Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States

Ecological Modelling

Volumn 277, Issue , 2014, Pages 1-12

  Li, Zhengpeng ^a   Liu, Shuguang ^b   Tan, Zhengxi ^b   Bliss, Norman B ^b   Young, Claudia J ^b   West, Tristram O ^a,c   Ogle, Stephen M ^d  

^a UNIVERSITY OF MARYLAND   (United States)

^b EARTH RESOURCES OBSERVATION AND SCIENCE EROS CENTER   (United States)

^c PACIFIC NORTHWEST NATIONAL LABORATORY   (United States)

^d Department of Environmental and Radiological Health Sciences   (United States)

Author keywords

Biogeochemical model; Crop inventory; Mid Continent Intensive Campaign; MODIS; Net primary production; USDA Cropland Data Layer

Indexed keywords

BIOGEOCHEMICAL MODELING; DATA LAYER; MID-CONTINENT INTENSIVE CAMPAIGN; MODIS; NET PRIMARY PRODUCTION;

BIOGEOCHEMISTRY; CARBON; CROPS; RADIOMETERS; SATELLITE IMAGERY;

ESTIMATION;

AGRICULTURAL LAND; CARBON CYCLE; CROP YIELD; ECOLOGICAL MODELING; ESTIMATION METHOD; INVENTORY; MODIS; NET PRIMARY PRODUCTION; SATELLITE DATA; SATELLITE IMAGERY; SPATIOTEMPORAL ANALYSIS;

MIDWEST; UNITED STATES;

EID: 84893489704     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2014.01.012     Document Type: Article

References (63)

1. Ahl D.E., Gower S.T., Mackay D.S., Burrows S.N., Norman J.M., Diak G.R. The effects of aggregated land cover data on estimating NPP in northern Wisconsin. Remote Sens. Environ. 2005, 97:1-14.
2. Baker J.M., Griffis T.J. Examining strategies to improve the carbon balance of corn/soybean agriculture using eddy covariance and mass balance techniques. Agric. For. Meteorol. 2005, 128:163-177.
3. Bandaru V., West T.O., Ricciuto D.M., César Izaurralde R. Estimating crop net primary production using national inventory data and MODIS-derived parameters. ISPRS J. Photogram. Rem. Sens. 2013, 80:61-71.
4. Boryan C., Yang Z., Mueller R., Craig M. Monitoring US agriculture: the US department of agriculture, national agricultural statistics service, cropland data layer program. Geocarto Int. 2011, 26:341-358.
5. Chapin I., Woodwell G., Randerson J.T., Rastetter E.B., Lovett G.M., Baldocchi D.D., et al. Reconciling carbon-cycle concepts, terminology, and methodology. Ecosystems 2006, 9:10.
6. Ciais P., Paris J.D., Marland G., Peylin P., Piao S.L., et al. The European carbon balance. Part 1: fossil fuel emissions. Glob. Change Biol. 2010, 16:1395-1408.
7. Cramer W., Kicklighter D.W., Bondeau A., Iii B.M., Churkina G., Nemry B., et al. Comparing global models of terrestrial net primary productivity (NPP): overview and key results. Glob. Change Biol. 1999, 5:1-15.
8. Egli D.B. Comparison of corn and soybean yields in the United States: historical trends and future prospects. Agron. J. 2008, 100:S-79-S-88.
9. Goetz S.J., Prince S.D., Goward S.N., Thawley M.M., Small J. Satellite remote sensing of primary production: an improved production efficiency modeling approach. Ecol. Model. 1999, 122:239-255.
10. Haberl H., Erb K.H., Krausmann F., Gaube V., Bondeau A., Plutzar C., et al. Quantifying and mapping the human appropriation of net primary production in earth's terrestrial ecosystems. Proc. Natl. Acad. Sci. U. S. A. 2007, 104:12942-12947.
11. Hansen M.C., Defries R.S., Townshend J.R.G., Sohlberg R. Global land cover classification at 1km spatial resolution using a classification tree approach. Int. J. Remote Sens. 2000, 21:1331-1364.
12. Hayes D.J., Turner D.P., Stinson G., McGuire A.D., Wei Y., West T.O., et al. Reconciling estimates of the contemporary North American carbon balance among terrestrial biosphere models, atmospheric inversions, and a new approach for estimating net ecosystem exchange from inventory-based data. Glob. Change Biol. 2012, 18:1282-1299.
13. Heinsch F.A., Reeves M., Votava P., Kang S., Milesi C., Zhao M., et al. User's guide GPP and NPP (MOD17A2/A3) products NASA MODIS Land Algorithm 2003, Available from: http://www.ntsg.umt.edu/sites/ntsg.umt.edu/files/modis/MOD17UsersGuide.pdf.
14. Hicke J.A., Lobell D.B., Asner G.P. Cropland area and net primary production computed from 30 years of USDA agricultural harvest data. Earth Interact. 2004, 8:1-20.
15. Holmes R.R., Koenig T.A., Karstensen K.A. Flooding in the United States Midwest, 2008 2010, USGS, (U.S. Geological Survey Professional Paper 1775, 64 p) Available from: http://pubs.usgs.gov/pp/1775/.
16. Imhoff M.L., Bounoua L., Ricketts T., Loucks C., Harriss R., Lawrence W.T. Global patterns in human consumption of net primary production. Nature 2004, 429:870-873.
17. Ito A. A historical meta-analysis of global terrestrial net primary productivity: are estimates converging?. Glob. Change Biol. 2011, 17:3161-3175.
18. Izaurralde R.C., Williams J.R., McGill W.B., Rosenberg N.J., Jakas M.C.Q. Simulating soil C dynamics with EPIC: model description and testing against long-term data. Ecol. Model. 2006, 192:362-384.
19. Jenkins J.C., Birdsey R.A., Pan Y. Biomass and NPP estimation for the mid-atlantic region (USA) using plot-level forest inventory data. Ecol. Appl. 2001, 11:1174-1193.
20. Johnson J.M.-F., Allmaras R.R., Reicosky D.C. Estimating source carbon from crop residues, roots and rhizodeposits using the national grain-yield database. Agron. J. 2006, 98:622-636.
21. 2 flux tower data. Agric. For. Meteorol. 2011, 151:1514-1528.
22. Li C., Frolking S., Crocker G.J., Grace P.R., Klfr J., KiSrchens M., Poulton P.R. Simulating trends in soil organic carbon in long-term experiments using the DNDC model. Geoderma 1997, 81:45-60.
23. Liu S. Quantifying the spatial details of carbon sequestration potential and performance. Carbon Sequestration and Its Role in the Global Carbon Cycle 2009, 117-128. American Geophysical Union, Washington, DC. B. McPherson, E. Sundquist (Eds.).
24. Liu S., Bliss N., Sundquist E., Huntington T.G. Modeling carbon dynamics in vegetation and soil under the impact of soil erosion and deposition. Global Biogeochem. Cycles 2003, 17:1074.
25. Liu S., Tan Z., Li Z., Zhao S., Yuan W. Are soils of Iowa USA currently a carbon sink or source? Simulated changes in SOC stock from 1972 to 2007. Agric. Ecosyst. Environ. 2011, 140:106-112.
26. Lobell D. The use of satellite data for crop yield gap analysis. Field Crops Res. 2013, 143:56-64.
27. Lobell D.B., Hicke J.A., Asner G.P., Field C.B., Tucker C.J., Los S.O. Satellite estimates of productivity and light use efficiency in United States agriculture. Glob. Change Biol. 2002, 8:722-735.
28. Michalak A.M., Jackson R.B., Marland G., Sabine C.L. A U.S. Carbon Cycle Science Plan 2011, the Carbon Cycle Science Working Group.
29. Monteith J.L. Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 1972, 9:747-766.
30. Monteith J.L., Moss C.J. Climate and the efficiency of crop production in Britain. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 1977, 281:277-294. (discussion).
31. National Agricultural Statistics Service (NASS), USDA Quick Stats: database 2011, Available at: www.nass.usda.gov/QuickStats.
32. Numerical Terradynamic Simulation Group (NTSG), University of Montana. Available from: (accessed November 2012). ftp://ftp.ntsg.umt.edu/pub/MODIS/NTSG_Products/.
33. Ogle S., Davis K., Andrews A., Gurney K., West T.O., Cooke R.B., et al. Science Plan: Mid-Continent Intensive Campaign of the North American Carbon Program. Greenbelt, MD 2006.
34. Parton W.J., Scurlock M.O., Ojima D.S. Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide. Global Biogeochem. Cycles 1993, 7:785-809.
35. Pittman K., Hansen M.C., Becker-Reshef I., Potapov P.V., Justice C.O. Estimating global cropland extent with multi-year MODIS data. Remote Sens. 2010, 2:1844-1863.
36. Portmann F.T., Siebert S., Döll P. MIRCA2000-global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Global Biogeochem. Cycles 2010, 24:GB1011.
37. Potter C.S., Randerson J.T., Field C.B., Matson P.A., Vitousek P.M., Mooney H.A., Klooster S.A. Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochem. Cycles 1993, 7:811-841.
38. Prince S.D. A model of regional primary production for use with coarse resolution satellite data. Int. J. Remote Sens. 1991, 12:1313-1330.
39. Prince S.D., Goward S.N. Global primary production: a remote sensing approach. J. Biogeogr. 1995, 22:815-835.
40. Prince S.D., Haskett J., Steininger M., Strand H., Wright R. Net primary production of U.S. Midwest croplands from agricultural harvest yield data. Ecol. Appl. 2001, 11:1194-1205.
41. Ramankutty N., Evan A.T., Monfreda C., Foley J.A. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochem. Cycles 2008, 22:GB1003.
42. Reeves M.C., Zhao M., Running S.W. Usefulness and limits of MODIS GPP for estimating wheat yield. Int. J. Remote Sens. 2005, 26:1403-1421.
43. Ruimy A., Saugier B., Dedieu G. Methodology for the estimation of terrestrial net primary production from remotely sensed data. J. Geophys. Res. 1994, 99:5263-5283.
44. Running S.W., Nemani R.R., Heinsch F.A., Zhao M., Reeves M., Hashimoto H. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 2004, 54:547-560.
45. Schnepf R. Midwest Floods of 2008: Potential Impact on Agriculture. Congressional Research Service Report for Congress 2008, http://www.nationalaglawcenter.org/assets/crs/RL34583.pdf.
46. 2 exchange across North America: results from the North American Carbon Program site synthesis. J. Geophys. Res. Biogeosci. 2010, 115:G00H05.
47. Scurlock J.M.O., Johnson K., Olson R.J. Estimating net primary productivity from grassland biomass dynamics measurements. Glob. Change Biol. 2002, 8:736-753.
48. Smith P., Davies C.A., Ogle S., Zanchi G., Bellarby J., Bird N., et al. Towards an integrated global framework to assess the impacts of land use and management change on soil carbon: current capability and future vision. Glob. Change Biol. 2012, 18:2089-2101.
49. Snapp S.S., Swinton S.M., Labarta R., Mutch D., Black J.R., Leep R., et al. Evaluating cover crops for benefits, costs and performance within cropping system niches. Agron. J. 2005, 97:322-332.
50. Tian H., Chen G., Liu M., Zhang C., Sun G., Lu C., et al. Model estimates of net primary productivity, evapotranspiration, and water use efficiency in the terrestrial ecosystems of the southern United States during 1895-2007. For. Ecol. Manag. 2010, 259:1311-1327.
51. Turner D.P., Gower S.T., Cohen W.B., Gregory M., Maiersperger T.K. Effects of spatial variability in light use efficiency on satellite-based NPP monitoring. Remote Sens. Environ. 2002, 80:397-405.
52. Turner D.P., Ritts W.D., Cohen W.B., Maeirsperger T.K., Gower S.T., Kirschbaum A.A., et al. Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring. Glob. Change Biol. 2005, 11:666-684.
53. United States Department of Agriculture (USDA) Major Land Resource Regions Custom Report (USDA Agriculture Handbook 296) 2006, USDA, Available from: http://soils.usda.gov/MLRAExplorer (accessed 22.10.12).
54. United States Department of Agriculture (USDA) 2007 Census of Agriculture, United States Summary and State Data 2009, Available from: http://www.agcensus.usda.gov/Publications/2007/Full_Report/ (accessed 22.09.12).
55. United States Department of Agriculture (USDA) Cropland Data Layer (CDL), created by the USDA, National Agricultural Statistics Service, Research and Development Division, Geospatial Information Branch, Spatial Analysis Research Section 2011, Available from: http://www.nass.usda.gov/research/Cropland/SARS1a.htm (accessed 12.06.11).
56. Verma S.B., Dobermann A., Cassman K.G., Walters D.T., Knops J.M., Arkebauer T.J., et al. Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems. Agric. For. Meteorol. 2005, 131:77-96.
57. West T.O., Brandt C.C., Marland G., De La Torre Ugarte D.G., Larson J., Hellwinckel C.M. Estimating regional changes in soil carbon with high spatial resolution. Soil Sci. Soc. Am. J. 2008, 72:285-294.
58. West T.O., Brandt C.C., Baskaran L.M., Hellwinckel C.M., Mueller R., Bernacchi C.J., et al. Cropland carbon fluxes in the United States: increasing geospatial resolution of inventory-based carbon accounting. Ecol. Appl. 2010, 20:1074-1086.
59. White M.A., Thornton P.E., Running S.W., Nemani R.R. Parameterization and sensitivity analysis of the biome-bgc terrestrial ecosystem model: net primary production controls. Earth Interact. 2000, 4:1-85.
60. Zhao M., Running S.W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 2012, 329:940-943.
61. Zhao M., Heinsch F.A., Nemani R.R., Running S.W. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens. Environ. 2005, 95:164-176.
62. Zhao M., Running S.W., Nemani R.R. Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses. J. Geophys. Res. Biogeosci. 2006, 111:G01002.
63. Zhao M., Running S.W., Heinsch F.A., Nemani R.R. MODIS-derived terrestrial primary production. Land Remote Sensing and Global Environmental Change, Remote Sensing and Digital Image Processing 11 2011, 635-660. Springer Science+Business Media, LLC, New York. 10.1007/978-1-4419-6749-7_28. B. Ramachandran (Ed.).