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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
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Comparing and combining data-driven and model-driven approaches to monitor wheat green area index with high spatio-temporal resolution satellites Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-19 Mingxia Dong, Shouyang Liu, Ruibo Jiang, Jianbo Qi, Benoit de Solan, Alexis Comar, Linyuan Li, Wenjuan Li, Yanfeng Ding, Frédéric Baret
Monitoring crops with high spatio-temporal resolution satellites provides valuable observations to ensure food security in the global change context. This study focuses on estimating the Green Area Index (GAI) to monitor wheat crops with a spatial resolution of 3 m and daily satellite observations from the SuperDove constellation. With an easier access to large training datasets of ground GAI measurements
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Sub-meter tree height mapping of California using aerial images and LiDAR-informed U-Net model Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-18 Fabien H. Wagner, Sophia Roberts, Alison L. Ritz, Griffin Carter, Ricardo Dalagnol, Samuel Favrichon, Mayumi C.M. Hirye, Martin Brandt, Philippe Ciais, Sassan Saatchi
Tree canopy height is one of the most important indicators of forest biomass, productivity, and ecosystem structure, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Net model adapted for regression to map the canopy height of all trees in the state of California with very high-resolution aerial imagery 0.6 m from the USDA-NAIP program. The U-Net model was
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Time series retrieval of Multi-wavelength Aerosol optical depth by adapting Transformer (TMAT) using Himawari-8 AHI data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-16 Lu She, Zhengqiang Li, Gerrit de Leeuw, Weile Wang, Yujie Wang, Lu Yang, Zixian Feng, Chen Yang, Yun Shi
Accurate aerosol optical depth (AOD) with high temporal resolution is required for dynamic monitoring changes of aerosol properties. The Himawari-8 Advanced Himawari imager (AHI) data with 10-min temporal resolution has been widely used for AOD retrieval but usually involves radiative transfer models and assumptions on surface reflection and aerosol properties that are hard to satisfy. This paper introduces
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Snow-cover remote sensing of conifer tree recovery in high-severity burn patches Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-16 Casey Menick, Wade Tinkham, Chad Hoffman, Melanie Vanderhoof, Jody Vogeler
The number of large, high-severity wildfires has been increasing across the western United States over the last several decades. It is not fully understood how changes in the frequency of large, severe wildfires may impact the resilience of conifer forests, due to alterations in regeneration success or failure. Our research investigates 30 years of conifer recovery patterns within 34 high-severity
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A SIF-based approach for quantifying canopy photosynthesis by simulating the fraction of open PSII reaction centers (qL) Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-16 Zhunqiao Liu, Chenhui Guo, Qiang Yu, Peng Zhu, Xiongbiao Peng, Mengqi Dong, Huanjie Cai, Xiaoliang Lu
Advances in retrieval of solar-induced chlorophyll fluorescence (SIF) provide a promising and independent approach for quantifying gross primary production (GPP) across spatial scales. Recent studies have highlighted the prominent role of the fraction of open Photosystem II (PSII) reaction centers, in mechanistically modeling GPP from remote sensing SIF. However, due to the limited availability of
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Boosting crop classification by hierarchically fusing satellite, rotational, and contextual data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-14 Valentin Barriere, Martin Claverie, Maja Schneider, Guido Lemoine, Raphaël d’Andrimont
Accurate early-season crop type classification is crucial for the crop production estimation and monitoring of agricultural parcels. However, the complexity of the plant growth patterns and their spatio-temporal variability present significant challenges. While current deep learning-based methods show promise in crop type classification from single- and multi-modal time series, most existing methods
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Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-14 Thiên-Anh Nguyen, Marc Rußwurm, Gaston Lenczner, Devis Tuia
Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of aerial imagery remains a challenge, due to changes in imaging technologies, sensors, and acquisition conditions, as well as irregular time gaps
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Remote sensing of diverse urban environments: From the single city to multiple cities Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-14 Gang Chen, Yuyu Zhou, James A. Voogt, Eleanor C. Stokes
Remote sensing of urban environments has unveiled a significant shift from single-city investigations to the inclusion of multiple cities. Originated from the ideas of the special issue entitled “Remote Sensing of the Urban Environment: Beyond the Single City,” this paper offers a comprehensive examination of the state of the science in multi-city remote sensing, and aims at fostering the rapid advancement
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Time-series land cover change detection using deep learning-based temporal semantic segmentation Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-14 Haixu He, Jining Yan, Dong Liang, Zhongchang Sun, Jun Li, Lizhe Wang
The process of sustainable urban development is accompanied by frequent and complex land cover changes, and thus, clarify accurate information on land cover changes can provide scientific data for urban management. To characterize urban development at an accurate spatiotemporal scale, a change detection model is not only required to provide accurate location (Where) and time (When) of the changes,
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Correction of photochemical reflectance index (PRI) by optical indices to predict non-photochemical quenching (NPQ) across various species Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-14 Yukiko Nakamura, Katsuto Tsujimoto, Tetsu Ogawa, Hibiki M. Noda, Kouki Hikosaka
Photochemical reflectance index (PRI) is an optical index that reflects the de-epoxidation state of the xanthophyll cycle. It is strongly correlated with non-photochemical quenching (NPQ) and is expected to be a good indicator of plant stress status in remote sensing. However, PRI is also influenced by other factors, such as leaf pigment composition, and varies widely among plant species, which prevented
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Detection of Land Surface Temperature anomalies using ECOSTRESS in Olkaria geothermal field Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-13 Agnieszka Soszynska, Thomas Groen, Eunice Bonyo, Harald van der Werff, Robert Hewson, Robert Reeves, Christoph Hecker
Geothermal systems can be used to produce low-emission energy throughout the day and night, regardless of the weather conditions. These features make geothermal systems a sustainable and reliable energy source, which can be exploited on a much larger scale than it is now. Remote sensing techniques can support detecting areas potentially suitable for geothermal energy production, thereby reducing the
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Detection of soil and canopy freeze/thaw state in the boreal region with L and C Band Synthetic Aperture Radar Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-13 Juval Cohen, Juha Lemmetyinen, Jorge Jorge Ruiz, Kimmo Rautiainen, Jaakko Ikonen, Anna Kontu, Jouni Pulliainen
Satellite instruments operating at microwave frequencies enable the monitoring of soil freeze/thaw (F/T) state of large areas. In this study, seasonal variations in space-borne SAR backscatter at L (1.2 GHz) and C (5.4 GHz) bands are investigated in the context of F/T detection in the boreal region of Finland. The focus is on the influence of canopy freezing during the winter and backscatter trends
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Detecting early winter open-water zones on Alaska rivers using dual-polarized C-band Sentinel-1 synthetic aperture radar (SAR) Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-13 Melanie Engram, Franz J. Meyer, Dana R.N. Brown, Sarah Clement, Allen C. Bondurant, Katie V. Spellman, Laura E. Oxtoby, Christopher D. Arp
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Enhanced observations from an optimized soil-canopy-photosynthesis and energy flux model revealed evapotranspiration-shading cooling dynamics of urban vegetation during extreme heat Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-11 Zhaowu Yu, Jiaqi Chen, Jike Chen, Wenfeng Zhan, Chenghao Wang, Wenjuan Ma, Xihan Yao, Siqi Zhou, Kai Zhu, Ranhao Sun
Previousstudies on the cooling of urban vegetation mainly focused on its transpiration or shading effect separately, neglecting to explore the combined evapotranspiration-shading cooling. Further, accurate quantification of evapotranspiration-shading cooling remains challenging due to heterogeneity of urban landscapes, which limits understanding of its high-resolution spatiotemporal patterns. Here
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Immediate and lagged vegetation responses to dry spells revealed by continuous solar-induced chlorophyll fluorescence observations in a tall-grass prairie Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-11 Yao Zhang, Mengyang Cai, Xiangming Xiao, Xi Yang, Mirco Migliavacca, Jeffrey Basara, Sha Zhou, Yuanzhizi Deng
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Augmenting daily MODIS LST with AIRS surface temperature retrievals to estimate ground temperature and permafrost extent in High Mountain Asia Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-11 Kyung Y. Kim, Ryan Haagenson, Prakrut Kansara, Harihar Rajaram, Venkataraman Lakshmi
Permafrost in High Mountain Asia (HMA) is becoming increasingly vulnerable to thaw due to climate change. However, the lack of either ground surface or borehole temperature data beyond the Tibetan Plateau prevents comprehensive assessments of its impact on the regional hydrologic cycle and local cascading hazards. Although past studies have generated estimates of permafrost extent in Central Asia,
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Inferring global terrestrial carbon fluxes from the synergy of Sentinel 3 & 5P with Gaussian process hybrid models Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-11 Pablo Reyes-Muñoz, Dávid D.Kovács, Katja Berger, Luca Pipia, Santiago Belda, Juan Pablo Rivera-Caicedo, Jochem Verrelst
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Analyzing surface deformation throughout China's territory using multi-temporal InSAR processing of Sentinel-1 radar data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-10 Guo Zhang, Zixing Xu, Zhenwei Chen, Shunyao Wang, Yutao Liu, Xuhui Gong
The damage caused by surface deformation is substantial and far-reaching. Although multi-temporal interferometric synthetic aperture radar (InSAR) technology is commonly used to monitor surface deformation, it remains challenging to rapidly extract surface deformation on a national scale, especially in China, which has an area of approximately 9.6 million km. We designed a set of robust parallel computing
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Nighttime light in China's coastal zone: The type classification approach using SDGSAT-1 Glimmer Imager Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-08 Mingming Jia, Haihang Zeng, Zuoqi Chen, Zongming Wang, Chunying Ren, Dehua Mao, Chuanpeng Zhao, Rong Zhang, Yeqiao Wang
Nighttime Light (NTL) is highly concentrated in China's coastal zone, leading to negative health impacts on both humans and wildlife. Particularly, in recent years, the widespread adoption of broad-spectrum Light-Emitting Diode (LED) light, a low-carbon technology providing substantial increases in luminosity, has led to certain ecological consequences. Thus, information regarding spatial distribution
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Uncovering the rapid expansion of photovoltaic power plants in China from 2010 to 2022 using satellite data and deep learning Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-08 Yuehong Chen, Jiayue Zhou, Yong Ge, Jinwei Dong
China's rapid deployment of solar photovoltaic (PV) power plants has positioned it as the global leader in cumulative installed capacity. The expansion patterns of PV power plants in China play a crucial role in promoting PV diffusion in markets, shaping policies, and analyzing environmental and social impacts. However, the current geospatial datasets of PV power plants available for China cannot fully
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Mapping vegetation height and identifying the northern forest limit across Canada using ICESat-2, Landsat time series and topographic data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-07 H. Travers-Smith, N.C. Coops, C. Mulverhill, M.A. Wulder, D. Ignace, T.C. Lantz
The northern forest-tundra ecotone is one of the fastest warming regions of the globe. Models of vegetation change generally predict a northward advance of boreal forests and corresponding retreat of the tundra. Previous satellite remote sensing analyses in this region have focused on mapping vegetation greenness and tree cover derived from optical multi-spectral sensors. Changes in vegetation structure
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Changes in glacier surface temperature across the Third Pole from 2000 to 2021 Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-06 Shaoting Ren, Tandong Yao, Wei Yang, Evan S. Miles, Huabiao Zhao, Meilin Zhu, Shenghai Li
Glacier surface temperature is not only an important indicator of climate change, but is also directly related to glacier physical processes and mass balance. Glaciers over the Third Pole have shown heterogeneous but accelerated mass loss over the past two decades. However, few studies have focused on changes of glacier surface temperature in this region. In this study, we investigate this change from
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Evaluation of light atmospheric plume inversion methods using synthetic XCO[formula omitted] satellite images to compute Paris CO[formula omitted] emissions Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-06 Alexandre Danjou, Grégoire Broquet, Jinghui Lian, François-Marie Bréon, Thomas Lauvaux
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Ocean-wave suppression for synthetic aperture radar images by depth counteraction method Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-05 Xiaopeng Chao, Qingsong Wang, Xiaoqing Wang, Jian Chen, Yuting Zhu
Surface waves are the most widely distributed texture in synthetic aperture radar (SAR) images acquired over the oceans, and long ocean waves are generally imaged in SAR images with a spatial resolution of 5 m as wave-looking structures. If these ocean-wave textures cause interference when detecting or extracting small-scale ocean features on the ocean surface, it is necessary to suppress them. However
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Soil moisture profile estimation by combining P-band SAR polarimetry with hydrological and multi-layer scattering models Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-05 Anke Fluhrer, Thomas Jagdhuber, Carsten Montzka, Maike Schumacher, Hamed Alemohammad, Alireza Tabatabaeenejad, Harald Kunstmann, Dara Entekhabi
An approach for estimating vertically continuous soil moisture profiles under varying vegetation covers by combining remote sensing with soil (hydrological) modeling is proposed. The approach uses decomposed soil scattering components, after the removal of the vegetation scattering components from fully polarimetric P-band SAR observations. By comparing these with hydrological simulations, soil moisture
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Spatiotemporal heterogeneity in global urban surface warming Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-01 Shuang Ge, Wenfeng Zhan, Shasha Wang, Huilin Du, Zihan Liu, Chenguang Wang, Chunli Wang, Sida Jiang, Pan Dong
The rapid urban warming in recent decades has posed significant risks to the health and well-being of urban residents. Previous studies have predominantly examined urban surface warming from an annual-mean and whole-city perspective. Spatiotemporal heterogeneity in surface warming trends within cities and throughout different periods within a yearly cycle remains largely unclear across global cities
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Sea ice detection using concurrent multispectral and synthetic aperture radar imagery Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-01 Martin S.J. Rogers, Maria Fox, Andrew Fleming, Louisa van Zeeland, Jeremy Wilkinson, J. Scott Hosking
Synthetic Aperture Radar (SAR) imagery is the primary data type used for sea ice mapping due to its spatiotemporal coverage and the ability to detect sea ice independent of cloud and lighting conditions. Automatic sea ice detection using SAR imagery remains problematic due to the presence of ambiguous signal and noise within the image. Conversely, ice and water are easily distinguishable using multispectral
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A river runs through it: Robust automated mapping of riparian woodlands and land surface phenology across dryland regions Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-01 Conor A. McMahon, Dar A. Roberts, John C. Stella, Anna T. Trugman, Michael B. Singer, Kelly K. Caylor
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Automated retrieval of internal wave phase speed and direction from pairs of SAR images with different look directions Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-01 Samantha Furtney, Roland Romeiser, Hans C. Graber
Synthetic aperture radar (SAR) is the premier instrument in satellite remote sensing for the detection of oceanic internal waves due to its sensitivity to changes in small-scale ocean surface roughness and relatively large spatial coverage. The satellite constellation COSMO-SkyMed offers the unique capability to acquire pairs of images of the same scene within 24 min, which is ideal for making internal
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Evidence of a bias-variance trade off when correcting for bias in Sentinel 2 forest LAI retrievals using radiative transfer models Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-01 Richard Fernandes, Najib Djamai, Kate Harvey, Gang Hong, Camryn MacDougall, Hemit Shah, Lixin Sun
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Solar-induced chlorophyll fluorescence sheds light on global evapotranspiration Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-01 Quan Zhang, Xuanqi Liu, Kai Zhou, Yang Zhou, Pierre Gentine, Ming Pan, Gabriel G. Katul
The significance of large-scale evapotranspiration (ET) to climate science, water resources management, flood routing, irreversible desertification, and crop yield is not in dispute. Current large-scale ET models combine empirical formulations with a suite of remotely sensed data products that include meteorological variables, vegetation indices and/or soil moisture. In recent years, solar-induced
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Optimization of InSAR based coseismic slip modeling for moderate earthquakes accounting for fore–aftershock sequence Remote Sens. Environ. (IF 13.5) Pub Date : 2024-03-01 Lejun Lu, Yu Zhou
Determination of coseismic slip distribution for moderate (moment magnitude, 6.5) earthquakes is challenging as the commonly-used interferometric synthetic aperture radar (InSAR) technique is unable to separate mainshock and fore–aftershocks due to its limited temporal resolution. In this study, we propose a new method of optimizing coseismic slip modeling by removing fore–aftershock contributions
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Efficacy of the SDGSAT-1 glimmer imagery in measuring sustainable development goal indicators 7.1.1, 11.5.2, and target 7.3 Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-29 Shaoyang Liu, Congxiao Wang, Zuoqi Chen, Wei Li, Lingxian Zhang, Bin Wu, Yan Huang, Yangguang Li, Jingwen Ni, Jianping Wu, Bailang Yu
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A novel surface energy balance-based approach to land surface temperature downscaling Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-29 Mohammad Karimi Firozjaei, Naeim Mijani, Majid Kiavarz, Si-Bo Duan, Peter M. Atkinson, Seyed Kazem Alavipanah
Spatial downscaling satellite sensor-derived land surface temperature (LST) is of great importance for various environmental applications. However, the energy balance at the land surface is complex, especially in urban environments. As a result, the complexity of land surface thermal processes and the resulting LST cannot be accurately modeled using common downscaling methods. Here, we propose a new
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GABLE: A first fine-grained 3D building model of China on a national scale from very high resolution satellite imagery Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-27 Xian Sun, Xingliang Huang, Yongqiang Mao, Taowei Sheng, Jihao Li, Zhirui Wang, Xue Lu, Xiaoliang Ma, Deke Tang, Kaiqiang Chen
Three-dimensional (3D) building models provide horizontal and vertical information of urban development patterns, which are significant to urbanization analysis, solar energy planning, carbon reduction and sustainability. Despite that many popular products on a global or national scale are proposed, these products usually focus on building extraction and height estimation at fairly coarse resolutions
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Automated forest inventory: Analysis of high-density airborne LiDAR point clouds with 3D deep learning Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-27 Binbin Xiang, Maciej Wielgosz, Theodora Kontogianni, Torben Peters, Stefano Puliti, Rasmus Astrup, Konrad Schindler
Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high-density point clouds with great potential for fine-scale forest inventory and analysis, but automatically partitioning those point clouds into meaningful entities like individual trees or tree components remains a challenge
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Imaging spectroscopy investigations in wet carbon ecosystems: A review of the literature from 1995 to 2022 and future directions Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-27 Thomas C. Ingalls, Jiwei Li, Yvonne Sawall, Roberta E. Martin, David R. Thompson, Gregory P. Asner
Earth is experiencing unprecedented climate change driven by anthropogenic activities. The Paris Climate Agreement is the most recent international agreement pushing nations to curtail greenhouse gas emissions and balance carbon sources and sinks. To help meet the standards set forth in the Paris Climate Agreement, countries can incorporate ecosystems known to sequester and store large amounts of carbon
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A study of annual tree-wise LiDAR intensity patterns of boreal species observed using a hyper-temporal laser scanning time series Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-27 Anna Shcherbacheva, Mariana B. Campos, Yunsheng Wang, Xinlian Liang, Antero Kukko, Juha Hyyppä, Samuli Junttila, Anna Lintunen, Ilkka Korpela, Eetu Puttonen
This study introduces the annual tree-wise intensity patterns of three boreal tree species, silver birch ( Roth.), Scots pine ( L., and Norway spruce ( H. Karst.), observed from a long-term hyper-temporal point cloud dataset collected with a permanent laser scanning (LiDAR) station. An annual LiDAR intensity pattern refers to the trend of variations of tree-wise calibrated LiDAR intensity values over
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Comparison of wave spectrum assimilation and significant wave height assimilation based on Chinese-French oceanography satellite observations Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-26 Chunxiao Wang, Songlin Li, Huaming Yu, Kejian Wu, Shuyan Lang, Ying Xu
Assimilating observation data into wave models can significantly improve the numerical simulation accuracy of ocean waves. Traditionally, assimilation efforts have predominantly focused on significant wave height (SWH). However, the launch of the China-France Oceanography SATellite (CFOSAT)has facilitated the acquisition to obtain global-scale wave spectrum data, introducing a transformative dimension
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Multi-spectral surface emissivity as an indicator of soil water content and soil water content changes in arid soils Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-26 D. Kool, N. Agam
Surface emissivity () is used to characterize surfaces and to determine surface temperature from thermal radiation data. While in many applications it is treated as a constant, it is known to change with surface water content. Several ASTER/SEVERI based studies have speculated that diurnal changes in over deserts are linked to diurnal soil water content cycles resulting from water vapor adsorption
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Estimating volume of large slow-moving deep-seated landslides in northern Canada from DInSAR-derived 2D and constrained 3D deformation rates Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-26 Sergey V. Samsonov, Andrée Blais-Stevens
Large slow-moving deep-seated landslides are observed in two different regions of northern Canada with advanced Differential Synthetic Aperture Radar (DInSAR). Two-dimensional vertical and horizontal east-west deformation rates and time series are computed from ascending and descending Sentinel-1 imagery acquired during 2017–2022. The landslides' east-west deformation rate is significantly larger than
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Leveraging past information and machine learning to accelerate land disturbance monitoring Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-26 Su Ye, Zhe Zhu, Ji Won Suh
Near real-time (NRT) monitoring of land disturbances holds great importance for delivering emergency aid, mitigating negative social and ecological impacts, and distributing resources for disaster recovery. Many past NRT techniques were built upon examining the overall change magnitude of a spectral anomaly with a predefined threshold, namely the unsupervised approach. However, their lack of fully
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Spectra-phenology integration for high-resolution, accurate, and scalable mapping of foliar functional traits using time-series Sentinel-2 data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-26 Shuwen Liu, Zhihui Wang, Ziyu Lin, Yingyi Zhao, Zhengbing Yan, Kun Zhang, Marco Visser, Philip A. Townsend, Jin Wu
Foliar functional traits are essential for understanding plant adaptation strategies and ecosystem function. Due to limited observational data, there is a growing interest in upscaling these traits from field sites to regional and global levels. However, limitations persist: (1) global/national scale upscaling that relies on plant functional type (PFT) maps, environmental variables or coarse resolution
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Seasonal dynamics of fallow and cropping lands in the broadacre cropping region of Australia Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-26 Zunyi Xie, Yan Zhao, Ruizhu Jiang, Miao Zhang, Graeme Hammer, Scott Chapman, Jason Brider, Andries B. Potgieter
Fallowing is an important strategy for enhancing soil health, water harvesting and crop yields, thus improving sustainability and reducing production risks in dryland farming systems in Australia. However, accurate data regarding the location, frequency, extent, and duration of fallow land is not readily available at high spatio-temporal resolutions before and during a cropping season. As a result
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National tree species mapping using Sentinel-1/2 time series and German National Forest Inventory data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-24 Lukas Blickensdörfer, Katja Oehmichen, Dirk Pflugmacher, Birgit Kleinschmit, Patrick Hostert
Spatially explicit and detailed information on tree species composition is critical for forest management, nature conservation and the assessment of forest ecosystem services. In many countries, forest attributes are monitored regularly through sample-based forest inventories. In combination with satellite imagery, data from such forest inventories have a great potential for developing large-area tree
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A self-adjusting method to generate daily consistent nighttime light data for the detection of short-term rapid human activities Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-24 Yang Hu, Xudong Zhou, Dai Yamazaki, Jin Chen
Nighttime light (NTL) has become an emerging indicator of the magnitude and changes in human settlement and activities. A recently released Lunar-BRDF-corrected NTL product, Black Marble VNP46A2, with enhanced temporal (daily) and spatial (15 arc-second) resolutions, has the potential to monitor human reactions to short-term events with rapid light intensity variations. However, the VNP46A2 NTL exhibits
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Mapping icebergs in sea ice: An analysis of seasonal SAR backscatter at C- and L-band Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-24 Laust Færch, Wolfgang Dierking, Nick Hughes, Anthony P. Doulgeris
Icebergs in the Arctic can pose a threat to maritime traffic and offshore installations and influence the properties of the upper ocean layer. While icebergs in open water are regularly monitored using C-band SAR satellites, less attention has been paid to icebergs in regions with a high areal fraction of sea ice, where detection using traditional methods is more difficult. In this study, we compare
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Spatial-temporal patterns of land surface evapotranspiration from global products Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-24 Ronglin Tang, Zhong Peng, Meng Liu, Zhao-Liang Li, Yazhen Jiang, Yongxin Hu, Lingxiao Huang, Yizhe Wang, Junrui Wang, Li Jia, Chaolei Zheng, Yongqiang Zhang, Ke Zhang, Yunjun Yao, Xuelong Chen, Yujiu Xiong, Zhenzhong Zeng, Joshua B. Fisher
Knowledge of spatio-temporal patterns of global land surface evapotranspiration (ET) is essential for understanding the exchanges of energy, water and carbon between the earth surface and atmosphere and responses to human activities, climate changes, and extreme weather events. This paper comprehensively reviewed accuracies and spatio-temporal patterns of 25 state-of-the-art global datasets of land
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Research on automatic recognition of active landslides using InSAR deformation under digital morphology: A case study of the Baihetan reservoir, China Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-24 Yang Liu, Xin Yao, Zhenkui Gu, Renjiang Li, Zhenkai Zhou, Xinghong Liu, Shu Jiang, Chuangchuang Yao, Shangfei Wei
Optical remote sensing and field investigations cannot satisfy the accuracy and timeliness requirements of active landslide detection. Interferometric synthetic aperture radar (InSAR) technology has become the mainstream method for observing active landslides in recent years, due to its advantages of a large detection range and high sensitivity to surface deformation. However, quickly and accurately
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Retrieval of temperature profiles in tropical cyclone Nanmadol from resampled advanced technology microwave sounder observations Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-22 Wenyu Li, Fuzhong Weng
In the past, satellite microwave sounding data are often utilized for retrieving the thermal structure of tropical cyclones (TCs). However, the spatial resolutions and scan pattern of the instruments vary from one to another and can affect the retrievals of TC structures. In this study, Backus-Gilbert Inversion (BGI), ATOVS and AVHRR Pre-processing Package (AAPP) filter and N × N field-of-views (FOVs)
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Temporal decorrelation of C-band radar data over wheat in a semi-arid area using sub-daily tower-based observations Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-22 Nadia Ouaadi, Lionel Jarlan, Ludovic Villard, Adnane Chakir, Saïd Khabba, Pascal Fanise, Mohamed Kasbani, Zoubair Rafi, Valerie Le Dantec, Jamal Ezzahar, Pierre-Louis Frison
Recent studies have shown that radar temporal coherence over tropical and boreal forests undergoes a diurnal cycle as a result of a combined effect of the wind-induced motion of scatterers and of the change and displacement of water within the plant in response to the transpiration process. Within this context, the objective of this paper is to investigate, for the first time, the diurnal cycle of
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Pakistan's 2022 floods: Spatial distribution, causes and future trends from Sentinel-1 SAR observations Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-21 Fang Chen, Meimei Zhang, Hang Zhao, Weigui Guan, Aqiang Yang
Floods are a great threat to Pakistan with increasing concern. As the consequences of increased extreme weather related to climate change, Pakistan experiences severe floods almost every year. This study aims to explore and analysis the actual inundated situation, magnitude, the possible causes of the 2022 devastating floods, and future trends. We presented an enhanced nationwide flood mapping method
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Sensitivity of spectral communities to shifts in benthic composition in Hawaiʻi Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-21 Dominica E. Harrison, Gregory P. Asner
Coral reef benthic communities have been mapped in broad categories, such as total coral cover, using remote sensing, yet we lack important details on how changes in benthic community composition translate to spectral changes observed in airborne and spaceborne data. Here, we carry out a spectral sensitivity analysis to enhance our quantitative understanding of benthic community reflectance spectra
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Mapping high-resolution surface current by incorporating geostrophic equilibrium with surface quasigeostrophic theory using multi-source satellite observations Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-20 Zhiqiang Chen, Xidong Wang, Haijin Cao, Xiangzhou Song
In the past three decades, altimeter-based remote sensing has been a widely used system to estimate ocean surface currents. However, it remains a great challenge to effectively resolve scales below ∼100 km at high latitudes and ∼ 300 km at mid-latitudes. In this study, we propose a scheme that utilizes geostrophic equilibrium and surface quasigeostrophy theory (SQG) to improve surface current resolution
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Co-seismic and post-seismic deformation associated with the 2018 Lombok, Indonesia, earthquake sequence, inferred from InSAR and seismic data analysis Remote Sens. Environ. (IF 13.5) Pub Date : 2024-02-20 Siyuan Zhao, Simon McClusky, Phil R. Cummins, Meghan S. Miller
In 2018, four deadly (Mw 6.2–6.9) earthquakes struck the north coast of Lombok Island on 28 July, 5 August, and 19 August. The slip distributions of the three mainshocks are modeled in this study by inverting the co-seismic deformation imaged using an interferometric analysis of Sentinel-1 synthetic aperture radar measurements (InSAR), based on rectangular dislocations embedded in a multi-layered elastic