Data supporting the remote sensing of winter cover crops in Maryland

Published: 30-06-2020| Version 3 | DOI: 10.17632/cbgkpyxvjz.3
Dean Hively,
Alison Thieme,
Sunita Yadav,
Gregory McCarty


This dataset supports the article "Using NASA earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed" in Remote Sensing of Environment (Thieme et al., 2020). Sampling data: The dataset includes field sampling data (1298 samples) of winter cover crops (WCC) on farms enrolled in the Maryland WCC cost-share program, collected in the winter and spring of each year from 2006 through 2013. Species sampled include wheat, barley and rye. Aboveground biomass (711 samples) was determined using 0.5m2 quadrats. Percent vegetative ground cover (587 samples) was determined for each sample based on nadir RGB photo analysis. Samples were collected in December and April of each year, three locations per field, ~10 fields per species of rye, barley, and wheat, = ~90 samples per season. The data were collected by U.S. Geological Survey and USDA-Agricultural Research Service, in collaboration with the Maryland Department of Agriculture (MDA), Maryland Association of Soil Conservation Districts, and the many farmers who allowed us access to their fields. Further detail on sampling methods is provided in Thieme et al (2020). Performance data: The dataset also includes satellite-derived performance measures and agronomic management information for all fields enrolled in the Maryland Department of Agriculture wintercover crop cost-share program, covering four counties (Queen Anne's, Somerset, Talbot, and Washington, Maryland, USA), for three years (2015, 2016, 2018). Agronomic management for each field includes cover crop species, previous crop species, planting date, and planting method. Performance data for each field include winter and springtime satellite-derived values for the normalized difference vegetation index (NDVI) and performance measures (biomass, percent ground cover) calculated from the NDVI. Further detail on methods used to derive NDVI values from harmonized Landsat-Sentinel surface reflectance imagery is provided in Thieme et al (2020). The agronomic enrollment data were provided by the Maryland Department of Agriculture. The precise geospatial locations of the fields are privacy protected, and therefore the data records are only identified by county. contact: W. Dean Hively, U.S. Geological Survey,


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