Utilizing Landsat and Sentinel-2 to remotely monitor and evaluate the performance of winter cover crops throughout Maryland

Authors: PEREDO, J. - Goddard Space Flight Center; WAYMAN, C. - Goddard Space Flight Center; WHONG, B. - Goddard Space Flight Center; THIEME, A. - Goddard Space Flight Center; KLINE, L.R. - Goddard Space Flight Center; YADAV, S. - Goddard Space Flight Center; EDER, B. - Goddard Space Flight Center; LENSKE, V. - Goddard Space Flight Center; PORTILLO, D. - Goddard Space Flight Center; MCCARTNEY, S. - Goddard Space Flight Center; FITZ, J. - Goddard Space Flight Center; ODDO, P.C. - Goddard Space Flight Center; KEPPLER, J. - Maryland Department Of Agriculture; BOLTEN, J. - Goddard Space Flight Center; McCarty, Gregory; IYON, A. - Maryland Department Of Agriculture

Submitted to: Photogrammetry and Remote Sensing International Archives
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/28/2020
Publication Date: 2/28/2020
Citation: Peredo, J., Wayman, C., Whong, B., Thieme, A., Kline, L., Yadav, S., Eder, B., Lenske, V., Portillo, D., McCartney, S., Fitz, J., Oddo, P., Keppler, J., Bolten, J., McCarty, G.W., Iyon, A. 2020. Utilizing Landsat and Sentinel-2 to remotely monitor and evaluate the performance of winter cover crops throughout Maryland. Photogrammetry and Remote Sensing International Archives. 42:125-130. https://doi.org/10.5194/isprs-archives-XLII-3-W11-125-2020.
DOI: https://doi.org/10.5194/isprs-archives-XLII-3-W11-125-2020

Interpretive Summary: 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. The effectiveness of cover crops depends on management practices and agronomic factors such as planting date, method, and crop species. 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 (DST) for satellite-based monitoring of cover crop performance throughout Maryland. A series of DEVELOP teams created the DST based on an interactive graphical user interface in Google Earth Engine which analyzes satellite imagery to calculate an index for measuring cover crop growth. With this DST, the MDA can analyze the effectiveness of cover crops 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.

Technical 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.