Remote Sensing of Environment最新文献

{"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}

{"title":"Coupling ecological concepts with an ocean-colour model: Parameterisation and forward modelling","authors":"Xuerong Sun ,&nbsp;Robert J.W. Brewin ,&nbsp;Shubha Sathyendranath ,&nbsp;Giorgio Dall’Olmo ,&nbsp;David Antoine ,&nbsp;Ray Barlow ,&nbsp;Astrid Bracher ,&nbsp;Malika Kheireddine ,&nbsp;Mengyu Li ,&nbsp;Dionysios E. Raitsos ,&nbsp;Fang Shen ,&nbsp;Gavin H. Tilstone ,&nbsp;Vincenzo Vellucci","doi":"10.1016/j.rse.2024.114487","DOIUrl":"10.1016/j.rse.2024.114487","url":null,"abstract":"<div><div>In the first part of this paper series (<span><span>Sun et al., 2023</span></span>), we developed an ecological model that partitions the total chlorophyll-a concentration (Chl-a) into three phytoplankton size classes (PSCs), pico-, nano-, and microplankton. The parameters of this model are controlled by sea surface temperature (SST), intended to capture shifts in phytoplankton size structure independently of variations in total Chl-a. In this second part of the series, we present an Ocean Colour Modelling Framework (OCMF), building on the classical Case-1 assumption, that explicitly incorporates our ecological model. The OCMF assumes the presence of the three PSCs and the existence of an independent background of non-algal particles. The framework assumes each phytoplankton group resides in a distinct optical environment, assigning chlorophyll-specific inherent optical properties to each group, both directly (phytoplankton) and indirectly (non-algal particulate and dissolved substances). The OCMF is parameterised, validated, and assessed using a large global dataset of inherent and apparent optical properties. We use the OCMF to explore the influence of variations in temperature and Chl-a on phytoplankton size structure and its resulting effects on ocean colour. We also discuss applications of the OCMF, such as its potential for inverse modelling and phytoplankton climate trend detection, which will be explored further in subsequent papers.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114487"},"PeriodicalIF":11.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637557","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":"Vegetation signal crosstalk present in official SMAP surface soil moisture retrievals","authors":"Wade T. Crow ,&nbsp;Andrew F. Feldman","doi":"10.1016/j.rse.2024.114466","DOIUrl":"10.1016/j.rse.2024.114466","url":null,"abstract":"<div><div>Successful surface soil moisture (SM) retrieval from space has been enabled by microwave satellite measurements of Earth's upwelling brightness temperature (<em>T</em>_B). Nevertheless, correction for the impact of vegetation on <em>T</em>_B emission remains a challenge for SM retrieval algorithms. Such correction is often performed in a simplified manner. For example, the Single Channel Algorithm (SCA) uses ancillary climatological normalized vegetation difference index values as a proxy for vegetation optical depth (<em>τ</em>) - resulting in SM retrievals that do not account for interannual <em>τ</em> variability. Official NASA Soil Moisture Active/Passive (SMAP) mission SM products are all based, to varying degrees, on the SCA. Here, we utilize an instrumental variable analysis and alternative SMAP SM retrievals derived from the Multi-Temporal Dual Channel Algorithm (MTDCA) – that better account for time variations in <em>τ</em> – as a benchmark for examining SMAP Level 3 SM retrievals for the presence of signal crosstalk associated with the neglect of interannual <em>τ</em> variability. Results suggest that failing to account for such variability introduces a spurious vegetation-based signal into monthly climatological SMAP SM anomalies. The SMAP Dual Channel Algorithm (DCA), which serves as the current SMAP baseline algorithm, reduces - but does not eliminate – this crosstalk. Results therefore suggest the need for caution when applying SMAP SM retrievals to science applications aimed at understanding SM coupling with the terrestrial biosphere.</div></div><div><h3>Plain language summary</h3><div>Satellite observations of natural microwave emission from Earth's land surface can be converted into estimates of both surface soil moisture and vegetation water content. Such estimates have a variety of applications. However, the separation of the vegetation signal from the soil moisture signal is challenging and often performed using only approximate methods. This paper uses a novel approach to evaluate how accurately state-of-the-art soil moisture retrieval algorithms perform such partitioning. Results suggests that spurious vegetation signals remain in existing soil moisture products - with some approaches removing it more than others. Such “crosstalk” between soil- and vegetation-based signals limits the value of existing satellite soil moisture products for agricultural and ecohydrological applications and motivates the development of improved retrieval algorithms.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114466"},"PeriodicalIF":11.1,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142601011","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":"Evaluating the utility of hyperspectral data to monitor local-scale β-diversity across space and time","authors":"Joseph J. Everest ,&nbsp;Elisa Van Cleemput ,&nbsp;Alison L. Beamish ,&nbsp;Marko J. Spasojevic ,&nbsp;Hope C. Humphries ,&nbsp;Sarah C. Elmendorf","doi":"10.1016/j.rse.2024.114507","DOIUrl":"10.1016/j.rse.2024.114507","url":null,"abstract":"<div><div>Plant functional traits are key drivers of ecosystem processes. However, plot-based monitoring of functional composition across both large spatial and temporal extents is a time-consuming and expensive undertaking. Airborne and satellite remote sensing platforms collect data across large spatial expanses, often repeatedly over time, raising the tantalising prospect of detection of biodiversity change over space and time through remotely sensed methods. Here, we test the degree to which in situ measurements of taxonomic and functional β-diversity, defined as pairwise dissimilarity either between sites, or between years within individual sites, is detectable in airborne hyperspectral imagery across both space and time in an alpine vascular plant community in the Front Range, Colorado, USA. Functional and taxonomic dissimilarity were significantly related to spectral dissimilarity across space, but lacked robust relationships with spectral dissimilarity over time. Biomass showed stronger relationships with spectral dissimilarity than either taxonomic or functional dissimilarity over space, but exhibited no significant associations with spectral dissimilarity over time. Comparative analyses using NDVI revealed that NDVI alone explains much of the variation explained by the full-range spectra. Our results support the use of hyperspectral data to detect fine-scale changes in vascular plant β-diversity over space, but suggest that methodological limitations still preclude the use of this technology for long-term monitoring and change detection.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114507"},"PeriodicalIF":11.1,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142601069","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":"Separation of the direct reflection of soil from canopy spectral reflectance","authors":"Peiqi Yang ,&nbsp;Christiaan van der Tol ,&nbsp;Jing Liu ,&nbsp;Zhigang Liu","doi":"10.1016/j.rse.2024.114500","DOIUrl":"10.1016/j.rse.2024.114500","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Separation of soil effects from top-of-canopy (TOC) reflectance is crucial for quantitative remote sensing of vegetation. Soil affects TOC reflectance via the soil-vegetation interaction and the direct reflection by soil. Various vegetation indices have been developed semi-empirically to mitigate the interferences caused by soil for specific applications, such estimating biomass and monitoring vegetation phenology. However, a practical approach to separate soil effects from the entire TOC spectral reflectance is still lacking. In this study, we investigate the radiative transfer process in a vegetation canopy with soil contamination and develop three methods to estimate the contribution of soil's direct reflection to TOC reflectance. Theoretical analysis reveals that the soil's direct reflection can be quantified and separated from TOC reflectance due to the distinct spectral characteristics of soil and vegetation. We identify three key features: a) Bands in the visible region where the reflectance of soil-uncontaminated green vegetation approaches zero due to strong pigment absorption. b) Two bands in the visible region where the vegetation reflectance is similar, but soil reflectance is distinguishable. c) Soil reflectance within the range of 400 nm to 1000 nm exhibits a near-linear dependence on wavelength. Using these features, we develop three methods to quantify the contribution of soil's direct reflection to TOC reflectance. For given soil reflectance, feature a) or b) alone allows estimating the fraction of soil that directly contributes to TOC reflectance, and thus the soil's direct reflection. Using all three features enables estimation of the soil's direct reflection without knowing soil reflectance.&lt;/div&gt;&lt;div&gt;The proposed methods, along with certain assumptions made during their development, are tested and evaluated using field and synthetic datasets of soil, leaf, and canopy. The evaluation of the three methods demonstrates that the estimation of the soil's direct reflection can be achieved through: i) Using TOC reflectance at approximately 675 nm and soil spectral reflectance, termed the red-band-based method (Method-RBB). ii) Using TOC reflectance at approximately 675 nm and 438 nm, along with soil spectral reflectance, termed as the two-band-based method (Method-TBB). iii) Using TOC reflectance at approximately 675 nm and 438 nm, assuming linear dependence of soil reflectance on wavelength in the visible and near-infrared region, termed as the linear-assumption-based method (Method-LAB). Our evaluation indicates that the linearity from 400 nm to 1000 nm holds true for a wide range of soil types. The conditions outlined in features a) and b) are valid for green vegetation with moderate to high leaf chlorophyll content: when leaf chlorophyll content exceeds 20 μg cm&lt;sup&gt;−2&lt;/sup&gt;, the leaf albedo at 675 nm is generally below 0.15, and the difference in leaf albedo at 675 nm and 438 nm is sufficiently small. The results reve","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114500"},"PeriodicalIF":11.1,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142609810","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":"Earth's record-high greenness and its attributions in 2020","authors":"Yulong Zhang ,&nbsp;Jiafu Mao ,&nbsp;Ge Sun ,&nbsp;Qinfeng Guo ,&nbsp;Jeffrey Atkins ,&nbsp;Wenhong Li ,&nbsp;Mingzhou Jin ,&nbsp;Conghe Song ,&nbsp;Jingfeng Xiao ,&nbsp;Taehee Hwang ,&nbsp;Tong Qiu ,&nbsp;Lin Meng ,&nbsp;Daniel M. Ricciuto ,&nbsp;Xiaoying Shi ,&nbsp;Xing Li ,&nbsp;Peter Thornton ,&nbsp;Forrest Hoffman","doi":"10.1016/j.rse.2024.114494","DOIUrl":"10.1016/j.rse.2024.114494","url":null,"abstract":"<div><div>Terrestrial vegetation is a crucial component of Earth's biosphere, regulating global carbon and water cycles and contributing to human welfare. Despite an overall greening trend, terrestrial vegetation exhibits a significant inter-annual variability. The mechanisms driving this variability, particularly those related to climatic and anthropogenic factors, remain poorly understood, which hampers our ability to project the long-term sustainability of ecosystem services. Here, by leveraging diverse remote sensing measurements, we pinpointed 2020 as a historic landmark, registering as the greenest year in modern satellite records from 2001 to 2020. Using ensemble machine learning and Earth system models, we found this exceptional greening primarily stemmed from consistent growth in boreal and temperate vegetation, attributed to rising CO₂ levels, climate warming, and reforestation efforts, alongside a transient tropical green-up linked to the enhanced rainfall. Contrary to expectations, the COVID-19 pandemic lockdowns had a limited impact on this global greening anomaly. Our findings highlight the resilience and dynamic nature of global vegetation in response to diverse climatic and anthropogenic influences, offering valuable insights for optimizing ecosystem management and informing climate mitigation strategies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114494"},"PeriodicalIF":11.1,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142601010","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}