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Land Surface Temperature product validation using NOAA's surface climate observation networks—Scaling methodology for the Visible Infrared Imager Radiometer Suite (VIIRS)

机译:使用NOaa表面气候进行地表温度产品验证观察网络 - 可见红外成像仪的缩放方法辐射计套件(VIIRs)

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摘要

NOAA will soon use the new Visible Infrared Imager Radiometer Suite (VIIRS) on the Joint Polar Satellite System (JPSS) as its primary polar-orbiting satellite imager. Employing a near real-time processing system, NOAA will generate a series of Environmental Data Records (EDRs) from VIIRS data. For example, the VIIRS Land Surface Temperature (LST) EDR will estimate the surface skin temperature over all global land areas and provide key information for monitoring Earth surface energy and water fluxes. Because both VIIRS and its processing algorithms are new, NOAA is conducting a rigorous calibration and validation program to understand and improve product quality. This paper presents a new validation methodology to estimate the quantitative uncertainty in the LST EDR, and contribute to improving the retrieval algorithm. It employs a physically-based approach to scaling up point LST measurements currently made operationally at many field and weather stations around the world. The scaling method consists of the merging information collected at different spatial resolutions within a land surface model to fully characterize large area (km×km scale) satellite products. The approach can be used to explore scaling issues over terrestrial surfaces spanning a large range of climate regimes and land cover types, including forests and mixed vegetated areas. The methodology was tested successfully with NASA/MODIS data, indicating an absolute error for MODIS LST products of 2.0 K at a mixed agricultural site (Bondville, IL) when accounting for scaling, and higher than 3 K without scaling. The VIIRS LST EDR requires a 1.5 K measurement accuracy and 2.5 K measurement precision. Ultimately, this validation approach should lead to an accurate and continuously-assessed VIIRS LST product suitable to support weather forecast, hydrological applications, or climate studies. It is readily adaptable to other moderate resolution satellite systems.
机译:NOAA将很快在联合极性卫星系统(JPSS)上使用新的可见红外成像仪透射率套件(VIIR)作为其主要北极轨道卫星成像仪。使用近实时处理系统,NOAA将从VIIR数据生成一系列环境数据记录(EDRS)。例如,Viirs陆地温度(LST)EDR将估计所有全球土地区域的表面皮肤温度,并提供用于监测地球表面能量和水通量的关键信息。因为VIIR及其处理算法都是新的,所以NOAA正在进行严格的校准和验证程序,以了解和提高产品质量。本文提出了一种新的验证方法,以估算LST EDR中的定量不确定性,并有助于提高检索算法。它采用了基于基于物理的方法来扩大目前在世界各地的许多领域和天气站运行的点LST测量。缩放方法包括在陆地面模型内以不同空间分辨率收集的合并信息,以完全表征大面积(KM×km Scale)卫星产品。该方法可用于探索跨越大型气候制度和陆地覆盖类型的陆地表面的缩放问题,包括森林和混合植被区域。使用NASA / MODIS数据成功测试了方法,表示在混合农业部位(Bondville,IL)的MODIS LST产品的绝对误差,当占缩放时,高于3 k而没有缩放。 VIIRS LST EDR需要1.5克测量精度和2.5 k测量精度。最终,这种验证方法应导致准确和不断评估的VIIRS LST产品,适用于支持天气预报,水文应用或气候研究。它易于适用于其他温和的卫星系统。

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