Remote Sensing of Environment最新文献

{"title":"Using river hypsometry to improve remote sensing of river discharge","authors":"Michael Durand ,&nbsp;Chunli Dai ,&nbsp;Joachim Moortgat ,&nbsp;Bidhyananda Yadav ,&nbsp;Renato Prata de Moraes Frasson ,&nbsp;Ziwei Li ,&nbsp;Kylie Wadkwoski ,&nbsp;Ian Howat ,&nbsp;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}

{"title":"Individual tree species classification using low-density airborne multispectral LiDAR data via attribute-aware cross-branch transformer","authors":"Lanying Wang ,&nbsp;Dening Lu ,&nbsp;Linlin Xu ,&nbsp;Derek T. Robinson ,&nbsp;Weikai Tan ,&nbsp;Qian Xie ,&nbsp;Haiyan Guan ,&nbsp;Michael A. Chapman ,&nbsp;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/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 <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}

{"title":"Surface energy balance-based surface urban heat island decomposition at high resolution","authors":"Fengxiang Guo ,&nbsp;Jiayue Sun ,&nbsp;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}

{"title":"Satellite-based estimation of monthly mean hourly 1-km urban air temperature using a diurnal temperature cycle model","authors":"Fan Huang ,&nbsp;Wenfeng Zhan ,&nbsp;Zihan Liu ,&nbsp;Huilin Du ,&nbsp;Pan Dong ,&nbsp;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_a) data. While satellite-derived land surface temperature (LST) data have been widely used to estimate T_a, high-resolution hourly T_a estimation in urban areas remains underexplored. Traditional methods typically rely on LST data from geostationary satellites and continuous 24-h T_a 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_a at 1-km resolution. This approach leverages a limited number of diurnal T_a 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_a into estimating 1-km DTC model parameters, primarily daily maximum and minimum T_a values. This method capitalizes on MODIS LST's ability to estimate daily T_a extremes and requires only four diurnal T_a observations within a daily cycle to estimate monthly mean hourly 1-km T_a. 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_a observations rivals that obtained using continuous 24-h T_a 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_a under clear-sky conditions. In conclusion, this study offers a feasible, efficient, and versatile method for accurately estimating monthly mean hourly 1-km T_a, 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}

{"title":"Observation-based quantification of aerosol transport using optical flow: A satellite perspective to characterize interregional transport of atmospheric pollution","authors":"Tianhao Zhang ,&nbsp;Yu Gu ,&nbsp;Bin Zhao ,&nbsp;Lunche Wang ,&nbsp;Zhongmin Zhu ,&nbsp;Yun Lin ,&nbsp;Xing Chang ,&nbsp;Xinghui Xia ,&nbsp;Zhe Jiang ,&nbsp;Hongrong Shi ,&nbsp;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}

{"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 ,&nbsp;M. Laiolo ,&nbsp;A. Campus ,&nbsp;F. Massimetti ,&nbsp;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}

{"title":"Towards robust validation strategies for EO flood maps","authors":"Tim Landwehr ,&nbsp;Antara Dasgupta ,&nbsp;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}

{"title":"Multitemporal UAV study of phenolic compounds in slash pine canopies","authors":"Zhaoying Song ,&nbsp;Cong Xu ,&nbsp;Qifu Luan ,&nbsp;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 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 (<em>h</em>^<em>2</em>) of PC ranging from 0 to 0.26 in 2021 and from 0 to 0.35 in 2022; The highest <em>h</em>^<em>2</em> 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}

{"title":"Spaceborne high-spectral-resolution lidar ACDL/DQ-1 measurements of the particulate backscatter coefficient in the global ocean","authors":"Yichen Yang ,&nbsp;Yudi Zhou ,&nbsp;Iwona S. Stachlewska ,&nbsp;Yongxiang Hu ,&nbsp;Xiaomei Lu ,&nbsp;Weibiao Chen ,&nbsp;Jiqiao Liu ,&nbsp;Wenbo Sun ,&nbsp;Suhui Yang ,&nbsp;Yuting Tao ,&nbsp;Lei Lin ,&nbsp;Weige Lv ,&nbsp;Lingying Jiang ,&nbsp;Lan Wu ,&nbsp;Chong Liu ,&nbsp;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>_bp in the global ocean, with a novel algorithm exploiting the column-integrated particulate and molecular signals. The ACDL-derived <em>b</em>_bp 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}

{"title":"Stability of cloud detection methods for Land Surface Temperature (LST) Climate Data Records (CDRs)","authors":"Claire E. Bulgin ,&nbsp;Ross I. Maidment ,&nbsp;Darren Ghent ,&nbsp;Christopher J. Merchant","doi":"10.1016/j.rse.2024.114440","DOIUrl":"10.1016/j.rse.2024.114440","url":null,"abstract":"<div><div>The stability of a climate data record (CDR) is essential for evaluating long-term trends in surface temperature using remote sensing products. In the case of a satellite-derived CDR of land surface temperature (LST), this includes the stability of processing steps prior to the estimation of the target climate variable. Instability in the masking of cloud-affected observations can result in non-geophysical trends in a LST CDR. This paper provides an assessment of cloud detection performance stability over a 25-year LST CDR generated using data from the second Along-Track Scanning Radiometer (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR), the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Sea and Land Surface Temperature Radiometer (SLSTR). We evaluate three cloud detection methodologies, one fully Bayesian, one naïve probabilistic and the operational threshold-based cloud mask provided with each sensor, at four in-situ ceilometer sites. Of the 12 algorithm-site combinations assessed, only two (17 %) were stable across the full timeseries with respect to both cloud contamination and missed clear-sky observations. Five (42 %) were stable with respect to missed clear-sky observations only. The associated impacts on LST trends in the CDR could be as large as (+/−)0.73 K per decade (0.43 K per decade above the target stability), which means that attention needs to be paid to this aspect of stability in order to understand uncertainty in long-term observed trends. Given that cloud detection stability has not to our knowledge been previously assessed for any target climate variable, this conclusion may apply more broadly to other satellite-derived CDRs.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114440"},"PeriodicalIF":11.1,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142369203","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}