The physical mechanisms behind correlations of earth observations and remote sensing products are of vital importance. The so-called ’near-infrared reflectance of vegetation’ (NIR_V) and gross primary production (GPP) show high correlations among different ecosystems and temporal scales but the underlying relationship is still poorly understood. NIR_V is defined as the product of normalized difference vegetation index (NDVI) and near-infrared (NIR) canopy reflectance (R^NIR). We examined this relationship in the case of a temperate deciduous forest in Germany. GPP, R^NIR and NIR_V all exhibited a strong rise during leaf development in spring and a continual decline after the maximum in early summer. The decline of NIR_V in late summer was mainly driven by the decline of R^NIR, since NDVI remained saturated.

Here we tested the R^NIR decline attributions to changes in leaf area index, leaf optical properties, canopy structure, sun-sensor geometry, or understory vegetation by measuring seasonal variations of those factors of the temperate deciduous forest. Leaf area was nearly constant between May and mid September, leaf albedo decreased slightly, leaf angles increased over time towards more vertical leaves, and understory reflectance decreased considerably.

We simulated the seasonal R^NIR decline of the forest using the radiative transfer model FRT and quantified the sensitivity of the decline to variations in the measured parameters. FRT captured well the observed seasonal R^NIR decline by Sentinel 2 using the measured optical and structural properties. Decreasing understory reflectance alone explained 43% of the simulated R^NIR decrease, while leaf angle variations explained 31%, the solar zenith angle (SZA) 21%, leaf albedo 7%, and LAI 0%. The effect size of the SZA depended on the viewing angle and would hence be different for different satellites and for local instruments. The results may help to better understand and help to track seasonal changes in forest structure and leaf optical properties using remote sensing techniques. They also suggest that the proposed link between the seasonal evolution of GPP and NIR_V may be weaker than expected.

地球观测和遥感产品相关性背后的物理机制至关重要。所谓的“植被近红外反射率”（NIR _V）和初级生产总值（GPP）显示出不同生态系统和时间尺度之间的高度相关性，但潜在的关系仍然知之甚少。NIR _V被定义为归一化差异植被指数 (NDVI) 和近红外 (NIR) 冠层反射率 (R ^NIR )的乘积。我们在德国温带落叶林的情况下研究了这种关系。GPP、R ^NIR和 NIR _V在春季叶片发育过程中均表现出强劲的上升趋势，并在初夏达到最大值后继续下降。由于 NDVI 保持饱和，夏末NIR _V的下降主要是由 R ^NIR的下降驱动的。

在这里，我们通过测量温带落叶林这些因素的季节性变化，测试了 R ^NIR下降归因于叶面积指数、叶子光学特性、冠层结构、太阳传感器几何形状或林下植被的变化。5 月至 9 月中旬叶面积几乎不变，叶反照率略有下降，随着时间的推移，叶角朝向更垂直的叶方向增加，林下反射率显着下降。

我们使用辐射传递模型 FRT模拟了森林的季节性 R ^NIR下降，并量化了下降对测量参数变化的敏感性。FRT使用测量的光学和结构特性很好地捕获了 Sentinel 2观察到的季节性 R ^NIR下降。仅降低林下反射率就解释了模拟 R ^{NIR 的}43%减少，而叶角变化解释了 31%，太阳天顶角 (SZA) 21%，叶反照率 7%，LAI 0%。SZA 的影响大小取决于视角，因此对于不同的卫星和本地仪器会有所不同。结果可能有助于使用遥感技术更好地理解和帮助跟踪森林结构和叶片光学特性的季节性变化。他们还建议 GPP 和 NIR _V的季节性演变之间的拟议联系可能比预期的要弱。