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Ground Comparisons at RadCalNet Sites to Determine the Equivalence of Sites within the Network

机译：RadCalNet站点的地面比较，以确定网络中站点的等效性

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摘要

The Radiometric Calibration Network (RadCalNet) routinely brings together data from several instrumented ground sites to provide users with top-of-atmosphere (TOA) reflectance data. These data are provided on cloud free days between 09:00 and 15:00 for the spectral range 400 to 1000 nm (and up to 2500 nm depending on available instrumentation) at a 10 nm spectral resolution. The data represents the nadir view of the ground. A key aspect to RadCalNet is a strict adherence to Sl-traceability leading to well-understood and defensible uncertainty analysis to ensure that the different sites operating within RadCalNet are consistent with one another. This process includes the requirement to validate uncertainty analyses. One way in which this can be achieved is through field-based comparisons between independently measured reflectance of the ground and the RadCalNet data product for that date / time. To test the potential of such comparisons for uncertainty validation, a comparison campaign has been undertaken by the UK's National Physical Laboratory (NPL) with the University of Arizona (UA) in March 2017 at the Railroad Valley radiometric test site in Nevada, USA using instruments developed for the purpose by UA and the Czech Metrology Institute (CMI). The measurements taken at the site with a new instrument, the Multispectral Transfer Radiometer (MuSTR) have been compared against the RadCalNet bottom-of-atmosphere (BOA) dataset to determine the equivalence of the reflectance. Radiances from MuSTR have also been compared against radiance measurements from the in-situ instrumentation at the site using a 48 % reflectance tarpaulin as a target. The comparisons presented here have demonstrated the utility of field-based comparisons for RadCalNet. In addition, a potential methodology for these comparisons has been developed and potential areas for improvement, including the systematic development of field-based uncertainty analyses, have been identified.
机译：辐射校准网络（RadCalNet）例行地将来自几个已测量地面站点的数据汇总在一起，以向用户提供大气层（TOA）反射率数据。这些数据是在09:00至15:00的无云日提供的，光谱范围为400至1000 nm（取决于可用的仪器，最大范围为2500 nm），光谱分辨率为10 nm。数据代表地面的最低点视图。 RadCalNet的一个关键方面是严格遵守Sl可追溯性，这导致了易于理解和合理的不确定性分析，以确保RadCalNet中运行的不同站点彼此一致。该过程包括验证不确定性分析的要求。实现此目标的一种方法是通过在该日期/时间独立测量的地面反射率与RadCalNet数据产品之间的基于字段的比较。为了测试这种比较用于不确定性验证的潜力，英国国家物理实验室（NPL）与亚利桑那大学（UA）于2017年3月在美国内华达州的铁路谷辐射测试站点进行了一项使用仪器的比较运动由UA和捷克计量学院（CMI）为此目的而开发。已使用新仪器多光谱传输辐射计（MuSTR）在现场进行的测量与RadCalNet大气底部（BOA）数据集进行了比较，以确定反射率的等效性。还使用48％反射率的防水油布作为目标，将MuSTR的辐射与现场仪器的辐射测量进行了比较。此处提供的比较已证明RadCalNet的基于字段的比较的实用性。此外，已经开发出了用于这些比较的潜在方法，并确定了潜在的改进领域，包括系统开发基于现场的不确定性分析。

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  • 来源
    《Sensors, systems, and next-generation satellites XXI》|2017年|104231B.1-104231B.13|共13页
  • 会议地点 Warsaw(PL)
  • 作者

    Tracy Scanlon; Claire Greenwell; Jeffrey Czapla-Myers; Nikolaus Anderson; Teresa Goodman; Kurt Thome; Emma Woolliams; Geiland Porrovecchio; Petr Linduska; Marek Smid; Nigel P. Fox;

  • 作者单位

    National Physical Laboratory, Hampton Road, Teddington, London, TW11 0LW, UK;

    National Physical Laboratory, Hampton Road, Teddington, London, TW11 0LW, UK;

    College of Optical Sciences, The University of Arizona, 1630 E. University Blvd., P.O. Box 210094, Tucson, AZ 85721-0094, USA;

    College of Optical Sciences, The University of Arizona, 1630 E. University Blvd., P.O. Box 210094, Tucson, AZ 85721-0094, USA;

    National Physical Laboratory, Hampton Road, Teddington, London, TW11 0LW, UK;

    NASA/Goddard Space Flight Center, Greenbelt, MD 20771, USA;

    National Physical Laboratory, Hampton Road, Teddington, London, TW11 0LW, UK;

    Czech Metrology Institute, LFM, V Botanice 4, Praha 5, Czech Republic;

    Czech Metrology Institute, LFM, V Botanice 4, Praha 5, Czech Republic;

    Czech Metrology Institute, LFM, V Botanice 4, Praha 5, Czech Republic;

    National Physical Laboratory, Hampton Road, Teddington, London, TW11 0LW, UK;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    RadCalNet; Comparisons; SI Traceability; Railroad Valley; MuSTR;

    机译：RadCalNet；比较； SI可追溯性；铁路谷MuSTR;

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