International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W11
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W11, 125–130, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W11-125-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W11, 125–130, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W11-125-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Feb 2020

14 Feb 2020

UTILIZING LANDSAT AND SENTINEL-2 TO REMOTELY MONITOR AND EVALUATE THE PERFORMANCE OF WINTER COVER CROPS THROUGHOUT MARYLAND

J. Peredo1, C. Wayman1, B. Whong1, A. Thieme1, L. R. Kline1, S. Yadav1, B. Eder1, V. Lenske1, D. Portillo1, S. McCartney1, J. Fitz1, P. Oddo1, J. Keppler2, D. Hively3, J. Bolten4, G. McCarty5, and A. Lyon2 J. Peredo et al.
  • 1Science Systems and Applications Inc. at NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA
  • 2Maryland Department of Agriculture, Annapolis, MD 21401, USA
  • 3USGS Lower Mississippi Gulf Science Center, Beltsville, MD 20705, USA
  • 4NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA
  • 5USDA-ARS Hydrology & Remote Sensing Laboratory, Beltsville Agricultural Research Center, Beltsville MD 20705, USA

Keywords: Remote Sensing, Graphical User Interface, Google Earth Engine, Normalized Difference Vegetation Index, Biomass, Percent Ground Cover, Time Series

Abstract. Winter cover crops have been shown to limit erosion and nutrient runoff from agricultural land. To promote their usage, the Maryland Department of Agriculture (MDA) subsidizes farmers who plant cover crops. Conventional verification of cover crop planting and analysis of subsequent crop performance requires on-the-ground fieldwork, which is costly and labor intensive. In partnership with the MDA, NASA's DEVELOP program utilized imagery from Landsat 5, Landsat 8, and the European Space Agency’s Sentinel-2 to create a decision support tool for satellite-based monitoring of cover crop performance throughout Maryland. Our teams created CCROP, an interactive graphical user interface, in Google Earth Engine which analyzes satellite imagery to calculate the normalized difference vegetation index (NDVI) of fields across the state. Linear regression models were applied to convert NDVI to estimates of crop biomass and percent green ground cover, with measure of fit (R2) values ranging from 0.4 to 0.7. These crop metrics were implemented into an interactive filtering tool within CCROP which allows users to examine cover crop performance based on a variety of growing parameters. CCROP also includes a time series analysis routine for examining the progression of NDVI throughout the spring to help determine farmer-induced termination dates of cover crops. With this decision support tool, the MDA can analyze the effectiveness of cover crops throughout the state with reduced need to manually spot-check enrolled production fields, and can identify variables influencing overall cover crop performance to optimize implementation of their winter cover crop program via adaptive management approaches.