areas exposed to surface O₃ pollution exceeding the World Health Organization (WHO) recommended long-term air quality guideline (AQG) level (peak-season MDA8 O₃ = 60 μg/m³). Moreover, the short-term population-risk exposure to daily surface O₃ pollution has shown a significant increasing trend of 1.2 % (<em>p</em> &lt; 0.001) of the days exceeding the WHO's recommended short-term AQG level (daily MDA8 O₃ = 100 μg/m³) per year during the 22-year period. The overall upward trend (0.73 μg/m³/yr, <em>p</em> &lt; 0.001) in peak-season surface O₃ pollution has led to an exceptionally large rate of increase of 953 (95 % confidence interval: 486, 1288) premature deaths per year from 2000 to 2021 in mainland China. Urgent action is required to develop comprehensive strategies aimed at mitigating surface O₃ pollution to enhance air quality in the future.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"317 ","pages":"Article 114459"},"PeriodicalIF":11.1,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142678168","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":"Assessing lead fraction derived from passive microwave images and improving estimates at pixel-wise level","authors":"Xi Zhao ,&nbsp;Jiaxing Gong ,&nbsp;Meng Qu ,&nbsp;Lijuan Song ,&nbsp;Xiao Cheng","doi":"10.1016/j.rse.2024.114517","DOIUrl":"10.1016/j.rse.2024.114517","url":null,"abstract":"<div><div>Passive microwave remote sensing provides unique pan-Arctic light- and cloud-independent daily coverage of lead fraction (LF) for Arctic winter and spring. In this study, we conducted a quantitative assessment of various sea ice concentration (SIC) data products and LF retrieval algorithms to evaluate their accuracy in deriving lead fractions at both overall and pixel-wise levels. Our results indicate that SIC data products are not sensitive to refrozen leads in winter but tend to display clear lead structures in spring. However, the absolute SIC values differ significantly from LF and cannot be directly used as a proxy. As for the LF retrieval algorithms, we proved that the overall accuracy can be largely improved by adjusting upper tie-points. To further minimize errors, we developed an Artificial Neural Network model that outperformed conventional approaches at the pixel-wise level, offering a more reliable estimation method for absolute fraction values.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114517"},"PeriodicalIF":11.1,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating anthropogenic CO2 emissions from China's Yangtze River Delta using OCO-2 observations and WRF-Chem simulations","authors":"Mengya Sheng ,&nbsp;Yun Hou ,&nbsp;Hao Song ,&nbsp;Xinxin Ye ,&nbsp;Liping Lei ,&nbsp;Peifeng Ma ,&nbsp;Zhao-Cheng Zeng","doi":"10.1016/j.rse.2024.114515","DOIUrl":"10.1016/j.rse.2024.114515","url":null,"abstract":"<div><div>Satellite-based measurements have emerged as an effective method for the top-down estimates of anthropogenic CO₂ emissions. Changes in the column-averaged dry-air mole fractions of CO₂ (XCO₂) in the atmosphere reflect contributions from both human activities and natural processes, posing challenges in accurately extracting anthropogenic XCO₂ signals and quantifying urban CO₂ emissions. Here, we introduce a novel method based on spatial autocorrelation to directly identify anthropogenic XCO₂ signals from satellite measurements of Orbiting Carbon Observatory-2 (OCO-2). These signals serve as constraints for atmospheric transport model simulations, enabling the verification of emission inventory over urban areas. Utilizing 35 OCO-2 overpasses over the Yangtze River Delta urban agglomeration, we demonstrate the effectiveness of local Moran's I statistics in detecting localized anthropogenic XCO₂ enhancements. The results show an average XCO₂ increase of 1.36–4.41 ppm in proximity to major cities and areas with intensive industrial activity. A case study near Nanjing, based on eight overpasses, reveals XCO₂ enhancements with peaks ranging from 2.26 to 4.72 ppm. To establish the relationship between these XCO₂ enhancements and CO₂ emissions, we conducted WRF-Chem simulations driven by emissions from the Emissions Database for Global Atmospheric Research (EDGAR). Discrepancies between observed and simulated XCO₂ enhancements were primarily attributed to uncertainties in the prior emissions, the calculation of urban XCO₂ enhancements from OCO-2 data, and complex atmospheric transport dynamics. From our estimates, the daily CO₂ emissions in Nanjing is 0.65 ± 0.15 MtCO₂/day, which is different from the EDGAR inventory by −10.5 % to 77.3 % (i.e., 0.17 ± 0.14 MtCO₂ /day). Error analysis suggests an uncertainty in CO₂ emission estimates associated with XCO₂ enhancement and wind speed ranging from 16 % to 32 % (i.e., 0.08–0.15 MtCO₂/day). This study proposes an objective approach to assess urban CO₂ emissions, leveraging satellite XCO₂ observations to improve accuracy and reliability in emission inventories.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114515"},"PeriodicalIF":11.1,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670805","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":"From theory to hydrological practice: Leveraging CYGNSS data over seven years for advanced soil moisture monitoring","authors":"Hoang Hai Nguyen ,&nbsp;Hyunglok Kim ,&nbsp;Wade Crow ,&nbsp;Simon Yueh ,&nbsp;Wolfgang Wagner ,&nbsp;Fangni Lei ,&nbsp;Jean-Pierre Wigneron ,&nbsp;Andreas Colliander ,&nbsp;Frédéric Frappart","doi":"10.1016/j.rse.2024.114509","DOIUrl":"10.1016/j.rse.2024.114509","url":null,"abstract":"<div><div>Soil moisture (SM) is a key variable in hydrometeorology and climate systems. With the growing interest in capturing fine-scale SM variability for effective hydroclimate applications, spaceborne L-band bistatic radar systems using Global Navigation Satellite System-Reflectometry (GNSS-R) technology hold great potential to meet the demand for high spatiotemporal resolution SM data. Although primarily designed for tropical cyclone monitoring purposes, the first GNSS-R satellite constellation – Cyclone Global Navigation Satellite System (CYGNSS) mission, has demonstrated the benefits of reliably monitoring diurnal SM dynamics through its initial stage of seven-year data record, thanks to its high revisit frequency at sub-daily intervals. Nevertheless, knowledge of SM retrieval from CYGNSS, particularly linked with its distinctive features, remains poorly understood, while numerous existing uncertainties and open issues can restrict its effective SM retrieval and practical applications in the next operating stages. Unlike other review papers, this work aims to bridge this knowledge gap in CYGNSS SM retrieval by highlighting noteworthy design properties based on analyses of its real-world data, while providing a synthesis of recent advances in eliminating external uncertainty factors and improving SM inversion methods.</div><div>Despite its potential, CYGNSS SM retrieval faces both general and particular challenges arising from common issues in retrieval algorithms for conventional GNSS-R satellites and unique data limitations tied to its technical design. Scientific debates over the contributions of coherent and incoherent components in total CYGNSS signals and accurate partitioning of these two parts are defined as the key algorithm-related challenges to resolve, along with correcting attenuation effects of vegetation and surface roughness. The data-related challenges involve variations in CYGNSS's spatial footprint, temporal frequency, and signal penetration depth across different land surface conditions, inadequate consideration of CYGNSS incidence angle change, excessive dependence on a reference SM dataset for inversion model calibration/training or validation, and computational demands for processing rapid multi-sampling CYGNSS data retrieval. Future research pathways highlight leveraging cutting-edge machine learning/deep learning algorithms to enhance CYGNSS SM data quantity and quality and better interpret its complex interactions with other hydroclimate variables. Assimilating CYGNSS SM data streams into physical models to improve the prediction of related variables and climate extremes also presents a promising prospect.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114509"},"PeriodicalIF":11.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142642868","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 dual-branch network for crop-type mapping of scattered small agricultural fields in time series remote sensing images","authors":"Yanjun Wu ,&nbsp;Zhenyue Peng ,&nbsp;Yimin Hu ,&nbsp;Rujing Wang ,&nbsp;Taosheng Xu","doi":"10.1016/j.rse.2024.114497","DOIUrl":"10.1016/j.rse.2024.114497","url":null,"abstract":"<div><div>With the rapid advancement of remote sensing technology, the recognition of agricultural field parcels using time-series remote sensing images has become an increasingly emphasized task. In this paper, we focus on identifying crops within scattered, irregular, and poorly defined agricultural fields in many Asian regions. We select two representative locations with small and scattered parcels and construct two new time-series remote sensing datasets (JM dataset and CF dataset). We propose a novel deep learning model DBL, the Dual-Branch Model with Long Short-Term Memory (LSTM), which utilizes main branch and supplementary branch to accomplish accurate crop-type mapping. The main branch is designed for capturing global receptive field and the supplementary is designed for temporal and spatial feature refinement. The experiments are conducted to evaluate the performance of the DBL compared with the state-of-the-art (SOTA) models. The results indicate that the DBL model performs exceptionally well on both datasets. Especially on the CF dataset characterized by scattered and irregular plots, the DBL model achieves an overall accuracy (OA) of 97.70% and a mean intersection over union (mIoU) of 90.70%. It outperforms all the SOTA models and becomes the only model to exceed 90% mark on the mIoU score. We also demonstrate the stability and robustness of the DBL across different agricultural regions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114497"},"PeriodicalIF":11.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142642867","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":"Developing Layered Occlusion Perception Model: Mapping community open spaces in 31 China cities","authors":"Yichen Lei ,&nbsp;Xiuyuan Zhang ,&nbsp;Shuping Xiong ,&nbsp;Ge Tan ,&nbsp;Shihong Du","doi":"10.1016/j.rse.2024.114498","DOIUrl":"10.1016/j.rse.2024.114498","url":null,"abstract":"<div><div>Community Open Spaces (COS) refer to the fine-grained and micro-open areas within communities that offer residents convenient opportunities for social interaction and health benefits. The mapping of COS using Very High Resolution (VHR) imagery can provide critical community-scale data for monitoring urban sustainable development goals (SDGs). However, the three-dimensional structure of COS often results in layered occlusion in two-dimensional satellite imagery, leading to the invisibility and fragmentation of ground COS features in VHR images. This study presents a novel Layered Occlusion Perception Model (LOPM) to address these challenges by accurately modeling and reconstructing the intricate layered structure of COS. Our approach involves the automatic generation of a comprehensive COS database and the joint training of a deep learning network to decompose occlusion relationships. The developed dual-layer map product, COS-1m, includes various elements and their coupled spaces, with a resolution of 1 m, covering 31 major cities in China. The results demonstrate that the proposed method achieved an overall accuracy of 86.39% and an average F1-score of 77.47% across these cities. COS-1m reveals that, on average, 60.51 km² of COS area per city is occluded, constituting 10.18% of the total COS area. This research advances the technology for layered monitoring of COS, fills a critical gap in community-scale SDG assessments by providing fine-grained COS data products, and offers valuable insights for urban planners and policymakers to promote more effective and sustainable urban development.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114498"},"PeriodicalIF":11.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637564","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":"Incorporating environmental stress improves estimation of photosynthesis from NIRvP in US Great Plains pasturelands and Midwest croplands","authors":"Lun Gao ,&nbsp;Kaiyu Guan ,&nbsp;Chongya Jiang ,&nbsp;Xiaoman Lu ,&nbsp;Sheng Wang ,&nbsp;Elizabeth A. Ainsworth ,&nbsp;Xiaocui Wu ,&nbsp;Min Chen","doi":"10.1016/j.rse.2024.114516","DOIUrl":"10.1016/j.rse.2024.114516","url":null,"abstract":"<div><div>Near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) is important for gross primary production (GPP) estimation. While NIRvP is a useful indicator of canopy structure and solar radiation, its association with heat or moisture stress is not fully understood. Thus, this research aimed to explore the impact of air temperature (Ta) and vapor pressure deficit (VPD) on the NIRvP-GPP relationship. Using Moderate Resolution Imaging Spectroradiometer (MODIS) observations, eddy-covariance measurements, and the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) data, we found that NIRvP cannot fully explain the response of plant photosynthesis to Ta and VPD at both seasonal and daily scales. Therefore, we incorporated a polynomial function of Ta and an exponential function of VPD to correct its seasonal response to stress and calibrated the GPP residual via a linear function of Ta and VPD time-varying derivatives to account for its daily response to stress. Leave-one-site-out cross-validation suggested that the improvements relative to its original version were especially noteworthy under stress conditions while less significant when there was no water or heat stress across grasslands and croplands. When compared to six other GPP models, the enhanced NIRvP model consistently outperformed them or performed comparably with the best model in terms of bias, RSME, and coefficient of determinant against measurements in grasslands and croplands. Moreover, we found that parameterizing the fraction of photosynthetically active radiation term using NIRv notably improved the performance of the classic MOD17 and vegetation photosynthesis model, with an average RMSE reduction of 13 % across grasslands and croplands. Overall, this study highlights the need to consider environmental stressors for improved NIRvP-based GPP and shed light on future improvements of LUE models.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114516"},"PeriodicalIF":11.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637897","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":"An adaptive spatiotemporal tensor reconstruction method for GIMMS-3g+ NDVI","authors":"Mengyang Cai ,&nbsp;Yao Zhang ,&nbsp;Xiaobin Guan ,&nbsp;Jinghao Qiu","doi":"10.1016/j.rse.2024.114511","DOIUrl":"10.1016/j.rse.2024.114511","url":null,"abstract":"<div><div>Satellite-derived normalized difference vegetation index (NDVI) is inevitably contaminated by clouds and aerosols, causing large uncertainties in depicting the seasonal and interannual variations of terrestrial ecosystems, and potentially misrepresents their responses to climate change and climate extremes. Although various methods have been developed to reconstruct NDVI time series using the similarity in time, space or their combination, they typically require known and accurate data quality information. It is still challenging to effectively reconstruct high-quality NDVI from Global Inventory Modeling and Mapping Studies-3rd Generation V1.2 (GIMMS-3g+), which is one of the longest observation records but lacks reliable data quality information. This study introduces an adaptive spatiotemporal tensor reconstruction algorithm that leverages the spatial and temporal patterns of vegetation to produce high-quality long-term NDVI datasets without the need of data quality information. The reconstruction process employs two different tensor completion models to satisfy the low-rank constraints. These two models can effectively remove the high-frequency noises originating from atmospheric contamination, while preserving the abrupt or low-frequency changes attributable to disturbances such as drought, even in the absence of data quality information. The resultant NDVI shows good consistency with observations from geostationary satellites. Regions that show a strong correlation (<em>r</em> &gt; 0.7) with geostationary satellite NDVI increased from 46.7 % (original GIMMS-3g+) to 62.2 % and 62.3 % (two reconstructions results) for East Asia, and from 41.4 % to 58.0 % and 59.0 % for Amazon. Our method also demonstrates superior performance to traditional methods such as Whittaker, HANTS, SG-filter, and comparable performance with the state-of-the-art ST-Tensor method when the fraction of contaminated observations is low. The proposed method can also be applied to other datasets such as EVI, LAI, etc., without additional data quality inputs. The resultant vegetation index dataset has the potential to improve plant phenology retrievals and evaluation of ecosystem responses to extremes.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114511"},"PeriodicalIF":11.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637556","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}