Vegetation indices derived from solar and photosynthetically active radiation (PAR) sensors (i.e. radiation derived) have been under-utilized in inferring ecosystem function, despite measurement capability at hundreds of sites. This under-utilization may be attributed to reported mismatches among the seasonality of radiation- and satellite-derived vegetation indices and canopy photosynthesis; herein referred to as measurement biases. Here biases in radiation derived reflectance and vegetation indices were assessed using a decadal record of satellite and ground based spectroradiometer data, ecosystem phenology and CO₂ fluxes, and radiation derived vegetation indices (i.e. the Normalized Difference Vegetation Index [NDVI], the two band Enhanced Vegetation Index [EVI2]) from a high latitude tundra site (i.e. Imnaviat). At Imnaviat, we found poor correspondence between the three types of reflectance and vegetation indices, especially during the latter part of the growing season. Radiation derived vegetation indices resulted in incorrect estimates of phenological timing of up to a month and poor relationships with canopy photosynthesis (i.e. Gross Ecosystem Exchange (GEE)). These mismatches were attributed to solar position (i.e. solar zenith and azimuth angle) and a method, based on the diel visible and near-infrared albedo variation, was developed to improve the performance of the vegetation indices. The ability of radiation derived vegetation indices to infer GEE and phenological dates drastically improved once radiation derived vegetation indices were corrected for solar position associated biases at Imnaviat. Moreover, radiation derived vegetation indices became better aligned with MODerate resolution Imaging Spectroradiometer (MODIS) satellite estimates after solar position associated biases were corrected at Imnaviat and at 25 Fluxnet sites (~90 site years) across North America. Corrections developed here provide a way forward in understanding daily ecosystem function or filling large gaps in eddy covariance data at a significant number of Fluxnet sites.

尽管有数百个站点的测量能力，但从太阳和光合有效辐射（PAR）传感器得出的植被指数（即辐射得出的）并未充分利用来推断生态系统功能。这种利用不足的原因可能是由于辐射和卫星衍生的植被指数与冠层光合作用的季节性之间存在失配。这里称为测量偏差。在此，使用基于卫星和地面的分光辐射计数据，生态系统物候和CO ₂的十年记录来评估辐射得出的反射率和植被指数的偏差。通量和辐射得出的植被指数（即归一化植被指数[NDVI]，两波段增强植被指数[EVI2]）来自高纬度苔原站点（即Imnaviat）。在Imnaviat，我们发现三种反射率与植被指数之间的对应关系不佳，尤其是在生长期的后期。辐射得出的植被指数导致长达一个月的物候时序估计不正确，并且与冠层光合作用的关系不佳（即总生态系统交换（GEE））。这些失配归因于太阳的位置（即太阳的天顶和方位角），并基于狄尔可见和近红外反照率变化开发了一种方法来改善植被指数的性能。一旦对Imnaviat的太阳位置相关偏差进行了校正，就可以大大提高辐射源植被指数推断GEE和物候日期的能力。此外，在北美的Imnaviat和25个Fluxnet站点（〜90站点年）校正了与太阳位置相关的偏差之后，辐射衍生的植被指数变得与MODerate分辨率成像光谱辐射仪（MODIS）卫星估计更好地吻合。本文开发的修正方法为理解日常生态系统功能或填补大量Fluxnet站点的涡度协方差数据中的巨大空白提供了前进的途径。在校正了Imnaviat和北美25个Fluxnet站点（约90个站点年）的太阳位置相关偏差之后，辐射衍生的植被指数与MODerate分辨率成像光谱辐射仪（MODIS）卫星估计值变得更加吻合。本文开发的修正方法为理解日常生态系统功能或填补大量Fluxnet站点的涡度协方差数据中的巨大空白提供了前进的途径。在校正了Imnaviat和北美25个Fluxnet站点（约90个站点年）的太阳位置相关偏差之后，辐射衍生的植被指数与MODerate分辨率成像光谱辐射仪（MODIS）卫星估计值变得更加吻合。本文开发的修正方法为理解日常生态系统功能或填补大量Fluxnet站点的涡度协方差数据中的巨大空白提供了前进的途径。