-
Mapping vegetation canopy height across the contiguous United States using ICESat-2 and ancillary datasets Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-28 Lonesome Malambo, Sorin Popescu
Mapping vegetation canopy height is a crucial aspect of understanding and management of vegetation ecosystems. In this study, we applied ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) canopy heights with ancillary Landsat, LANDFIRE (Landscape Fire and Resource Management Planning Tools) and topographic variables to produce a spatially explicit 30-m canopy height product across the contiguous
-
Intercomparison of very high-resolution surface soil moisture products over Catalonia (Spain) Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-28 Nadia Ouaadi, Lionel Jarlan, Michel Le Page, Mehrez Zribi, Giovani Paolini, Bouchra Ait Hssaine, Maria Jose Escorihuela, Pascal Fanise, Olivier Merlin, Nicolas Baghdadi, Aaron Boone
The surface soil moisture (SSM) is a key variable for monitoring hydrological, meteorological and agricultural processes. It can be estimated from active and passive microwave remote sensing data. While coarse-resolution SSM products (> 1 km) have already been evaluated for a large range of ecosystems, such assessments lack very high-spatial-resolution SSM products, although they are increasingly available
-
Correcting confounding canopy structure, biochemistry and soil background effects improves leaf area index estimates across diverse ecosystems from Sentinel-2 imagery Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-28 Liang Wan, Youngryel Ryu, Benjamin Dechant, Yorum Hwang, Huaize Feng, Yanghui Kang, Sungchan Jeong, Jeongho Lee, Changhyun Choi, Joonhwan Bae
-
Modeling slope instabilities with multi-temporal InSAR considering hydrogeological triggering factors: A case study across Badong County in the Three Gorges Area Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-28 Zhuge Xia, Mahdi Motagh, Wandi Wang, Tao Li, Mimi Peng, Chao Zhou, Sadra Karimzadeh
-
Continuous Sargassum monitoring across the Caribbean Sea and Central Atlantic using multi-sensor satellite observations Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-27 Yue Sun, Mengqiu Wang, Mingqing Liu, Zhongbin B. Li, Zhaotong Chen, Bowen Huang
Recurrent transnational blooms across the Caribbean Sea and Atlantic Ocean have received growing attention. Different multispectral sensors, including Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imager Radiometer Suite (VIIRS), and Ocean and Land Color Instrument (OLCI), have been used to map their distributions. However, the synergistic use of multi-sensor observations
-
Forward and backward full-pol scattering analysis using SMAP reflectometer and radar datasets Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-27 Adrian Perez-Portero, Joan Francesc Munoz-Martin, Nereida Rodriguez-Alvarez, Xavier Bosch-Lluis, Kamal Oudrhiri
Over the last two decades, there has been a notable increase in the use of bistatic radar configurations for remote sensing, particularly employing Signals-of-Opportunity (SoOp), such as Global Navigation Satellite System - Reflectometry (GNSS-R). Unlike the more common monostatic backward radar configuration, GNSS-R forward scattering is dominated by different mechanisms. The forward scattering measurement
-
Improving interpretability of deep active learning for flood inundation mapping through class ambiguity indices using multi-spectral satellite imagery Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-25 Hyunho Lee, Wenwen Li
Flood inundation mapping is a critical task for responding to the increasing risk of flooding linked to global warming. Significant advancements of deep learning in recent years have triggered its extensive applications, including flood inundation mapping. To cope with the time-consuming and labor-intensive data labeling process in supervised learning, deep active learning strategies are one of the
-
Deep learning-based geological map generation using geological routes Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-24 Chaoling Li, Fengdan Li, Chang Liu, Zhen Tang, Si Fu, Min Lin, Xia Lv, Shuang Liu, Yuanyuan Liu
-
Characterizing satellite-derived freeze/thaw regimes through spatial and temporal clustering for the identification of growing season constraints on vegetation productivity Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-23 Ramon Melser, Nicholas C. Coops, Chris Derksen
Vegetation growth and productivity in Canada's boreal are governed by a characteristically short growing season, which is largely driven by the Freeze/Thaw(F/T) cycles that constrain the supply of water and nutrients through seasonally frozen soils. Since much of the vegetation in the Canadian boreal consists of evergreen species which do not experience large seasonal cycles in photosynthetic biomass
-
Soil moisture estimation based on FY-3E backscattering data for enhanced daily coverage to SMAP observations in the dawn-dusk orbit Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-23 Peilin Song, Xiangzhuo Liu, Ling Sun, Xiaochun Zhai, Jiao Wang, Liang He, Yuanyuan Wang, Yongqiang Zhang, Guicai Li
Surface soil moisture estimates (SSM) from microwave sensors onboard the polar-orbit satellites are important data sources for investigating hydrological signatures of the global terrestrial area. However, daily SSM products from any single satellite platform have significant inter-swath gaps and thus incomplete spatial coverage in low latitudes. This can lead to a longer revisit cycle (2–3 days) than
-
Monitoring construction changes using dense satellite time series and deep learning Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-22 Ji Won Suh, Zhe Zhu, Yongquan Zhao
-
MDINEOF: A scheme to recover land surface temperatures under cloudy-sky conditions by incorporating radiation fluxes Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-21 Chuanye Shi, Tianxing Wang, Shiyao Wang, Aolin Jia, Xiaopo Zheng, Wanchun Leng, Yihan Du
Land Surface Temperature (LST) is widely used as a crucial parameter to monitor the energy exchange and cycle processes over the globe. Constructing seamless LST datasets with high accuracy is crucial to study its influence on climate. However, existing remote sensing methods for estimating LST are inevitably limited by cloud contaminations, leading to spatiotemporal discontinuity of the derived LST
-
On the ambiguity removal of wind direction derived from space-borne SAR imagery using deep learning Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-17 Hongyu Yang, Chao Fang, Sheng Wang, Jianing Shao, Xiaofeng Yang
This paper presents a scheme for retrieving wind direction without 180° ambiguity over the sea solely based on Synthetic Aperture Radar (SAR) images. The dataset utilized for training, validating, and testing wind direction estimation deep neural network consists of 19,210 spatiotemporal match-ups of Sentinel-1 sub-images from Google Earth Engine (GEE) platform and wind direction records from National
-
Surface water temperature observations and ice phenology estimations for 1.4 million lakes globally Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-17 Maartje C. Korver, Bernhard Lehner, Jeffrey A. Cardille, Laura Carrea
Water temperature and ice cover are critical characteristics of the ecological, biogeochemical, and physical functioning of a lake. Site-specific observations of temperature and ice, however, are not available for most lakes in the world. Yet this information is crucial to understanding the global role of lakes in the functioning of the bio- and hydrosphere. Here, we present the LakeTEMP dataset, referring
-
Learning the variations in annual spectral-temporal metrics to enhance the transferability of regression models for land cover fraction monitoring Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-16 Vu-Dong Pham, Fabian Thiel, David Frantz, Akpona Okujeni, Franz Schug, Sebastian van der Linden
Monitoring the Earth by annually mapping land cover (LC) fractions helps to better understand the ongoing processes and changes of land use and land management. At 10 to 30 m spatial resolution, the combination of time-series data aggregation, specifically spectral-temporal metrics (STM), and regression-based unmixing models has been shown to be highly effective in quantifying LC fractions over large
-
Characterizing oil spills using deep learning and spectral-spatial-geometrical features of HY-1C/D CZI images Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-16 Junnan Jiao, Yingcheng Lu, Chuanmin Hu
Marine oil spills cause pollution to the environment, where timely information on the oil characteristics (i.e., oil types, concentration, thickness) is essential for spill response and post-spill assessment. Existing remote sensing models (including deep-learning or DL) have been applied to both synthetic aperture radar (SAR) and optical remote sensing images, which are primarily for presence/absence
-
Tracking states and transitions in semiarid rangelands: A spatiotemporal archetypal analysis of productivity dynamics using wavelets Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-16 O.A. Bruzzone, S.I. Hurtado, D.V. Perri, R.A. Maddio, M.E. Sello, M.H. Easdale
Climate change poses challenges in classifying ecosystem dynamics, as they are influenced by shifting dynamics resulting from changes in climate forces and meteorological variables, including temperature and water availability. To address this, our study presents a novel approach using Continuous Wavelet Transform (CWT) and power spectrum analysis to classify vegetation dynamics, considering the time-dependent
-
Evaluating the performance of airborne and spaceborne lidar for mapping biomass in the United States' largest dry woodland ecosystem Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-16 Michael J. Campbell, Jessie F. Eastburn, Philip E. Dennison, Jody C. Vogeler, Atticus E.L. Stovall
The ability of remote sensing to accurately quantify live aboveground biomass (AGB) varies by ecosystem. Given their important role in global carbon dynamics, deriving accurate, spatially and temporally explicit AGB estimates in dryland ecosystems is uniquely valuable. However, the shorter stature and sparser cover of vegetation in dryland ecosystems makes remote sensing of AGB particularly challenging
-
3D Monte Carlo differentiable radiative transfer with DART Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-15 Yingjie Wang, Abdelaziz Kallel, Zhijun Zhen, Nicolas Lauret, Jordan Guilleux, Eric Chavanon, Jean-Philippe Gastellu-Etchegorry
Understanding the sensitivity of remote sensing (RS) observation to land surface parameters (e.g., reflectance and temperature) is very important for estimating the accuracy of RS products and optimizing inversion algorithms. The most precise method for quantifying this sensitivity is physical modelling of derivative propagation in simulated 3D landscapes. However, to our knowledge, present land surface
-
Reconstruction of seamless harmonized Landsat Sentinel-2 (HLS) time series via self-supervised learning Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-15 Hao Liu, Hankui K. Zhang, Bo Huang, Lin Yan, Khuong K. Tran, Yuean Qiu, Xiaoyang Zhang, David P. Roy
The Harmonized Landsat Sentinel-2 (HLS) data, harmonizing Landsat-8/9 and Sentinel-2 imagery, offers frequent 30 m resolution multispectral observations but is often contaminated by clouds, shadows, and snow that reduce the availability of good-quality surface observations. Traditional techniques for reconstructing HLS time series, such as polynomial, logistic, or harmonic functions that model seasonal
-
The value of hyperspectral UAV imagery in characterizing tundra vegetation Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-15 Pauli Putkiranta, Aleksi Räsänen, Pasi Korpelainen, Rasmus Erlandsson, Tiina H.M. Kolari, Yuwen Pang, Miguel Villoslada, Franziska Wolff, Timo Kumpula, Tarmo Virtanen
-
Repeat GEDI footprints measure the effects of tropical forest disturbances Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-14 Amelia Holcomb, Patrick Burns, Srinivasan Keshav, David A. Coomes
More of the Amazon rainforest is disturbed each year than completely deforested, but the impact of these disturbances on the carbon cycle remains poorly understood. Recent algorithmic advances using optical and radar remote sensing have improved detection of disturbances at fine spatiotemporal resolution, but quantifying changes in forest structure and biomass associated with these detected disturbances
-
Deep learning techniques for enhanced sea-ice types classification in the Beaufort Sea via SAR imagery Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-13 Yan Huang, Yibin Ren, Xiaofeng Li
This study proposes a dual-branch encoder U-Net (DBU-Net) deep learning model to classify sea ice types based on synthetic aperture radar (SAR) images in the Beaufort Sea. The DBU-Net can segment multi-year ice (MYI), first-year ice (FYI), open water (OW), and leads on SAR images. We design a dual-branch encoder to fuse the polarization and the grey-level co-occurrence matrix (GLCM) information of
-
Coastal wind retrievals from corrected QuikSCAT Normalized Radar Cross Sections Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-13 Giuseppe Grieco, Marcos Portabella, Ad Stoffelen, Anton Verhoef, Jur Vogelzang, Andrea Zanchetta, Stefano Zecchetto
-
Sandy desertification monitoring with the Relative Normalized Silica Index (RNSI) based on SDGSAT-1 thermal infrared image Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-13 Ziyu Yang, Xiaosong Li, Tong Shen, Amos Tiereyangn Kabo-bah, Hanwen Cui, Xingxu Dong, Lei Huang
The Silica Index (SI) obtained from thermal infrared bands is an effective way for monitoring sandy desertification. Sustainable Development Goals Science Satellite 1 (SDGSAT-1) enriches the thermal infrared imagery globally with three thermal infrared bands. However, the use of SDGSAT-1 for monitoring sandy desertification remains underexplored, especially with radiance instead of emissivity. This
-
A multi-source change detection algorithm supporting user customization and near real-time deforestation detections Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-11 Ian R. McGregor, Grant Connette, Josh M. Gray
The abundance of free and accessible satellite data has revolutionized our ability to study deforestation with remote sensing. Recent advances have enabled us to monitor deforestation in near real-time (NRT), and a number of operational NRT alert systems using both optical and synthetic aperture radar (SAR) data have been developed. Yet despite their success, there are three primary issues with current
-
Surface soil moisture from combined active and passive microwave observations: Integrating ASCAT and SMAP observations based on machine learning approaches Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-10 Hongliang Ma, Jiangyuan Zeng, Xiang Zhang, Jian Peng, Xiaojun Li, Peng Fu, Michael H. Cosh, Husi Letu, Shaohua Wang, Nengcheng Chen, Jean-Pierre Wigneron
The fusion of active and passive microwave measurements is expected to provide more robust surface soil moisture (SSM) mapping across various environmental conditions compared to the use of a single sensor. Thus, the integration of the newest L-band passive (i.e., Soil Moisture Active Passive, SMAP) and the active (i.e., the Advanced Scatterometer, ASCAT) observations provides an opportunity for SSM
-
Fractional cover mapping of wildland-urban interface fuels using Landsat, Sentinel 1 and PALSAR imagery Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-10 L. Collins, L. Guindon, C. Lloyd, S.W. Taylor, S. White
Fuels within the immediate vicinity of a house (e.g., within 30–60 m), referred to as the ‘home-ignition zone’, are important determinants of structure damage during wildfires. Methods for mapping home-ignition zone fuels using earth observing satellites are lacking, limiting the capacity to quantify the spatial and temporal dynamics of urban fuel hazard and wildfire risk. Here, we (i) develop a methodology
-
Transitioning from remote sensing archaeology to space archaeology: Towards a paradigm shift Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-09 Lei Luo, Xinyuan Wang, Huadong Guo
Remote Sensing Archaeology (RSA) is an innovative sub-discipline within archaeology that utilizes general Remote Sensing (RS) techniques to analyze data from ancient human-made structures. This analysis significantly contributes to understanding various facets of human history, such as cultures, practices, diversity, and evolution. Our study anticipates the forthcoming changes in the roles of RS archaeologists
-
Using CloudSat to Advance the Global Precipitation Climatology Project (GPCP) over Antarctica Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-09 Mohammad Reza Ehsani, Ali Behrangi, Cristian Román-Palacios, George J. Huffman, Robert F. Adler
Remote sensing-based precipitation products face several challenges in high latitudes and specifically over frozen surfaces (i.e., snow and ice). Consequently, precipitation estimates tend to be lower in quality over these regions, including Antarctica, the coldest continent on Earth. In this study, we developed a method for adjusting precipitation estimates over Antarctica by leveraging CloudSat's
-
Unmixing-based forest recovery indicators for predicting long-term recovery success Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-09 Lisa Mandl, Alba Viana-Soto, Rupert Seidl, Ana Stritih, Cornelius Senf
Recovery from forest disturbances is a pivotal metric of forest resilience. Forests globally are facing unprecedented levels of both natural and anthropogenic disturbances, yet our understanding of their recovery from these disturbances remains incomplete. Remote sensing is an effective tool for understanding post-disturbance recovery, but existing approaches largely rely on spectral recovery indicators
-
Monitoring saltwater intrusion to estuaries based on UAV and satellite imagery with machine learning models Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-08 Dingshen Jiang, Chunyu Dong, Zhimin Ma, Xianwei Wang, Kairong Lin, Fang Yang, Xiaohong Chen
-
Arctic Sea ice leads detected using sentinel-1B SAR image and their responses to atmosphere circulation and sea ice dynamics Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-08 Meng Qu, Ruibo Lei, Yue Liu, Na Li
-
Totaling river discharge of the third pole from satellite imagery Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-08 Jie Xu, Lei Wang, Tandong Yao, Deliang Chen, Gang Wang, Zhaowei Jing, Fan Zhang, Yuyang Wang, Xiuping Li, Yinsheng Zhang, Yuanwei Wang, Tian Zeng, Chenhao Chai, Hu Liu, Ruishun Liu, Junshui Long, Xinfeng Fan, Ranjeet Bhlon, Baiqing Xu
The high-mountain Third Pole (TP) in Asia is undergoing rapid warming, profoundly impacting river discharge. Changes in precipitation and the degradation of glaciers and permafrost exert a substantial impact on TP rivers, affecting millions downstream. Nevertheless, conventional estimation methods that rely on in-situ observations and models face considerable challenges due to inconsistent data quality
-
Sub-daily live fuel moisture content estimation from Himawari-8 data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-08 Xingwen Quan, Rui Chen, Marta Yebra, David Riaño, Víctor Resco de Dios, Xing Li, Binbin He, Rachael H. Nolan, Anne Griebel, Matthias M. Boer, Yuanqi Sun
-
Corrigendum to “Improving estimates of sub-daily gross primary production from solar-induced chlorophyll fluorescence by accounting for light distribution within canopy” [Remote Sensing of Environment - 300 (2024) 113919] Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-07 Ruonan Chen, Liangyun Liu, Xinjie Liu, Zhunqiao Liu, Lianhong Gu, Uwe Rascher
-
General method of precipitable water vapor retrieval from remote sensing satellite near-infrared data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-07 Qingzhi Zhao, Zhi Ma, Jinfang Yin, Yibin Yao, Wanqiang Yao, Zheng Du, Wei Wang
The use of remote sensing technique to monitor atmospheric water vapor is significant for weather and climate studies. However, the general methods of retrieving precipitable water vapor (PWV) with high precision and high resolution using remote sensing satellite has hardly been investigated, which becomes the focus of this paper. A general remote sensing PWV retrieval (GRPR) method that uses level-1
-
Revisiting the quantification of power plant CO2 emissions in the United States and China from satellite: A comparative study using three top-down approaches Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-06 Cheng He, Xiao Lu, Yuzhong Zhang, Zhu Liu, Fei Jiang, Youwen Sun, Meng Gao, Yiming Liu, Haipeng Lin, Jiani Yang, Xiaojuan Lin, Yurun Wang, Chengyuan Hu, Shaojia Fan
-
Solar zenith angle-based calibration of Himawari-8 land surface temperature for correcting diurnal retrieval error characteristics Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-06 Yi Yu, Luigi J. Renzullo, Tim R. McVicar, Thomas G. Van Niel, Dejun Cai, Siyuan Tian, Yichuan Ma
-
Learning spectral-indices-fused deep models for time-series land use and land cover mapping in cloud-prone areas: The case of Pearl River Delta Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-04 Zhiwei Li, Qihao Weng, Yuhan Zhou, Peng Dou, Xiaoli Ding
Mapping of highly dynamic changes in land use and land cover (LULC) can be hindered by various cloudy conditions with optical satellite images. These conditions result in discontinuities in high-temporal-density LULC mapping. In this paper, we developed an integrated time series mapping method to enhance the LULC mapping accuracy and frequency in cloud-prone areas by incorporating spectral-indices-fused
-
Assimilating ASCAT normalized backscatter and slope into the land surface model ISBA-A-gs using a Deep Neural Network as the observation operator: Case studies at ISMN stations in western Europe Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-02 Xu Shan, Susan Steele-Dunne, Sebastian Hahn, Wolfgang Wagner, Bertrand Bonan, Clement Albergel, Jean-Christophe Calvet, Ou Ku
ASCAT normalized backscatter () and slope () contain valuable information about soil moisture and vegetation. While has been assimilated to constrain soil moisture, sometimes together with Leaf Area Index (LAI), this study is the first to assimilate directly to constrain vegetation states. Here, we assimilate and slope into the ISBA-A-gs LSM using the Simplified Extended Kalman Filter (SEKF) using
-
Multimodal aircraft flight altitude inversion from SDGSAT-1 thermal infrared data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-05-01 Xiaoxuan Zhou, Liyuan Li, Jianing Yu, Long Gao, Rongguo Zhang, Zhuoyue Hu, Fansheng Chen
Accurately detecting and localizing high-speed aircraft is significant for monitoring global flight activities, conducting searches, and performing emergency rescues. Typically, the spatial position of an air target is obtained through active communication or multi-satellite observation. Here, we propose a multimodal flight altitude inversion method using a single thermal infrared payload (SDGSAT-1
-
Long-term assessment and analysis of the radiometric quality of standard data products for Chinese Gaofen-1/2/6/7 optical remote sensing satellites Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-30 Litao Li, Yonghua Jiang, Xin Shen, Deren Li
The first Chinese Gaofen (GF) remote sensing satellite was launched in August 2013 and has been in orbit for more than 10 years, providing a rich variety of image product data for remote sensing applications in various industries, with other remote sensing satellites of the GF series. To ensure the reliability of the information generated via remote sensing applications, all remote sensing images must
-
Corrigendum to “Spectra-phenology integration for high-resolution, accurate, and scalable mapping of foliar functional traits using time-series Sentinel-2 data” [Remote Sensing of Environment 305 (2024) 114082] Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-29 Shuwen Liu, Zhihui Wang, Ziyu Lin, Yingyi Zhao, Zhengbing Yan, Kun Zhang, Marco Visser, Philip A. Townsend, Jin Wu
-
This is MATE: A Multiple scAttering correcTion rEtrieval algorithm for accurate lidar profiling of seawater optical properties Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-29 Yatong Chen, Xiaoyu Cui, Qiuling Gu, Yudi Zhou, Hongkai Zhao, Han Zhang, Shizhe Ma, Peituo Xu, Henrich Frielinghaus, Lan Wu, Chong Liu, Wenbo Sun, Suhui Yang, Miao Hu, Qun Liu, Dong Liu
Lidar has the capability to measure seawater vertical optical properties efficiently both day-time and night-time, though accurate retrieval is still challenging due to multiple scattering. Herein, we propose a Multiple scAttering correcTion and rEtrieval (MATE) algorithm suitable for shipborne, airborne and spaceborne lidars. The MATE algorithm provides the synchronous depth-resolved absorption, backscattering
-
Corrigendum to “Quantification of wetland vegetation communities features with airborne AVIRIS-NG, UAVSAR, and UAV LiDAR data in Peace-Athabasca Delta” [Remote Sensing of Environment, volume 294 (2023) 113646] (Wang et al. 2024) Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-27 Chao Wang, Kevin P. Timoney, Tamlin M. Pavelsky
-
Estimation of global ecosystem isohydricity from solar-induced chlorophyll fluorescence and meteorological datasets Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-27 Jinru Xue, Alfredo Huete, Zhunqiao Liu, Yakai Wang, Xiaoliang Lu
Plants exhibit varying strategies for optimizing the trade-off between CO uptake and water loss through transpiration in response to increasing air or soil dryness. Anisohydric plants generally keep their stomata open to maintain or enhance carbon uptake, but this exposes them to a greater risk of hydraulic failure. In contrast, isohydric plants tend to maintain hydraulic integrity by enforcing stricter
-
Two-stage, model-assisted estimation using remotely sensed auxiliary data Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-26 Ronald E. McRoberts, Erik Næsset, Juha Heikkinen, Victor Strimbu
The utility of remotely sensed auxiliary data for increasing the precision of sample-based inventory estimates of population parameters is well-established. To this end, the model-assisted estimators with remotely sensed auxiliary data are particularly effective for use with continuous dependent variables. The model-assisted estimators take somewhat different forms, depending on the sampling design
-
Net fluxes of broadband shortwave and photosynthetically active radiation complement NDVI and near infrared reflectance of vegetation to explain gross photosynthesis variability across ecosystems and climate Remote Sens. Environ. (IF 13.5) Pub Date : 2024-04-26 Kanishka Mallick, Joseph Verfaillie, Tianxin Wang, Ariane Arias Ortiz, Daphne Szutu, Koong Yi, Yanghui Kang, Robert Shortt, Tian Hu, Mauro Sulis, Zoltan Szantoi, Gilles Boulet, Joshua B. Fisher, Dennis Baldocchi
A significant challenge in global change research is understanding how vegetation interacts with the environment to influence ecosystem gross primary productivity (GPP) through carbon assimilation. One emerging objective is to consistently predict GPP fluctuations worldwide by establishing a robust scaling relationship between GPP measured at flux towers and satellite spectral reflectance data. However
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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