## 1 Introduction  
Knowledge about land use/land cover (LULC) is fundamental for natural resource management, agricultural policy making, and regional and urban planning. Most reliable data sources for LULC information are periodic surveys from governmental agencies, e.g., the National Resource Inventory and the National Agricultural Statistics Service (NASS), both in the United States Department of Agriculture (USDA) [1; 2]. However, those datasets often lack spatial and temporal details, which prevents a comprehensive analysis of land change. Remote sensing technology can complement field observations and surveys. Conventional classification approaches, such as those applied in the National Land Cover Dataset (NLCD) [3; 4; 5] or the Cropland Data Layer (CDL) [6], were developed in an era of data scarcity and limited computational power and data storage. Thus, they have focused on mapping annual land cover from multispectral data from one or just a few image dates. However, in areas with frequent morning cloud cover, collecting even a few cloud-free scenes over a year can be challenging. The recent rapid increase of accessible Earth observation data coupled with improved computing and storage capabilities is leading to the emergence of methods for mapping land cover using multi-date imagery and dense image time series [7]. Compared to the traditional approach, the use of image time series often improves classification accuracy by incorporating both spectral and temporal profiles [8; 9; 10]. Land surface phenology (LSP) has been a useful approach to characterize seasonal vegetation dynamics on vegetation index time series [11]. Over the past few years, several efforts have been made to map LULC using phenological metrics derived from satellite image time series with promising outcomes [12; 13; 14; 15; 16; 17; 18]. Due to the relatively low return interval of orbital sensors with spatial resolutions finer than 50 m, many studies--with notable exceptions [13; 16; 18]--have relied on MODIS time series to capture phenological characteristics of land surfaces, thus often producing cover maps at spatial resolutions (e.g., 250-1000 m) that are coarse relative to human land uses, such as agriculture and settlements. To overcome limited temporal coverage of Landsat-like data and map land covers at finer spatial resolutions, [PERSON] et al. [13] and [PERSON] et al. [16] fused the MODIS Normalized Different Vegetation Index (NDVI) [19] with Landsat and Gaofen-1 NDVI time series, respectively. Although each produced land cover maps at finer spatial resolution (30 m for Landsat and 16 m for GF-1), neither [PERSON] et al. [13] nor [PERSON] et al. [16] were able to map more than Level-1 NLCD Land Cover Classification System, except foriferous and broadleaf forest in [PERSON] et al. [16] (Level-2 NLCD). In 2016, the United States Geological Survey reorganized the Landsat archive into a tiered collection, namely the Landsat Collections, to facilitate time series analysis and data stacking [20]. Taking advantage of the Landsat Collections data, [PERSON] et al. [18] performed a phenometrically-based classification for sample areas in South Dakota using all available Tier-1 (highest quality) images from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI). At each pixel, an Enhanced Vegetation Index (EVI) time series calculated from Landsat Collections data was simulated as a convex quadratic function of accumulated growing degree-days (AGDD), i.e., a measure of accumulated heat from January 1 onward whenever the average temperature exceeded 0. Results showed that classification using only phenometrics generated from the fitted model could accurately map broad thematic land cover classes (water, developed, grassland) as well as commodity crops (corn/maize, soybean, wheat) in Codington and Roberts counties in South Dakota for two years (2012 and 2014). However, they also pointed out some challenges of the phenometrically-based classification.