Pub Date : 2024-10-07DOI: 10.1016/j.rse.2024.114450
Red Willow Coleman , David R. Thompson , Philip G. Brodrick , Eyal Ben Dor , Evan Cox , Carlos Pérez García-Pando , Todd Hoefen , Raymond F. Kokaly , John M. Meyer , Francisco Ochoa , Gregory S. Okin , Daniela Heller Pearlshtien , Gregg Swayze , Robert O. Green
The Earth surface Mineral dust source InvesTigation (EMIT) is an imaging spectrometer launched to the International Space Station in July 2022 to measure the mineral composition of Earth’s dust-producing regions. We present a systematic accuracy assessment of the EMIT surface reflectance product in two parts. First, we characterize the surface reflectance product’s overall performance using multiple independent vicarious calibration field experiments with hand-held and automated field spectrometers. We find that the EMIT surface reflectance product has a standard error of % in absolute reflectance units for temporally coincident observations. Discrepancies rise to % for spectra acquired at different dates and times of day, which we attribute mainly to changes in solar geometry. Second, we develop an error budget that explains the differences between EMIT and in-situ field spectrometer data. We find that uncertainties in spatial footprints, field spectroscopy, and the EMIT-reported measurement were sufficient to explain discrepancies in most cases. Our approach did not detect any systematic calibration or reflectance errors in the timespan considered. Together, these findings demonstrate that a space-based imaging spectrometer can acquire high-quality spectra across a wide range of observational and atmospheric conditions.
{"title":"An accuracy assessment of the surface reflectance product from the EMIT imaging spectrometer","authors":"Red Willow Coleman , David R. Thompson , Philip G. Brodrick , Eyal Ben Dor , Evan Cox , Carlos Pérez García-Pando , Todd Hoefen , Raymond F. Kokaly , John M. Meyer , Francisco Ochoa , Gregory S. Okin , Daniela Heller Pearlshtien , Gregg Swayze , Robert O. Green","doi":"10.1016/j.rse.2024.114450","DOIUrl":"10.1016/j.rse.2024.114450","url":null,"abstract":"<div><div>The Earth surface Mineral dust source InvesTigation (EMIT) is an imaging spectrometer launched to the International Space Station in July 2022 to measure the mineral composition of Earth’s dust-producing regions. We present a systematic accuracy assessment of the EMIT surface reflectance product in two parts. First, we characterize the surface reflectance product’s overall performance using multiple independent vicarious calibration field experiments with hand-held and automated field spectrometers. We find that the EMIT surface reflectance product has a standard error of <span><math><mrow><mo>±</mo><mn>1</mn><mo>.</mo><mn>0</mn></mrow></math></span>% in absolute reflectance units for temporally coincident observations. Discrepancies rise to <span><math><mrow><mo>±</mo><mn>2</mn><mo>.</mo><mn>7</mn></mrow></math></span> % for spectra acquired at different dates and times of day, which we attribute mainly to changes in solar geometry. Second, we develop an error budget that explains the differences between EMIT and in-situ field spectrometer data. We find that uncertainties in spatial footprints, field spectroscopy, and the EMIT-reported measurement were sufficient to explain discrepancies in most cases. Our approach did not detect any systematic calibration or reflectance errors in the timespan considered. Together, these findings demonstrate that a space-based imaging spectrometer can acquire high-quality spectra across a wide range of observational and atmospheric conditions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114450"},"PeriodicalIF":11.1,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383854","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 : 2024-10-07DOI: 10.1016/j.rse.2024.114455
Michael Durand , Chunli Dai , Joachim Moortgat , Bidhyananda Yadav , Renato Prata de Moraes Frasson , Ziwei Li , Kylie Wadkwoski , Ian Howat , Tamlin M. Pavelsky
Remote sensing has the potential to dramatically advance river discharge monitoring globally, but precision of primary data (water surface elevation (WSE) and river width) remains a limiting factor. WSE can be measured from altimeters, and river width from imagers, but the measurements historically have not been made concurrently from space. This is changing with the advent of the Surface Water and Ocean Topography (SWOT) mission and is anticipated by the combination of high-resolution commercial imagery and DEMs from ArcticDEM. WSE and width respond to changing flow conditions as modulated by the three-dimensional structure of the river channel bed and banks. The relationship between WSE and width thus increases monotonically and is essentially the hypsometric curve of the river. In this study, we explore how simultaneous measurements of WSE and width, combined with the monotonic nature of the river hypsometric curve, can be used to improve measurements of river discharge. First, we present an algorithm to compute the river hypsometric curve from noisy measurements of WSE and width. Second, we demonstrate a method to compute estimates of WSE and width constrained to the river hypsometric curve, and we analyze the probability distribution function of the hypsometrically constrained WSE and width estimates. Specifically, we show that the variance of width and WSE is reduced by invoking a hypsometric constraint, at the cost of an induced correlation between the WSE and width errors. Third, we show that river discharge estimated with the hypsometrically constrained WSE and width is more precise than that without hypsometric constraint, and we predict the expected reduction in discharge error. Fourth, we look at six example river reaches measured by ArcticDEM. The WSE root mean square error had a median across the six reaches of 39.3 cm, which was improved to 33.4 cm across the six reaches using the hypsometric constraint. The discharge predictions were similarly improved: the constrained height and width produce more accurate discharge estimates for five of the six reaches and show reduced variation among flow laws. With the launch of SWOT, river hypsometry constraints applied to simultaneous measurement of WSE and width will support new discharge estimates globally.
{"title":"Using river hypsometry to improve remote sensing of river discharge","authors":"Michael Durand , Chunli Dai , Joachim Moortgat , Bidhyananda Yadav , Renato Prata de Moraes Frasson , Ziwei Li , Kylie Wadkwoski , Ian Howat , Tamlin M. Pavelsky","doi":"10.1016/j.rse.2024.114455","DOIUrl":"10.1016/j.rse.2024.114455","url":null,"abstract":"<div><div>Remote sensing has the potential to dramatically advance river discharge monitoring globally, but precision of primary data (water surface elevation (WSE) and river width) remains a limiting factor. WSE can be measured from altimeters, and river width from imagers, but the measurements historically have not been made concurrently from space. This is changing with the advent of the Surface Water and Ocean Topography (SWOT) mission and is anticipated by the combination of high-resolution commercial imagery and DEMs from ArcticDEM. WSE and width respond to changing flow conditions as modulated by the three-dimensional structure of the river channel bed and banks. The relationship between WSE and width thus increases monotonically and is essentially the hypsometric curve of the river. In this study, we explore how simultaneous measurements of WSE and width, combined with the monotonic nature of the river hypsometric curve, can be used to improve measurements of river discharge. First, we present an algorithm to compute the river hypsometric curve from noisy measurements of WSE and width. Second, we demonstrate a method to compute estimates of WSE and width constrained to the river hypsometric curve, and we analyze the probability distribution function of the hypsometrically constrained WSE and width estimates. Specifically, we show that the variance of width and WSE is reduced by invoking a hypsometric constraint, at the cost of an induced correlation between the WSE and width errors. Third, we show that river discharge estimated with the hypsometrically constrained WSE and width is more precise than that without hypsometric constraint, and we predict the expected reduction in discharge error. Fourth, we look at six example river reaches measured by ArcticDEM. The WSE root mean square error had a median across the six reaches of 39.3 cm, which was improved to 33.4 cm across the six reaches using the hypsometric constraint. The discharge predictions were similarly improved: the constrained height and width produce more accurate discharge estimates for five of the six reaches and show reduced variation among flow laws. With the launch of SWOT, river hypsometry constraints applied to simultaneous measurement of WSE and width will support new discharge estimates globally.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114455"},"PeriodicalIF":11.1,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383855","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 : 2024-10-05DOI: 10.1016/j.rse.2024.114456
Lanying Wang , Dening Lu , Linlin Xu , Derek T. Robinson , Weikai Tan , Qian Xie , Haiyan Guan , Michael A. Chapman , Jonathan Li
Traditional forest inventory supplies essential data for forest monitoring and management, including tree species, but obtaining individual tree-level information is increasingly crucial. Airborne Light Detection and Ranging (LiDAR) with multispectral observation offers rich information for improved forest inventory mapping with reliable individual tree attributes. Although deep learning techniques have shown promise in tree species classification, they are not sufficiently explored for individual tree-level classification using low-density (less than 30 point/m2) Airborne Multispectral LiDAR (AML) data. This study therefore explores the feasibility of using a deep learning (DL) framework for processing low-density AML point clouds to enhance tree species classification in challenging forest environments. A point-based deep learning network with a dual-branch mechanism combined Cross-Branch Attention modules named Attribute-Aware Cross-Branch (AACB) Transformer is designed for AML data to better differentiate tree species from delineated individual trees. In addition, a channel merging approach is introduced, which is suited to prepare the training samples of deep learning networks and reduces the computational costs. This study was tested with an average 9 points/m2 AML point cloud for 6 tree species including Populus tremuloides, Larix laricina, Acer saccharum, Picea abies, Pinus resinosa, and Pinus strobus from a Canadian mixed forest. The overall accuracies achieved 83.1 %, 85.8 %, and 95.3 % at species, genus, and leaf-type levels, respectively. The comparison between the proposed method and other widely used tree species classification methods demonstrates the effectiveness of the proposed approach in enhancing tree species classification accuracy. We discuss potentials and remaining challenges, and our findings allow to further improve tree species classification of low-density AML point clouds by DL technology.
传统的森林资源清查为森林监测和管理提供了包括树种在内的重要数据，但获取单棵树木级别的信息越来越重要。带有多光谱观测功能的机载光探测与测距（LiDAR）可提供丰富的信息，通过可靠的单棵树木属性改进森林资源清查制图。虽然深度学习技术在树种分类方面已显示出良好的前景，但在使用低密度（小于 30 点/平方米）机载多光谱激光雷达（AML）数据进行单棵树木级分类方面，还没有进行充分的探索。因此，本研究探索了使用深度学习（DL）框架处理低密度 AML 点云的可行性，以增强具有挑战性的森林环境中的树种分类。针对 AML 数据设计了一种基于点的深度学习网络，该网络具有双分支机制，结合了名为 "属性感知交叉分支（AACB）转换器 "的交叉分支注意模块，以便更好地从划定的单个树木中区分树种。此外，还引入了一种通道合并方法，该方法适用于准备深度学习网络的训练样本，并可降低计算成本。这项研究使用平均每平方米 9 个点的 AML 点云对加拿大混交林中的 6 个树种进行了测试，这些树种包括震颤杨（Populus tremuloides）、Larix laricina、糖槭（Acer saccharum）、枞树（Picea abies）、树脂松（Pinus resinosa）和石松（Pinus strobus）。在种、属和叶片类型层面，总体准确率分别达到 83.1%、85.8% 和 95.3%。该方法与其他广泛使用的树种分类方法进行了比较，证明了该方法在提高树种分类准确性方面的有效性。我们讨论了该方法的潜力和仍然存在的挑战，我们的发现有助于通过 DL 技术进一步改进低密度 AML 点云的树种分类。
{"title":"Individual tree species classification using low-density airborne multispectral LiDAR data via attribute-aware cross-branch transformer","authors":"Lanying Wang , Dening Lu , Linlin Xu , Derek T. Robinson , Weikai Tan , Qian Xie , Haiyan Guan , Michael A. Chapman , Jonathan Li","doi":"10.1016/j.rse.2024.114456","DOIUrl":"10.1016/j.rse.2024.114456","url":null,"abstract":"<div><div>Traditional forest inventory supplies essential data for forest monitoring and management, including tree species, but obtaining individual tree-level information is increasingly crucial. Airborne Light Detection and Ranging (LiDAR) with multispectral observation offers rich information for improved forest inventory mapping with reliable individual tree attributes. Although deep learning techniques have shown promise in tree species classification, they are not sufficiently explored for individual tree-level classification using low-density (less than 30 point/m<sup>2</sup>) Airborne Multispectral LiDAR (AML) data. This study therefore explores the feasibility of using a deep learning (DL) framework for processing low-density AML point clouds to enhance tree species classification in challenging forest environments. A point-based deep learning network with a dual-branch mechanism combined Cross-Branch Attention modules named Attribute-Aware Cross-Branch (AACB) Transformer is designed for AML data to better differentiate tree species from delineated individual trees. In addition, a channel merging approach is introduced, which is suited to prepare the training samples of deep learning networks and reduces the computational costs. This study was tested with an average 9 points/m<sup>2</sup> AML point cloud for 6 tree species including <em>Populus tremuloides</em>, <em>Larix laricina</em>, <em>Acer saccharum</em>, <em>Picea abies</em>, <em>Pinus resinosa</em>, and <em>Pinus strobus</em> from a Canadian mixed forest. The overall accuracies achieved 83.1 %, 85.8 %, and 95.3 % at species, genus, and leaf-type levels, respectively. The comparison between the proposed method and other widely used tree species classification methods demonstrates the effectiveness of the proposed approach in enhancing tree species classification accuracy. We discuss potentials and remaining challenges, and our findings allow to further improve tree species classification of low-density AML point clouds by DL technology.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114456"},"PeriodicalIF":11.1,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377648","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 : 2024-10-05DOI: 10.1016/j.rse.2024.114447
Fengxiang Guo , Jiayue Sun , Die Hu
Urban heat island (UHI) is among the most pronounced human impacts on Earth. To formulate locally adapted mitigation strategies, a comprehensive understanding of the influencing mechanisms of UHI at high resolution is imperative. Based on surface energy balance, we attributed surface UHI (SUHI) into five biophysical terms (surface radiation, anthropogenic heat, convection, evapotranspiration and heat storage term) using Sentinel-2 and Landsat-8 images in Beijing. The simulated SUHI intensity, derived by combining all five contribution terms, exhibited a good consistency but a higher spatial resolution, than SUHI intensity extracted from Landsat-8 land surface temperature product. SUHI intensity tended to decrease from the old city to outsides, attributed to the decrease of evapotranspiration, solar radiation and anthropogenic heat term. The convection and heat storage term play a positive role in reducing SUHI. Among urban morphological blocks, low-rise and high-density blocks had the strongest SUHI, with the evapotranspiration term contributing the most. The results highlighted the capacity of the urban surface to evaporate water in affecting Beijing SUHI. The proposed method provides one useful tool to analyze the drivers of SUHI from the aspect of heat formation, which can be potentially applied worldwide for large-scale comparisons of how urbanization affects UHI.
{"title":"Surface energy balance-based surface urban heat island decomposition at high resolution","authors":"Fengxiang Guo , Jiayue Sun , Die Hu","doi":"10.1016/j.rse.2024.114447","DOIUrl":"10.1016/j.rse.2024.114447","url":null,"abstract":"<div><div>Urban heat island (UHI) is among the most pronounced human impacts on Earth. To formulate locally adapted mitigation strategies, a comprehensive understanding of the influencing mechanisms of UHI at high resolution is imperative. Based on surface energy balance, we attributed surface UHI (SUHI) into five biophysical terms (surface radiation, anthropogenic heat, convection, evapotranspiration and heat storage term) using Sentinel-2 and Landsat-8 images in Beijing. The simulated SUHI intensity, derived by combining all five contribution terms, exhibited a good consistency but a higher spatial resolution, than SUHI intensity extracted from Landsat-8 land surface temperature product. SUHI intensity tended to decrease from the old city to outsides, attributed to the decrease of evapotranspiration, solar radiation and anthropogenic heat term. The convection and heat storage term play a positive role in reducing SUHI. Among urban morphological blocks, low-rise and high-density blocks had the strongest SUHI, with the evapotranspiration term contributing the most. The results highlighted the capacity of the urban surface to evaporate water in affecting Beijing SUHI. The proposed method provides one useful tool to analyze the drivers of SUHI from the aspect of heat formation, which can be potentially applied worldwide for large-scale comparisons of how urbanization affects UHI.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114447"},"PeriodicalIF":11.1,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377647","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 : 2024-10-04DOI: 10.1016/j.rse.2024.114453
Fan Huang , Wenfeng Zhan , Zihan Liu , Huilin Du , Pan Dong , Xinya Wang
Cities worldwide face escalating climate change risks, underscoring the need for spatially and temporally resolved urban air temperature (Ta) data. While satellite-derived land surface temperature (LST) data have been widely used to estimate Ta, high-resolution hourly Ta estimation in urban areas remains underexplored. Traditional methods typically rely on LST data from geostationary satellites and continuous 24-h Ta observations from weather stations. To address these limitations, we introduce a method that combines a diurnal temperature cycle (DTC) model with a random forest model to estimate monthly mean hourly urban Ta at 1-km resolution. This approach leverages a limited number of diurnal Ta observations from weather stations, MODIS LST data, and ancillary information. The core idea of the proposed method is to transform the estimation of monthly mean hourly 1-km Ta into estimating 1-km DTC model parameters, primarily daily maximum and minimum Ta values. This method capitalizes on MODIS LST's ability to estimate daily Ta extremes and requires only four diurnal Ta observations within a daily cycle to estimate monthly mean hourly 1-km Ta. Station-based five-fold cross-validation yields overall RMSE values consistently below 1.0 °C across nine cities with diverse geographic and climatic contexts. The accuracy achieved with only four diurnal Ta observations rivals that obtained using continuous 24-h Ta observations. Even with a limited training set of ten stations, the overall RMSE remains below 1.0 °C for most cities. The proposed method proves effective for both single-city and multi-city modeling and can estimate daily hourly 1-km Ta under clear-sky conditions. In conclusion, this study offers a feasible, efficient, and versatile method for accurately estimating monthly mean hourly 1-km Ta, which can be readily applied to other cities and holds potential for various applications.
全世界的城市都面临着不断升级的气候变化风险，这就更加需要具有空间和时间分辨率的城市气温（Ta）数据。虽然源自卫星的陆地表面温度（LST）数据已被广泛用于估算气温，但城市地区的高分辨率每小时气温估算仍未得到充分开发。传统方法通常依赖于地球静止卫星的 LST 数据和气象站连续 24 小时的 Ta 观测数据。为了解决这些局限性，我们引入了一种将昼夜温度周期（DTC）模型与随机森林模型相结合的方法，以 1 千米的分辨率估算城市每小时的月平均 Ta 值。这种方法利用了气象站有限数量的昼夜气温观测数据、MODIS LST 数据和辅助信息。该方法的核心思想是将估算月平均每小时 1 公里 Ta 值转化为估算 1 公里 DTC 模型参数，主要是每日最大和最小 Ta 值。该方法利用了 MODIS LST 估算日极端 Ta 值的能力，只需在一个日周期内进行四次昼夜 Ta 观测，即可估算月平均每小时 1 公里 Ta 值。在地理和气候环境各异的九个城市中，基于站点的五倍交叉验证得出的总体 RMSE 值始终低于 1.0 °C。仅使用四个昼夜Ta观测数据所获得的准确度，可与使用连续24小时Ta观测数据所获得的准确度相媲美。即使使用有限的 10 个站点的训练集，大多数城市的总体 RMSE 仍低于 1.0 ℃。事实证明，所提出的方法对单个城市和多个城市的建模都很有效，并能在晴空条件下估算每日每小时 1 公里的 Ta 值。总之，本研究为精确估算月平均每小时 1 千米 Ta 值提供了一种可行、高效和通用的方法，该方法可随时应用于其他城市，并具有多种应用潜力。
{"title":"Satellite-based estimation of monthly mean hourly 1-km urban air temperature using a diurnal temperature cycle model","authors":"Fan Huang , Wenfeng Zhan , Zihan Liu , Huilin Du , Pan Dong , Xinya Wang","doi":"10.1016/j.rse.2024.114453","DOIUrl":"10.1016/j.rse.2024.114453","url":null,"abstract":"<div><div>Cities worldwide face escalating climate change risks, underscoring the need for spatially and temporally resolved urban air temperature (T<sub>a</sub>) data. While satellite-derived land surface temperature (LST) data have been widely used to estimate T<sub>a</sub>, high-resolution hourly T<sub>a</sub> estimation in urban areas remains underexplored. Traditional methods typically rely on LST data from geostationary satellites and continuous 24-h T<sub>a</sub> observations from weather stations. To address these limitations, we introduce a method that combines a diurnal temperature cycle (DTC) model with a random forest model to estimate monthly mean hourly urban T<sub>a</sub> at 1-km resolution. This approach leverages a limited number of diurnal T<sub>a</sub> observations from weather stations, MODIS LST data, and ancillary information. The core idea of the proposed method is to transform the estimation of monthly mean hourly 1-km T<sub>a</sub> into estimating 1-km DTC model parameters, primarily daily maximum and minimum T<sub>a</sub> values. This method capitalizes on MODIS LST's ability to estimate daily T<sub>a</sub> extremes and requires only four diurnal T<sub>a</sub> observations within a daily cycle to estimate monthly mean hourly 1-km T<sub>a</sub>. Station-based five-fold cross-validation yields overall RMSE values consistently below 1.0 °C across nine cities with diverse geographic and climatic contexts. The accuracy achieved with only four diurnal T<sub>a</sub> observations rivals that obtained using continuous 24-h T<sub>a</sub> observations. Even with a limited training set of ten stations, the overall RMSE remains below 1.0 °C for most cities. The proposed method proves effective for both single-city and multi-city modeling and can estimate daily hourly 1-km T<sub>a</sub> under clear-sky conditions. In conclusion, this study offers a feasible, efficient, and versatile method for accurately estimating monthly mean hourly 1-km T<sub>a</sub>, which can be readily applied to other cities and holds potential for various applications.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114453"},"PeriodicalIF":11.1,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374397","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 : 2024-10-03DOI: 10.1016/j.rse.2024.114457
Tianhao Zhang , Yu Gu , Bin Zhao , Lunche Wang , Zhongmin Zhu , Yun Lin , Xing Chang , Xinghui Xia , Zhe Jiang , Hongrong Shi , Wei Gong
Interregional transport plays a significant role in haze formation with varying and disputable contribution extent. Current research on quantitatively analyzing interregional atmospheric pollution transport has mainly relied on meteorological and chemical models. However, these models are typically affected by uncertainties due to the assumptions and simplifications inherent in the numerical simulations and source emission estimations. In this study, a comprehensive optical flow framework is developed to offer a new perspective on quantitative characterization of interregional transport of atmospheric pollution based on synergistic observations from geostationary and sun-synchronous satellites. In this framework, the high-frequency continuous aerosol observing images are regarded as video in computer vision, and an aerosol dynamic optical flow algorithm is proposed by incorporating aerosol-specific assumptions and constraints, overcoming the limitation that traditional optical flow methods are typically confined to rigid bodies. Results demonstrate that the developed optical flow framework could distinguish the aerosol transport process from other dynamic processes of aerosol development and accurately capture the fast-changing details of transport processes. Moreover, the satellite-based optical flow framework achieves aerosol transport results comparable to those of widely accepted model-based methods, demonstrating the physical interpretation of pixel-based optical flow results and highlighting its effectiveness in quantitative characterization of the atmospheric pollution transport process via the Aerosol Transport Index (ATI). Furthermore, a case analysis of long-term assessments of interregional transport of atmospheric pollution indicates that Beijing acts as a “sink” of atmospheric pollution, and a downward trend could be found from the annually averaged transported aerosol net loadings due to the emission reduction policy. Compared with model-based methods, the satellite-based optical flow framework is directly grounded in observations and does not rely on emission inventories that take years to update. Therefore, it not only helps improve understanding the patterns of atmospheric pollution interregional transport, but also provides a more efficient and economical way to assess the effectiveness of regional joint control policy.
{"title":"Observation-based quantification of aerosol transport using optical flow: A satellite perspective to characterize interregional transport of atmospheric pollution","authors":"Tianhao Zhang , Yu Gu , Bin Zhao , Lunche Wang , Zhongmin Zhu , Yun Lin , Xing Chang , Xinghui Xia , Zhe Jiang , Hongrong Shi , Wei Gong","doi":"10.1016/j.rse.2024.114457","DOIUrl":"10.1016/j.rse.2024.114457","url":null,"abstract":"<div><div>Interregional transport plays a significant role in haze formation with varying and disputable contribution extent. Current research on quantitatively analyzing interregional atmospheric pollution transport has mainly relied on meteorological and chemical models. However, these models are typically affected by uncertainties due to the assumptions and simplifications inherent in the numerical simulations and source emission estimations. In this study, a comprehensive optical flow framework is developed to offer a new perspective on quantitative characterization of interregional transport of atmospheric pollution based on synergistic observations from geostationary and sun-synchronous satellites. In this framework, the high-frequency continuous aerosol observing images are regarded as video in computer vision, and an aerosol dynamic optical flow algorithm is proposed by incorporating aerosol-specific assumptions and constraints, overcoming the limitation that traditional optical flow methods are typically confined to rigid bodies. Results demonstrate that the developed optical flow framework could distinguish the aerosol transport process from other dynamic processes of aerosol development and accurately capture the fast-changing details of transport processes. Moreover, the satellite-based optical flow framework achieves aerosol transport results comparable to those of widely accepted model-based methods, demonstrating the physical interpretation of pixel-based optical flow results and highlighting its effectiveness in quantitative characterization of the atmospheric pollution transport process via the Aerosol Transport Index (ATI). Furthermore, a case analysis of long-term assessments of interregional transport of atmospheric pollution indicates that Beijing acts as a “sink” of atmospheric pollution, and a downward trend could be found from the annually averaged transported aerosol net loadings due to the emission reduction policy. Compared with model-based methods, the satellite-based optical flow framework is directly grounded in observations and does not rely on emission inventories that take years to update. Therefore, it not only helps improve understanding the patterns of atmospheric pollution interregional transport, but also provides a more efficient and economical way to assess the effectiveness of regional joint control policy.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114457"},"PeriodicalIF":11.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142370140","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 : 2024-10-03DOI: 10.1016/j.rse.2024.114388
S. Aveni , M. Laiolo , A. Campus , F. Massimetti , D. Coppola
Detecting early signs of impending eruptions and monitoring the evolution of volcanic phenomena are fundamental objectives of applied volcanology, both essential for timely assessment of associated hazards. Thermal remote sensing proves to be a cost-effective, yet reliable, information source for these purposes, especially for the hundreds of volcanoes still lacking conventional ground-based monitoring networks. In this work, we present an innovative and effective single band TIR-based (11.45 μm) algorithm (TIRVolcH), capable of detecting thermal anomalies in a broad range of volcanic settings, from low-temperature hydrothermal systems to high-temperature effusive events. Based on the processing of Visible Infrared Imaging Radiometer Suite (VIIRS) scenes, the algorithm offers an unprecedented trade-off between spatial (375 m) and temporal resolution (multiple acquisitions per day), having the potential to detect thermal anomalies for pixel-integrated temperatures as low as 0.5 K above the background, while maintaining a false positive rate of ∼1.8 %. The analysis of decadal time series of VIIRS data (2012−2023), acquired at three different volcanoes, reveals how the algorithm can: (i) detect hydrothermal crises at fumarolic fields (Vulcano, Italy), (ii) unveil thermal unrest preceding dome extrusions and explosive eruptions (Agung, Indonesia), and (iii) spatially trace lava flows extent and quantify their advancement rate, as well as track their long-term cooling behaviour (La Palma, Spain).
We envisage that the algorithm will prove instrumental for detecting early signs of volcanic activity and following the evolution of eruptive phenomena, providing a useful tool for hazard management and risk reduction applications. Furthermore, the compilation of statistically robust multidecadal thermal datasets will provide novel insights and new perspectives into volcano monitoring, laying the ground for forthcoming higher-resolution TIR missions.
{"title":"TIRVolcH: Thermal Infrared Recognition of Volcanic Hotspots. A single band TIR-based algorithm to detect low-to-high thermal anomalies in volcanic regions.","authors":"S. Aveni , M. Laiolo , A. Campus , F. Massimetti , D. Coppola","doi":"10.1016/j.rse.2024.114388","DOIUrl":"10.1016/j.rse.2024.114388","url":null,"abstract":"<div><div>Detecting early signs of impending eruptions and monitoring the evolution of volcanic phenomena are fundamental objectives of applied volcanology, both essential for timely assessment of associated hazards. Thermal remote sensing proves to be a cost-effective, yet reliable, information source for these purposes, especially for the hundreds of volcanoes still lacking conventional ground-based monitoring networks. In this work, we present an innovative and effective single band TIR-based (11.45 μm) algorithm (TIRVolcH), capable of detecting thermal anomalies in a broad range of volcanic settings, from low-temperature hydrothermal systems to high-temperature effusive events. Based on the processing of Visible Infrared Imaging Radiometer Suite (VIIRS) scenes, the algorithm offers an unprecedented trade-off between spatial (375 m) and temporal resolution (multiple acquisitions per day), having the potential to detect thermal anomalies for pixel-integrated temperatures as low as 0.5 K above the background, while maintaining a false positive rate of ∼1.8 %. The analysis of decadal time series of VIIRS data (2012−2023), acquired at three different volcanoes, reveals how the algorithm can: (i) detect hydrothermal crises at fumarolic fields (Vulcano, Italy), (ii) unveil thermal unrest preceding dome extrusions and explosive eruptions (Agung, Indonesia), and (iii) spatially trace lava flows extent and quantify their advancement rate, as well as track their long-term cooling behaviour (La Palma, Spain).</div><div>We envisage that the algorithm will prove instrumental for detecting early signs of volcanic activity and following the evolution of eruptive phenomena, providing a useful tool for hazard management and risk reduction applications. Furthermore, the compilation of statistically robust multidecadal thermal datasets will provide novel insights and new perspectives into volcano monitoring, laying the ground for forthcoming higher-resolution TIR missions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114388"},"PeriodicalIF":11.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374396","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 : 2024-10-03DOI: 10.1016/j.rse.2024.114439
Tim Landwehr , Antara Dasgupta , Björn Waske
Flood maps based on Earth Observation (EO) data inform critical decision-making in almost every stage of the disaster management cycle, directly impacting the ability of affected individuals and governments to receive aid as well as informing policies on future adaptation. However, flood map validation also presents a challenge in the form of class imbalance between flood and non-flood classes, which has rarely been investigated. There are currently no established best practices for addressing this issue, and the accuracy of these maps is often viewed as a mere formality, which leads to a lack of user trust in flood map products and a limitation in their operational use and uptake. This paper provides the first comprehensive assessment of the impact of current EO-based flood map validation practices. Using flood inundation maps derived from Sentinel-1 synthetic aperture radar data with synthetically generated controlled errors and Copernicus Emergency Management Service flood maps as the ground truth, binary metrics were statistically evaluated for the quantification of flood detection accuracy for events under varying flood conditions. Especially, class specific metrics were found to be sensitive to the class imbalance, i.e. larger flood magnitudes result in higher metric scores, thus being naturally biased towards overpredicting classifiers. Metric stability across error percentiles and flood magnitudes was assessed through standard deviation calculated by bootstrapping to quantify the impact of sample selection subjectivity, where stratified sampling schemes exhibited the lowest standard deviation consistently. Thoughtful sample and response design were critical, with probability-based random sampling and proportional or equal class allocation vital to producing robust accuracy estimates comparable across study sites, error classes, and flood magnitudes. Results suggest that popular evaluation metrics such as the F1-Score are in fact unsuitable for accurate characterization of map quality and are not comparable across different study sites or events. Overall accuracy and MCC are shown to be the most robust performance metrics when sampling designs are optimized, and bootstrapping is demonstrated to be a necessary tool for estimating variability in map accuracy observed due to the spatial sampling of validation points. Results presented herein pave the way for the development of global flood map validation guidelines, to support wider use of and trust in EO-derived flood risk and recovery products, eventually allowing us to unlock the full potential of EO for improved flood resilience.
基于地球观测（EO）数据的洪水地图为灾害管理周期几乎每个阶段的关键决策提供信息，直接影响受灾个人和政府接受援助的能力，并为未来适应政策提供信息。然而，洪水地图验证也面临着一个挑战，即洪水等级与非洪水等级之间的不平衡，而这一问题很少得到研究。目前还没有既定的最佳实践来解决这一问题，这些地图的准确性往往被视为一种形式，导致用户对洪水地图产品缺乏信任，限制了其实际使用和吸收。本文首次全面评估了当前基于 EO 的洪水地图验证实践的影响。使用从哨兵-1 号合成孔径雷达数据中提取的洪水淹没图，并以合成生成的受控误差和哥白尼应急管理服务洪水地图作为地面实况，对二进制指标进行了统计评估，以量化在不同洪水条件下的洪水检测精度。特别是，研究发现，针对具体类别的指标对类别不平衡很敏感，即洪水量级越大，指标得分越高，因此自然会偏向于预测过高的分类器。不同误差百分位数和洪水量级的指标稳定性通过自举法计算的标准偏差进行评估，以量化样本选择主观性的影响，其中分层抽样方案的标准偏差始终最低。深思熟虑的样本和响应设计至关重要，基于概率的随机抽样和比例或等分级分配对于在不同研究地点、误差等级和洪水量级之间产生可比较的稳健准确性估计至关重要。结果表明，F1 分数等流行的评估指标实际上并不适合用于准确描述地图质量，而且在不同研究地点或事件之间也不具有可比性。结果表明，在优化采样设计时，总体精度和 MCC 是最稳健的性能指标，而且自举法是估算验证点空间采样导致的地图精度变化的必要工具。本文介绍的结果为制定全球洪水地图验证指南铺平了道路，以支持更广泛地使用和信任源自 EO 的洪水风险和恢复产品，最终使我们能够释放 EO 的全部潜力，提高抗洪能力。
{"title":"Towards robust validation strategies for EO flood maps","authors":"Tim Landwehr , Antara Dasgupta , Björn Waske","doi":"10.1016/j.rse.2024.114439","DOIUrl":"10.1016/j.rse.2024.114439","url":null,"abstract":"<div><div>Flood maps based on Earth Observation (EO) data inform critical decision-making in almost every stage of the disaster management cycle, directly impacting the ability of affected individuals and governments to receive aid as well as informing policies on future adaptation. However, flood map validation also presents a challenge in the form of class imbalance between flood and non-flood classes, which has rarely been investigated. There are currently no established best practices for addressing this issue, and the accuracy of these maps is often viewed as a mere formality, which leads to a lack of user trust in flood map products and a limitation in their operational use and uptake. This paper provides the first comprehensive assessment of the impact of current EO-based flood map validation practices. Using flood inundation maps derived from Sentinel-1 synthetic aperture radar data with synthetically generated controlled errors and Copernicus Emergency Management Service flood maps as the ground truth, binary metrics were statistically evaluated for the quantification of flood detection accuracy for events under varying flood conditions. Especially, class specific metrics were found to be sensitive to the class imbalance, i.e. larger flood magnitudes result in higher metric scores, thus being naturally biased towards overpredicting classifiers. Metric stability across error percentiles and flood magnitudes was assessed through standard deviation calculated by bootstrapping to quantify the impact of sample selection subjectivity, where stratified sampling schemes exhibited the lowest standard deviation consistently. Thoughtful sample and response design were critical, with probability-based random sampling and proportional or equal class allocation vital to producing robust accuracy estimates comparable across study sites, error classes, and flood magnitudes. Results suggest that popular evaluation metrics such as the F1-Score are in fact unsuitable for accurate characterization of map quality and are not comparable across different study sites or events. Overall accuracy and MCC are shown to be the most robust performance metrics when sampling designs are optimized, and bootstrapping is demonstrated to be a necessary tool for estimating variability in map accuracy observed due to the spatial sampling of validation points. Results presented herein pave the way for the development of global flood map validation guidelines, to support wider use of and trust in EO-derived flood risk and recovery products, eventually allowing us to unlock the full potential of EO for improved flood resilience.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114439"},"PeriodicalIF":11.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142370131","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 : 2024-10-02DOI: 10.1016/j.rse.2024.114454
Zhaoying Song , Cong Xu , Qifu Luan , Yanjie Li
Phenolic compounds (PC) are important secondary metabolites in plants, playing a crucial role in plant defense mechanisms against pathogens and other plants. Monitoring PC levels is important for understanding tree stress and implementing effective breeding programs. However, traditional methods for monitoring PC are time-consuming, prone to altering the phenolic composition, and mostly applicable only on a small scale. In this study, we evaluated the performance of Unoccupied Aerial Vehicles (UAV) multispectral imaging in estimating the canopy phenolic content in slash pine over an 11-month period in 2021 and a seven-month period in 2022. Three machine learning models including Partial least squares regression (PLSR), Random forest (RF) and Support Vector Machine (SVM) were compared to determine the optimal predictive model for canopy PC. The RF model provided the best predictive results, with R2 values of 0.82 for the validation set and 0.94 for the calibration set. Additionally, the study assesses the heritable variation in canopy PC over time, with the monthly heritability (h2) of PC ranging from 0 to 0.26 in 2021 and from 0 to 0.35 in 2022; The highest h2 levels were observed in July and September 2021and July 2022. The findings demonstrate significant genetic control over the variation of PC. Furthermore, we observed higher breeding values and genetic gains in July and November, which further supports the strong correlation between PC and environmental factors such as temperature and light intensity. To the best of our knowledge, this is the first study to employ time-series UAV multispectral imaging to predict secondary metabolites in pine trees and estimate their genetic variation over time. As a proof of concept, these findings provide more reliable information for tree breeding programs, ultimately enhancing their overall performance.
酚类化合物（PC）是植物体内重要的次级代谢产物，在植物抵御病原体和其他植物的防御机制中发挥着至关重要的作用。监测酚类化合物的含量对于了解树木的胁迫和实施有效的育种计划非常重要。然而，传统的 PC 监测方法耗时长，容易改变酚类成分，而且大多只适用于小规模监测。在本研究中，我们评估了无人飞行器（UAV）多光谱成像技术在估算2021年为期11个月和2022年为期7个月的斜伐松树冠酚含量方面的性能。比较了三种机器学习模型，包括偏最小二乘回归（PLSR）、随机森林（RF）和支持向量机（SVM），以确定冠层 PC 的最佳预测模型。RF 模型提供了最佳预测结果，验证集的 R2 值为 0.82，校准集的 R2 值为 0.94。此外，该研究还评估了冠层 PC 随时间变化的遗传变异，2021 年 PC 的月遗传率 (h2) 在 0 至 0.26 之间，2022 年在 0 至 0.35 之间；2021 年 7 月和 9 月以及 2022 年 7 月的 h2 水平最高。这些研究结果表明，遗传对 PC 的变化具有重要的控制作用。此外，我们还观察到 7 月和 11 月的育种值和遗传增益较高，这进一步证实了 PC 与温度和光照强度等环境因素之间的密切联系。据我们所知，这是第一项利用时间序列无人机多光谱成像技术预测松树次生代谢物并估算其遗传变异的研究。作为概念验证，这些发现为树木育种计划提供了更可靠的信息，最终提高了其整体性能。
{"title":"Multitemporal UAV study of phenolic compounds in slash pine canopies","authors":"Zhaoying Song , Cong Xu , Qifu Luan , Yanjie Li","doi":"10.1016/j.rse.2024.114454","DOIUrl":"10.1016/j.rse.2024.114454","url":null,"abstract":"<div><div>Phenolic compounds (PC) are important secondary metabolites in plants, playing a crucial role in plant defense mechanisms against pathogens and other plants. Monitoring PC levels is important for understanding tree stress and implementing effective breeding programs. However, traditional methods for monitoring PC are time-consuming, prone to altering the phenolic composition, and mostly applicable only on a small scale. In this study, we evaluated the performance of Unoccupied Aerial Vehicles (UAV) multispectral imaging in estimating the canopy phenolic content in slash pine over an 11-month period in 2021 and a seven-month period in 2022. Three machine learning models including Partial least squares regression (PLSR), Random forest (RF) and Support Vector Machine (SVM) were compared to determine the optimal predictive model for canopy PC. The RF model provided the best predictive results, with R<sup>2</sup> values of 0.82 for the validation set and 0.94 for the calibration set. Additionally, the study assesses the heritable variation in canopy PC over time, with the monthly heritability (<em>h</em><sup><em>2</em></sup>) of PC ranging from 0 to 0.26 in 2021 and from 0 to 0.35 in 2022; The highest <em>h</em><sup><em>2</em></sup> levels were observed in July and September 2021and July 2022. The findings demonstrate significant genetic control over the variation of PC. Furthermore, we observed higher breeding values and genetic gains in July and November, which further supports the strong correlation between PC and environmental factors such as temperature and light intensity. To the best of our knowledge, this is the first study to employ time-series UAV multispectral imaging to predict secondary metabolites in pine trees and estimate their genetic variation over time. As a proof of concept, these findings provide more reliable information for tree breeding programs, ultimately enhancing their overall performance.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114454"},"PeriodicalIF":11.1,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142369237","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 : 2024-10-02DOI: 10.1016/j.rse.2024.114444
Yichen Yang , Yudi Zhou , Iwona S. Stachlewska , Yongxiang Hu , Xiaomei Lu , Weibiao Chen , Jiqiao Liu , Wenbo Sun , Suhui Yang , Yuting Tao , Lei Lin , Weige Lv , Lingying Jiang , Lan Wu , Chong Liu , Dong Liu
Spaceborne lidars have demonstrated outstanding global ocean observation in terms of sampling at day- and night-time and penetrating thin cloud and aerosol layers. A spaceborne high-spectral-resolution lidar (HSRL) has the potential to provide accurate optical properties by decreasing the number of assumptions in the retrieval algorithm in comparison with classical elastic spaceborne lidar. In this paper, we report the first ocean application from both particulate and molecular scattering measurements of spaceborne HSRL, namely Aerosol and Carbon Detection Lidar (ACDL) onboard China DQ-1 satellite. We use the ACDL/DQ-1 HSRL to quantify particulate backscatter coefficient bbp in the global ocean, with a novel algorithm exploiting the column-integrated particulate and molecular signals. The ACDL-derived bbp data agree well with MODIS-derived data through along-track and global comparisons. It also presents high correlations with the Argo floats in-situ data under various spatial and temporal matching windows. The ACDL/DQ-1 is anticipated to become an important part of the global ocean satellite observations addressing some limitations of traditional passive ocean colour observation.
{"title":"Spaceborne high-spectral-resolution lidar ACDL/DQ-1 measurements of the particulate backscatter coefficient in the global ocean","authors":"Yichen Yang , Yudi Zhou , Iwona S. Stachlewska , Yongxiang Hu , Xiaomei Lu , Weibiao Chen , Jiqiao Liu , Wenbo Sun , Suhui Yang , Yuting Tao , Lei Lin , Weige Lv , Lingying Jiang , Lan Wu , Chong Liu , Dong Liu","doi":"10.1016/j.rse.2024.114444","DOIUrl":"10.1016/j.rse.2024.114444","url":null,"abstract":"<div><div>Spaceborne lidars have demonstrated outstanding global ocean observation in terms of sampling at day- and night-time and penetrating thin cloud and aerosol layers. A spaceborne high-spectral-resolution lidar (HSRL) has the potential to provide accurate optical properties by decreasing the number of assumptions in the retrieval algorithm in comparison with classical elastic spaceborne lidar. In this paper, we report the first ocean application from both particulate and molecular scattering measurements of spaceborne HSRL, namely Aerosol and Carbon Detection Lidar (ACDL) onboard China DQ-1 satellite. We use the ACDL/DQ-1 HSRL to quantify particulate backscatter coefficient <em>b</em><sub>bp</sub> in the global ocean, with a novel algorithm exploiting the column-integrated particulate and molecular signals. The ACDL-derived <em>b</em><sub>bp</sub> data agree well with MODIS-derived data through along-track and global comparisons. It also presents high correlations with the Argo floats <em>in-situ</em> data under various spatial and temporal matching windows. The ACDL/DQ-1 is anticipated to become an important part of the global ocean satellite observations addressing some limitations of traditional passive ocean colour observation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114444"},"PeriodicalIF":11.1,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142369204","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}
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