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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
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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