Using NASA earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed

Authors: THIEME, A. - Goddard Space Flight Center; YADAV, S. - Goddard Space Flight Center; ODDO, P.C. - Goddard Space Flight Center; FITZ, J.M. - Goddard Space Flight Center; MCCARTNEY, S. - Goddard Space Flight Center; KEPPLER, J. - Maryland Department Of Agriculture; McCarty, Gregory; HIVELY, W.D. - Us Geological Survey (USGS)

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/15/2020
Publication Date: 7/10/2020
Citation: Thieme, A., Yadav, S., Oddo, P., Fitz, J., McCartney, S., Keppler, J., McCarty, G.W., Hively, W. 2020. Using NASA earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed. Remote Sensing of Environment. 248:111943. https://doi.org/10.1016/j.rse.2020.111943.
DOI: https://doi.org/10.1016/j.rse.2020.111943

Interpretive Summary: The Maryland Agricultural Water Quality Cost-Share (MACS) Program run by the Maryland Department of Agriculture (MDA) is designed to share the costs of maintaining water quality and pays farmers to grow winter cover crops. The program began providing cash incentives in 2005 for farmers to grow winter cover crops to reduce nutrient and sediment loss. The program offers a variety of incentives based on crop species and agronomic management techniques. However, performance of winter cover crops can vary based on many additional factors such as early or late planting, field preparation, local and annual climate variability, or crop species planted before the cover crop. The MDA has now developed the capacity to digitize field boundaries for all fields enrolled in their cover crop programs (approximately 26,000 fields per year) and has requested assistance to apply remote sensing tools to analyze cover crop performance at statewide scale. A NASA DEVELOP team used the Google Earth Engine cloud computing platform to automate the acquisition, compositing, and extraction of wintertime vegetation data from Landsat 5, Landsat 8, and Sentinel-2 imagery. Using their best model, the DEVELOP team estimated cover crop performance within each individual field boundary, linking cover crop performance to agronomic management data for each field. Winter 2017 results indicate that wheat and rye fields tend to have a lower performance in comparison to barley, and that early planting, along with planting methods that increase seed-soil contact, increases performance. By combining the capabilities of Google Earth Engine for large scale image processing with the MDA's geospatial enrollment dataset, the team created a scalable cover crop performance analysis. The tool can be modified for different seasonal cutoffs, utilize new sensors to capture phenology in winter and spring, and scale to larger regions for use in adaptive management winter cover crops planted for environmental benefit. It is now expected that MDA will adopt this tool for operational assessment of winter cover crop performance in Maryland.

Technical Abstract: Planting cover crops provides a range of benefits to farmers including increased soil health, reduced soil and water erosion, and improved water quality. Winter cover crops such as barley, rye, and wheat help improve soil structure by increasing porosity and adding organic matter that helps bind soil particles together while preventing agricultural nutrients from leaching into waterways. They are an essential component of conservation management practices such as those promoted by the Maryland Department of Agriculture and Chesapeake Bay Program Partnership. The Maryland Department of Agriculture oversees an environmental cost-sharing program that offers subsidies to farmers to plant winter cover crops. The effectiveness of mitigating soil and nutrient loss varies based on factors including cover crop species, planting date, planting method, nutrient inputs, temperature, and precipitation. The U.S. Geological Survey and the U.S. Department of Agriculture – Agricultural Research Service have worked in partnership with the Maryland Department of Agriculture to develop satellite remote sensing techniques for measuring cover crop performance. The Maryland Department of Agriculture has now developed the capacity to digitize field boundaries for all fields enrolled in their cover crop programs (approximately 26,000 fields per year) and has requested assistance to apply remote sensing tools to analyze cover crop performance at statewide scale. A NASA DEVELOP team used the Google Earth Engine cloud computing platform to automate the acquisition, compositing, and extraction of wintertime vegetation data from Landsat 5, Landsat 8, and Sentinel-2 imagery. The team calibrated cover crop performance models using linear regression between satellite vegetation indices and U.S. Geological Survey/U.S. Department of Agriculture ground truth data collected on farm fields within four Maryland counties from 2006 to 2012 (1,296 samples). The best model to estimate winter cover crop biomass (p<=0.01, R2=0.6) included three independent variables (Normalized Difference Vegetation Index, planting method, and elapsed growing degrees since planting), whereas the best model to estimate percent ground cover included only the Normalized Difference Vegetation Index (p<=0.01, R2=0.68). Using these models, the DEVELOP team estimated cover crop performance within each individual field boundary, linking cover crop performance to agronomic management data for each field. Winter 2017 results indicate that wheat and rye fields tend to have a lower performance in comparison to barley, and that early planting, along with planting methods that increase seed-soil contact, increases performance. By combining the capabilities of Google Earth Engine for large scale image processing with the Maryland Department of Agriculture’s geospatial enrollment dataset, the team created a scalable cover crop performance analysis. It is expected to have multiple applications for Maryland Department of Agriculture including the rapid identification of underperforming cover crop fields, the ability to distinguish fields with high or low biomass, and information to evaluate the environmental outcomes of various agronomic management strategies. The tool can be modified for different seasonal cutoffs, utilize new sensors to capture phenology in winter and spring, and scale to larger regions for use in adaptive management winter cover crops planted for environmental benefit.