# Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland
## 1 Introduction  
While the spatial and temporal dynamics of snow cover are important variables in climatological and hydrological studies because of their relationships with fluxes of environmental energy and mass, they also confound satellite-based efforts to monitor vegetation phenology, often the purpose of time-series analysis of remotely sensed data. Phenology is the science of recording natural events to identify changes in seasonal and annual cycles of plants and animals. Monitoring phenology over time can account for changes in the ecology of species in response to climate change [1].  
Remote sensing makes possible frequent, repeat monitoring of vegetation canopies, especially in difficult-to-access locations. Researchers use indexes such as the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Green Chromatic Coordinate (GCC) to track vegetation phenology and to quantify and monitor plant stages and plant responses to weather and climate [2, 3, 4, 5, 6, 7, 8, 9, 10].  
Establishing an accurate and precise record of vegetation phenology across expansive geographic regions to validate satellite observations is the primary purpose of a system of terrestrial cameras comprising the PhenoCam Network [2, 4, 5, 11]. Scientists have established PhenoCams to monitor vegetation in many different ecoregions, including tallgrass prairie, the focus of this study [3, 12, 13]. PhenoCams are fixed terrestrial webcoms taking frequent, repeat oblique pictures that are used to monitor vegetation phenology and other changes in land surfaces, including snow cover dynamics [7, 14]. These systems are low-cost [15], and there are 393 cameras currently located across the U.S. [16].  
In prairie, Fractional Snow Cover (FSC), the percentage of an area of land covered by snow, creates challenges for accurate phenology monitoring from satellite sensors. Forexample, the presence of snow affects EVI by decreasing the values from the true vegetation signal [17; 18; 19]. In addition, snow presence affects the near-surface remote sensing GCC index; when snow starts melting; there is an increase in the GCC, but this is not related to vegetation growth itself [16].  
The purpose of this study is to produce an FSC dataset from PhenoCam images that we can later use to validate and improve satellite-based measures of grassland phenology, specifically the dates defining the start and ends of annual growing seasons. We estimated FSC using PhenoCam images taken from the University of North Dakota (UND) Oakville Prairie Biological Field Station (Oakville) near Grand Forks. This research sought to answer the following questions: (1) can PhenoCam images be used to estimate FSC in a semi-automatic way?; (2) does the near-infrared band available in our PhenoCam imagery improve FSC estimation?; and (3) during five recent winters, 2015-2016 to 2019-2020, how did FSC vary in Oakville?  
Below we review the use of remote sensing to monitor snow, as well as the area of study, its location, and its weather patterns. Then we describe PhenoCam data, our methodology, and the algorithm we developed to estimate FSC. We conclude that our algorithm could be used to estimate FSC with high accuracy using PhenoCam data in grassland settings.