The use of near-surface remote sensing for monitoring vegetation phenology has advanced greatly over the past decade. The Phenocam Network has deployed more than 500 web-enabled cameras across the globe that use digital repeat photography to capture color information and measure changes in vegetation phenology across diverse ecosystems. Vegetation indices (VIs) such as the Green Chromatic Coordinate (GCC), Normalized Difference Vegetation Index (NDVI), and two-band Enhanced Vegetation Index (EVI2) have been derived from phenocam imagery. However, it is often necessary to scale these metrics to align them with satellite imagery since phenocam data are not standardised to surface reflectance. We developed a method to convert phenocam digital numbers (DNs) to align with Harmonized Landsat-8 and Sentinel-2 surface (HLS) reflectance products using a Gaussian process. We applied our method across six grassland phenocam sites. The Gaussian Process regression was on average able to account for 77.4 percent of the variation in the HLS surface reflectance, and our resulting phenocam VIs had a high level of agreement with the modeled HLS VIs with an R² of 0.811. This technique provides a novel method for standardising phenocam imagery, easing comparison between multiple phenocam locations and satellite or other sensors that have a standardised surface reflectance product.

在过去的十年中，近地遥感技术在监测植被物候方面的应用已大大提高。Phenocam网络已在全球范围内部署了500多个基于Web的相机，这些相机使用数字重复摄影来捕获色彩信息并测量不同生态系统中植被物候的变化。植被指数（VI），例如绿色色坐标（GCC），归一化差异植被指数（NDVI）和两波段增强植被指数（EVI2），均已从phenocam影像中得出。但是，通常通常需要对这些指标进行缩放，以使其与卫星图像对齐，这是因为phenocam数据并未针对表面反射率进行标准化。我们开发了一种方法，可以使用高斯过程转换phenocam数字编号（DNs）以与协调的Landsat-8和Sentinel-2表面（HLS）反射产品对齐。我们在六个草原phenocam地点应用了我们的方法。高斯过程回归平均能够解释HLS表面反射率变化的77.4％，并且我们得到的phenocam VI与具有R的建模HLS VI具有高度的一致性0.811中的²。该技术提供了一种新颖的方法来标准化phenocam影像，简化多个phenocam位置与具有标准化表面反射率产品的卫星或其他传感器之间的比较。