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The value of hyperspectral UAV imagery in characterizing tundra vegetation Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-15 Pauli Putkiranta, Aleksi Räsänen, Pasi Korpelainen, Rasmus Erlandsson, Tiina H.M. Kolari, Yuwen Pang, Miguel Villoslada, Franziska Wolff, Timo Kumpula, Tarmo Virtanen
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Repeat GEDI footprints measure the effects of tropical forest disturbances Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-14 Amelia Holcomb, Patrick Burns, Srinivasan Keshav, David A. Coomes
More of the Amazon rainforest is disturbed each year than completely deforested, but the impact of these disturbances on the carbon cycle remains poorly understood. Recent algorithmic advances using optical and radar remote sensing have improved detection of disturbances at fine spatiotemporal resolution, but quantifying changes in forest structure and biomass associated with these detected disturbances
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Deep learning techniques for enhanced sea-ice types classification in the Beaufort Sea via SAR imagery Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-13 Yan Huang, Yibin Ren, Xiaofeng Li
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Coastal wind retrievals from corrected QuikSCAT Normalized Radar Cross Sections Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-13 Giuseppe Grieco, Marcos Portabella, Ad Stoffelen, Anton Verhoef, Jur Vogelzang, Andrea Zanchetta, Stefano Zecchetto
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Sandy desertification monitoring with the Relative Normalized Silica Index (RNSI) based on SDGSAT-1 thermal infrared image Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-13 Ziyu Yang, Xiaosong Li, Tong Shen, Amos Tiereyangn Kabo-bah, Hanwen Cui, Xingxu Dong, Lei Huang
The Silica Index (SI) obtained from thermal infrared bands is an effective way for monitoring sandy desertification. Sustainable Development Goals Science Satellite 1 (SDGSAT-1) enriches the thermal infrared imagery globally with three thermal infrared bands. However, the use of SDGSAT-1 for monitoring sandy desertification remains underexplored, especially with radiance instead of emissivity. This
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A multi-source change detection algorithm supporting user customization and near real-time deforestation detections Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-11 Ian R. McGregor, Grant Connette, Josh M. Gray
The abundance of free and accessible satellite data has revolutionized our ability to study deforestation with remote sensing. Recent advances have enabled us to monitor deforestation in near real-time (NRT), and a number of operational NRT alert systems using both optical and synthetic aperture radar (SAR) data have been developed. Yet despite their success, there are three primary issues with current
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Surface soil moisture from combined active and passive microwave observations: Integrating ASCAT and SMAP observations based on machine learning approaches Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-10 Hongliang Ma, Jiangyuan Zeng, Xiang Zhang, Jian Peng, Xiaojun Li, Peng Fu, Michael H. Cosh, Husi Letu, Shaohua Wang, Nengcheng Chen, Jean-Pierre Wigneron
The fusion of active and passive microwave measurements is expected to provide more robust surface soil moisture (SSM) mapping across various environmental conditions compared to the use of a single sensor. Thus, the integration of the newest L-band passive (i.e., Soil Moisture Active Passive, SMAP) and the active (i.e., the Advanced Scatterometer, ASCAT) observations provides an opportunity for SSM
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Fractional cover mapping of wildland-urban interface fuels using Landsat, Sentinel 1 and PALSAR imagery Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-10 L. Collins, L. Guindon, C. Lloyd, S.W. Taylor, S. White
Fuels within the immediate vicinity of a house (e.g., within 30–60 m), referred to as the ‘home-ignition zone’, are important determinants of structure damage during wildfires. Methods for mapping home-ignition zone fuels using earth observing satellites are lacking, limiting the capacity to quantify the spatial and temporal dynamics of urban fuel hazard and wildfire risk. Here, we (i) develop a methodology
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Transitioning from remote sensing archaeology to space archaeology: Towards a paradigm shift Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-09 Lei Luo, Xinyuan Wang, Huadong Guo
Remote Sensing Archaeology (RSA) is an innovative sub-discipline within archaeology that utilizes general Remote Sensing (RS) techniques to analyze data from ancient human-made structures. This analysis significantly contributes to understanding various facets of human history, such as cultures, practices, diversity, and evolution. Our study anticipates the forthcoming changes in the roles of RS archaeologists
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Using CloudSat to Advance the Global Precipitation Climatology Project (GPCP) over Antarctica Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-09 Mohammad Reza Ehsani, Ali Behrangi, Cristian Román-Palacios, George J. Huffman, Robert F. Adler
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Unmixing-based forest recovery indicators for predicting long-term recovery success Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-09 Lisa Mandl, Alba Viana-Soto, Rupert Seidl, Ana Stritih, Cornelius Senf
Recovery from forest disturbances is a pivotal metric of forest resilience. Forests globally are facing unprecedented levels of both natural and anthropogenic disturbances, yet our understanding of their recovery from these disturbances remains incomplete. Remote sensing is an effective tool for understanding post-disturbance recovery, but existing approaches largely rely on spectral recovery indicators
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Monitoring saltwater intrusion to estuaries based on UAV and satellite imagery with machine learning models Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-08 Dingshen Jiang, Chunyu Dong, Zhimin Ma, Xianwei Wang, Kairong Lin, Fang Yang, Xiaohong Chen
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Arctic Sea ice leads detected using sentinel-1B SAR image and their responses to atmosphere circulation and sea ice dynamics Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-08 Meng Qu, Ruibo Lei, Yue Liu, Na Li
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Totaling river discharge of the third pole from satellite imagery Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-08 Jie Xu, Lei Wang, Tandong Yao, Deliang Chen, Gang Wang, Zhaowei Jing, Fan Zhang, Yuyang Wang, Xiuping Li, Yinsheng Zhang, Yuanwei Wang, Tian Zeng, Chenhao Chai, Hu Liu, Ruishun Liu, Junshui Long, Xinfeng Fan, Ranjeet Bhlon, Baiqing Xu
The high-mountain Third Pole (TP) in Asia is undergoing rapid warming, profoundly impacting river discharge. Changes in precipitation and the degradation of glaciers and permafrost exert a substantial impact on TP rivers, affecting millions downstream. Nevertheless, conventional estimation methods that rely on in-situ observations and models face considerable challenges due to inconsistent data quality
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Sub-daily live fuel moisture content estimation from Himawari-8 data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-08 Xingwen Quan, Rui Chen, Marta Yebra, David Riaño, Víctor Resco de Dios, Xing Li, Binbin He, Rachael H. Nolan, Anne Griebel, Matthias M. Boer, Yuanqi Sun
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General method of precipitable water vapor retrieval from remote sensing satellite near-infrared data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-07 Qingzhi Zhao, Zhi Ma, Jinfang Yin, Yibin Yao, Wanqiang Yao, Zheng Du, Wei Wang
The use of remote sensing technique to monitor atmospheric water vapor is significant for weather and climate studies. However, the general methods of retrieving precipitable water vapor (PWV) with high precision and high resolution using remote sensing satellite has hardly been investigated, which becomes the focus of this paper. A general remote sensing PWV retrieval (GRPR) method that uses level-1
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Revisiting the quantification of power plant CO2 emissions in the United States and China from satellite: A comparative study using three top-down approaches Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-06 Cheng He, Xiao Lu, Yuzhong Zhang, Zhu Liu, Fei Jiang, Youwen Sun, Meng Gao, Yiming Liu, Haipeng Lin, Jiani Yang, Xiaojuan Lin, Yurun Wang, Chengyuan Hu, Shaojia Fan
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Solar zenith angle-based calibration of Himawari-8 land surface temperature for correcting diurnal retrieval error characteristics Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-06 Yi Yu, Luigi J. Renzullo, Tim R. McVicar, Thomas G. Van Niel, Dejun Cai, Siyuan Tian, Yichuan Ma
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Learning spectral-indices-fused deep models for time-series land use and land cover mapping in cloud-prone areas: The case of Pearl River Delta Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-04 Zhiwei Li, Qihao Weng, Yuhan Zhou, Peng Dou, Xiaoli Ding
Mapping of highly dynamic changes in land use and land cover (LULC) can be hindered by various cloudy conditions with optical satellite images. These conditions result in discontinuities in high-temporal-density LULC mapping. In this paper, we developed an integrated time series mapping method to enhance the LULC mapping accuracy and frequency in cloud-prone areas by incorporating spectral-indices-fused
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Assimilating ASCAT normalized backscatter and slope into the land surface model ISBA-A-gs using a Deep Neural Network as the observation operator: Case studies at ISMN stations in western Europe Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-02 Xu Shan, Susan Steele-Dunne, Sebastian Hahn, Wolfgang Wagner, Bertrand Bonan, Clement Albergel, Jean-Christophe Calvet, Ou Ku
ASCAT normalized backscatter () and slope () contain valuable information about soil moisture and vegetation. While has been assimilated to constrain soil moisture, sometimes together with Leaf Area Index (LAI), this study is the first to assimilate directly to constrain vegetation states. Here, we assimilate and slope into the ISBA-A-gs LSM using the Simplified Extended Kalman Filter (SEKF) using
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Multimodal aircraft flight altitude inversion from SDGSAT-1 thermal infrared data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-01 Xiaoxuan Zhou, Liyuan Li, Jianing Yu, Long Gao, Rongguo Zhang, Zhuoyue Hu, Fansheng Chen
Accurately detecting and localizing high-speed aircraft is significant for monitoring global flight activities, conducting searches, and performing emergency rescues. Typically, the spatial position of an air target is obtained through active communication or multi-satellite observation. Here, we propose a multimodal flight altitude inversion method using a single thermal infrared payload (SDGSAT-1
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Long-term assessment and analysis of the radiometric quality of standard data products for Chinese Gaofen-1/2/6/7 optical remote sensing satellites Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-30 Litao Li, Yonghua Jiang, Xin Shen, Deren Li
The first Chinese Gaofen (GF) remote sensing satellite was launched in August 2013 and has been in orbit for more than 10 years, providing a rich variety of image product data for remote sensing applications in various industries, with other remote sensing satellites of the GF series. To ensure the reliability of the information generated via remote sensing applications, all remote sensing images must
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Corrigendum to “Spectra-phenology integration for high-resolution, accurate, and scalable mapping of foliar functional traits using time-series Sentinel-2 data” [Remote Sensing of Environment 305 (2024) 114082] Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-29 Shuwen Liu, Zhihui Wang, Ziyu Lin, Yingyi Zhao, Zhengbing Yan, Kun Zhang, Marco Visser, Philip A. Townsend, Jin Wu
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This is MATE: A Multiple scAttering correcTion rEtrieval algorithm for accurate lidar profiling of seawater optical properties Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-29 Yatong Chen, Xiaoyu Cui, Qiuling Gu, Yudi Zhou, Hongkai Zhao, Han Zhang, Shizhe Ma, Peituo Xu, Henrich Frielinghaus, Lan Wu, Chong Liu, Wenbo Sun, Suhui Yang, Miao Hu, Qun Liu, Dong Liu
Lidar has the capability to measure seawater vertical optical properties efficiently both day-time and night-time, though accurate retrieval is still challenging due to multiple scattering. Herein, we propose a Multiple scAttering correcTion and rEtrieval (MATE) algorithm suitable for shipborne, airborne and spaceborne lidars. The MATE algorithm provides the synchronous depth-resolved absorption, backscattering
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Corrigendum to “Quantification of wetland vegetation communities features with airborne AVIRIS-NG, UAVSAR, and UAV LiDAR data in Peace-Athabasca Delta” [Remote Sensing of Environment, volume 294 (2023) 113646] (Wang et al. 2024) Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-27 Chao Wang, Kevin P. Timoney, Tamlin M. Pavelsky
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Estimation of global ecosystem isohydricity from solar-induced chlorophyll fluorescence and meteorological datasets Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-27 Jinru Xue, Alfredo Huete, Zhunqiao Liu, Yakai Wang, Xiaoliang Lu
Plants exhibit varying strategies for optimizing the trade-off between CO uptake and water loss through transpiration in response to increasing air or soil dryness. Anisohydric plants generally keep their stomata open to maintain or enhance carbon uptake, but this exposes them to a greater risk of hydraulic failure. In contrast, isohydric plants tend to maintain hydraulic integrity by enforcing stricter
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Two-stage, model-assisted estimation using remotely sensed auxiliary data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-26 Ronald E. McRoberts, Erik Næsset, Juha Heikkinen, Victor Strimbu
The utility of remotely sensed auxiliary data for increasing the precision of sample-based inventory estimates of population parameters is well-established. To this end, the model-assisted estimators with remotely sensed auxiliary data are particularly effective for use with continuous dependent variables. The model-assisted estimators take somewhat different forms, depending on the sampling design
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Net fluxes of broadband shortwave and photosynthetically active radiation complement NDVI and near infrared reflectance of vegetation to explain gross photosynthesis variability across ecosystems and climate Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-26 Kanishka Mallick, Joseph Verfaillie, Tianxin Wang, Ariane Arias Ortiz, Daphne Szutu, Koong Yi, Yanghui Kang, Robert Shortt, Tian Hu, Mauro Sulis, Zoltan Szantoi, Gilles Boulet, Joshua B. Fisher, Dennis Baldocchi
A significant challenge in global change research is understanding how vegetation interacts with the environment to influence ecosystem gross primary productivity (GPP) through carbon assimilation. One emerging objective is to consistently predict GPP fluctuations worldwide by establishing a robust scaling relationship between GPP measured at flux towers and satellite spectral reflectance data. However
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Late summer northwestward Amazon plume pathway under the action of the North Brazil Current rings Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-23 L. Olivier, G. Reverdin, J. Boutin, R. Laxenaire, D. Iudicone, S. Pesant, Paulo H.R. Calil, J. Horstmann, D. Couet, J.M. Erta, P. Huber, H. Sarmento, A. Freire, A. Koch-Larrouy, J.-L. Vergely, P. Rousselot, S. Speich
The North Brazil Current (NBC) flows offshore of the mouth of the Amazon River and seasonally sheds anticyclonic rings (NBC rings) that propagate northwestward and interact with the Amazon River plume (ARP). Mesoscale features have a high temporal variability that is hard to monitor from current weekly and monthly sea surface salinity (SSS) satellite fields. Novel SSS fields with a higher temporal
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Modeling gross primary production and transpiration from sun-induced chlorophyll fluorescence using a mechanistic light-response approach Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-18 Quentin Beauclaire, Simon De Cannière, François Jonard, Natacha Pezzetti, Laura Delhez, Bernard Longdoz
Sun-induced chlorophyll fluorescence (SIF) is a promising optical remote sensing signal which is directly linked to photosynthesis, allowing for the monitoring of gross primary production (GPP). Although empirical relationships between these variables have demonstrated the potential of SIF for site-specific GPP estimations, a better physiological understanding of the link between SIF and GPP would
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Assessment of snow cover mapping algorithms from Landsat surface reflectance data and application to automated snowline delineation Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-17 Xiongxin Xiao, Shuang Liang
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Spectral-temporal traits in Sentinel-1 C-band SAR and Sentinel-2 multispectral remote sensing time series for 61 tree species in Central Europe Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-17 Christian Schulz, Michael Förster, Stenka Valentinova Vulova, Alby Duarte Rocha, Birgit Kleinschmit
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Quantifying vegetation species functional traits along hydrologic gradients in karst wetland based on 3D mapping with UAV hyperspectral point cloud Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-17 Bolin Fu, Liwei Deng, Weiwei Sun, Hongchang He, Huajian Li, Yong Wang, Yeqiao Wang
Karst wetlands, recognized for their unique hydrology and remarkable biodiversity, play a crucial role in global carbon sequestration and the terrestrial carbon cycle. However, understanding the relationships between hydrology and the spatial distribution, functional traits, and diversity of karst wetland vegetation is challenging. This study proposes a novel self-supervised deep learning method, the
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Mangrove species mapping in coastal China using synthesized Sentinel-2 high-separability images Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-17 Chuanpeng Zhao, Mingming Jia, Rong Zhang, Zongming Wang, Chunying Ren, Dehua Mao, Yeqiao Wang
The absence of national-scale mangrove species maps has hindered the precise estimation of their blue carbon storage ecological value evaluation, and effective management of protected areas. Mangroves typically grow in harsh intertidal environments, with non-mono species distributed together, and exhibit varied species compositions and appearances along the latitudes. Previous studies demonstrated
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Stratified burn severity assessment by integrating spaceborne spectral and waveform attributes in Great Xing'an Mountain Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-15 Simei Lin, Linyuan Li, Shangbo Liu, Ge Gao, Xun Zhao, Ling Chen, Jianbo Qi, Qin Shen, Huaguo Huang
Burn severity assessment is critical for understanding the pattern of post-fire vegetation recovery and ecosystem resilience. Previous studies proposed various field criteria (e.g., Composite Burn Index (CBI)) to quantify burn severity from strata level to total site level, yet suffering from surveyors' subjective interpretation across site conditions. High-resolution passive remote sensing allows
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Ocean eddy detection based on YOLO deep learning algorithm by synthetic aperture radar data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-12 Nannan Zi, Xiao-Ming Li, Martin Gade, Han Fu, Sisi Min
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Satellite-based tracking of reservoir operations for flood management during the 2018 extreme weather event in Kerala, India Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-11 Sarath Suresh, Faisal Hossain, Sanchit Minocha, Pritam Das, Shahzaib Khan, Hyongki Lee, Konstantinos Andreadis, Perry Oddo
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Soil moisture profile estimation under bare and vegetated soils using combined L-band and P-band radiometer observations: An incoherent modeling approach Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-11 Foad Brakhasi, Jeffrey P. Walker, Jasmeet Judge, Pang-Wei Liu, Xiaoji Shen, Nan Ye, Xiaoling Wu, In-Young Yeo, Edward Kim, Yann Kerr, Thomas Jackson
Effective water management in agriculture requires a comprehensive understanding of the distribution of water content throughout the soil profile to the root zone. This knowledge empowers farmers and water managers to make informed decisions regarding irrigation timing and quantity for optimizing crop growth. To estimate the soil moisture profile, this study utilized combined L- and P-band radiometry
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Changes in the lithosphere, atmosphere, and ionosphere before and during the Mw = 7.7 Jamaica 2020 earthquake Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-10 Dedalo Marchetti, Kaiguang Zhu, Alessandro Piscini, Essam Ghamry, Xuhui Shen, Rui Yan, Xiaodan He, Ting Wang, Wenqi Chen, Jiami Wen, Yiqun Zhang, Yuqi Cheng, Mengxuan Fan, Donghua Zhang, Hanshuo Zhang, Guido Ventura
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Global estimates of L-band vegetation optical depth and soil permittivity of snow-covered boreal forests and permafrost landscape using SMAP satellite data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-09 Divya Kumawat, Ardeshir Ebtehaj, Mike Schwank, Xiaojun Li, Jean-Pierre Wigneron
The tau-omega model is expanded to properly simulate L-band microwave emission of the soil–snow–vegetation continuum through a closed-form solution of Maxwell’s equations, considering the intervening dry snow layer as a loss-less medium. The error standard deviations of a least-squared inversion are 0.1 and 3.5 for VOD and ground permittivity, over moderately dense vegetation and a snow density ranging
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Retrieval of ground, snow, and forest parameters from space borne passive L band observations. A case study over Sodankylä, Finland Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-09 Manu Holmberg, Juha Lemmetyinen, Mike Schwank, Anna Kontu, Kimmo Rautiainen, Ioanna Merkouriadi, Johanna Tamminen
Previous studies have indicated and shown the feasibility of retrieving snow density from ground based passive microwave measurements at the L band (2GHz) from theoretical and experimental viewpoints. This paper expands the previous studies by presenting a case study of the retrieval problem with space borne brightness temperature measurements from the SMOS satellite over Sodankylä, Finland. To successfully
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A generalized model for mapping sunflower areas using Sentinel-1 SAR data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-06 Abdul Qadir, Sergii Skakun, Nataliia Kussul, Andrii Shelestov, Inbal Becker-Reshef
Existing crop mapping models, rely heavily on reference (calibration) data obtained from remote sensing observations. However, the transferability of such models in space and time, without the need for additional extensive datasets remains a significant challenge. There is still a large gap in developing generalized classification models capable of mapping specific or multiple crops with minimal calibration
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Four-decades of sediment transport variations in the Yellow River on the Loess Plateau using Landsat imagery Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-05 Zhiqiang Qiu, Dong Liu, Mengwei Duan, Panpan Chen, Chen Yang, Keyu Li, Hongtao Duan
The Yellow River is globally recognized for its significant sediment load, primarily attributed to its passage through the Loess Plateau. Notably, effective soil erosion control measures have led to a substantial decrease in sediment transport since the 1950s. However, a lack of comprehensive and detailed data impedes understanding of long-term spatiotemporal changes in suspended sediment concentration
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Filling gaps in cloudy Landsat LST product by spatial-temporal fusion of multi-scale data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-05 Qunming Wang, Yijie Tang, Xiaohua Tong, Peter M. Atkinson
Land surface temperature (LST) is an important factor in studies of surface energy fluxes between the Earth's surface and atmosphere. The Landsat LST product has been applied widely due to its fine spatial resolution and high data quality. Frequent cloud coverage, however, results in different degrees of gaps in the Landsat LST images, restricting greatly their application in practical cases requiring
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Lighting characteristics of public space in urban functional areas based on SDGSAT-1 glimmer imagery:A case study in Beijing, China Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-04 Saimiao Liu, Yi Zhou, Futao Wang, Shixin Wang, Zhenqing Wang, Yanchao Wang, Gang Qin, Ping Wang, Ming Liu, Lei Huang
The artificial light sources in urban areas constitute the primary source of stable nighttime illumination, which can be utilized to reflect the nighttime human activity and the conditions of public space lighting. The previous studies have focused on the evaluation of lighting environments in specific scenarios and the public's perception of these lighting environments. There has been limited analysis
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Assimilation of RCM data in the Canadian ice concentration analysis system Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-04 Alexander S. Komarov, Alain Caya, Lynn Pogson, Mark Buehner
The sea and lake ice concentration pan-Arctic analysis system at Environment and Climate Change Canada (ECCC) initializes both the short-range Arctic sea ice forecasting models and numerical weather prediction tools. In this study, our previously developed approach for deriving ice concentration from RADARSAT-2 was adjusted to become applicable to the RADARSAT Constellation Mission (RCM) data. This
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The PROLIB leaf radiative transfer model: Simulation of the dorsiventrality of leaves from visible to mid-wave infrared Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-03 Hanyu Shi, Stéphane Jacquemoud, Jingyi Jiang, Minqiang Zhou, Sophie Fabre, Andrew D. Richardson, Shuang Wang, Xuju Jiang, Zhiqiang Xiao
Many plant species have dorsiventral leaves that have significant differences in optical properties from one side to the other. Several studies have revealed that ignoring this asymmetry induces significant errors in plant canopy reflectance, and current leaf models simulating leaf dorsiventrality are limited to the 0.4–2.5 m wavelength range. This article, partly based on two recently collected datasets
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Branch architecture quantification of large-scale coniferous forest plots using UAV-LiDAR data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-02 Shangshu Cai, Wuming Zhang, Shuhang Zhang, Sisi Yu, Xinlian Liang
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Impact of atmospheric dryness on solar-induced chlorophyll fluorescence: Tower-based observations at a temperate forest Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-01 Koong Yi, Rong Li, Todd M. Scanlon, Manuel T. Lerdau, Joseph A. Berry, Xi Yang
Solar-induced chlorophyll fluorescence (SIF) is widely accepted as a proxy for gross primary productivity (GPP). Among the various SIF measurements, tower-based SIF measurements allow for continuous monitoring of SIF variation at a canopy scale with high temporal resolution, making it suitable for monitoring highly variable plant physiological responses to environmental changes. However, because of
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Multi-sensor satellite imagery reveals spatiotemporal changes in peatland water table after restoration Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-30 Aleksi Isoaho, Lauri Ikkala, Lassi Päkkilä, Hannu Marttila, Santtu Kareksela, Aleksi Räsänen
Remote sensing (RS) has been suggested as a tool to spatially monitor the status of peatland ecosystem functioning after restoration. However, there have been only a few studies in which post-restoration hydrological changes have been quantified with RS-based modelling. To address this gap, we developed an approach to assess post-restoration spatiotemporal changes in the peatland water table (WT) with
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A stepwise unmixing model to address the scale gap issue present in downscaling of geostationary meteorological satellite surface temperature images Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-30 Fei Xu, Xiaolin Zhu, Jin Chen, Wenfeng Zhan
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Cooling and optimizing urban heat island based on a thermal knowledge-informed multi-type ant colony model Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-30 Zhaomin Tong, Jiaming Yang, Yaolin Liu, Ziyi Zhang, Sui Liu, Yanchi Lu, Bowen Pang, Rui An
In the context of rapid urbanization and global warming, the urban heat island (UHI) intensifies the risk of heat-related mortality, endangering the health of urban residents. Urban greening effectively mitigates severe urban heating climates, but increasing green space without restrictions is undesirable due to the scarcity of urban land. Accurately characterizing the scope and intensity of UHI and
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Monitoring of chlorophyll content in local saltwort species Suaeda salsa under water and salt stress based on the PROSAIL-D model in coastal wetland Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-28 Sen Zhang, Jia Tian, Xia Lu, Qingjiu Tian, Shuang He, Yali Lin, Shan Li, Wei Zheng, Tao Wen, Xinyuan Mu, Jun Zhang, Yurong Li
As the invasion of alien species intensifies, the native salt marsh vegetation, especially the ecosystem of (), in Chinese coastal wetlands has been severely disrupted, significantly impeding its functionality within coastal wetland ecosystems. Chlorophyll content (C) is an important parameter for monitoring the growth and health status of vegetation. However, the remote sensing mechanism of C for
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Towards robust classification of multi-view remote sensing images with partial data availability Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-28 Maofan Zhao, Qingyan Meng, Lifeng Wang, Linlin Zhang, Xinli Hu, Wenxu Shi
Utilizing remote sensing to monitor and obtain the land use information is crucial for sustainable development goals (SDGs), including sustainable agriculture, urbanization processes, land reclamation, etc. The development of remote sensing big data and deep learning has greatly promoted the use of multi-source images to understand land use. However, in practical applications, missing data often occurs
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Evaluating deep learning methods applied to Landsat time series subsequences to detect and classify boreal forest disturbances events: The challenge of partial and progressive disturbances Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-28 Pauline Perbet, Luc Guindon, Jean-François Côté, Martin Béland
The monitoring of forest ecosystems is significantly affected by the lack of consistent historical data of low-severity (forest partially disturbed) or gradual disturbance (e.g. eastern spruce budworm epidemic). The goal of this paper is to explore the use of a subset of Landsat time series and deep learning models to identify both the type and the year of disturbances, including low-severity and gradual
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Diversity of 3D APAR and LAI dynamics in broadleaf and coniferous forests: Implications for the interpretation of remote sensing-based products Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-26 Jasmin Kesselring, Felix Morsdorf, Daniel Kükenbrink, Jean-Philippe Gastellu-Etchegorry, Alexander Damm
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CloudS2Mask: A novel deep learning approach for improved cloud and cloud shadow masking in Sentinel-2 imagery Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-23 Nicholas Wright, John M.A. Duncan, J. Nik Callow, Sally E. Thompson, Richard J. George
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Quantifying decadal stability of lake reflectance and chlorophyll-a from medium-resolution ocean color sensors Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-23 Xiaohan Liu, Mark Warren, Nick Selmes, Stefan G.H. Simis
Multi-decadal time-series of Lake Water-Leaving Reflectance (LWLR), part of the Lakes Essential Climate Variable, have typically been interrupted for the 2012–2016 period due to lack of an ocean color sensor with capabilities equivalent to MERIS (2002−2012) and OLCI (2016 - present). Here we assess, for the first time, the suitability of MODIS/Aqua to estimate LWLR and the derived concentration of
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Mapping proglacial headwater streams in High Mountain Asia using PlanetScope imagery Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-22 Jonathan A. Flores, Colin J. Gleason, Craig B. Brinkerhoff, Merritt E. Harlan, M. Malisse Lummus, Leigh A. Stearns, Dongmei Feng
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A transferable approach to assessing green infrastructure types (GITs) and their effects on surface urban heat islands with multi-source geospatial data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-20 Linlin Lu, Huadong Guo, Qihao Weng, Carlos Bartesaghi-Koc, Paul Osmond, Qingting Li
Urban green infrastructure (GI) is essential for mitigating surface urban heat islands (SUHIs) and strengthening urban resilience to climate change, thereby contributing to the achievement of sustainable development goals in urban areas. A ‘green infrastructure types’ (GITs) scheme was recently developed to examine the role of amount, composition, and configuration of GI in providing effective thermal