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Comprehensive gridded dataset of photosynthetically active radiation in the upper ocean from 1958 to 2022 Remote Sens. Environ. (IF 11.1) Pub Date : 2024-07-11 Jérôme Castant, Vincent Vantrepotte, Robert Frouin, Grégory Beaugrand
Photosynthetically Active Radiation (PAR) plays a crucial role in shaping marine ecosystems, influencing primary production, species interaction, and phytoplankton seasonal dynamics. However, comprehensive long-term (gap-free) datasets for both surface PAR and the diffuse Attenuation Coefficient of Photosynthetically Active Radiation (KPAR) are currently lacking. In this study, we introduce two new
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Revealing post-megafire spectral and compositional recovery in the Siberian boreal forest using Landsat time series and regression-based unmixing approach Remote Sens. Environ. (IF 11.1) Pub Date : 2024-07-05 Suri G. Bao, Wen J. Wang, Zhihua Liu, Hankui K. Zhang, Lei Wang, Jun Ma, Hongchao Sun, Shengjie Ba, Yeqiao Wang, Hong S. He
Megafires trigger abrupt ecosystem changes and may lead to alternative successional pathways, thereby undermining the recovery and resilience of forests. However, comprehensive multi-perspective assessments of post-megafire forest recovery remain limited. In this study, we assessed short- and long-term spectral and compositional recovery following the 1987 Black Dragon Megafire in the Siberian boreal
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Canopy reflectance modeling of row aquatic vegetation: AVRM and AVMC Remote Sens. Environ. (IF 11.1) Pub Date : 2024-07-05 Guanhua Zhou, Chen Tian, Yaxin Han, Chunyue Niu, Haoyu Miao, Guifei Jing, Franz Pablo Antezana Lopez, Guangjian Yan, Hilana Saleh Mahmoud Najjar, Feng Zhao, Shubha Sathyendranath
Row aquatic vegetation is characterized by distinctive features as inundated habitats and individuals arranged in rows. However, current radiative transfer models have not yet taken into account both the water background and the row structure. To address this problem, we developed a hybrid radiative transfer and geometric optical model (Aquatic Vegetation Row Model, AVRM) for row vegetation and taken
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A novel hybrid GNSS, GRACE, and InSAR joint inversion approach to constrain water loss during a record-setting drought in California Remote Sens. Environ. (IF 11.1) Pub Date : 2024-07-04 G. Carlson, S. Werth, M. Shirzaei
Water years 2020 and 2021 in California were two of the driest on record and the most recent series of dry years during a two-decade-long mega-drought. The 2020–2021 drought period, characterized by low precipitation and high temperatures, had devastating effects, including an increase in ongoing groundwater overdraft, manifesting in rapid subsidence in California's Central Valley. Here, we present
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Global deep learning model for delineation of optically shallow and optically deep water in Sentinel-2 imagery Remote Sens. Environ. (IF 11.1) Pub Date : 2024-07-04 Galen Richardson, Neve Foreman, Anders Knudby, Yulun Wu, Yiwen Lin
In aquatic remote sensing, algorithms commonly used to map environmental variables rely on assumptions regarding the optical environment. Specifically, some algorithms assume that the water is optically deep, i.e., that the influence of bottom reflectance on the measured signal is negligible. Other algorithms assume the opposite and are based on an estimation of the bottom-reflected part of the signal
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Heterogeneity in ice-wedge permafrost degradation revealed across spatial scales Remote Sens. Environ. (IF 11.1) Pub Date : 2024-07-04 Katherine N. Braun, Christian G. Andresen
Permafrost thaw exhibits an array of spatially heterogenous patterns. As the Arctic continues to warm, those spatial patterns of permafrost thaw, or degradation, are becoming increasingly intricate and dynamic. In particular, ice-wedge permafrost degradation contains a high degree of spatial heterogeneity as ice wedges transition through undegraded, degraded, and stabilized stages. Developing accurate
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Examining CNN terrain model for TanDEM-X DEMs using ICESat-2 data in Southeastern United States Remote Sens. Environ. (IF 11.1) Pub Date : 2024-07-04 Eric Guenther, Lori Magruder, Amy Neuenschwander, Donald Maze-England, James Dietrich
Accurate large-area Digital Terrain Models (DTMs) are crucial for many science applications. Spaceborne Synthetic Aperture Radar (SAR) platforms are often used to create these DTMs as they provide an effective tool to collect surface elevations across a wide extent. However, SAR-derived digital elevation models (DEMs) cannot accurately measure ground elevations in the presence of forests. This work
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Estimation and validation of InSAR-derived surface displacements at temperate raised peatlands Remote Sens. Environ. (IF 11.1) Pub Date : 2024-07-04 Alexis Hrysiewicz, Jennifer Williamson, Chris D. Evans, A. Jonay Jovani-Sancho, Nathan Callaghan, Justin Lyons, Jake White, Joanna Kowalska, Nina Menichino, Eoghan P. Holohan
Peatland surface motion derived from satellite-based Interferometry of Synthetic Aperture Radar (InSAR) is potentially a proxy for groundwater level variations and greenhouse gas emissions from peat soils. Ground validation of these motions at equivalent temporal resolution has proven problematic, either because of limitations of traditional surveying methods or because of limitations with past InSAR
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Sensitivity of a carbon-based primary production model on satellite ocean color products Remote Sens. Environ. (IF 11.1) Pub Date : 2024-07-02 Luping Song, Zhongping Lee, Shaoling Shang, Jinghui Wu
Satellite remote sensing plays a crucial role in estimating global primary production. One well-known model is the carbon-based production model (CbPM), which focuses on the estimation of carbon biomass () via particulate backscattering coefficient at 443 nm ((443)) and emphasizes the physiological characteristics of phytoplankton. However, as there are various remote sensing algorithms for the estimation
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HIDYM: A high-resolution gross primary productivity and dynamic harvest index based crop yield mapper Remote Sens. Environ. (IF 11.1) Pub Date : 2024-07-02 Weiguo Yu, Dong Li, Hengbiao Zheng, Xia Yao, Yan Zhu, Weixing Cao, Lin Qiu, Tao Cheng, Yongguang Zhang, Yanlian Zhou
Reliable prediction of field-level crop yield over large regions is a prerequisite for informed decision-making in precision crop management. One of common Earth observation approaches is to predict crop yield through the estimation of gross primary productivity (GPP) and a fixed crop-specific harvest index (HI), but few studies have considered the spatio-temporal dynamics of HI. Although some studies
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Causal inference reveals the dominant role of interannual variability of carbon sinks in complicated environmental-terrestrial ecosystems Remote Sens. Environ. (IF 11.1) Pub Date : 2024-07-02 Chaoya Dang, Zhenfeng Shao, Peng Fu, Qingwei Zhuang, Xiaodi Xu, Jiaxin Qian
Climate factors (CFs) are key variables shaping the interannual variability (IAV) of terrestrial ecosystem carbon sinks. However, the dominant CFs influencing the IAV of terrestrial carbon sinks remains debated, as CFs are coupled via land-atmosphere interactions. Here, the dominant factors influencing the IAV of global terrestrial net ecosystem production (NEP) were quantified using the convergent
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Different glacier surge patterns revealed by Sentinel-2 imagery derived quasi-monthly flow velocity at west Kunlun Shan, Karakoram, Hindu Kush and Pamir Remote Sens. Environ. (IF 11.1) Pub Date : 2024-07-02 Gang Li, Zhuoqi Chen, Yanting Mao, Zhibin Yang, Xiao Chen, Xiao Cheng
The twin optical Sentinel-2 A/B satellites, with their 5-day repeat observations, have proven to be capable of deriving high temporal resolution glacier velocity fields. This study proposes a data processing procedure for deriving quasi-monthly glacier flow velocity fields for the “Karakoram-Pamir anomaly” region. Each Sentinel-2 acquisition is performed offset-tracking (OT) to its next three almost
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GEOSIF: A continental-scale sub-daily reconstructed solar-induced fluorescence derived from OCO-3 and GK-2A over Eastern Asia and Oceania Remote Sens. Environ. (IF 11.1) Pub Date : 2024-07-02 Sungchan Jeong, Youngryel Ryu, Xing Li, Benjamin Dechant, Jiangong Liu, Juwon Kong, Wonseok Choi, Jianing Fang, Xu Lian, Pierre Gentine
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Influence of temperate forest autumn leaf phenology on segmentation of tree species from UAV imagery using deep learning Remote Sens. Environ. (IF 11.1) Pub Date : 2024-07-02 Myriam Cloutier, Mickaël Germain, Etienne Laliberté
Remote sensing of forests has become increasingly accessible with the use of unoccupied aerial vehicles (UAV), along with deep learning, allowing for repeated high-resolution imagery and the capturing of phenological changes at larger spatial and temporal scales. In temperate forests during autumn, leaf senescence occurs when leaves change colour and drop. However, the influence of leaf senescence
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Persistent global greening over the last four decades using novel long-term vegetation index data with enhanced temporal consistency Remote Sens. Environ. (IF 11.1) Pub Date : 2024-07-02 Sungchan Jeong, Youngryel Ryu, Pierre Gentine, Xu Lian, Jianing Fang, Xing Li, Benjamin Dechant, Juwon Kong, Wonseok Choi, Chongya Jiang, Trevor F. Keenan, Sandy P. Harrison, Iain Colin Prentice
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The effect of artificial intelligence evolving on hyperspectral imagery with different signal-to-noise ratio, spectral and spatial resolutions Remote Sens. Environ. (IF 11.1) Pub Date : 2024-07-01 Jianxin Jia, Xiaorou Zheng, Yueming Wang, Yuwei Chen, Mika Karjalainen, Shoubin Dong, Runuo Lu, Jianyu Wang, Juha Hyyppä
Hyperspectral images are increasingly being used in classification and identification. Data users prefer hyperspectral imagery with high spatial resolution, finer spectral resolution, and high signal-to-noise ratio (SNR). However, tradeoffs exist in these core parameters in imagery acquired by different hyperspectral sensor systems. Data users may find it difficult to utilize all the advantages of
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Generation of country-scale canopy height maps over Gabon using deep learning and TanDEM-X InSAR data Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-29 Daniel Carcereri, Paola Rizzoli, Luca Dell’Amore, José-Luis Bueso-Bello, Dino Ienco, Lorenzo Bruzzone
Operational canopy height mapping at high resolution remains a challenging task at country-level. Most of the existing state-of-the-art inversion methods propose physically-based schemes which are specifically tuned for local scales. Only few approaches in the literature have attempted to produce country or global scale estimates, mostly by means of data-driven approaches and multi-spectral data sources
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Characterizing the spatial structure and aliasing effect of ocean tide loading on InSAR measurements Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-28 Zhou Wu, Ruya Xiao, Mi Jiang, Vagner G. Ferreira
Ocean tide loading (OTL) displacements, shown as long-wavelength errors in Interferometric Synthetic Aperture Radar (InSAR), must be considered in large-scale applications. Despite efforts to explore the impacts of OTL on InSAR, most studies use individual interferograms and simple metrics, which fail to characterize the spatial structure of OTL. Moreover, the OTL contribution to InSAR time series
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Multitemporal airborne imaging spectrometry and fluorometry reveal contrasting photoprotective responses of trees Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-28 Ran Wang, John A. Gamon, Sabrina E. Russo, Aime Valentin Nishimwe, Hugh Ellerman, Brian Wardlow
The Photochemical Reflectance Index (PRI) and solar induced fluorescence (SIF) provide information on plant photosynthetic activity. PRI and SIF are both strongly influenced by irradiance, but uncertainties related to the interpretation of these light responses at large spatial scales remain, partly due to a shortage of suitable data from aircraft or satellite platforms. The goal of this study was
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Constructing a long-term global dataset of direct and diffuse radiation (10 km, 3 h, 1983–2018) separating from the satellite-based estimates of global radiation Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-28 Wenjun Tang, Junmei He, Changkun Shao, Jun Song, Zongtao Yuan, Bowen Yan
In addition to global radiation (R), direct radiation (R) and diffuse radiation (R) are important fundamental data urgently needed in scientific and industrial fields. However, compared with R, R and R have received little attention in the past, either in observations or in satellite retrievals, mainly due to the high cost of their observations and the difficulty of retrieving them effectively from
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Using interpenetrating subsampling to incorporate interpreter variability into estimation of the total variance of land cover area estimates Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-28 Dingfan Xing, Stephen V. Stehman
Reference data obtained by interpreters is a key component of sample-based estimation of area of land cover and land cover change. However, interpreters may disagree when assigning the reference class label for a given sample unit and this inconsistency between interpreters contributes to the overall uncertainty of the estimated area. Interpenetrating subsampling (IPS) offers a practical way to incorporate
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On the shoreline monitoring via earth observation: An isoradiometric method Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-28 F. Caldareri, A. Sulli, N. Parrino, G. Dardanelli, S. Todaro, A. Maltese
Shoreline variations, triggered by climate change, eustatism, and tectonic, drive the coastal landscape evolution over multiple spatial and temporal scales. Among the many different existing coast types, sandy coasts are the most sensitive to coastal erosion and accretion processes and, at the same time, often host valuable anthropogenic assets. The rapid and ongoing evolution of these coastal environments
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Continental aerosol properties and absorption retrieval using random forest machine learning method specific to geostationary remote sensing Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-28 Fangwen Bao, Shengbiao Wu, Jinhui Gao, Shuyun Yuan, Yiwen Liu, Kai Huang
The utilization of satellite remote sensing images for retrieving aerosol optical parameters has been extensively discussed over the past few decades. While employing machine learning models is indeed a viable approach, a significant portion of these studies still rely on redundant data. Moreover, the discussion regarding aerosol absorption, a crucial factor for determining aerosol radiative impact
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Not just a pretty picture: Mapping Leaf Area Index at 10 m resolution using Sentinel-2 Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-28 Richard Fernandes, Gang Hong, Luke A. Brown, Jadu Dash, Kate Harvey, Simha Kalimipalli, Camryn MacDougall, Courtney Meier, Harry Morris, Hemit Shah, Abhay Sharma, Lixin Sun
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Uncertainty estimates in the NISAR high-resolution soil moisture retrievals from multi-scale algorithm Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-27 Preet Lal, Gurjeet Singh, Narendra N. Das, Dara Entekhabi, Rowena B. Lohman, Andreas Colliander
It is important to know the amount of systematic and random uncertainties in any state variable to improve its geophysical application potential. The expected high-resolution (200 [m]) soil moisture product from the NASA-ISRO Synthetic Aperture Radar (NISAR) mission is no exception. Thus, knowing the quality of the soil moisture retrievals through the estimation of various error sources is imperative
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Unveiling the hidden dynamics of intermittent surface water: A remote sensing framework Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-27 Zhen Xiao, Runkui Li, Mingjun Ding, Panli Cai, Jingxian Guo, Haiyu Fu, Xiaoping Zhang, Xianfeng Song
Intermittent surface water frequently transitioning between water and land over months and years, plays a crucial and increasingly significant role in both social and ecological systems. However, their vital and dramatic dynamics have mainly remained invisible due to monitoring limitations. We present a new remote sensing framework to capture the long-term monthly dynamics of surface water bodies,
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Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-27 Benjamin Dechant, Jens Kattge, Ryan Pavlick, Fabian D. Schneider, Francesco M. Sabatini, Álvaro Moreno-Martínez, Ethan E. Butler, Peter M. van Bodegom, Helena Vallicrosa, Teja Kattenborn, Coline C.F. Boonman, Nima Madani, Ian J. Wright, Ning Dong, Hannes Feilhauer, Josep Peñuelas, Jordi Sardans, Jesús Aguirre-Gutiérrez, Peter B. Reich, Pedro J. Leitão, Jeannine Cavender-Bares, Isla H. Myers-Smith,
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Comparing the quantum use efficiency of red and far-red sun-induced fluorescence at leaf and canopy under heat-drought stress Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-26 Sebastian Wieneke, Javier Pacheco-Labrador, Miguel D. Mahecha, Sílvia Poblador, Sara Vicca, Ivan A. Janssens
Sun-Induced chlorophyll Fluorescence (SIF) is the most promising remote sensing signal to monitor photosynthesis in space and time. However, under stress conditions its interpretation is often complicated by factors such as light absorption and plant morphological and physiological adaptations. To ultimately derive the quantum yield of fluorescence () at the photosystem from canopy measurements, the
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Assessment of Sentinel-6 SAR mode and reprocessed Jason-3 sea level measurements over global coastal oceans Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-26 Fukai Peng, Xiaoli Deng, Yunzhong Shen
With dedicated coastal processing strategies and advanced Delay-Doppler technique, the quality of altimeter data from Low-Resolution Mode (LRM) and Synthetic Aperture Radar (SAR) mode altimeters in coastal areas have been greatly improved. In this study, we present a new 20-Hz along-track sea level anomaly (SLA) dataset of Jason-3 within 100 km to the global coastlines using the modified SCMR (Seamless
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Estimates of the global ocean surface dissolved oxygen and macronutrients from satellite data Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-26 Harish Kumar Kashtan Sundararaman, Palanisamy Shanmugam
Marine ecosystems are complex and dynamic in nature and influenced by various environmental factors such as temperature, salinity, ocean currents, nutrient availability, light penetration, and anthropogenic activities. Macronutrients (nitrate, phosphate, and silicate) and dissolved oxygen (DO) are crucial properties for determining the health, function, and dynamics of marine ecosystems. There are
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Forcing conditions of cross-shelf plumes on a wide continental shelf, Winyah Bay, South Atlantic Bight Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-25 Steven L. Dykstra, Gabrielle Ricche, George Marmorino, Alexander E. Yankovsky
Buoyant cross-shelf river plumes can extend far offshore through the combined effect of buoyancy and wind forcing, creating a critical land-ocean link in global biogeochemical cycles. On the Carolinas continental shelf, cross-shelf plume structure has been analyzed using satellite imagery, with forcing conditions represented by an estuarine Richardson number, wind stress, and alongshore pressure gradient
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A novel physics-based cloud retrieval algorithm based on neural networks (CRANN) from hyperspectral measurements in the O2-O2 band Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-25 Wenwu Wang, Husi Letu, Huazhe Shang, Jian Xu, Huanhuan Yan, Lianru Gao, Chao Yu, Jianbin Gu, Jinhua Tao, Na Xu, Lin Chen, Liangfu Chen
Clouds play a crucial role in the Earth's climate system and their properties can be detected by hyperspectral measurements from space. With the increasing spectral resolution, traditional retrieval methods based on look-up tables (LUT) and optimal estimation are limited in both efficiency and accuracy compared with machine learning methods. However, the machine learning techniques used to establish
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Differentiable modeling for soil moisture retrieval by unifying deep neural networks and water cloud model Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-22 Zhenghao Li, Qiangqiang Yuan, Qianqian Yang, Jie Li, Tianjie Zhao
Machine learning has been widely used in high-spatial-resolution surface soil moisture (SSM) retrieval studies, but in recent years, this purely data-driven retrieval method has been controversial due to its lack of physical interpretability and generalization ability. Physical retrieval models based on the theory of radiative transfer equations respect physical laws, but their retrieval accuracy is
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The critical role of cross-polarized backscatter in understanding L-band PolSAR data in forested and urban environments Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-22 Dingfeng Duan, Yong Wang, Yin Zhang
Ambiguity exists in interpreting polarimetric synthetic aperture radar (PolSAR) L-band backscatter data from forested and urban environments. Two types of ambiguity are studied. The first is between flooded forests and urban buildings with small azimuth angles, and the other is between upland forests and urban buildings with large azimuth angles. To resolve the ambiguity, we developed an algorithm
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Domain knowledge-driven variational recurrent networks for drought monitoring Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-22 Mengxue Zhang, Miguel-Ángel Fernández-Torres, Gustau Camps-Valls
In the context of climate change, droughts, increasingly frequent and severe, necessitate effective monitoring. Existing methods, such as drought indices and data-driven models, face important limitations. Drought indices are built on prior expert knowledge but lack calibration based on actual drought events, while data-driven models prioritize goodness of fit over real event identification, undermining
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Seasonal crustal movements in Northeast Japan revisited Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-20 Shuo Zheng, Kosuke Heki, Zizhan Zhang, Haoming Yan
Seasonal movements of global navigation satellite system (GNSS) stations in Northeast (NE) Japan are mainly driven by elastic loading of snow, reaching a few meters deep along the western flank of the backbone range. Here we study them in a comprehensive manner to solve remaining problems. GNSS stations in the inland area show sharp and strong subsidence peaks in winter and remain flat in other seasons
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Estimating aboveground biomass dynamics of wheat at small spatial scale by integrating crop growth and radiative transfer models with satellite remote sensing data Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-20 Pengcheng Hu, Bangyou Zheng, Qiaomin Chen, Swaantje Grunefeld, Malini Roy Choudhury, Javier Fernandez, Andries Potgieter, Scott C. Chapman
Aboveground biomass (AGB) of plants is an agroecological indicator that can be used to monitor crop growth status and quantify biomass carbon stock in agricultural ecosystems. Although satellite remote sensing data and crop models are widely employed to estimate AGB dynamics, their application in small-scale research plots is often constrained by the unavailability of freely and timely high-resolution
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Retrieving global single-layer liquid cloud thickness from OCO-2 hyperspectral oxygen A-band Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-20 Siwei Li, Jie Yang
Cloud geometrical thickness (CGT) retrieved from satellite observations is vital in the investigation of cloud microphysics and radiative balance. However, there are few algorithms designed for retrieving CGT over land by using passive remote sensing observations. This is due to the inherent difficulties in correctly retrieving atmospheric profiles because of inadequate knowledge of land surface reflection
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A machine learning model for estimating the temperature of small rivers using satellite-based spatial data Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-20 Daniel Philippus, Anneliese Sytsma, Ashley Rust, Terri S. Hogue
The influence of anthropogenic activity and land cover alteration on stream temperatures has major ecological implications, such as limiting fish survival. While these ecological impacts have been extensively studied at varying spatial scales for major rivers, our understanding of the range and complexity of this relationship across climates and geographies for smaller rivers (less than 60 m wide)
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Retrieval of snow liquid water content from radiative transfer model, field data and PRISMA satellite data Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-20 C. Ravasio, R. Garzonio, B. Di Mauro, E. Matta, C. Giardino, M. Pepe, E. Cremonese, P. Pogliotti, C. Marin, R. Colombo
The amount of liquid water (LWC) present in the snowpack is critical for predicting wet snow avalanches, forecasting meltwater release, and assessing water availability in river basins. However, measuring this variable using traditional in situ methods is challenging. Space imaging spectroscopy is emerging as a promising approach to map the spatial and temporal variations of snow parameters. While
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Estimating river discharge across scales with a novel regional gauging method driven by Sentinel satellite data Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-20 Hong Lin, Xiao Cheng, Junguo Liu, Qian Shi, Teng Li, Lei Zheng, Xuejiao Hou, Jinyang Du
Accurately monitoring river discharge is paramount for effective water resource management, and the utilization of remote sensing has emerged as a promising approach. However, estimating river discharge from a single pixel is limited in its ability across various river sizes: suffering from the applicability of single-pixel size to different river scales. Here, we develop an innovative regional gauging
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Evaluation of road network power conservation based on SDGSAT-1 glimmer imagery Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-15 Fang Chen, Lei Wang, Ning Wang, Huadong Guo, Cheng Chen, Cheng Ye, Ying Dong, Taichang Liu, Bo Yu
Nighttime road lighting is crucial for transportation and substantially contributes to power consumption. To enhance energy efficiency, numerous Light Emitting Diode (LED) lamps have been deployed across urban road networks. Assessing their effectiveness, however, has been challenging due to the coarse spatial resolution of traditional glimmer imagery. This study leverages high-resolution imagery from
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Novel 3D photosynthetic traits derived from the fusion of UAV LiDAR point cloud and multispectral imagery in wheat Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-15 Yangyang Gu, Yongqing Wang, Yapeng Wu, Timothy A. Warner, Tai Guo, Hongxu Ai, Hengbiao Zheng, Tao Cheng, Yan Zhu, Weixing Cao, Xia Yao
Photosynthesis is the material basis for crop growth and yield formation. Rapid and real-time monitoring of photosynthetic parameters is essential for crop stress monitoring, light use efficiency assessment, and yield prediction. Methods based on passive optical remote sensing can accurately monitor the photosynthetic traits of crops. However, they are limited in capturing one- or two-dimensional information
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Contrasting response of regional spring Arctic Sea ice variations on Indian summer monsoon rainfall Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-14 Juhi Yadav, Avinash Kumar, Seong-Joong Kim, Rohit Srivastava, Rahul Mohan, M. Ravichandran
Understanding the relationship between Arctic Sea Ice Concentration (SIC) and Indian Summer Monsoon Rainfall (ISMR) is crucial for analysing regional climate change. Present study investigates this connection using satellite observations and simulations from Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 coupled climate models, focusing on external forcing from 1979 to 2021. Rotated
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Tropical cyclone signatures in SAR ocean radial Doppler Velocity Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-14 Yury Yu. Yurovsky, Vladimir N. Kudryavtsev, Maria V. Yurovskaya, Pavel D. Pivaev, Semyon A. Grodsky
Ocean surface radial Doppler Velocity (DV) signatures of Tropical Cyclones (TC) at moderate incidence angles are analyzed using a semi-empirical DV model. This model, originally named KaDOP, is based on the physics of Ka-band Doppler radar backscattering from the sea surface and represents the DV as a sum of components due to surface currents, long surface waves, Bragg scattering, and wave breaking
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Estimation of canopy photon recollision probability from airborne laser scanning Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-12 Siying He, Jianbo Qi, Di Wang, Kai Yan, Huaguo Huang
The past two decades have witnessed the widespread application of the spectral invariant theory (-theory) in modeling the shortwave radiation absorbed or scattered by vegetation. The basic principle of this theory is that the canopy reflectance is solely determined by the optical properties of leaves and the photon recollision probability . As one of the spectral invariants, serves as a critical bond
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A novel GNSS and precipitation-based integrated drought characterization framework incorporating both meteorological and hydrological indicators Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-12 Hai Zhu, Kejie Chen, Shunqiang Hu, Ji Wang, Ziyue Wang, Jiafeng Li, Junguo Liu
The Global Navigation Satellite System (GNSS) has become instrumental in developing drought indices, particularly meteorological drought indicators derived from atmospheric precipitable water vapor and hydrological drought indicators based on inverted terrestrial water storage changes. However, these indices traditionally focus on individual aspects of droughts, either meteorological or hydrological
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Progressive gap-filling in optical remote sensing imagery through a cascade of temporal and spatial reconstruction models Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-12 Jingan Wu, Tongwen Li, Liupeng Lin, Chao Zeng
Filling gaps caused by thick cloud cover or sensor malfunctions has always posed a significant challenge in the preprocessing of optical remote sensing images. The concept of similar pixels, derived from spatial and temporal similarities in remote sensing scenes, has been widely embraced and extensively applied, leading to the development of various gap-filling models. However, in complex scenarios
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A remote sensing model for coral recruitment habitat Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-12 Ben Radford, Marji Puotinen, Defne Sahin, Nader Boutros, Mathew Wyatt, James Gilmour
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Does intensity-based weighting of multiple-return terrestrial LiDAR data improve leaf area density estimates? Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-12 Eric R. Kent, Brian N. Bailey
Terrestrial LiDAR scanning (TLS) data can be used to efficiently estimate plant canopy structural variables including leaf area density (LAD). This is done by estimating the transmission probability of LiDAR beams through a canopy, which is directly related to the local LAD. Advancements in TLS technology have enabled instruments that can record multiple object intersection points along a single laser
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Optimal model-based temperature inputs for global soil moisture and vegetation optical depth retrievals from SMAP Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-11 Yao Xiao, Xiaojun Li, Lei Fan, Gabrielle De Lannoy, Jian Peng, Frédéric Frappart, Ardeshir Ebtehaj, Patricia de Rosnay, Zanpin Xing, Ling Yu, Guanyu Dong, Simon H. Yueh, Andress Colliander, Jean-Pierre Wigneron
The accuracy of global L-band soil moisture (SM) and vegetation optical depth (L-VOD) products retrieved through the τ-ω model is highly dependent on temperature inputs obtained from model-based temperature products. However, the performance of these temperature products in the retrieval of global-scale SM and L-VOD has not yet been evaluated. Therefore, this study aimed to evaluate four commonly used
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Estimation of boreal forest biomass from ICESat-2 data using hierarchical hybrid inference Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-10 Petri Varvia, Svetlana Saarela, Matti Maltamo, Petteri Packalen, Terje Gobakken, Erik Næsset, Göran Ståhl, Lauri Korhonen
The ICESat-2, launched in 2018, carries the ATLAS instrument, which is a photon-counting spaceborne lidar that provides profile samples over the terrain. While primarily designed for snow and ice monitoring, there has been a great interest in using ICESat-2 to predict forest above-ground biomass density (AGBD). As ICESat-2 is on a polar orbit, it provides good spatial coverage of boreal forests.
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Land cover fraction mapping across global biomes with Landsat data, spatially generalized regression models and spectral-temporal metrics Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-08 Franz Schug, Kira A. Pfoch, Vu-Dong Pham, Sebastian van der Linden, Akpona Okujeni, David Frantz, Volker C. Radeloff
Mapping land cover in highly heterogeneous landscapes is challenging, and classifications have inherent limitations where the spatial resolution of remotely sensed data exceeds the size of small objects. For example, classifications based on 30-m Landsat data do not capture urban or other heterogeneous environments well. This limitation may be overcome by quantifying the subpixel fractions of different
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Downscaling canopy photochemical reflectance index to leaf level by correcting for the soil effects Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-08 Peiqi Yang
The photochemical reflectance index (PRI) is a promising remote sensing signal for monitoring vegetation physiology. Variations in leaf PRI are usually attributed to either the energy-dependent xanthophyll cycle or the carotenoid-chlorophyll ratio, both indicative of leaf physiology. However, canopy PRI is subject to soil, canopy structure, and incident and viewing angles, and thus has a weaker and
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Building up a data engine for global urban mapping Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-08 Yuhan Zhou, Qihao Weng
Global urban mapping is vital for understanding various environmental challenges and supporting Sustainable Development Goal 11. Although deep learning models present a potential unified solution, their effectiveness is intrinsically tied to the quality and diversity of the training data, which often present limitations in existing research. To overcome these limitations, this paper introduced a data
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ICESat-2 and ocean particulates: A roadmap for calculating Kd from space-based lidar photon profiles Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-08 E.F. Eidam, K. Bisson, C. Wang, C. Walker, A. Gibbons
ICESat-2's Advanced Topographic Laser Altimeter System (ATLAS) has emerged as a useful tool for calculating attenuation signals in natural surface waters, thus improving our understanding of particulates from open-ocean plankton to nearshore suspended terrigenous sediments. While several studies have employed methods based on Beer's Law to derive attenuation coefficients (including through a machine-learning
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Tracking Darwin's footprints but with LiDAR for booting up the 3D and even beyond-3D understanding of plant intelligence Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-07 Yi Lin
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A deep learning-based super-resolution method for building height estimation at 2.5 m spatial resolution in the Northern Hemisphere Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-04 Yinxia Cao, Qihao Weng
Building height is an important indicator for assessing the level of urban development along the vertical dimension. Existing large-scale building height estimation studies focus on coarse spatial resolution (e.g., 10, 500, and 1000 m), which cannot reveal height variations across buildings in urban areas. High-resolution images (e.g., < 5 m resolution) can support building-scale height estimation
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Evaluating epistemic uncertainty estimation strategies in vegetation trait retrieval using hybrid models and imaging spectroscopy data Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-04 José Luis García-Soria, Miguel Morata, Katja Berger, Ana Belén Pascual-Venteo, Juan Pablo Rivera-Caicedo, Jochem Verrelst
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Full-waveform hyperspectral LiDAR data decomposition via ranking central locations of natural target echoes (Rclonte) at different wavelengths Remote Sens. Environ. (IF 11.1) Pub Date : 2024-06-01 Jie Bai, Zheng Niu, Yanru Huang, Kaiyi Bi, Yuwen Fu, Shuai Gao, Mingquan Wu, Li Wang
The novel hyperspectral LiDAR (HSL) system exhibits the aptitude to simultaneously capture both spectral and geometric information from the hyperspectral waveform data. However, conventional single-wavelength decomposition methods may not be compatible with HSL waveforms due to higher levels of unstable noise, more complex waveform shapes, and inconsistent time delay effects at different wavelengths