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

REMOTE SENSING OF ENVIRONMENT

卷:248

Article

Thieme, Alison^1,2  Yadav, Sunita^1,3  Oddo, Perry C.^1,4  Fitz, John M.^1,2  McCartney, Sean^1,5  King, LeeAnn⁶  Keppler, Jason⁷  McCarty, Gregory W.⁶  Hively, W. Dean⁸ 

[1] NASA DEVELOP Natl Program, MS 307, Hampton, VA 23681 USA

[2] Univ Maryland, Dept Geog Sci, 2181 Samuel J LeFrak Hall,7251 Preinkert Dr, College Pk, MD 20742 USA

[3] Foreign Agr Serv, USDA, 1400 Independence Ave SW, Washington, DC 20250 USA

[4] Univ Space Res Assoc USRA, 7178 Columbia Gateway Dr, Columbia, MD 21046 USA

[5] Sci Syst & Applicat Inc, 10210 Greenbelt Rd,Suite 600, Lanham, MD 20706 USA

[6] ARS, USDA, Hydrol & Remote Sensing Lab, Rm 104,Bldg 007 BARC W,10300 Baltimore Ave, Beltsville, MD 20705 USA

[7] Maryland Dept Agr, 50 Harry S Truman Pkwy, Annapolis, MD 21401 USA

[8] US Geol Survey, Lower Mississippi Gulf Water Sci Ctr, Rm 104,Bldg 007 BARC W,10300 Baltimore Ave, Beltsville, MD 20705 USA

关键词: Google Earth Engine;    Remote sensing;    Cover crop;    Conservation;    Chesapeake Bay;    Biomass;    Ground cover;    Adaptive management;    BMP;    Vegetation index;   

DOI  :  10.1016/j.rse.2020.111943

来源: Elsevier

【 摘 要 】

Winter cover crops such as barley, rye, and wheat help to improve soil structure by increasing porosity, aggregate stability, and organic matter, while reducing the loss of agricultural nutrients and sediments into waterways. The environmental performance of cover crops is affected by choice of species, planting date, planting method, nutrient inputs, temperature, and precipitation. The Maryland Department of Agriculture (MDA) oversees an agricultural cost-share program that provides farmers with funding to cover costs associated with planting winter cover crops, and the U.S. Geological Survey (USGS) and the U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS) have partnered with the MDA to develop satellite remote sensing techniques for measuring cover crop performance. The MDA has developed the capacity to digitize field boundaries for all fields enrolled in their cover crop programs (> 26,000 fields per year) to support a remote sensing performance analysis at a statewide scal,e and has requested assistance with the associated imagery processing from the National Aeronautics and Space Administration (NASA). Using the Google Earth Engine (GEE) cloud computing platform, scripts were developed to process Landsat 5/7/8 and Harmonized Sentinel-2 imagery to measure winter cover crop performance. We calibrated cover crop performance models using linear regression between satellite vegetation indices and USGS / USDA-ARS field sampling data collected on Maryland farms between 2006 and 2012 (1298 samples). Satellite-derived Normalized Difference Vegetation Index (NDVI) values showed significant correlation with the natural logarithm of cover crop biomass (p <= 0.01, R-2 = 0.56) and with observed percent vegetative ground cover (p <= 0.01, R-2 = 0.68). The GEE scripts were used to composite seasonal maximum NDVI values for each enrolled cover crop field and calculate performance metrics for the winter and spring seasons of three enrollment years (2014-15, 2015-16, and 2017-18) for four Maryland counties. Results from winter 2017-18 demonstrate that rye and barley fields had higher biomass than wheat fields, and that early planting, along with planting methods that increase seed-soil contact, increased performance. The processing capabilities of GEE will support the MDA in scaling up remote sensing performance analysis statewide, providing information to evaluate the environmental outcomes associated with 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 of winter cover crops planted for environmental benefit.

【 授权许可】

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