Disentangling the individual contributions from vegetation and soil in measured canopy reflectance is a grand challenge to the remote sensing and ecophysiology communities. Since Solar Induced chlorophyll Fluorescence (SIF) is uniquely emitted from vegetation, it can be used to evaluate how well reflectance-based vegetation indices (VIs) can separate the vegetation and soil components. Due to the residual soil background contributions, Near-infrared (NIR) reflectance of vegetation (NIRv) and Difference Vegetation index (DVI) present offsets when compared to SIF (i.e., the value of NIRv or DVI is non-zero when SIF is zero). In this study, we proposed a simple framework for estimating the true NIR reflectance of vegetation from Hyperspectral measurements (NIRvH) with minimal soil impacts. NIRvH takes advantage of the spectral shape variations in the red-edge region to minimize the soil effects. We evaluated the capability of NIRvH, NIRv and DVI in isolating the true NIR reflectance of vegetation using the data from both the model-based simulations and Hyperspectral Plant imaging spectrometer (HyPlant) measurements. Benchmarked by simultaneously measured SIF, NIRvH has the smallest offset (0–0.037), as compared to an intermediate offset of 0.047–0.062 from NIRv, and the largest offset of 0.089–0.112 from DVI. The magnitude of the offset can vary with different soil reflectance spectra across spatio-temporal scales, which may lead to bias in the downstream NIRv-based photosynthesis estimates. NIRvH and SIF measurements from the same sensor platform avoided complications due to different geometry, footprint and time of observation across sensors when studying the radiative transfer of reflected photons and SIF. In addition, NIRvH was primarily determined by canopy structure rather than chlorophyll content and soil brightness. Our work showcases that NIRvH is promising for retrieving canopy structure parameters such as leaf area index and leaf inclination angle, and for estimating fluorescence yield with current and forthcoming hyperspectral satellite measurements.

在测量的冠层反射率中解开来自植被和土壤的个体贡献是遥感和生态生理学界面临的巨大挑战。由于太阳能诱导叶绿素荧光 (SIF) 是从植被中唯一发出的，因此可用于评估基于反射率的植被指数 (VI) 将植被和土壤成分分开的程度。由于残余土壤背景贡献，植被的近红外 (NIR) 反射率 (NIRv) 和差异植被指数 (DVI) 与 SIF 相比存在偏移（即，当 SIF 为零时，NIRv 或 DVI 的值不为零）。在这项研究中，我们提出了一个简单的框架，用于通过高光谱测量 (NIRvH) 估算植被的真实 NIR 反射率，同时对土壤的影响最小。NIRvH 利用红边区域的光谱形状变化来最小化土壤影响。我们使用基于模型的模拟和高光谱植物成像光谱仪 (HyPlant) 测量的数据评估了 NIRvH、NIRv 和 DVI 在隔离植被真实 NIR 反射率方面的能力。以同时测量的 SIF 为基准，NIRvH 具有最小的偏移 (0–0.037)，而与 NIRv 的中间偏移为 0.047–0.062，而与 DVI 的最大偏移为 0.089–0.112。偏移量的大小会随着时空尺度上的不同土壤反射光谱而变化，这可能导致下游基于 NIRv 的光合作用估计出现偏差。来自同一传感器平台的 NIRvH 和 SIF 测量避免了由于几何形状不同而导致的并发症，在研究反射光子和 SIF 的辐射传输时，跨传感器的观察足迹和时间。此外，NIRvH 主要由冠层结构而不是叶绿素含量和土壤亮度决定。我们的工作表明，NIRvH 有望用于检索冠层结构参数，例如叶面积指数和叶倾角，以及通过当前和即将进行的高光谱卫星测量来估计荧光产量。