Frontiers in Remote Sensing最新文献

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Retrieving BRDFs from UAV-based radiometers for fiducial reference measurements: caveats and recommendations 从无人机辐射计读取 BRDF,用于靶标参考测量:注意事项和建议
Frontiers in Remote Sensing Pub Date : 2023-11-20 DOI: 10.3389/frsen.2023.1285800
Sebastian Schunke, Vincent Leroy, Yves Govaerts
{"title":"Retrieving BRDFs from UAV-based radiometers for fiducial reference measurements: caveats and recommendations","authors":"Sebastian Schunke, Vincent Leroy, Yves Govaerts","doi":"10.3389/frsen.2023.1285800","DOIUrl":"https://doi.org/10.3389/frsen.2023.1285800","url":null,"abstract":"Surface Bidirectional reflectance distribution function (BRDF) is a key intrinsic geophysical variable depending only on the characteristics of the observed medium. It is therefore the most suitable measurand to support the definition of fiducial reference measurements (FRM). Field acquisition of surface reflectance data relies on substantial assumptions and simplifications, often without accounting for their impact. For example, the BRDF is a theoretical concept and can never be measured in the field. In contrast, the hemispherical conical reflectance factor (HCRF), which is the measurand obtained during field campaigns, is impacted by all scene elements and is not intrinsic to the surface. This study analyses the impact of four parameters (atmospheric scattering, measurement device field of view cropping, acquisition duration, non-Lambertian reference panels) on HCRF estimation. Simulations are performed on a 3D vegetation scene, using the new radiative transfer model Eradiate. It is found that among the aforementioned parameters, atmospheric scattering alone leads to a relative root-mean-square error (RRMSE) of more than 10% between HCRF and reference Bidirectional reflectance factor (BRF).","PeriodicalId":502669,"journal":{"name":"Frontiers in Remote Sensing","volume":"30 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139258222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Editorial: Advances in deep learning approaches applied to remotely sensed images 社论:应用于遥感图像的深度学习方法的进展
Frontiers in Remote Sensing Pub Date : 2023-11-15 DOI: 10.3389/frsen.2023.1281162
Cláudia Maria Almeida, Lichao Mou, Friedrich Fraundorfer
{"title":"Editorial: Advances in deep learning approaches applied to remotely sensed images","authors":"Cláudia Maria Almeida, Lichao Mou, Friedrich Fraundorfer","doi":"10.3389/frsen.2023.1281162","DOIUrl":"https://doi.org/10.3389/frsen.2023.1281162","url":null,"abstract":"","PeriodicalId":502669,"journal":{"name":"Frontiers in Remote Sensing","volume":"10 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139272397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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