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Generating vegetation leaf area index earth system data record from multiple sensors. Part 1: Theory

机译:从多个传感器生成植被叶面积指数地球系统数据记录。第1部分:理论

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The generation of multi-decade long Earth System Data Records (ESDRs) of Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) from remote sensing measurements of multiple sensors is key to monitoring long-term changes in vegetation due to natural and anthropogenic influences. Challenges in developing such ESDRs include problems in remote sensing science (modeling of variability in global vegetation, scaling, atmospheric correction) and sensor hardware (differences in spatial resolution, spectral bands, calibration, and information content). In this paper, we develop a physically based approach for deriving LAI and FPAR products from the Advanced Very High Resolution Radiometer (AVHRR) data that are of comparable quality to the Moderate resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products, thus realizing the objective of producing a long (multi-decadal) time series of these products. The approach is based on the radiative transfer theory of canopy spectral invariants which facilitates parameterization of the canopy spectral bidirectional reflectance factor (BRF). The methodology permits decoupling of the structural and radiometric components and obeys the energy conservation law. The approach is applicable to any optical sensor, however, it requires selection of sensor-specific values of configurable parameters, namely, the single scattering albedo and data uncertainty. According to the theory of spectral invariants, the single scattering albedo is a function of the spatial scale, and thus, accounts for the variation in BRF with sensor spatial resolution. Likewise, the single scattering albedo accounts for the variation in spectral BRF with sensor bandwidths. The second adjustable parameter is data uncertainty. which accounts for varying information content of the remote sensing measurements, i.e., Normalized Difference Vegetation Index (NDVI, low information content), vs. spectral BRF (higher information content). Implementation of this approach indicates good consistency in LAI values retrieved from NDVI (AVHRR-mode) and spectral BRF (MODIS-mode). Specific details of the implementation and evaluation of the derived products are detailed in the second part of this two-paper series. (C) 2008 Elsevier Inc. All rights reserved.
机译:从多个传感器的遥感测量中生成叶面积指数(LAI)的数十年长地球系统数据记录(ESDR)和植被吸收的光合有效辐射分数(FPAR)是监测植被长期变化的关键由于自然和人为因素的影响。开发此类ESDR所面临的挑战包括遥感科学(全球植被变化建模,缩放,大气校正)和传感器硬件(空间分辨率,光谱带,校准和信息内容的差异)方面的问题。在本文中,我们开发了一种基于物理的方法,用于从高级超高分辨率辐射计(AVHRR)数据得出LAI和FPAR产品,这些数据的质量与中分辨率成像光谱仪(MODIS)LAI和FPAR产品相当,从而实现了目标生产这些产品的较长(数十年)时间序列。该方法基于冠层光谱不变量的辐射传递理论,该理论促进了冠层光谱双向反射系数(BRF)的参数化。该方法允许将结构和辐射测量组件分离,并遵守节能法。该方法适用于任何光学传感器,但是,它需要选择传感器特定的可配置参数值,即单个散射反照率和数据不确定性。根据光谱不变性理论,单个散射反照率是空间尺度的函数,因此,BRF随传感器空间分辨率而变化。同样,单个散射反照率解释了频谱BRF随传感器带宽的变化。第二个可调参数是数据不确定性。它说明了遥感测量的不同信息内容,即归一化植被指数(NDVI,信息含量低)与光谱BRF(信息含量高)的关系。此方法的实现表明从NDVI(AVHRR模式)和频谱BRF(MODIS模式)检索到的LAI值具有良好的一致性。此两篇文章系列的第二部分详细介绍了实现和评估派生产品的具体细节。 (C)2008 Elsevier Inc.保留所有权利。

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