Pub Date : 2024-07-02DOI: 10.1016/j.rse.2024.114298
Gang Li , Zhuoqi Chen , Yanting Mao , Zhibin Yang , Xiao Chen , Xiao Cheng
The twin optical Sentinel-2 A/B satellites, with their 5-day repeat observations, have proven to be capable of deriving high temporal resolution glacier velocity fields. This study proposes a data processing procedure for deriving quasi-monthly glacier flow velocity fields for the “Karakoram-Pamir anomaly” region. Each Sentinel-2 acquisition is performed offset-tracking (OT) to its next three almost cloud-free acquisitions to increase number of redundant observations. The detector mosaicking errors are eliminated for the two different Sentinel-2 satellites OT. A preliminary flow speed and direction referenced method is taken to remove the wrong matching of OT, and an iterative SVD (singular value decomposition) method solves the glacier velocity time series and removes the observation with large residual. Between Oct 2017 and Sep 2021, the derived results capture several surged glaciers initiating and/or ending their surging phases throughout the region. Additionally, two types of surging glaciers are identified solely on the basis of their high temporal resolution flow rates time series. The first type exhibits a short surging phase of just a few years and exhibited no seasonal variation in their flow rates, as exemplified by Rimo's southern tributary, which experienced a full surging phase lasting for approximately two years and reaching maximum speeds exceeding 10 m/day within the study period. The second type behaves similarly to normal glaciers, albeit with a glacier front that advances and exhibits much higher summer speeds (>3.5 m/day) than during stagnation, such as Gando at western Pamir. Normal glaciers exhibit annual speed-ups and slowdowns, with acceleration typically beginning in late April or early May and ending before September.
{"title":"Different glacier surge patterns revealed by Sentinel-2 imagery derived quasi-monthly flow velocity at west Kunlun Shan, Karakoram, Hindu Kush and Pamir","authors":"Gang Li , Zhuoqi Chen , Yanting Mao , Zhibin Yang , Xiao Chen , Xiao Cheng","doi":"10.1016/j.rse.2024.114298","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114298","url":null,"abstract":"<div><p>The twin optical Sentinel-2 A/B satellites, with their 5-day repeat observations, have proven to be capable of deriving high temporal resolution glacier velocity fields. This study proposes a data processing procedure for deriving quasi-monthly glacier flow velocity fields for the “Karakoram-Pamir anomaly” region. Each Sentinel-2 acquisition is performed offset-tracking (OT) to its next three almost cloud-free acquisitions to increase number of redundant observations. The detector mosaicking errors are eliminated for the two different Sentinel-2 satellites OT. A preliminary flow speed and direction referenced method is taken to remove the wrong matching of OT, and an iterative SVD (singular value decomposition) method solves the glacier velocity time series and removes the observation with large residual. Between Oct 2017 and Sep 2021, the derived results capture several surged glaciers initiating and/or ending their surging phases throughout the region. Additionally, two types of surging glaciers are identified solely on the basis of their high temporal resolution flow rates time series. The first type exhibits a short surging phase of just a few years and exhibited no seasonal variation in their flow rates, as exemplified by Rimo's southern tributary, which experienced a full surging phase lasting for approximately two years and reaching maximum speeds exceeding 10 m/day within the study period. The second type behaves similarly to normal glaciers, albeit with a glacier front that advances and exhibits much higher summer speeds (>3.5 m/day) than during stagnation, such as Gando at western Pamir. Normal glaciers exhibit annual speed-ups and slowdowns, with acceleration typically beginning in late April or early May and ending before September.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":null,"pages":null},"PeriodicalIF":11.1,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479897","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}
Climate factors (CFs) are key variables shaping the interannual variability (IAV) of terrestrial ecosystem carbon sinks. However, the dominant CFs influencing the IAV of terrestrial carbon sinks remains debated, as CFs are coupled via land-atmosphere interactions. Here, the dominant factors influencing the IAV of global terrestrial net ecosystem production (NEP) were quantified using the convergent cross-mapping (CCM) technique. This analysis was conducted with distinct global terrestrial NEP datasets deriving from process-based ecosystem models, machine learning techniques, and eddy covariance flux towers. Results revealed that the spatial patterns of IAV of global terrestrial NEP were dominated by water availability (WA) and temperature (Ts). Ts mainly controlled the IAV of terrestrial NEP in mid to high-latitude regions of the Northern Hemisphere (NH), while WA exerted dominance over low and mid-latitude regions in both the NH and the Southern Hemisphere. Moreover, the energy limitation and water limitation explained the spatial pattern of Ts and WA dominant on NEP. Further analysis found that WA and Ts also played a dominant role in gross primary productivity (GPP) and terrestrial ecosystem respiration (TER), proving that WA and Ts were the dominant factors affecting NEP. In addition, we found a weakening trend in causal linkages of CFs to NEP in the temporal domain. This study used causal analysis to reveal the spatial patterns of water and heat dominating the NEP, providing support for improved assessment and prediction of terrestrial carbon sinks under climate change.
{"title":"Causal inference reveals the dominant role of interannual variability of carbon sinks in complicated environmental-terrestrial ecosystems","authors":"Chaoya Dang , Zhenfeng Shao , Peng Fu , Qingwei Zhuang , Xiaodi Xu , Jiaxin Qian","doi":"10.1016/j.rse.2024.114300","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114300","url":null,"abstract":"<div><p>Climate factors (CFs) are key variables shaping the interannual variability (IAV) of terrestrial ecosystem carbon sinks. However, the dominant CFs influencing the IAV of terrestrial carbon sinks remains debated, as CFs are coupled via land-atmosphere interactions. Here, the dominant factors influencing the IAV of global terrestrial net ecosystem production (NEP) were quantified using the convergent cross-mapping (CCM) technique. This analysis was conducted with distinct global terrestrial NEP datasets deriving from process-based ecosystem models, machine learning techniques, and eddy covariance flux towers. Results revealed that the spatial patterns of IAV of global terrestrial NEP were dominated by water availability (WA) and temperature (Ts). Ts mainly controlled the IAV of terrestrial NEP in mid to high-latitude regions of the Northern Hemisphere (NH), while WA exerted dominance over low and mid-latitude regions in both the NH and the Southern Hemisphere. Moreover, the energy limitation and water limitation explained the spatial pattern of Ts and WA dominant on NEP. Further analysis found that WA and Ts also played a dominant role in gross primary productivity (GPP) and terrestrial ecosystem respiration (TER), proving that WA and Ts were the dominant factors affecting NEP. In addition, we found a weakening trend in causal linkages of CFs to NEP in the temporal domain. This study used causal analysis to reveal the spatial patterns of water and heat dominating the NEP, providing support for improved assessment and prediction of terrestrial carbon sinks under climate change.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":null,"pages":null},"PeriodicalIF":11.1,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141480077","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}
Pub Date : 2024-07-01DOI: 10.1016/j.rse.2024.114291
Jianxin Jia , Xiaorou Zheng , Yueming Wang , Yuwei Chen , Mika Karjalainen , Shoubin Dong , Runuo Lu , Jianyu Wang , Juha Hyyppä
Hyperspectral images are increasingly being used in classification and identification. Data users prefer hyperspectral imagery with high spatial resolution, finer spectral resolution, and high signal-to-noise ratio (SNR). However, tradeoffs exist in these core parameters in imagery acquired by different hyperspectral sensor systems. Data users may find it difficult to utilize all the advantages of hyperspectral imagery. How to select hyperspectral data with optimal parameter configuration has been one of the essential issues for data users, which also affects the back-end applications. With advancements in computer science, various artificial intelligence algorithms from conventional machine learning to deep learning have been utilized for hyperspectral images classification and identification. Few researchers study the mechanism between the core parameters of hyperspectral imaging spectrometers and advanced artificial intelligence algorithms, which affects the application efficiency and accuracy. In this paper, we delved into the evolution of machine learning and deep learning models applied to imagery acquired by different hyperspectral sensor systems having different SNR, spectral, and spatial resolutions. Additionally, we also considered the tradeoffs among the core parameters of hyperspectral imagers. We used two conventional machine learning models, including the classification and regression tree (CART) and random forest (RF), two deep learning methods based on convolution neural network architectures—3D convolutional neural network (3D-CNN) and hamida, and two deep learning methods based on vision transformers architectures—transformer models vision transformer (VIT) and robust vision transformer (RVT), to compare the characteristics of different algorithms. In addition, five hyperspectral datasets with different species categories and scene distributions and aggregated datasets with different spatial resolutions, spectral resolutions, and SNRs were used to validate our study. The experimental results indicate that: (1) The overall accuracy (OA) using CART, RF, 3D-CNN, and VIT models decreased with coarser spectral resolution, but almost remained unchanged using the RVT classifier. The number of class and classification species affect the results. (2) The influence of spatial resolution on classification accuracy is related to the scene complexity, target size, and classification purpose. The coarser spatial resolution can achieve higher OA than the original spatial resolution for the uniform scene distribution. For the datasets with small objects and intersections of different species, OA first increased, plateaued, and then decreased with coarser spatial resolution. (3) The SNR has an obvious impact on OA for the CART and RF classifiers, and the impact decreased for deep learning models, especially for the VIT and RVT models, which were almost unaffected by SNR. Additionally, slight variations in experimental results were observ
高光谱图像正越来越多地用于分类和识别。数据用户更喜欢空间分辨率高、光谱分辨率更精细和信噪比(SNR)高的高光谱图像。然而,不同的高光谱传感器系统获取的图像在这些核心参数上存在差异。数据用户可能会发现很难利用高光谱图像的所有优势。如何选择参数配置最优的高光谱数据一直是数据用户面临的基本问题之一,这也影响到后端应用。随着计算机科学的发展,从传统机器学习到深度学习的各种人工智能算法已被用于高光谱图像的分类和识别。很少有研究人员研究高光谱成像光谱仪的核心参数与先进的人工智能算法之间的机制,这影响了应用的效率和准确性。在本文中,我们深入研究了机器学习和深度学习模型在不同高光谱传感器系统获取的图像上的应用演变,这些传感器系统具有不同的信噪比、光谱和空间分辨率。此外,我们还考虑了高光谱成像仪核心参数之间的权衡。我们使用了两种传统的机器学习模型,包括分类回归树(CART)和随机森林(RF),两种基于卷积神经网络架构的深度学习方法--三维卷积神经网络(3D-CNN)和hamida,以及两种基于视觉变换器架构的深度学习方法--变换器模型视觉变换器(VIT)和鲁棒性视觉变换器(RVT),以比较不同算法的特点。此外,我们还使用了五个不同物种类别和场景分布的高光谱数据集以及不同空间分辨率、光谱分辨率和信噪比的聚合数据集来验证我们的研究。实验结果表明(1) 使用 CART、RF、3D-CNN 和 VIT 模型的总体准确率(OA)随着光谱分辨率的提高而降低,但使用 RVT 分类器的总体准确率几乎保持不变。类的数量和分类物种对结果有影响。(2) 空间分辨率对分类精度的影响与场景复杂度、目标大小和分类目的有关。在场景分布均匀的情况下,较粗的空间分辨率可以获得比原始空间分辨率更高的 OA。对于小目标和不同物种交叉的数据集,随着空间分辨率的提高,OA 先是增加,然后趋于平稳,最后降低。(3)信噪比对 CART 和 RF 分类器的 OA 有明显影响,而对深度学习模型的影响则有所减小,尤其是 VIT 和 RVT 模型,几乎不受信噪比的影响。此外,不同场景分布和类别的数据集的实验结果也略有不同。此外,我们还详细分析了传统机器学习和深度学习模型在实验结果中的作用。这项研究有助于深入理解高光谱成像仪的核心参数与用于高光谱分类的人工智能算法之间的关系。它有助于弥补前端高光谱成像仪、中端模型和后端应用之间的知识鸿沟,进一步推动高光谱成像技术的发展。
{"title":"The effect of artificial intelligence evolving on hyperspectral imagery with different signal-to-noise ratio, spectral and spatial resolutions","authors":"Jianxin Jia , Xiaorou Zheng , Yueming Wang , Yuwei Chen , Mika Karjalainen , Shoubin Dong , Runuo Lu , Jianyu Wang , Juha Hyyppä","doi":"10.1016/j.rse.2024.114291","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114291","url":null,"abstract":"<div><p>Hyperspectral images are increasingly being used in classification and identification. Data users prefer hyperspectral imagery with high spatial resolution, finer spectral resolution, and high signal-to-noise ratio (SNR). However, tradeoffs exist in these core parameters in imagery acquired by different hyperspectral sensor systems. Data users may find it difficult to utilize all the advantages of hyperspectral imagery. How to select hyperspectral data with optimal parameter configuration has been one of the essential issues for data users, which also affects the back-end applications. With advancements in computer science, various artificial intelligence algorithms from conventional machine learning to deep learning have been utilized for hyperspectral images classification and identification. Few researchers study the mechanism between the core parameters of hyperspectral imaging spectrometers and advanced artificial intelligence algorithms, which affects the application efficiency and accuracy. In this paper, we delved into the evolution of machine learning and deep learning models applied to imagery acquired by different hyperspectral sensor systems having different SNR, spectral, and spatial resolutions. Additionally, we also considered the tradeoffs among the core parameters of hyperspectral imagers. We used two conventional machine learning models, including the classification and regression tree (CART) and random forest (RF), two deep learning methods based on convolution neural network architectures—3D convolutional neural network (3D-CNN) and hamida, and two deep learning methods based on vision transformers architectures—transformer models vision transformer (VIT) and robust vision transformer (RVT), to compare the characteristics of different algorithms. In addition, five hyperspectral datasets with different species categories and scene distributions and aggregated datasets with different spatial resolutions, spectral resolutions, and SNRs were used to validate our study. The experimental results indicate that: (1) The overall accuracy (OA) using CART, RF, 3D-CNN, and VIT models decreased with coarser spectral resolution, but almost remained unchanged using the RVT classifier. The number of class and classification species affect the results. (2) The influence of spatial resolution on classification accuracy is related to the scene complexity, target size, and classification purpose. The coarser spatial resolution can achieve higher OA than the original spatial resolution for the uniform scene distribution. For the datasets with small objects and intersections of different species, OA first increased, plateaued, and then decreased with coarser spatial resolution. (3) The SNR has an obvious impact on OA for the CART and RF classifiers, and the impact decreased for deep learning models, especially for the VIT and RVT models, which were almost unaffected by SNR. Additionally, slight variations in experimental results were observ","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":null,"pages":null},"PeriodicalIF":11.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0034425724003092/pdfft?md5=b6b63290497a1f21b4c5543bf078af7e&pid=1-s2.0-S0034425724003092-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-29DOI: 10.1016/j.rse.2024.114270
Daniel Carcereri , Paola Rizzoli , Luca Dell’Amore , José-Luis Bueso-Bello , Dino Ienco , Lorenzo Bruzzone
Operational canopy height mapping at high resolution remains a challenging task at country-level. Most of the existing state-of-the-art inversion methods propose physically-based schemes which are specifically tuned for local scales. Only few approaches in the literature have attempted to produce country or global scale estimates, mostly by means of data-driven approaches and multi-spectral data sources. In this paper, we propose a robust deep learning approach that exploits single-pass interferometric TanDEM-X data to generate accurate forest height estimates from a single interferometric bistatic acquisition. The model development is driven by considerations on both the final performance and the trustworthiness of the model for large-scale deployment in the context of tropical forests. We train and test our model over the five tropical sites of the AfriSAR 2016 campaign, situated in the West Central state of Gabon, performing spatial cross-validation experiments to test its generalization capability. We define a specific training dataset and input predictors to develop a robust model for country-scale inference, by finding an optimal trade-off between the model performance and the large-scale reliability. The proposed model achieves an overall estimation bias of 0.12 m, a mean absolute error of 3.90 m, a root mean squared error of 5.08 m and a coefficient of determination of 0.77. Finally, we generate a time-tagged country-scale canopy height map of Gabon at 25 m resolution, discussing the potential and challenges of these kinds of products for their application in different scenarios and for the monitoring of forest changes.
{"title":"Generation of country-scale canopy height maps over Gabon using deep learning and TanDEM-X InSAR data","authors":"Daniel Carcereri , Paola Rizzoli , Luca Dell’Amore , José-Luis Bueso-Bello , Dino Ienco , Lorenzo Bruzzone","doi":"10.1016/j.rse.2024.114270","DOIUrl":"https://doi.org/10.1016/j.rse.2024.114270","url":null,"abstract":"<div><p>Operational canopy height mapping at high resolution remains a challenging task at country-level. Most of the existing state-of-the-art inversion methods propose physically-based schemes which are specifically tuned for local scales. Only few approaches in the literature have attempted to produce country or global scale estimates, mostly by means of data-driven approaches and multi-spectral data sources. In this paper, we propose a robust deep learning approach that exploits single-pass interferometric TanDEM-X data to generate accurate forest height estimates from a single interferometric bistatic acquisition. The model development is driven by considerations on both the final performance and the trustworthiness of the model for large-scale deployment in the context of tropical forests. We train and test our model over the five tropical sites of the AfriSAR 2016 campaign, situated in the West Central state of Gabon, performing spatial cross-validation experiments to test its generalization capability. We define a specific training dataset and input predictors to develop a robust model for country-scale inference, by finding an optimal trade-off between the model performance and the large-scale reliability. The proposed model achieves an overall estimation bias of 0.12<!--> <!-->m, a mean absolute error of 3.90<!--> <!-->m, a root mean squared error of 5.08<!--> <!-->m and a coefficient of determination of 0.77. Finally, we generate a time-tagged country-scale canopy height map of Gabon at 25<!--> <!-->m resolution, discussing the potential and challenges of these kinds of products for their application in different scenarios and for the monitoring of forest changes.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":null,"pages":null},"PeriodicalIF":11.1,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0034425724002888/pdfft?md5=32bc7f400201d7da018dd0b2be3b2173&pid=1-s2.0-S0034425724002888-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-28DOI: 10.1016/j.rse.2024.114297
Zhou Wu , Ruya Xiao , Mi Jiang , Vagner G. Ferreira
Ocean tide loading (OTL) displacements, shown as long-wavelength errors in Interferometric Synthetic Aperture Radar (InSAR), must be considered in large-scale applications. Despite efforts to explore the impacts of OTL on InSAR, most studies use individual interferograms and simple metrics, which fail to characterize the spatial structure of OTL. Moreover, the OTL contribution to InSAR time series remains relatively unexplored. The aliasing effect and related biases due to OTL, which are common to space-geodetic time series, are primarily theoretical with few practical observations for InSAR. This study comprehensively explores the statistical properties of OTL and their impacts on InSAR measurements, using the Southwest United Kingdom and Northwest France as study areas. Spatially, OTL artifacts on interferograms exhibit an escalating magnitude along the principal direction that aligns with the coastline's orientation. Temporally, the aliasing effect originating from OTL introduces periodic signals with prominent 15/64-day cycles into the Sentinel-1 InSAR time series, causing high velocity biases (up to ∼1 cm/yr) and uncertainties (up to ∼5 mm/yr) for short time spans. Applying OTL correction mitigates the noise level in the displacement time series, leading to a 16% improvement in accuracy, as validated against the Global Navigation Satellite System (GNSS). The study proposes the “overlapping effect” concept, which links InSAR tropospheric delay errors and OTL effects. It underscores the importance of accurate error assessment and removal. Neglecting this interaction may result in a 13% underestimation of the tropospheric error correction efficacy.
{"title":"Characterizing the spatial structure and aliasing effect of ocean tide loading on InSAR measurements","authors":"Zhou Wu , Ruya Xiao , Mi Jiang , Vagner G. Ferreira","doi":"10.1016/j.rse.2024.114297","DOIUrl":"10.1016/j.rse.2024.114297","url":null,"abstract":"<div><p>Ocean tide loading (OTL) displacements, shown as long-wavelength errors in Interferometric Synthetic Aperture Radar (InSAR), must be considered in large-scale applications. Despite efforts to explore the impacts of OTL on InSAR, most studies use individual interferograms and simple metrics, which fail to characterize the spatial structure of OTL. Moreover, the OTL contribution to InSAR time series remains relatively unexplored. The aliasing effect and related biases due to OTL, which are common to space-geodetic time series, are primarily theoretical with few practical observations for InSAR. This study comprehensively explores the statistical properties of OTL and their impacts on InSAR measurements, using the Southwest United Kingdom and Northwest France as study areas. Spatially, OTL artifacts on interferograms exhibit an escalating magnitude along the principal direction that aligns with the coastline's orientation. Temporally, the aliasing effect originating from OTL introduces periodic signals with prominent 15/64-day cycles into the Sentinel-1 InSAR time series, causing high velocity biases (up to ∼1 cm/yr) and uncertainties (up to ∼5 mm/yr) for short time spans. Applying OTL correction mitigates the noise level in the displacement time series, leading to a 16% improvement in accuracy, as validated against the Global Navigation Satellite System (GNSS). The study proposes the “overlapping effect” concept, which links InSAR tropospheric delay errors and OTL effects. It underscores the importance of accurate error assessment and removal. Neglecting this interaction may result in a 13% underestimation of the tropospheric error correction efficacy.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":null,"pages":null},"PeriodicalIF":11.1,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462700","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}
Pub Date : 2024-06-28DOI: 10.1016/j.rse.2024.114275
Fangwen Bao , Shengbiao Wu , Jinhui Gao , Shuyun Yuan , Yiwen Liu , Kai Huang
The utilization of satellite remote sensing images for retrieving aerosol optical parameters has been extensively discussed over the past few decades. While employing machine learning models is indeed a viable approach, a significant portion of these studies still rely on redundant data. Moreover, the discussion regarding aerosol absorption, a crucial factor for determining aerosol radiative impact and distinguishing aerosol components, is limited in current machine learning studies. In this study, we propose a random forest model to retrieve high-precision aerosol properties and their absorption over land from Himawari-8 geostationary satellite images. Remarkably, this model attains a high degree of accuracy in estimating aerosol optical depth (AOD), absorption aerosol optical depth (AAOD), and single scattering albedo (SSA) of heavy air mass using only seven primary predictors (observational radiances or their mathematical combinations, geometries, and wavelength). For AOD, the new random forest model demonstrates excellent performance on an hourly scale (R2 ≥ 0.89, MAE < 0.07, RMSE <0.13), >80% of the samples fall within the expected error (EE) range. Concerning AAOD, the validation indicates that at least 65% of AAODs have a bias of ≤50%, with an R2 exceeding 0.78, MAE ≤ 0.008 and RMSE ≤0.016. SSA also demonstrates a high accuracy (R2 ≥ 0.57, MAE < 0.03, RMSE <0.05), with >70% of the results have an error ≤ 0.03. Through more comprehensive independent spatiotemporal cross validation, it can be determined that the model also offers reliable spatial and temporal predictions. The proposed RF model is capable of learning aerosol properties under most atmosphere scenarios, providing a reasonable conversion from predictors to AOD and AAOD/SSA under high aerosol loadings. The spatial patterns of these parameters suggest that the retrievals show considerable potential in capturing high aerosol loading in East Asia and biomass burning in Southeast Asia. The method introduced in this study offers a new approach to obtaining aerosol properties from geostationary satellite remote sensing, featuring a flexible process, simple inputs, high accuracy, and enhanced robustness. Additionally, it furnishes supplementary insights into aerosol absorption, presenting new possibilities in determining aerosol radiative impact and distinguishing aerosol components.
{"title":"Continental aerosol properties and absorption retrieval using random forest machine learning method specific to geostationary remote sensing","authors":"Fangwen Bao , Shengbiao Wu , Jinhui Gao , Shuyun Yuan , Yiwen Liu , Kai Huang","doi":"10.1016/j.rse.2024.114275","DOIUrl":"10.1016/j.rse.2024.114275","url":null,"abstract":"<div><p>The utilization of satellite remote sensing images for retrieving aerosol optical parameters has been extensively discussed over the past few decades. While employing machine learning models is indeed a viable approach, a significant portion of these studies still rely on redundant data. Moreover, the discussion regarding aerosol absorption, a crucial factor for determining aerosol radiative impact and distinguishing aerosol components, is limited in current machine learning studies. In this study, we propose a random forest model to retrieve high-precision aerosol properties and their absorption over land from Himawari-8 geostationary satellite images. Remarkably, this model attains a high degree of accuracy in estimating aerosol optical depth (AOD), absorption aerosol optical depth (AAOD), and single scattering albedo (SSA) of heavy air mass using only seven primary predictors (observational radiances or their mathematical combinations, geometries, and wavelength). For AOD, the new random forest model demonstrates excellent performance on an hourly scale (R<sup>2</sup> ≥ 0.89, MAE < 0.07, RMSE <0.13), >80% of the samples fall within the expected error (EE) range. Concerning AAOD, the validation indicates that at least 65% of AAODs have a bias of ≤50%, with an R<sup>2</sup> exceeding 0.78, MAE ≤ 0.008 and RMSE ≤0.016. SSA also demonstrates a high accuracy (R<sup>2</sup> ≥ 0.57, MAE < 0.03, RMSE <0.05), with >70% of the results have an error ≤ 0.03. Through more comprehensive independent spatiotemporal cross validation, it can be determined that the model also offers reliable spatial and temporal predictions. The proposed RF model is capable of learning aerosol properties under most atmosphere scenarios, providing a reasonable conversion from predictors to AOD and AAOD/SSA under high aerosol loadings. The spatial patterns of these parameters suggest that the retrievals show considerable potential in capturing high aerosol loading in East Asia and biomass burning in Southeast Asia. The method introduced in this study offers a new approach to obtaining aerosol properties from geostationary satellite remote sensing, featuring a flexible process, simple inputs, high accuracy, and enhanced robustness. Additionally, it furnishes supplementary insights into aerosol absorption, presenting new possibilities in determining aerosol radiative impact and distinguishing aerosol components.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":null,"pages":null},"PeriodicalIF":11.1,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462817","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}
Pub Date : 2024-06-28DOI: 10.1016/j.rse.2024.114289
Dingfan Xing , Stephen V. Stehman
Reference data obtained by interpreters is a key component of sample-based estimation of area of land cover and land cover change. However, interpreters may disagree when assigning the reference class label for a given sample unit and this inconsistency between interpreters contributes to the overall uncertainty of the estimated area. Interpenetrating subsampling (IPS) offers a practical way to incorporate interpreter variability into an unbiased estimator of the total variance. This method requires partitioning the full sample into g nonoverlapping groups with the sample units in each group then evaluated by a different interpreter and each interpreter determines the reference class data for only one group. The total variance is estimated by the among group variability of the g estimates of area. IPS was applied to estimate the total variance of land cover area estimates for a sample of 300 pixels selected from the Puget Sound region of the Northwest United States. The reference land cover data were obtained by seven interpreters who each labeled all 300 pixels. These data provided a unique opportunity to explore properties of IPS such as variability over different random partitions of the sample into groups and variability over different subsets of interpreters. IPS estimates of total variance were produced for each land cover class for group sizes of g = 2 through g = 6 and all possible combinations of the seven interpreters for each group size. The estimated total variance decreased with increasing number of groups. Incorporating interpreter variance increased the estimated total variance by a factor ranging from 1.08 (agriculture) to 7.06 (grass/shrub) in simple random sampling. The total variance estimates varied substantially over the random partitions of the sample into groups, but this variability decreased as the group size increased. Compared with other total variance estimators, the IPS estimator is simpler to compute and is more cost effective because it does not require repeat interpretations
{"title":"Using interpenetrating subsampling to incorporate interpreter variability into estimation of the total variance of land cover area estimates","authors":"Dingfan Xing , Stephen V. Stehman","doi":"10.1016/j.rse.2024.114289","DOIUrl":"10.1016/j.rse.2024.114289","url":null,"abstract":"<div><p>Reference data obtained by interpreters is a key component of sample-based estimation of area of land cover and land cover change. However, interpreters may disagree when assigning the reference class label for a given sample unit and this inconsistency between interpreters contributes to the overall uncertainty of the estimated area. Interpenetrating subsampling (IPS) offers a practical way to incorporate interpreter variability into an unbiased estimator of the total variance. This method requires partitioning the full sample into <em>g</em> nonoverlapping groups with the sample units in each group then evaluated by a different interpreter and each interpreter determines the reference class data for only one group. The total variance is estimated by the among group variability of the <em>g</em> estimates of area. IPS was applied to estimate the total variance of land cover area estimates for a sample of 300 pixels selected from the Puget Sound region of the Northwest United States. The reference land cover data were obtained by seven interpreters who each labeled all 300 pixels. These data provided a unique opportunity to explore properties of IPS such as variability over different random partitions of the sample into groups and variability over different subsets of interpreters. IPS estimates of total variance were produced for each land cover class for group sizes of <em>g</em> = 2 through <em>g</em> = 6 and all possible combinations of the seven interpreters for each group size. The estimated total variance decreased with increasing number of groups. Incorporating interpreter variance increased the estimated total variance by a factor ranging from 1.08 (agriculture) to 7.06 (grass/shrub) in simple random sampling. The total variance estimates varied substantially over the random partitions of the sample into groups, but this variability decreased as the group size increased. Compared with other total variance estimators, the IPS estimator is simpler to compute and is more cost effective because it does not require repeat interpretations</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":null,"pages":null},"PeriodicalIF":11.1,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141463123","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}
Pub Date : 2024-06-28DOI: 10.1016/j.rse.2024.114292
Wenjun Tang , Junmei He , Changkun Shao , Jun Song , Zongtao Yuan , Bowen Yan
In addition to global radiation (Rg), direct radiation (Rdir) and diffuse radiation (Rdif) are important fundamental data urgently needed in scientific and industrial fields. However, compared with Rg, Rdir and Rdif have received little attention in the past, either in observations or in satellite retrievals, mainly due to the high cost of their observations and the difficulty of retrieving them effectively from satellites. In this study, a long-term global gridded dataset of Rdir and Rdif was constructed by separating from a high-precision satellite-based product of Rg using the Light Gradient Boosting Machine (LightGBM) model, trained with in-situ observations measured at the Baseline Surface Radiation Network (BSRN). The inputs to construct the dataset are the four variables of Rg, the cloud transmittance for Rg, the ratio of Rdif to Rg under clear sky condition (call the clear diffuse ratio), and the cosine of the solar zenith angle. The developed dataset was validated against in-situ observations and compared with other satellite-based products. Evaluations against the BSRN observations indicated that our proposed method has good generality and outperforms the machine learning-based direct estimation method of Hao et al. (2020). Independent validations were further performed against the observations measured at 17 China Meteorological Administration (CMA) radiation stations and the estimation based on sunshine duration observations at >2400 CMA routine meteorological stations, respectively. It was found that the accuracies of our estimates for both Rdir and Rdif were improved when upscaled to ≥ 30 km. Comparisons with three other satellite-based products indicate that our developed dataset of both Rdir and Rdif was generally more accurate than the global products of the Earth's Radiant Energy System (CERES) and Hao et al. (2020) based on the Deep Space Climate Observatory/Earth Polychromatic Imaging Camera (EPIC) (DSCOVER/EPIC) satellite, and the regional gridded product (JIEA) of Jiang et al. (2020a). The dataset developed in this study will contribute to ecological research and solar engineering applications.
除全球辐射(R)外,直接辐射(R)和漫射辐射(R)也是科学和工业领域急需的重要基础数据。然而,与 R 相比,R 和 R 在过去的观测或卫星检索中很少受到关注,主要原因是其观测成本高昂,且难以从卫星上有效检索。在这项研究中,利用光梯度提升机(LightGBM)模型,从基于卫星的高精度 R 产品中分离出 R 和 R 的长期全球网格数据集,该数据集是利用基线地表辐射网(BSRN)测量的原位观测数据训练而成的。构建数据集的输入是 R 的四个变量、R 的云透射率、晴空条件下 R 与 R 的比率(称为晴空漫射比)以及太阳天顶角的余弦。所开发的数据集根据现场观测结果进行了验证,并与其他卫星产品进行了比较。对 BSRN 观测数据的评估表明,我们提出的方法具有良好的通用性,优于 Hao 等人(2020 年)基于机器学习的直接估算方法。此外,还分别对中国气象局 17 个辐射站的观测数据和中国气象局大于 2400 个常规气象站的日照时数观测数据进行了独立验证。结果发现,当放大到≥ 30 km 时,我们对 R 和 R 的估计精度都有所提高。与其他三个卫星产品的比较表明,我们开发的 R 和 R 数据集总体上比地球辐射能量系统(CERES)和郝等人(2020 年)基于深空气候观测站/地球多色成像相机(EPIC)(DSCOVER/EPIC)卫星的全球产品以及蒋等人(2020a)的区域网格产品(JIEA)更准确。本研究开发的数据集将有助于生态研究和太阳能工程应用。
{"title":"Constructing a long-term global dataset of direct and diffuse radiation (10 km, 3 h, 1983–2018) separating from the satellite-based estimates of global radiation","authors":"Wenjun Tang , Junmei He , Changkun Shao , Jun Song , Zongtao Yuan , Bowen Yan","doi":"10.1016/j.rse.2024.114292","DOIUrl":"10.1016/j.rse.2024.114292","url":null,"abstract":"<div><p>In addition to global radiation (R<sub>g</sub>), direct radiation (R<sub>dir</sub>) and diffuse radiation (R<sub>dif</sub>) are important fundamental data urgently needed in scientific and industrial fields. However, compared with R<sub>g</sub>, R<sub>dir</sub> and R<sub>dif</sub> have received little attention in the past, either in observations or in satellite retrievals, mainly due to the high cost of their observations and the difficulty of retrieving them effectively from satellites. In this study, a long-term global gridded dataset of R<sub>dir</sub> and R<sub>dif</sub> was constructed by separating from a high-precision satellite-based product of R<sub>g</sub> using the Light Gradient Boosting Machine (LightGBM) model, trained with in-situ observations measured at the Baseline Surface Radiation Network (BSRN). The inputs to construct the dataset are the four variables of R<sub>g</sub>, the cloud transmittance for R<sub>g</sub>, the ratio of R<sub>dif</sub> to R<sub>g</sub> under clear sky condition (call the clear diffuse ratio), and the cosine of the solar zenith angle. The developed dataset was validated against in-situ observations and compared with other satellite-based products. Evaluations against the BSRN observations indicated that our proposed method has good generality and outperforms the machine learning-based direct estimation method of Hao et al. (2020). Independent validations were further performed against the observations measured at 17 China Meteorological Administration (CMA) radiation stations and the estimation based on sunshine duration observations at >2400 CMA routine meteorological stations, respectively. It was found that the accuracies of our estimates for both R<sub>dir</sub> and R<sub>dif</sub> were improved when upscaled to ≥ 30 km. Comparisons with three other satellite-based products indicate that our developed dataset of both R<sub>dir</sub> and R<sub>dif</sub> was generally more accurate than the global products of the Earth's Radiant Energy System (CERES) and Hao et al. (2020) based on the Deep Space Climate Observatory/Earth Polychromatic Imaging Camera (EPIC) (DSCOVER/EPIC) satellite, and the regional gridded product (JIEA) of Jiang et al. (2020a). The dataset developed in this study will contribute to ecological research and solar engineering applications.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":null,"pages":null},"PeriodicalIF":11.1,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141463125","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}
Pub Date : 2024-06-28DOI: 10.1016/j.rse.2024.114269
Richard Fernandes , Gang Hong , Luke A. Brown , Jadu Dash , Kate Harvey , Simha Kalimipalli , Camryn MacDougall , Courtney Meier , Harry Morris , Hemit Shah , Abhay Sharma , Lixin Sun
Achieving the Global Climate Observing System goal of 10 m resolution leaf area index (LAI) maps is critical for applications related to climate adaptation, sustainable agriculture, and ecosystem monitoring. Five strategies for producing 10 m LAI maps from Sentinel-2 (S2) imagery are evaluated: i. bi-cubic interpolation of 20 m resolution S2 LAI maps from the Simplified Level 2 Prototype Processor Version 1 (SL2PV1) as currently performed by the Sentinel Applications Platform (SNAP), ii. applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using bi-cubic interpolation (BICUBIC), iii. Applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using Area to Point Regression Kriging (ATPRK), iv. using a recalibrated version of SL2PV1 (SL2PV2) requiring only three S2 10m bands, and iv) a novel use of the previously developed Active Learning Regularization (ALR) approach to locally approximate the SL2PV1 algorithm using only 10 m bands.
Algorithms were assessed in terms of per-pixel accuracy and spatial metrics when comparing 10 m LAI maps produced using either actual S2 imagery or S2 imagery synthesized from airborne hyperspectral imagery to reference 10 m LAI maps traceable to in-situ fiducial reference measurements at 10 sites across the continental US. ATPRK and ALR algorithms had the lowest precision error of ∼0.15 LAI, compared to 0.19 LAI for SNAP and BICUBIC and 0.35 LAI for SL2PV2, and ranked highest in terms of local correlation and Structural Similarity Index measure as well as qualitative agreement with reference maps. SL2PV2 LAI showed evidence of saturation over forests related to decreased sensitivity of input visible reflectance. All algorithms had a similar uncertainty of ∼0.55 LAI compared to traceable reference maps, due to the trade-off between bias and precision. However, ATPRK and ALR uncertainty reduced to 0.11 LAI and 0.16 LAI, respectively, when compared to reference maps that ignored canopy clumping. These results suggest that both ATPRK and ALR are suitable for producing 10 m S2 LAI maps assuming bias due to local clumping can be corrected in the underlying SL2PV1 algorithm.
{"title":"Not just a pretty picture: Mapping Leaf Area Index at 10 m resolution using Sentinel-2","authors":"Richard Fernandes , Gang Hong , Luke A. Brown , Jadu Dash , Kate Harvey , Simha Kalimipalli , Camryn MacDougall , Courtney Meier , Harry Morris , Hemit Shah , Abhay Sharma , Lixin Sun","doi":"10.1016/j.rse.2024.114269","DOIUrl":"10.1016/j.rse.2024.114269","url":null,"abstract":"<div><p>Achieving the Global Climate Observing System goal of 10 m resolution leaf area index (LAI) maps is critical for applications related to climate adaptation, sustainable agriculture, and ecosystem monitoring. Five strategies for producing 10 m LAI maps from Sentinel-2 (S2) imagery are evaluated: i. bi-cubic interpolation of 20 m resolution S2 LAI maps from the Simplified Level 2 Prototype Processor Version 1 (SL2PV1) as currently performed by the Sentinel Applications Platform (SNAP), ii. applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using bi-cubic interpolation (BICUBIC), iii. Applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using Area to Point Regression Kriging (ATPRK), iv. using a recalibrated version of SL2PV1 (SL2PV2) requiring only three S2 10m bands, and iv) a novel use of the previously developed Active Learning Regularization (ALR) approach to locally approximate the SL2PV1 algorithm using only 10 m bands.</p><p>Algorithms were assessed in terms of per-pixel accuracy and spatial metrics when comparing 10 m LAI maps produced using either actual S2 imagery or S2 imagery synthesized from airborne hyperspectral imagery to reference 10 m LAI maps traceable to in-situ fiducial reference measurements at 10 sites across the continental US. ATPRK and ALR algorithms had the lowest precision error of ∼0.15 LAI, compared to 0.19 LAI for SNAP and BICUBIC and 0.35 LAI for SL2PV2, and ranked highest in terms of local correlation and Structural Similarity Index measure as well as qualitative agreement with reference maps. SL2PV2 LAI showed evidence of saturation over forests related to decreased sensitivity of input visible reflectance. All algorithms had a similar uncertainty of ∼0.55 LAI compared to traceable reference maps, due to the trade-off between bias and precision. However, ATPRK and ALR uncertainty reduced to 0.11 LAI and 0.16 LAI, respectively, when compared to reference maps that ignored canopy clumping. These results suggest that both ATPRK and ALR are suitable for producing 10 m S2 LAI maps assuming bias due to local clumping can be corrected in the underlying SL2PV1 algorithm.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":null,"pages":null},"PeriodicalIF":11.1,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0034425724002876/pdfft?md5=f00c5a7f3787d5a87bb85b680544500d&pid=1-s2.0-S0034425724002876-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141463173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-28DOI: 10.1016/j.rse.2024.114286
F. Caldareri , A. Sulli , N. Parrino , G. Dardanelli , S. Todaro , A. Maltese
Shoreline variations, triggered by climate change, eustatism, and tectonic, drive the coastal landscape evolution over multiple spatial and temporal scales. Among the many different existing coast types, sandy coasts are the most sensitive to coastal erosion and accretion processes and, at the same time, often host valuable anthropogenic assets. The rapid and ongoing evolution of these coastal environments poses challenges for their management, necessitating cost-effective and highly reliable methods for measuring these changes. Many remotely sensed shoreline extraction methods have been proposed in the literature, providing valuable tools for improving coastal management. Even if these methodologies allow the demarcation of the shoreline, its pixelated shape usually requires refinement through subsequent smoothing or vector generalization processes. It is important to note that the position of the thus extracted coastline is not a direct result of a measured physical quantity but rather a product of these refinement techniques. To address this problem, we developed a sub-pixel resolution method for extracting shorelines from remotely sensed images of sandy beaches, leveraging the radiometric signature of the shoreline. Validated through precise Global Navigation Satellite System field surveys for positioning the beach foreshore, this method was successfully applied to three beaches in Sicily, in the central Mediterranean, all exhibiting similar microtidal conditions. Its robust design allows for application across various satellite images, employing a straightforward radiometric interpolation method adaptable to different spatial resolutions. This method would be a valuable tool for coastal managers in detecting and mitigating coastal erosion and developing and maintaining anthropogenic coastal assets.
{"title":"On the shoreline monitoring via earth observation: An isoradiometric method","authors":"F. Caldareri , A. Sulli , N. Parrino , G. Dardanelli , S. Todaro , A. Maltese","doi":"10.1016/j.rse.2024.114286","DOIUrl":"10.1016/j.rse.2024.114286","url":null,"abstract":"<div><p>Shoreline variations, triggered by climate change, eustatism, and tectonic, drive the coastal landscape evolution over multiple spatial and temporal scales. Among the many different existing coast types, sandy coasts are the most sensitive to coastal erosion and accretion processes and, at the same time, often host valuable anthropogenic assets. The rapid and ongoing evolution of these coastal environments poses challenges for their management, necessitating cost-effective and highly reliable methods for measuring these changes. Many remotely sensed shoreline extraction methods have been proposed in the literature, providing valuable tools for improving coastal management. Even if these methodologies allow the demarcation of the shoreline, its pixelated shape usually requires refinement through subsequent smoothing or vector generalization processes. It is important to note that the position of the thus extracted coastline is not a direct result of a measured physical quantity but rather a product of these refinement techniques. To address this problem, we developed a sub-pixel resolution method for extracting shorelines from remotely sensed images of sandy beaches, leveraging the radiometric signature of the shoreline. Validated through precise Global Navigation Satellite System field surveys for positioning the beach foreshore, this method was successfully applied to three beaches in Sicily, in the central Mediterranean, all exhibiting similar microtidal conditions. Its robust design allows for application across various satellite images, employing a straightforward radiometric interpolation method adaptable to different spatial resolutions. This method would be a valuable tool for coastal managers in detecting and mitigating coastal erosion and developing and maintaining anthropogenic coastal assets.</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":null,"pages":null},"PeriodicalIF":11.1,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0034425724003043/pdfft?md5=a19e4552172efffa32dc4f5780d7b1ce&pid=1-s2.0-S0034425724003043-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141463445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}