Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland

Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy mass flux. However, variability snow can confound satellite-based efforts monitor vegetation phenology. This research explores the utility PhenoCam Network cameras estimate Fractional Cover (FSC) grassland. The goal operationalize FSC estimates from PhenoCams inform improve determination phenological metrics. study site Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. We developed a semi-automated process images through Python coding. Compared with previous employing RGB only, our use monochrome + NIR (near-infrared) reduced pixel misclassification increased accuracy. results had average RMSE less than 8% compared visual estimates. Our pixel-based accuracy assessment showed that overall selected for validation was 92%. promising outcome, although not every system has capability.