气候变化领域集成服务门户(CCPortal): Using NASA Earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed

CCPortal

DOI	10.1016/j.rse.2020.111943
	Using NASA Earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed
	Thieme A.; Yadav S.; Oddo P.C.; Fitz J.M.; McCartney S.; King L.; Keppler J.; McCarty G.W.; Hively W.D.
发表日期	2020
ISSN	00344257
卷号	248
英文摘要	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, R2 = 0.56) and with observed percent vegetative ground cover (p ≤0.01, R2 = 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. Project support: This project was supported by the USGS Land Change Science Program within the Land Resources Mission Area, the USDA Choptank River Conservation Effects Assessment Project (CEAP), the USDA Lower Chesapeake Bay Long Term Agricultural Research (LTAR) Project; the Maryland Department of Agriculture; and the NASA DEVELOP National Program. © 2020
英文关键词	Adaptive management; Biomass; BMP; Chesapeake Bay; Conservation; Cover crop; Google Earth Engine; Ground cover; Remote sensing; Vegetation index
语种	英语
scopus关键词	Aggregates; Agricultural robots; Engines; Environmental management; Forestry; NASA; Nutrients; Remote sensing; Satellites; Seed; Space optics; Vegetation; Agricultural Research Services; Chesapeake Bay watershed; Cloud computing platforms; Environmental performance; Normalized difference vegetation index; Satellite remote sensing; U.s. department of agricultures; U.s. geological surveys; Crops; adaptive management; aggregate stability; air temperature; barley; biomass; correlation; cover crop; Landsat; NDVI; organic matter; porosity; precipitation (chemistry); regression analysis; satellite data; soil structure; wheat; winter; Chesapeake Bay; Choptank River; United States; Hordeum; Secale cereale; Triticum aestivum
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179208
作者单位	NASA DEVELOP National Program, MS 307, Hampton, VA 23681, United States; University of Maryland, Department of Geographical Sciences, College Park, 2181 Samuel J. LeFrak Hall, 7251 Preinkert Drive, College ParkMD 20742, United States; U.S. Department of Agriculture, Foreign Agricultural Service, 1400 Independence Avenue S.W., Washington, DC, 20250, United States; Universities Space Research Association (USRA), 7178 Columbia Gateway Drive, Columbia, MD 21046, United States; Science Systems & Applications, Inc., 10210 Greenbelt Road, Suite 600, Lanham, MD 20706, United States; U.S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Rm 104 Bldg 007 BARC-W, 10300 Baltimore Ave, Beltsville, MD 20705, United States; Maryland Department of Agriculture, 50 Harry S. Truman Parkway, Annapolis, MD 21401, United States; U.S. Geological Survey, Lower Mississippi-Gulf Water Science Center, Rm 104 Bldg 007 BARC-W, 10300 Baltimore Ave, Beltsville, MD 20705, United States
推荐引用方式 GB/T 7714	Thieme A.,Yadav S.,Oddo P.C.,et al. Using NASA Earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed[J],2020,248.
APA	Thieme A..,Yadav S..,Oddo P.C..,Fitz J.M..,McCartney S..,...&Hively W.D..(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.
MLA	Thieme A.,et al."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(2020).