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Characterization Approaches to Place Invariant Sites on SI-Traceable Scales

机译:将不变位点置于SI跟踪标度上的表征方法

摘要

The effort to understand the Earth's climate system requires a complete integration of remote sensing imager data across time and multiple countries. Such an integration necessarily requires ensuring inter-consistency between multiple sensors to create the data sets needed to understand the climate system. Past efforts at inter-consistency have forced agreement between two sensors using sources that are viewed by both sensors at nearly the same time, and thus tend to be near polar regions over snow and ice. The current work describes a method that would provide an absolute radiometric calibration of a sensor rather than an inter-consistency of a sensor relative to another. The approach also relies on defensible error budgets that eventually provides a cross comparison of sensors without systematic errors. The basis of the technique is a model-based, SI-traceable prediction of at-sensor radiance over selected sites. The predicted radiance would be valid for arbitrary view and illumination angles and for any date of interest that is dominated by clear-sky conditions. The effort effectively works to characterize the sites as sources with known top-of-atmosphere radiance allowing accurate intercomparison of sensor data that without the need for coincident views. Data from the Advanced Spaceborne Thermal Emission and Reflection and Radiometer (ASTER), Enhanced Thematic Mapper Plus (ETM+), and Moderate Resolution Imaging Spectroradiometer (MODIS) are used to demonstrate the difficulties of cross calibration as applied to current sensors. Special attention is given to the differences caused in the cross-comparison of sensors in radiance space as opposed to reflectance space. The radiance comparisons lead to significant differences created by the specific solar model used for each sensor. The paper also proposes methods to mitigate the largest error sources in future systems. The results from these historical intercomparisons provide the basis for a set of recommendations to ensure future SI-traceable cross calibration using future missions such as CLARREO and TRUTHS. The paper describes a proposed approach that relies on model-based, SI-traceable predictions of at-sensor radiance over selected sites. The predicted radiance would be valid for arbitrary view and illumination angles and for any date of interest that is dominated by clear-sky conditions. The basis of the method is highly accurate measurements of at-sensor radiance of sufficient quality to understand the spectral and BRDF characteristics of the site and sufficient historical data to develop an understanding of temporal effects from changing surface and atmospheric conditions.
机译:理解地球气候系统的工作要求跨时间和多个国家的遥感影像数据完全集成。这样的整合必然要求确保多个传感器之间的相互一致性,以创建理解气候系统所需的数据集。过去在一致性方面的努力已经迫使两个传感器之间使用几乎同时被两个传感器观察到的信号源达成一致,因此往往接近冰雪上的极地地区。当前的工作描述了一种方法,该方法将提供传感器的绝对辐射度校准,而不是传感器相对于另一个的相互一致性。该方法还依赖可辩护的错误预算,该预算最终提供了传感器的交叉比较而没有系统错误。该技术的基础是对选定位置的传感器辐射的基于模型的,SI可追踪的预测。预测的辐射率对于任意视角和照明角度以及晴空条件主导的任何关注日期都是有效的。这项工作有效地将这些站点定性为具有已知大气顶辐射源的源,从而允许传感器数据的精确比对,而无需重合视图。来自先进的星载热发射与反射和辐射计(ASTER),增强型专题测绘仪Plus(ETM +)和中分辨率成像光谱仪(MODIS)的数据用于证明应用于电流传感器的交叉校准的困难。要特别注意辐射空间中与反射空间相对的传感器交叉比较中的差异。辐射度比较导致每个传感器使用的特定太阳能模型产生的显着差异。本文还提出了减轻未来系统中最大错误源的方法。这些历史比较的结果为一系列建议提供了基础,这些建议可确保使用将来的任务(例如CLARREO和TRUTHS)来确保将来的SI可追溯的交叉校准。本文介绍了一种建议的方法,该方法依赖于所选位置上基于模型的,SI跟踪的传感器辐射亮度预测。预测的辐射率对于任意视角和照明角度以及晴空条件主导的任何关注日期都是有效的。该方法的基础是对传感器辐射的高度精确测量,其质量足以了解站点的光谱和BRDF特性,并具有足够的历史数据以了解对不断变化的地面和大气条件的时间影响。

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    Thome Kurtis;

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  • 年度 2012
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