Remote Sensing of Environment最新文献

{"title":"Exploring spectral and phylogenetic diversity links with functional structure of aquatic plant communities","authors":"Paolo Villa, Andrea Berton, Rossano Bolpagni, Michele Caccia, Maria B. Castellani, Alice Dalla Vecchia, Francesca Gallivanone, Lorenzo Lastrucci, Erika Piaser, Andrea Coppi","doi":"10.1016/j.rse.2024.114582","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114582","url":null,"abstract":"As freshwater ecosystems are threatened globally, the conservation of aquatic plant diversity is becoming a priority. In the last decade, remote sensing has opened up new opportunities to measure biodiversity, especially across terrestrial biomes, and the combination of spectral features with additional information derived from community phylogeny can further advance the accurate characterisation of plant functional diversity across scales. In this study, we explored the use of spectral features extracted from centimetre resolution hyperspectral imagery collected by a drone and phylogenetic metrics derived from a fully resolved supertree to estimate functional diversity (richness, divergence, and evenness) using non-linear parametric and machine learning models within communities of floating hydrophytes and helophytes sampled from different sites. Our results show that all three functional diversity metrics can be estimated from spectral features using machine learning models (random forest; R² = 0.90–0.92), while parametric models perform worse (generalised additive models; R² = 0.40–0.79), especially for community evenness. Merging phylogenetic and spectral features improves modelling performance for functional richness and divergence (R² = 0.95–0.96) using machine learning, but only significantly benefits community evenness estimation when parametric models are used. The combination of imaging spectroscopy and phylogenetic analysis can provide a quantitative way to capture variability in plant communities across scales and gradients, to the benefit of ecologists focused on the study and monitoring of biodiversity and related processes.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"26 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding temperature variations in mountainous regions: The relationship between satellite-derived land surface temperature and in situ near-surface air temperature","authors":"Yaping Mo, Nick Pepin, Harold Lovell","doi":"10.1016/j.rse.2024.114574","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114574","url":null,"abstract":"Mountain systems significantly influence both regional and global climates, and are vital for biodiversity, water resources, and economic activities. Many mountainous regions are experiencing more rapid temperature changes than environments at lower elevations. Whilst <em>in situ</em> weather stations offer critical data on near-surface air temperature (T_air) patterns, the lack of high-elevation stations may lead to an underestimation of warming in mountainous regions. Land surface temperature (LST), which has a strong relationship with T_air and can potentially be measured globally by satellites irrespective of extreme terrain, presents an important alternative for comprehensively assessing temperature dynamics. In this study, we review studies on the relationship between satellite-derived LST and <em>in situ</em> T_air, particularly in mountainous regions, by conducting a meta-analysis of the research literature and discussing the factors driving the LST-T_air relationship. Our review reveals several research biases, including the regions that are the focus of studies to date (<em>e.g.</em> hemispheric and continent biases) and the elevation ranges that have <em>in situ</em> T_air data. We highlight the need for further research in mountain environments to better understand the impacts of climate change on these critical regions.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"41 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of leaf-wood separation algorithms on aboveground biomass estimation from terrestrial laser scanning","authors":"Shilin Chen, Hans Verbeeck, Louise Terryn, Wouter A.J. Van den Broeck, Matheus Boni Vicari, Mathias Disney, Niall Origo, Di Wang, Zhouxin Xi, Chris Hopkinson, Wenxia Dai, Meilian Wang, Sruthi M. Krishna Moorthy, Jie Shao, Roberto Ferrara, David W. MacFarlane, Kim Calders","doi":"10.1016/j.rse.2024.114581","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114581","url":null,"abstract":"Leaf-wood separation plays an important role in estimating aboveground biomass (AGB) of trees from terrestrial laser scanning (TLS) point clouds. Yet, leaf-wood separation studies have predominantly focused on reporting the accuracy of leaf and wood point separation. Assessments of the impact of these algorithms on the subsequent AGB estimations, based on commonly used quantitative structure models (QSMs), have been limited. Therefore, in this study, we quantified the impact of 11 published leaf-wood separation algorithms on QSM-based tree AGB estimation using an independent benchmarking dataset. The benchmarking dataset consists of AGB measured for 20 destructively harvested trees from a mixed temperate forest in Harvard Forest and AGB estimated from QSMs built on manually segmented tree point clouds of 856 broadleaved trees in Wytham Woods under leaf-off conditions. These benchmarking AGB values were compared to the AGB estimated from QSMs built on the leaf-removed point clouds resulting from the different separation algorithms performed on the leaf-on tree point clouds of the same trees. The results of the study indicated that for most of the algorithms, the leaf-removed AGB estimates for both coniferous and broadleaved trees underestimated the AGB (conifers: −17 % to −3 %, broadleaf: −14 % to −2 %) compared to the destructively measured AGB in Harvard Forest. In Wytham Woods, leaf-removed AGB estimates from all separation algorithms consistently underestimated the AGB (−46 % to −24 %) compared to the AGB from the leaf-off point clouds. Most leaf-wood separation algorithms performed better on broadleaved trees than on coniferous trees. Moreover, significant differences were observed among different algorithms in estimating AGB for trees of the same forest type. For coniferous trees, the relative difference (RD) of leaf-removed AGB estimates from QSMs and separation algorithms ranged from −27 % to 16 %, among which the best performing algorithms demonstrated similar optimal performance, with small RD values of approximately −3 % to 2 %. For broadleaved trees, the leaf-removed AGB estimates from QSMs and eight separation algorithms, as well as leaf-off point cloud estimates (approximately at 10 %), were closely in agreement with the harvested benchmark values, among which the best performing algorithms had a RD value approximately within ±2 %. Additionally, most separation algorithms could lead to better estimates of trunk biomass than branch biomass, whereas the estimation for branch biomass consistently exhibited varying degrees of underestimation. These findings provide a timely reference for utilizing leaf-wood separation algorithms for QSM-based AGB estimation.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"32 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating remote sensing with OpenStreetMap data for comprehensive scene understanding through multi-modal self-supervised learning","authors":"Lubin Bai, Xiuyuan Zhang, Haoyu Wang, Shihong Du","doi":"10.1016/j.rse.2024.114573","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114573","url":null,"abstract":"OpenStreetMap (OSM) contains valuable geographic knowledge for remote sensing (RS) interpretation. They can provide correlated and complementary descriptions of a given region. Integrating RS images with OSM data can lead to a more comprehensive understanding of a geographic scene. But due to the significant differences between them, little progress has been made in data fusion for RS and OSM data, and how to extract, interact, and collaborate the information from multiple geographic data sources remains largely unexplored. In this work, we focus on designing a multi-modal self-supervised learning (SSL) approach to fuse RS images and OSM data, which can extract meaningful features from the two complementary data sources in an unsupervised manner, resulting in comprehensive scene understanding. We harmonize the parts of information extraction, interaction, and collaboration for RS and OSM data into a unified SSL framework, named Rose. For information extraction, we start from the complementarity between the two modalities, designing an OSM encoder to harmoniously align with the ViT image encoder. For information interaction, we leverage the spatial correlation between RS and OSM data to guide the cross-attention module, thereby enhancing the information transfer. For information collaboration, we design the joint mask-reconstruction learning strategy to achieve cooperation between the two modalities, which reconstructs the original inputs by referring to information from both sources. The three parts are interlinked and blending seamlessly into a unified framework. Finally, Rose can generate three kinds of representations, i.e., RS feature, OSM feature, and RS-OSM fusion feature, which can be used for multiple downstream tasks. Extensive experiments on land use semantic segmentation, population estimation, and carbon emission estimation tasks demonstrate the multitasking capability, label efficiency, and robustness to noise of Rose. Rose can associate RS images and OSM data at a fine level of granularity, enhancing its effectiveness on fine-grained tasks like land use semantic segmentation. The code can be found at <span><span>https://github.com/bailubin/Rose</span><svg aria-label=\"Opens in new window\" focusable=\"false\" height=\"20\" viewbox=\"0 0 8 8\"><path d=\"M1.12949 2.1072V1H7V6.85795H5.89111V2.90281L0.784057 8L0 7.21635L5.11902 2.1072H1.12949Z\"></path></svg></span>.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"80 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ground surface displacement measurement from SAR imagery using deep learning","authors":"Jinwoo Kim, Hyung-Sup Jung, Zhong Lu","doi":"10.1016/j.rse.2024.114577","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114577","url":null,"abstract":"Offset tracking using synthetic aperture radar (SAR) amplitude imagery is a valuable technique for detecting large ground displacements. However, the traditional offset tracking methods with the SAR datasets are computationally intensive and require significant time for processing. We have developed a novel cross-connection Siamese ResNet (CC-ResSiamNet). The model leverages multi-kernel offset tracking for preprocessing, followed by deep learning architectures that incorporate U-Net, cross-connections, and residual and attention blocks to predict pixel offsets between two SAR amplitude images. It is trained and tested on 200 K pairs of reference and secondary SAR amplitude images, alongside corresponding target offset data from Alaska's glaciers. The comparative analysis with multiple deep learning models confirmed that our designed model is highly generalizable, achieving rapid convergence, minimal overfitting, and high prediction accuracy. Through multi-scenario inference with glacier movements, earthquakes, and volcanic eruptions worldwide, the model demonstrates strong performance, closely matching the accuracy of traditional methods while offering significantly faster processing times through parallel computing. The model's rapid displacement mapping capability shows particular promise for improving disaster response and near real-time surface monitoring. While the approach encounters challenges in accurately capturing small-scale displacements, it opens new possibilities for SAR-based surface displacement prediction using machine learning. This research highlights the advantages of combining deep learning with SAR imagery for advancing geophysical analysis, with future applications anticipated as more commercial and scientific SAR missions launch globally.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"111 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coupled hydrologic-electromagnetic framework to model permafrost active layer organic soil dielectric properties","authors":"Kazem Bakian-Dogaheh, Yuhuan Zhao, John S. Kimball, Mahta Moghaddam","doi":"10.1016/j.rse.2024.114560","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114560","url":null,"abstract":"Arctic permafrost soils contain a vast reservoir of soil organic carbon (SOC) vulnerable to increasing mobilization and decomposition from polar warming and permafrost thaw. How these SOC stocks are responding to global warming is uncertain, partly due to a lack of information on the distribution and status of SOC over vast Arctic landscapes. Soil moisture and organic matter vary substantially over the short vertical distance of the permafrost active layer. The hydrological properties of this seasonally thawed soil layer provide insights for understanding the dielectric behavior of water inside the soil matrix, which is key for developing more effective physics-based radar remote sensing retrieval algorithms for large-scale mapping of SOC. This study provides a coupled hydrologic-electromagnetic framework to model the frequency-dependent dielectric behavior of active layer organic soil. For the first time, we present joint measurement and modeling of the water matric potential, dielectric permittivity, and basic physical properties of 66 soil samples collected across the Alaskan Arctic tundra. The matric potential measurement allows for estimating the soil water retention curve, which helps determine the relaxation time through the Eyring equation. The estimated relaxation time of water molecules in soil is then used in the Debye model to predict the water dielectric behavior in soil. A multi-phase dielectric mixing model is applied to incorporate the contribution of various soil components. The resulting organic soil dielectric model accepts saturation water fraction, organic matter content, mineral texture, temperature, and microwave frequency as inputs to calculate the effective soil dielectric characteristic. The developed dielectric model was validated against lab-measured dielectric data for all soil samples and exhibited robust accuracy. We further validated the dielectric model against field-measured dielectric profiles acquired from five sites on the Alaskan North Slope. Model behavior was also compared against other existing dielectric models, and an in-depth discussion on their validity and limitations in permafrost soils is given. The resulting organic soil dielectric model was then integrated with a multi-layer electromagnetic scattering forward model to simulate radar backscatter under a range of soil profile conditions and model parameters. The results indicate that low frequency (P-, L-band) polarimetric synthetic aperture radars (SARs) have the potential to map water and carbon characteristics in permafrost active layer soils using physics-based radar retrieval algorithms.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"31 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint mapping of melt pond bathymetry and water volume on sea ice using optical remote sensing images and physical reflectance models","authors":"Chuan Xiong, Xudong Li","doi":"10.1016/j.rse.2024.114571","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114571","url":null,"abstract":"Melt ponds are a common phenomenon on the surface of Arctic sea ice during the summer, and their low albedo strongly influences the energy balance of the Arctic sea ice. Estimating Melt Pond Fraction (MPF) and Melt Pond Depth (MPD) using optical remote sensing is crucial for a better understanding of rapid climate change in the Arctic region. However, current retrieval algorithms for monitoring Arctic melt ponds using optical imagery often fail to estimate MPD. In this study, a radiative transfer model for melt ponds is establish to describe the relationship between melt pond reflectance and its physical properties. Using Sentinel-2 observation data, we propose a novel algorithm for the simultaneous retrieval of MPF and MPD, thereby enabling the estimation of Melt Pond Volume (MPV). This method does not depend on prior assumptions regarding the spectral reflectance of sea ice and melt ponds, and it accounts for the spatiotemporal variability in their reflectance. Compared with other high-resolution MPF and MPD products, the results of this study demonstrate comparable spatial distributions. The root mean square error (RMSE) of the retrieved MPF is less than 10 %, and the RMSE for MPD is approximately 24.51 cm. The analysis of melt pond evolution along the MOSAiC track shows the rapid expansion of melt ponds and their significant spatial variability. Ultimately, using Google Earth Engine (GEE) and machine learning, a dataset of MPF, MPD, and MPV for the Arctic from 2013 to 2023 is generated from 57,842 Landsat-8 images. Correlation analysis shows that MPF, MPD, and MPV all have a positive correlation with downward surface radiation. The approach outlined in this study is entirely based on remote sensing imagery, demonstrating significant potential for large scale application. This offers new opportunities for estimating the volume of water stored in Arctic summer melt ponds.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"19 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative characterization of global nighttime light: A method for measuring energy intensity based on radiant flux and SNPP-VIIRS data","authors":"Haihang Zeng, Mingming Jia, Xiangyu Ning, Zhaohui Xue, Rong Zhang, Chuanpeng Zhao, Yangyang Yan, Zongming Wang","doi":"10.1016/j.rse.2024.114576","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114576","url":null,"abstract":"Nighttime light (NTL) remote sensing has become an important tool to study human activities and their impact on the environment. However, accurately and quantitatively measuring NTL has remained a challenge. In this study, we propose using radiant flux as a more precise measure of NTL energy intensity, which takes into account both radiance and image pixel area. To achieve this, we develop a conversion model from radiance to radiant flux based on the SNPP-VIIRS dataset and calculate the global radiant flux map for 2022. A validation of the model was conducted in 50 representative cities worldwide, confirming its rationality and accuracy. The use of radiant flux provides a more intuitive reflection of NTL energy intensity and eliminates system error caused by variations in pixel areas. This research emphasizes the importance of using a quantitative measurement for NTL and highlights the potential for further evaluation of socio-economic parameters and ecological impacts using NTL radiant flux.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"87 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A flexible framework for built-up height mapping using ICESat-2 photons and multisource satellite observations","authors":"Xiayu Tang, Guojiang Yu, Xuecao Li, Hannes Taubenböck, Guohua Hu, Yuyu Zhou, Cong Peng, Donglie Liu, Jianxi Huang, Xiaoping Liu, Peng Gong","doi":"10.1016/j.rse.2024.114572","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114572","url":null,"abstract":"Built-up heights serve as a nexus in understanding the complex relationship between urban forms and socioeconomic activities. With the advent of remote sensing technology, built-up height mapping from satellite observations has become available over the past years. However, the absence of high-precision sample data poses a significant limitation to built-up height mapping at large (regional or global) scales, particularly in developing regions. To address this issue, we proposed a flexible mapping framework to derive precise building height samples using the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data for built-up height estimation. First, we calculated building heights from ICESat-2 photons using advanced algorithms such as Random Sample Consensus (RANSAC) linear fitting and cloth simulation filtering. Then, we constructed large-scale built-up height samples by aggregating the height information into grid cells with optimal size. Finally, aided by these grids with height information from ICEsat-2 and other satellite observations from Sentinel data as well as the digital surface model (DSM), we mapped built-up heights in two mega-cities (i.e., New York and Shenzhen) using the random forest regression model. Our results demonstrate building height estimation using ICESat-2 data generally exhibits in relation to other studies high accuracy, showing great potential to support large-scale built-up height mapping using satellite observations. We found the optimal grid size for built-up height mapping is around 300 m, after a comprehensive sensitivity analysis regarding the building fraction within the grid and its size. Overall, the mapped built-up heights are reliable, with relatively low mean absolute errors (MAE) of 2.69 m in New York and 3.87 m in Shenzhen, similar to or better than previous studies. By leveraging high-precision elevation data provided by the ICESat-2 data, our proposed approach can effectively collect samples in regions with limited information on building heights, showing great potential for large-scale built-up height monitoring and supporting future urban studies.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"64 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling multimodal consolidation process of the newly reclaimed HKIA 3rd runway from satellite SAR interferometry, ICA analytics and Terzaghi consolidation theory","authors":"Zhuo Jiang, Guoqiang Shi, Songbo Wu, Xiaoli Ding, Chaoying Zhao, Man Sing Wong, Zhong Lu","doi":"10.1016/j.rse.2024.114561","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114561","url":null,"abstract":"The three-runway system expansion project of the Hong Kong International Airport (HKIA) began with the land reclamation to the north of its original runway. To facilitate quick stabilization, the Deep Cement Mixing (DCM) in this project was featured as the novel reclamation method firstly applied in Hong Kong. Understanding ground deformation and underground consolidation is crucial for subsequent soil improvement, civil construction, and future planning at the new platform. Synthetic Aperture Radar Interferometry (InSAR) is used to investigate the spatiotemporal characteristics of land deformation following the completion of the third runway pavement. A combined strategy of persistent scatterer (PS) and distributed scatterer (DS) interferometry was implemented to address low radar coherence at the site. The new reclamation is subject to varying degrees of land subsidence, with a maximum monitored sinking rate to be ∼150 mm/year during September 2021 and October 2023. Whereas the 3rd runway was urgently transformed to operation, spatial details of consolidation status of this new land were not yet evaluated. We applied the Independent Component Analysis (ICA) to identify the underlying sources leading to the measured deformation from InSAR. Three distinct sources have been unveiled, including an exponential decay signal (a quick compaction subsidence of surficial materials), a linear signal (a continuous subsiding from marine deposits) and a periodic signal (thermal effects correlated with buildings and bridges). Notably, the linear deformation component is mainly located in areas with prefabricated vertical drains (PVD), which is strongly correlating with the current monitored subsidence pattern. We incorporated the Terzaghi consolidation theory to further characterize InSAR displacement and estimate the subsidence decay property, consolidation time, ultimate primary settlement and consolidation degree at the 3rd runway, with unprecedented spatial details. Our results indicate the DCM method achieves geological stability more rapidly than the PVD method, with a time advantage of approximately 0.08–1.39 years. Meanwhile, DCM can effectively control the primary settlement to 29 % - 83 % of the PVD method. This research benefits our understanding of the consolidation process at the 3rd runway and offer reliable and detailed data of underground properties. This facilitates more accurate planning of follow-up reinforcement measures at specific locations if needed, which also serves as a valuable reference for future reclamation practices in Hong Kong, particularly using the DCM method.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"87 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142833054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}