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Surface energy balance-based surface urban heat island decomposition at high resolution Remote Sens. Environ. (IF 11.1) Pub Date : 2024-10-05 Fengxiang Guo, Jiayue Sun, Die Hu
Urban heat island (UHI) is among the most pronounced human impacts on Earth. To formulate locally adapted mitigation strategies, a comprehensive understanding of the influencing mechanisms of UHI at high resolution is imperative. Based on surface energy balance, we attributed surface UHI (SUHI) into five biophysical terms (surface radiation, anthropogenic heat, convection, evapotranspiration and heat
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Individual tree species classification using low-density airborne multispectral LiDAR data via attribute-aware cross-branch transformer Remote Sens. Environ. (IF 11.1) Pub Date : 2024-10-05 Lanying Wang, Dening Lu, Linlin Xu, Derek T. Robinson, Weikai Tan, Qian Xie, Haiyan Guan, Michael A. Chapman, Jonathan Li
Traditional forest inventory supplies essential data for forest monitoring and management, including tree species, but obtaining individual tree-level information is increasingly crucial. Airborne Light Detection and Ranging (LiDAR) with multispectral observation offers rich information for improved forest inventory mapping with reliable individual tree attributes. Although deep learning techniques
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TIRVolcH: Thermal Infrared Recognition of Volcanic Hotspots. A single band TIR-based algorithm to detect low-to-high thermal anomalies in volcanic regions. Remote Sens. Environ. (IF 11.1) Pub Date : 2024-10-03 S. Aveni, M. Laiolo, A. Campus, F. Massimetti, D. Coppola
Detecting early signs of impending eruptions and monitoring the evolution of volcanic phenomena are fundamental objectives of applied volcanology, both essential for timely assessment of associated hazards. Thermal remote sensing proves to be a cost-effective, yet reliable, information source for these purposes, especially for the hundreds of volcanoes still lacking conventional ground-based monitoring
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Satellite-based estimation of monthly mean hourly 1-km urban air temperature using a diurnal temperature cycle model Remote Sens. Environ. (IF 11.1) Pub Date : 2024-10-04 Fan Huang, Wenfeng Zhan, Zihan Liu, Huilin Du, Pan Dong, Xinya Wang
Cities worldwide face escalating climate change risks, underscoring the need for spatially and temporally resolved urban air temperature (Ta) data. While satellite-derived land surface temperature (LST) data have been widely used to estimate Ta, high-resolution hourly Ta estimation in urban areas remains underexplored. Traditional methods typically rely on LST data from geostationary satellites and
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Towards robust validation strategies for EO flood maps Remote Sens. Environ. (IF 11.1) Pub Date : 2024-10-03 Tim Landwehr, Antara Dasgupta, Björn Waske
Flood maps based on Earth Observation (EO) data inform critical decision-making in almost every stage of the disaster management cycle, directly impacting the ability of affected individuals and governments to receive aid as well as informing policies on future adaptation. However, flood map validation also presents a challenge in the form of class imbalance between flood and non-flood classes, which
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Observation-based quantification of aerosol transport using optical flow: A satellite perspective to characterize interregional transport of atmospheric pollution Remote Sens. Environ. (IF 11.1) Pub Date : 2024-10-03 Tianhao Zhang, Yu Gu, Bin Zhao, Lunche Wang, Zhongmin Zhu, Yun Lin, Xing Chang, Xinghui Xia, Zhe Jiang, Hongrong Shi, Wei Gong
Interregional transport plays a significant role in haze formation with varying and disputable contribution extent. Current research on quantitatively analyzing interregional atmospheric pollution transport has mainly relied on meteorological and chemical models. However, these models are typically affected by uncertainties due to the assumptions and simplifications inherent in the numerical simulations
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Deployment-invariant probability of detection characterization for aerial LiDAR methane detection Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-30 Michael J. Thorpe, Aaron Kreitinger, Dominic T. Altamura, Cameron D. Dudiak, Bradley M. Conrad, David R. Tyner, Matthew R. Johnson, Jason K. Brasseur, Peter A. Roos, William M. Kunkel, Asa Carre-Burritt, Jerry Abate, Tyson Price, David Yaralian, Brandon Kennedy, Edward Newton, Erik Rodriguez, Omar Ibrahim Elfar, Daniel J. Zimmerle
Accurate detection sensitivity characterization of remote methane monitoring technologies is critical for designing, implementing, and auditing effective emissions monitoring and mitigation programs. Several research groups have developed test methods based on single/double-blind controlled release protocols and regression-based data analysis techniques to create probability of detection (PoD) models
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Kuroshio path variability inferred from satellite-derived sea surface topography in the northwestern Pacific Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-30 Ying-Chih Fang, Wei-Teh Li, Shao-Hua Chen
The Kuroshio has a fundamental impact on the regional oceanography of the northwestern Pacific. But identification of the Kuroshio path (KP), an abstraction of the course along which the Kuroshio mainstream moves, has not yet been established in a systematic manner. We optimally track the KP and study its variability in the northwestern Pacific south of ∼31°N, where eddy activity is rich. An automatic
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Urban surface-emitted longwave radiation estimation from high spatial resolution thermal infrared images using a hybrid method Remote Sens. Environ. (IF 11.1) Pub Date : 2024-10-01 Songyi Lin, Huazhong Ren, Rongyuan Liu, Jinxiang Li, Shanshan Chen, Yuanjian Teng, Wenjie Fan, Baozhen Wang, Yu Liu
Accurate estimation of the surface-emitted longwave radiation (SELR) has important scientific value in understanding its spatiotemporal dynamics and surface thermal environment. Thermal infrared (TIR) images with high spatial resolution offer enhanced data support for studying SELR of complex surfaces, such as urban surface. This study proposes a new urban-oriented hybrid (UoHy) method, which considers
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Two-stage estimation of hourly diffuse solar radiation across China using end-to-end gradient boosting with sequentially boosted features Remote Sens. Environ. (IF 11.1) Pub Date : 2024-10-01 Lu Chen, Haoze Shi, Hong Tang, Xin Yang, Chao Ji, Zhigang Li, Yuhong Tu
Diffuse solar radiation (DR) constitutes a vital component of solar energy reaching the surface of the Earth. The demand for extensive temporal and spatial coverage of DR data has intensified in the realms of solar energy harvesting, agriculture, and climate change. However, until now, long-term DR observations have only been available from 17 stations across mainland China. Consequently, there is
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A theoretical demonstration on the independence of distance and incidence angle effects for small-footprint hyperspectral LiDAR: Basic physical concepts Remote Sens. Environ. (IF 11.1) Pub Date : 2024-10-01 Jie Bai, Zheng Niu, Li Wang
Distance and incidence angle effects play crucial roles in determining the raw intensity captured by light detection and ranging (LiDAR) systems. For these two effects, the emergence of hyperspectral LiDAR necessitates a deep theoretical exploration of potential coupling relationships and wavelength dependence. From a theoretical standpoint, this study provides a systematic demonstration, based on
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New top-down estimation of daily mass and number column density of black carbon driven by OMI and AERONET observations Remote Sens. Environ. (IF 11.1) Pub Date : 2024-10-01 Jian Liu, Jason Blake Cohen, Pravash Tiwari, Zhewen Liu, Steve Hung-Lam Yim, Pawan Gupta, Kai Qin
This work uses a mixture of observations from surface remote sensing (AERONET) and satellite remote sensing (OMI) to uniquely compute the atmospheric column loading of black carbon (BC) mass concentration density (MCD) and number concentration density (NCD) on a grid-by-grid, day-by-day basis at 0.25°x0.25° over rapidly developing and biomass burning (BB) impacted regions in South, Southeast, and East
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Sensor-generic adjacency-effect correction for remote sensing of coastal and inland waters Remote Sens. Environ. (IF 11.1) Pub Date : 2024-10-01 Yulun Wu, Anders Knudby, Nima Pahlevan, David Lapen, Chuiqing Zeng
The adjacency effect distorts the top-of-atmosphere (TOA) spectral signals of coastal and inland waters and is a major challenge for optical remote sensing of nearshore aquatic environments. We introduce a closed-form expression that corrects for the adjacency effect prior to atmospheric correction. The method is included in an open-source Python tool, which ingests level-1 imagery and calculates the
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Stability of cloud detection methods for Land Surface Temperature (LST) Climate Data Records (CDRs) Remote Sens. Environ. (IF 11.1) Pub Date : 2024-10-02 Claire E. Bulgin, Ross I. Maidment, Darren Ghent, Christopher J. Merchant
The stability of a climate data record (CDR) is essential for evaluating long-term trends in surface temperature using remote sensing products. In the case of a satellite-derived CDR of land surface temperature (LST), this includes the stability of processing steps prior to the estimation of the target climate variable. Instability in the masking of cloud-affected observations can result in non-geophysical
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Spaceborne high-spectral-resolution lidar ACDL/DQ-1 measurements of the particulate backscatter coefficient in the global ocean Remote Sens. Environ. (IF 11.1) Pub Date : 2024-10-02 Yichen Yang, Yudi Zhou, Iwona S. Stachlewska, Yongxiang Hu, Xiaomei Lu, Weibiao Chen, Jiqiao Liu, Wenbo Sun, Suhui Yang, Yuting Tao, Lei Lin, Weige Lv, Lingying Jiang, Lan Wu, Chong Liu, Dong Liu
Spaceborne lidars have demonstrated outstanding global ocean observation in terms of sampling at day- and night-time and penetrating thin cloud and aerosol layers. A spaceborne high-spectral-resolution lidar (HSRL) has the potential to provide accurate optical properties by decreasing the number of assumptions in the retrieval algorithm in comparison with classical elastic spaceborne lidar. In this
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Towards transferable building damage assessment via unsupervised single-temporal change adaptation Remote Sens. Environ. (IF 11.1) Pub Date : 2024-10-01 Zhuo Zheng, Yanfei Zhong, Liangpei Zhang, Marshall Burke, David B. Lobell, Stefano Ermon
Rapid and accurate assessment of building damage in sudden-onset disasters is crucial for effective humanitarian assistance and disaster response. However, the occurrence of disasters is highly uncertain, e.g., unexpected geographic location and hazards, which challenge the conventional building damage assessment model on generalization and transferability. Unfortunately, there is little public literature
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Potential of SDGSAT-1 nighttime light data in extracting urban main roads Remote Sens. Environ. (IF 11.1) Pub Date : 2024-10-01 Bin Wu, Yu Wang, Hailan Huang, Shaoyang Liu, Bailang Yu
The Sustainable Development Science Satellite 1 (SDGSAT-1) provides a novel nighttime light (NTL) data product with medium spatial resolution, captured by its unique Glimmer Imager (GLI) sensor. Unlike traditional NTL products, the exceptional resolution of SDGSAT-1 NTL data allows for distinct visualization of urban road networks. Although recent studies have validated the effectiveness of SDGSAT-1
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Multitemporal UAV study of phenolic compounds in slash pine canopies Remote Sens. Environ. (IF 11.1) Pub Date : 2024-10-02 Zhaoying Song, Cong Xu, Qifu Luan, Yanjie Li
Phenolic compounds (PC) are important secondary metabolites in plants, playing a crucial role in plant defense mechanisms against pathogens and other plants. Monitoring PC levels is important for understanding tree stress and implementing effective breeding programs. However, traditional methods for monitoring PC are time-consuming, prone to altering the phenolic composition, and mostly applicable
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CROPUP: Historical products are all you need? An end-to-end cross-year crop map updating framework without the need for in situ samples Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-26 Lei Lei, Xinyu Wang, Liangpei Zhang, Xin Hu, Yanfei Zhong
In situ samples are essential for crop mapping, but the collection of samples is time-consuming and labor-intensive, and the samples are usually only valid for the current year, due to the crop rotation across years. In this paper, we discuss an alternative solution, i.e., whether using transfer learning to mine useful information from historical products can achieve cross-year crop mapping without
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An analysis of the potentials of L-band SAR satellites for measuring azimuth motion Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-26 Cunren Liang, Eric J. Fielding, Zhen Liu, Takeshi Motohka, Ryo Natsuaki, Sang-Ho Yun
Azimuth or along-track (approximately north-south) motion is critical in constructing three-dimensional ground motion with synthetic aperture radar (SAR) satellites orbiting the Earth in sun-synchronous polar orbit. The main problem of measuring azimuth motion with short-wavelength SAR data is decorrelation. A fleet of newly launched and upcoming long-wavelength L-band SAR satellites bring new opportunities
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Mapping urban construction sites in China through geospatial data fusion: Methods and applications Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-25 Chaoqun Zhang, Ziyue Chen, Lei Luo, Qiqi Zhu, Yuheng Fu, Bingbo Gao, Jianqiang Hu, Liurun Cheng, Qiancheng Lv, Jing Yang, Manchun Li, Lei Zhou, Qiao Wang
The rapid increase in Urban Construction Sites (UCSs) due to urbanization has become a global trend. UCSs are crucial for timely tracking of urban expansion and renewal progress, understanding settlement environments and human activities, and achieving Sustainable Development Goals (SDGs) 3 and 11. However, distinguishing UCSs from other land covers remains challenging, whether using spatial texture
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Machine learning forecast of surface solar irradiance from meteo satellite data Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-25 Alessandro Sebastianelli, Federico Serva, Andrea Ceschini, Quentin Paletta, Massimo Panella, Bertrand Le Saux
In order to facilitate the shift towards sustainable practices and to support the transition to renewable energy, there is a requirement for faster and more accurate predictions of solar irradiance. Surface solar energy predictions are essential for the establishment of solar farms and the enhancement of energy grid management. This paper presents a novel approach to forecast surface solar irradiance
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Deep learning solver unites SDGSAT-1 observations and Navier–Stokes theory for oceanic vortex streets Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-24 He Gao, Baoxiang Huang, Ge Chen, Linghui Xia, Milena Radenkovic
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Global aerosol retrieval over land from Landsat imagery integrating Transformer and Google Earth Engine Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-24 Jing Wei, Zhihui Wang, Zhanqing Li, Zhengqiang Li, Shulin Pang, Xinyuan Xi, Maureen Cribb, Lin Sun
Landsat imagery offers remarkable potential for various applications, including land monitoring and environmental assessment, thanks to its high spatial resolution and over 50 years of data records. However, the presence of atmospheric aerosols greatly hinders the precision of land classification and the quantitative retrieval of surface parameters. There is a pressing need for reliable and accurate
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The EnMAP spaceborne imaging spectroscopy mission: Initial scientific results two years after launch Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-23 Sabine Chabrillat, Saskia Foerster, Karl Segl, Alison Beamish, Maximilian Brell, Saeid Asadzadeh, Robert Milewski, Kathrin J. Ward, Arlena Brosinsky, Katrin Koch, Daniel Scheffler, Stephane Guillaso, Alexander Kokhanovsky, Sigrid Roessner, Luis Guanter, Hermann Kaufmann, Nicole Pinnel, Emiliano Carmona, Tobias Storch, Tobias Hank, Sebastian Fischer
Imaging spectroscopy has been a recognized and established remote sensing technology since the 1980s, mainly using airborne and field-based platforms to identify and quantify key bio- and geo-chemical surface and atmospheric compounds, based on characteristic spectral reflectance features in the visible-near infrared (VNIR) and short-wave infrared (SWIR). Spaceborne missions, a leap in technology,
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Retrieval of high-resolution melting-season albedo and its implications for the Karakoram Anomaly Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-22 Fuming Xie, Shiyin Liu, Yu Zhu, Xinyi Qing, Shucheng Tan, Yongpeng Gao, Miaomiao Qi, Ying Yi, Hui Ye, Muhammad Mannan Afzal, Xianhe Zhang, Jun Zhou
Glacial responses to climate change exhibit considerable heterogeneity. Although global glaciers are generally thinning and retreat, glaciers in the Karakoram region are distinct in their surging or advancing, exhibiting nearly zero or positive mass balance—a phenomenon known as the Karakoram Anomaly. This anomaly has sparked significant scientific interest, prompting extensive research into glacier
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Monitoring river discharge from space: An optimization approach with uncertainty quantification for small ungauged rivers Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-21 Daniel Scherer, Christian Schwatke, Denise Dettmering, Florian Seitz
The number of in-situ stations measuring river discharge, one of the Essential Climate Variables (ECV), is declining steadily, and numerous basins have never been gauged. With the aim of improving data availability worldwide, we propose an easily applicable and transferable approach to estimate reach-scale discharge solely using remote sensing data that is suitable for filling gaps in the in-situ network
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Void filling of digital elevation models based on terrain feature-guided diffusion model Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-21 Ji Zhao, Yingying Yuan, Yuting Dong, Yaozu Li, Changliang Shao, Haixia Yang
Digital Elevation Models (DEMs) are pivotal in scientific research and engineering because they provide essential topographic and geomorphological information. Voids in DEM data result in the loss of terrain information, significantly impacting its broad applicability. Although spatial interpolation methods are frequently employed to address these voids, they suffer from accuracy degradation and struggle
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Automated grounding line delineation using deep learning and phase gradient-based approaches on COSMO-SkyMed DInSAR data Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-20 Natalya Ross, Pietro Milillo, Luigi Dini
The grounding line marks the transition between a glacier's floating and grounded parts and serves as a crucial parameter for monitoring sea level changes and assessing glacier retreat. The Differential Interferometric Synthetic Aperture Radar (DInSAR) technique for grounding line mapping currently requires the involvement of human experts, which becomes challenging with the continuously growing volume
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A new constant scattering angle solar geometry definition for normalization of GOES-R ABI reflectance times series to support land surface phenology studies Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-19 Shuai Gao, Xiaoyang Zhang, Hankui K. Zhang, Yu Shen, David P. Roy, Weile Wang, Crystal Schaaf
The Advanced Baseline Imager (ABI) sensors on the Geostationary Operational Environment Satellite-R series (GOES-R) broaden the application of global vegetation monitoring due to their higher temporal (5–15 min) and appropriate spatial (0.5–1 km) resolution compared to previous geostationary and current polar-orbiting sensing systems. Notably, ABI Land Surface Phenology (LSP) quantification may be
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Monitoring road development in Congo Basin forests with multi-sensor satellite imagery and deep learning Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-16 Bart Slagter, Kurt Fesenmyer, Matthew Hethcoat, Ethan Belair, Peter Ellis, Fritz Kleinschroth, Marielos Peña-Claros, Martin Herold, Johannes Reiche
Road development has affected many remote tropical forests around the world and has accelerated human-induced deforestation, forest degradation and biodiversity loss. The development of roads in tropical forests is largely driven by industrial selective logging, which can provide a sustainable source of revenue for developing countries while avoiding more detrimental forms of forest degradation or
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Dynamic assessment of the impact of compound dry-hot conditions on global terrestrial water storage Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-14 Zhiming Han, Hongbo Zhang, Jinxia Fu, Zhengshi Wang, Limin Duan, Wenrui Zhang, Zhi Li
Precipitation and temperature are critical factors influencing terrestrial water storage (TWS) can lead to unexpected TWS losses when compounded by dryness and high temperatures. Yet, a dynamic assessment of the individual and combined effects of these conditions on TWS is lacking. This study proposes a framework to assess TWS loss driven by compound dry-hot conditions (CDHC) and dynamically evaluates
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Subfield-level crop yield mapping without ground truth data: A scale transfer framework Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-13 Yuchi Ma, Sang-Zi Liang, D. Brenton Myers, Anu Swatantran, David B. Lobell
Ongoing advances in satellite remote sensing data and machine learning methods have enabled crop yield estimation at various spatial and temporal resolutions. While yield mapping at broader scales (e.g., state or county level) has become common, mapping at finer scales (e.g., field or subfield) has been limited by the lack of ground truth data for model training and evaluation. Here we present a scale
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A new dataset of leaf optical traits to include biophysical parameters in addition to spectral and biochemical assessment Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-13 Reisha D. Peters, Scott D. Noble
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Mapping global drought-induced forest mortality based on multiple satellite vegetation optical depth data Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-13 Xiang Zhang, Xu Zhang, Berhanu Keno Terfa, Won-Ho Nam, Jiangyuan Zeng, Hongliang Ma, Xihui Gu, Wenying Du, Chao Wang, Jian Yang, Peng Wang, Dev Niyogi, Nengcheng Chen
The frequency and intensity of global drought events are continuously increasing, posing an elevated risk of forest mortality worldwide. Accurately understanding the impact of drought on forests, particularly the distribution of mortality due to drought, is crucial for scientifically understanding global ecological drought. Atmospheric indicators and soil moisture are typically correlated with tree
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Retrieval of moisture content of common Sphagnum peat moss species from hyperspectral and multispectral data Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-11 Susanna Karlqvist, Iuliia Burdun, Sini-Selina Salko, Jussi Juola, Miina Rautiainen
Peatlands store enormous amounts of carbon in a peat layer, the formation and preservation of which can only occur under waterlogged conditions. Monitoring peatland moisture conditions is critically important because a decrease in moisture leads to peat oxidation and the release of accumulated carbon back into the atmosphere as a greenhouse gas. Optical remote sensing enables the indirect monitoring
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Automatic extraction of glacial lakes from Landsat imagery using deep learning across the Third Pole region Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-11 Qian Tang, Guoqing Zhang, Tandong Yao, Marc Wieland, Lin Liu, Saurabh Kaushik
The Tibetan Plateau and surroundings, commonly referred to as the Third Pole region, has the largest ice store outside the Arctic and Antarctic regions. Glacial lakes in the Third Pole region are expanding rapidly as glaciers thin and retreat. The Landsat satellite series is the most popular for mapping glacial lakes, benefiting from long-term archived data and suitable spatial resolution (30 m since
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Land surface temperature retrieval from SDGSAT-1 thermal infrared spectrometer images: Algorithm and validation Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-11 Yuanjian Teng, Huazhong Ren, Yonghong Hu, Changyong Dou
Launched by China in 2021, the Sustainable Development Goals Science Satellite 1 (SDGSAT-1) is the world's first science satellite dedicated to serving the United Nations 2030 Agenda for Sustainable Development Goals. In keeping with international aims of this 2030 agenda, the SDGSAT-1 data will be made available for open accee without any restrictions. The Thermal Infrared Spectrometer (TIS) onboard
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Airborne thermal infrared hyperspectral image temperature and emissivity retrieval based on inter-channel correlated automatic atmospheric compensation and TES Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-11 Du Wang, Li-Qin Cao, Lyu-Zhou Gao, Yan-Fei Zhong
Land Surface Temperature (LST) and Land Surface Emissivity (LSE) are key properties of natural materials essential for scientific analysis. Existing retrieval techniques, however, impede the automatic retrieval of LST and LSE across various observational contexts due to the frequent unavailability of in-situ atmospheric data or blackbody references for atmospheric compensation. To address this, we
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DRMAT: A multivariate algorithm for detecting breakpoints in multispectral time series Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-11 Yang Li, Michael A. Wulder, Zhe Zhu, Jan Verbesselt, Dainius Masiliūnas, Yanlan Liu, Gil Bohrer, Yongyang Cai, Yuyu Zhou, Zhaowei Ding, Kaiguang Zhao
Ecosystem dynamics and ecological disturbances manifest as breakpoints in long-term multispectral remote sensing time series. Typically, these breakpoints are captured using univariate methods applied individually to each band, with subsequent integration of the results. However, multivariate analysis provides a promising way to fully incorporate the multispectral bands into breakpoints detection methods
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Mobile laser scanning as reference for estimation of stem attributes from airborne laser scanning Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-10 Raul de Paula Pires, Eva Lindberg, Henrik Jan Persson, Kenneth Olofsson, Johan Holmgren
The acquisition of high-quality reference data is essential for effectively modelling forest attributes. Incorporating close-range Light Detection and Ranging (LiDAR) systems into the reference data collection stage of remote sensing-based forest inventories can not only increase data collection efficiency but also increase the number of attributes measured with high quality. Therefore, we propose
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Sea surface current estimation using optical satellite imagery of Kelvin wakes and AIS data Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-09 Koen Haakman, Martin Verlaan, Avelon Gerritsma, Arne van der Hout
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Nearshore satellite-derived bathymetry from a single-pass satellite video: Improvements from adaptive correlation window size and modulation transfer function Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-07 Adrien N. Klotz, Rafael Almar, Yohan Quenet, Erwin W.J. Bergsma, David Youssefi, Stephanie Artigues, Nicolas Rascle, Boubou Aldiouma Sy, Abdoulaye Ndour
Accurate nearshore bathymetry estimation remains a critical challenge, impacting coastal forecasting evolution assessments through the inaccuracies in both in-situ and remote sensing surveys. This article introduces the Satellite Derived Bathymetry (SDB) temporal correlation method, showcasing its ability in deriving accurate nearshore bathymetry from one minute spaceborne videos. The approach utilises
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The potential of NIRvP in estimating evapotranspiration Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-07 Cha Ersi, Bilige Sudu, Ziming Song, Yongbin Bao, Sicheng Wei, Jiquan Zhang, Zhijun Tong, Xingpeng Liu, Wuni Le, Su Rina
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Monitoring spatially heterogeneous riparian vegetation around permanent waterholes: A method to integrate LiDAR and Landsat data for enhanced ecological interpretation of Landsat fPAR time-series Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-07 Marcelo Henriques, Tim R. McVicar, Kate L. Holland, Edoardo Daly
The vegetation dynamics in highly heterogeneous landscapes (e.g., riparian vegetation surrounding waterholes and oases) are difficult to detect from large (e.g., MODIS) and moderate (e.g., Landsat) spatial resolution remote sensing products. Within a “classify-to-monitor” approach, a method to monitor spatially heterogeneous riparian vegetation dynamics is developed by integrating high spatial resolution
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Improving estimation of diurnal land surface temperatures by integrating weather modeling with satellite observations Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-07 Wei Chen, Yuyu Zhou, Ulrike Passe, Tao Zhang, Chenghao Wang, Ghassem R. Asrar, Qi Li, Huidong Li
Land surface temperature (LST) derived from satellite observations and weather modeling has been widely used for investigating Earth surface-atmosphere energy exchange and radiation budget. However, satellite-derived LST has a trade-off between spatial and temporal resolutions and missing observations caused by clouds, while there are limitations such as potential bias and expensive computation in
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Improvement of NDVI mixture model for fractional vegetation cover estimation with consideration of shaded vegetation and soil components Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-05 Xihan Mu, Yang Yang, Hui Xu, Yuhan Guo, Yongkang Lai, Tim R. McVicar, Donghui Xie, Guangjian Yan
The fraction of green vegetation is a widely-used indicator of vegetation abundance at regional and/or global scales. The pixel mixture model, especially the dimidiate pixel model (DPM, also referred to as two-endmember model) based on the normalized difference vegetation index (NDVI), plays an important role in the accurate estimation of fractional vegetation cover (FVC) remote sensing. The two components
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Improving water level monitoring in small to medium-sized rivers: An enhanced footprint filter-based conditional threshold retracker approach Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-05 Xilin Hu, Chenhui Jiang, Dejun Zhu, Danxun Li
Satellite altimetry data has become essential for studying the dynamics of water bodies, especially in regions with limited or inaccessible data. Traditional low-resolution mode (LRM) satellites' accuracy cannot be guaranteed when it comes to assessing water levels in small- (< 200 m in width) and medium-sized (200–800 m in width) rivers. Synthetic aperture radar (SAR) altimeters, exemplified by Sentinel-3 A
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Monitoring volcanic CO2 flux by the remote sensing of vegetation on Mt. Etna, Italy Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-04 Nicole K. Guinn, Craig Glennie, Marco Liuzzo, Giovanni Giuffrida, Sergio Gurrieri
Volcanic CO is widely acknowledged as an important geochemical precursor for volcanic activity; however, obtaining observations through remote sensing remains a challenge. It is well established that volcanic CO diffusely degases during magma ascent, and the volatiles interact with the ecosystem on the surface through CO fertilization, which can improve vegetation health. A normalized difference vegetation
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New insights into distinguishing temperate deciduous swamps from upland forests and shrublands with SAR Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-04 Sarah Banks, Koreen Millard, Laura Dingle-Robertson, Jason Duffe
Although wetlands are widely recognized for thier important role in providing ecosystem services, their abundance, spatial extent, and condition remain poorly constrained and at-risk of decline. Accurate mapping and monitoring are therefore essential for their protection. However, distinguishing swamps from upland forests and shrublands is especially challenging because optical sensors cannot detect
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Evaluating SWOT's interferometric capabilities for mapping intertidal topography Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-03 Edward Salameh, Damien Desroches, Julien Deloffre, Roger Fjørtoft, Ernesto Tonatiuh Mendoza, Imen Turki, Laurent Froideval, Romain Levaillant, Simon Déchamps, Nicolas Picot, Benoit Laignel, Frédéric Frappart
The Surface Water and Ocean Topography (SWOT) mission, originally designed for observing ocean and inland water bodies, can be a valuable tool for mapping the topography of intertidal flats. This study provides the first demonstration of SWOT's ability to measure intertidal topography using simultaneous interferometric acquisitions. Observations acquired between April and July 2023, during the calibration/validation
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A source-free unsupervised domain adaptation method for cross-regional and cross-time crop mapping from satellite image time series Remote Sens. Environ. (IF 11.1) Pub Date : 2024-09-03 Sina Mohammadi, Mariana Belgiu, Alfred Stein
Precise and timely information about crop types plays a crucial role in various agriculture-related applications. However, crop type mapping methods often face significant challenges in cross-regional and cross-time scenarios with high discrepancies between temporal-spectral characteristics of crops from different regions and years. Unsupervised domain adaptation (UDA) methods have been employed to
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Flood inundation monitoring using multi-source satellite imagery: a knowledge transfer strategy for heterogeneous image change detection Remote Sens. Environ. (IF 11.1) Pub Date : 2024-08-31 Bofei Zhao, Haigang Sui, Junyi Liu, Weiyue Shi, Wentao Wang, Chuan Xu, Jindi Wang
Flood emergency mapping is essential for flood management, often requiring near real-time extraction of large-scale flood extents by combining pre- and post-event multi-source remote sensing images. Pre-event optical imagery delineates the normal water extent, providing a benchmark for estimation of post-flood changes. Synthetic aperture radar (SAR) imagery provides rapid and accurate interpretation
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Phase unwrapping of SAR interferogram from modified U-net via training data simulation and network structure optimization Remote Sens. Environ. (IF 11.1) Pub Date : 2024-08-30 Won-Kyung Baek, Hyung-Sup Jung
Phase unwrapping is the process of retrieving the true phase values from observed wrapped phases by adding the correct multiples of 2π. This process is crucial in synthetic aperture radar (SAR) interferometry, and numerous studies have aimed to enhance its performance. This study explored phase unwrapping using a modified U-Net regression model by optimizing both the network structure and training
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A LiDAR-driven three-dimensional simulation model for far-red solar-induced chlorophyll fluorescence in forests Remote Sens. Environ. (IF 11.1) Pub Date : 2024-08-30 Shichao Jin, Chunhui Zhan, Weiwei Liu, Lixia Ma, Zhaohui Li, Xiaokang Zhang, Yunfei Wu, Qian Zhang, Guang Zheng, Yongguang Zhang
Solar-induced chlorophyll fluorescence (SIF) is a subtle but informative probe of plant photosynthesis. Quantifying the three-dimensional (3D) distribution of SIF benefits a better understanding of photosynthesis variations over heterogeneous canopies. Although radiative transfer models (RTMs) provide a solid theoretical basis for simulating the 3D SIF distribution, most RTMs use virtual scenes with
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L1-Tree: A novel algorithm for constructing 3D tree models and estimating branch architectural traits using terrestrial laser scanning data Remote Sens. Environ. (IF 11.1) Pub Date : 2024-08-30 Yuhao Feng, Yanjun Su, Jiatong Wang, Jiabo Yan, Xiaotian Qi, Eduardo Eiji Maeda, Matheus Henrique Nunes, Xiaoxia Zhao, Xiaoqiang Liu, Xiaoyong Wu, Chen Yang, Jiamin Pan, Kai Dong, Danhua Zhang, Tianyu Hu, Jingyun Fang
Branch architecture provides crucial information for the understanding of plant trait variability and the adaptive strategies employed by trees in response to their environment. High-fidelity terrestrial laser scanning (TLS) data provide an accurate, efficient, and non-destructive means for constructing three-dimensional (3D) tree models and estimating architectural traits. However, the complex canopy
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Assessment of the spaceborne EnMAP hyperspectral data for alteration mineral mapping: A case study of the Reko Diq porphyry Cu[sbnd]Au deposit, Pakistan Remote Sens. Environ. (IF 11.1) Pub Date : 2024-08-30 Saeid Asadzadeh, Xiaodong Zhou, Sabine Chabrillat
For over four decades, spaceborne multispectral data have played a crucial role in supporting mineral exploration and geologic mapping. The spaceborne multispectral datasets, however, have a restricted number of bands with coarse spectral resolution and, thus are very limited in mineral mapping. The advent of high-quality spaceborne imaging spectroscopic data like the Environmental Mapping and Analysis
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High-resolution population maps derived from Sentinel-1 and Sentinel-2 Remote Sens. Environ. (IF 11.1) Pub Date : 2024-08-30 Nando Metzger, Rodrigo Caye Daudt, Devis Tuia, Konrad Schindler
Detailed population maps play an important role in diverse fields ranging from humanitarian action to urban planning. Generating such maps in a timely and scalable manner presents a challenge, especially in data-scarce regions. To address it we have developed , a population mapping method whose only inputs are free, globally available satellite images from Sentinel-1 and Sentinel-2; and a small number
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SCARF: A new algorithm for continuous prediction of biomass dynamics using machine learning and Landsat time series Remote Sens. Environ. (IF 11.1) Pub Date : 2024-08-30 Yingchun Fu, Runhao Li, Zhe Zhu, Yufei Xue, Hu Ding, Xinyu Wang, Jiaming Na, Weijie Xia
We developed the SCARF (Spatial Mismatch and Systematic Prediction Error Corrected cAscade Random Forests) algorithm for continuous prediction of biomass dynamics using machine learning and Landsat Time Series (LTS). Our approach addresses the challenges posed by the cloudy subtropical forests in southern China, where monitoring biomass dynamics is notoriously difficult. To derive spectral-temporal
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An automated sample generation method by integrating phenology domain optical-SAR features in rice cropping pattern mapping Remote Sens. Environ. (IF 11.1) Pub Date : 2024-08-28 Jingya Yang, Qiong Hu, Wenjuan Li, Qian Song, Zhiwen Cai, Xinyu Zhang, Haodong Wei, Wenbin Wu
Accurate spatio-temporal information on rice cropping patterns is vital for predicting grain production, managing water resource and assessing greenhouse gas emissions. However, current automated mapping of rice cropping patterns at regional scale is heavily constrained by insufficient training samples and frequent cloudy weathers in major rice-producing areas. To tackle this challenge, we proposed