Effects of in-situ and reanalysis climate data on estimation of cropland gross primary production using the Vegetation Photosynthesis Model

Agricultural and Forest Meteorology

Volumn 213, Issue , 2015, Pages 240-250

  Jin, Cui ^a   Xiao, Xiangming ^a,b   Wagle, Pradeep ^a   Griffis, Timothy ^c   Dong, Jinwei ^a   Wu, Chaoyang ^d   Qin, Yuanwei ^a   Cook, David R ^e  

^a UNIVERSITY OF OKLAHOMA   (United States)

^b FUDAN UNIVERSITY   (China)

^c UNIVERSITY OF MINNESOTA   (United States)

^d INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^e ARGONNE NATIONAL LABORATORY   (United States)

Author keywords

AmeriFlux; Downward shortwave radiation; MODIS; NARR; Vegetation indices; Vegetation Photosynthesis Model (VPM)

Indexed keywords

AGRICULTURAL LAND; AGRICULTURAL MODELING; AGRICULTURAL PRODUCTION; AIR TEMPERATURE; DATA SET; EDDY COVARIANCE; ESTIMATION METHOD; IN SITU MEASUREMENT; IRRIGATION SYSTEM; MAIZE; MODIS; PHOTOSYNTHESIS; PREDICTION; PRIMARY PRODUCTION; RAINFED AGRICULTURE; SATELLITE IMAGERY; SEASONALITY; SHORTWAVE RADIATION; SOYBEAN;

NORTH AMERICA;

GLYCINE MAX; ZEA MAYS;

EID: 84938800554     PISSN: 01681923     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.agrformet.2015.07.003     Document Type: Article

References (83)

1. Abatzoglou J.T. Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol. 2013, 33(1):121-131.
2. Babst F., Mueller R.W., Hollmarm R. Verification of NCEP reanalysis shortwave radiation with mesoscale remote sensing data. IEEE Geosci. Remote Sens. Lett. 2008, 5(1):34-37.
3. Baldocchi D., et al. FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Am. Meteorol. Soc. 2001, 82(11):2415-2434.
4. Baldocchi D., Sturtevant C., Contributors F. Does day and night sampling reduce spurious correlation between canopy photosynthesis and ecosystem respiration?. Agric. Forest Meteorol. 2015, 207(0):117-126.
5. Bush B.C., Valero F.P.J., Simpson A.S., Bignone L. Characterization of thermal effects in pyranometers: a data correction algorithm for improved measurement of surface insolation. J. Atmos. Ocean Technol. 2000, 17(2):165-175.
6. Cai W.W., et al. Improved estimations of gross primary production using satellite-derived photosynthetically active radiation. J. Geophys. Res. Biogeosci. 2014, 119(1):110-123.
7. Chen J.M., Liu J., Cihlar J., Goulden M.L. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol. Model. 1999, 124(2-3):99-119.
8. Chen T., van der Werf G.R., Gobron N., Moors E.J., Dolman A.J. Global cropland monthly gross primary production in the year 2000. Biogeosciences 2014, 11(14):3871-3880.
9. Decker M., et al. Evaluation of the reanalysis products from GSFC, NCEP, and ECMWF using flux tower observations. J. Clim. 2012, 25(6):1916-1944.
10. Desai A.R., et al. Cross-site evaluation of eddy covariance GPP and RE decomposition techniques. Agric. Forest Meteorol. 2008, 148(6-7):821-838.
11. Dong J., et al. Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought. Remote Sens. Environ. 2015, 162:154-168.
12. Eck T.F., Dye D.G. Satellite estimation of incident photosynthetically active radiation using ultraviolet reflectance. Remote Sens. Environ. 1991, 38(2):135-146.
13. Falge E., et al. Gap filling strategies for defensible annual sums of net ecosystem exchange. Agric. Forest Meteorol. 2001, 107(1):43-69.
14. Feng X., et al. Net primary productivity of China's terrestrial ecosystems from a process model driven by remote sensing. J. Environ. Manag. 2007, 85(3):563-573.
15. Frouin R., Pinker R.T. Estimating photosynthetically active radiation (Par) at the earths surface from satellite-observations. Remote Sens. Environ. 1995, 51(1):98-107.
16. Gitelson A.A., et al. Relationship between gross primary production and chlorophyll content in crops: implications for the synoptic monitoring of vegetation productivity. J. Geophys. Res. Atmos. 2006, 111(D8).
17. Gleckler P.J., Taylor K.E., Doutriaux C. Performance metrics for climate models. J. Geophys. Res. Atmos. 2008, 113(D6).
18. Gonzalez J.A., Calbo J. Modelled and measured ratio of PAR to global radiation under cloudless skies. Agric. Forest Meteorol. 2002, 110(4):319-325.
19. 2 exchange in C-3/C-4 managed ecosystem. Agric. Forest Meteorol. 2005, 132(1-2):1-19.
20. Guanter L., et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl. Acad. Sci. U. S. A. 2014, 111(14):E1327-E1333.
21. Gupta H.V., Kling H., Yilmaz K.K., Martinez G.F. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J. Hydrol. 2009, 377(1-2):80-91.
22. Haberl H., 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(31):12942-12945.
23. He M., et al. Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity. Agric. Forest Meteorol. 2013, 173(0):28-39.
24. Heinsch F.A., et al. Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. IEEE Trans. Geosci. Remote Sens. 2006, 44(7):1908-1925.
25. Huntzinger D.N., et al. The North American carbon program multi-scale synthesis and terrestrial model intercomparison project - Part 1: Overview and experimental design. Geosci. Model Dev. 2013, 6(6):2121-2133.
26. Jacovides C.P., Timvios F.S., Papaioannou G., Asimakopoulos D.N., Theofilou C.M. Ratio of PAR to broadband solar radiation measured in Cyprus. Agric. Forest Meteorol. 2004, 121(3-4):135-140.
27. Jiang X.Y., Yang Z.L. Projected changes of temperature and precipitation in Texas from downscaled global climate models. Clim. Res. 2012, 53(3):229-244.
28. Jin H.A., et al. Validation of global land surface satellite (GLASS) downward shortwave radiation product in the rugged surface. J. Mt. Sci. Engl. 2013, 10(5):812-823.
29. 2 flux tower data. Agric. Forest Meteorol. 2011, 151(12):1514-1528.
30. Karnauskas K.B., Ruiz-Barradas A., Nigam S., Busalacchi A.J. North American droughts in ERA-40 global and NCEP North American Regional Reanalyses: a palmer drought severity index perspective. J. Clim. 2008, 21(10):2102-2123.
31. Karnieli A., et al. Use of NDVI and land surface temperature for drought assessment: merits and limitations. J. Clim. 2010, 23(3):618-633.
32. Kennedy A., Dong X.Q., Xi B.K. A comparison of MERRA and NARR reanalyses with the DOE ARM SGP data. J. Clim. 2011, 24(17):4541-4557.
33. Landis D.A., Gardiner M.M., van der Werf W., Swinton S.M. Increasing corn for biofuel production reduces biocontrol services in agricultural landscapes. Proc. Natl. Acad. Sci. U. S. A. 2008, 105(51):20552-20557.
34. Lobell D.B., Asner G.P. Climate and management contributions to recent trends in U.S. Agric. Yields Sci. 2003, 299(5609):1032.
35. Markovic M., Jones C.G., Winger K., Paquin D. The surface radiation budget over North America: gridded data assessment and evaluation of regional climate models. Int. J. Climatol. 2009, 29(15):2226-2240.
36. Mesinger F., et al. North American regional reanalysis. Bull. Am. Meteorol. Soc. 2006, 87(3):343-360.
37. Meyers T.P., Hollinger S.E. An assessment of storage terms in the surface energy balance of maize and soybean. Agric. Forest Meteorol. 2004, 125(1-2):105-115.
38. Monteith J.L. Solar-radiation and productivity in tropical ecosystems. J. Appl. Ecol. 1972, 9(3):747-766.
39. Monteith J.L., Moss C.J. Climate and the efficiency of crop production in Britain [and discussion]. Philos. Trans. R. Soc. B: Biol. Sci. 1977, 281:277-294.
40. Papaioannou G., Papanikolaou N., Retalis D. Relationships of photosynthetically active radiation and shortwave irradiance. Theor. Appl. Climatol. 1993, 48(1):23-27.
41. Papale D., et al. Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences 2006, 3(4):571-583.
42. Peng Y., Gitelson A.A. Application of chlorophyll-related vegetation indices for remote estimation of maize productivity. Agric. Forest Meteorol. 2011, 151(9):1267-1276.
43. Peng Y., Gitelson A.A. Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content. Remote Sens. Environ. 2012, 117:440-448.
44. Peng Y., Gitelson A.A., Keydan G., Rundquist D.C., Moses W. Remote estimation of gross primary production in maize and support for a new paradigm based on total crop chlorophyll content. Remote Sens. Environ. 2011, 115(4):978-989.
45. Pinker R.T., Zhao M.S., Wang H.M., Wood E.F. Impact of satellite based PAR on estimates of terrestrial net primary productivity. Int. J. Remote Sens. 2010, 31(19):5221-5237.
46. Prince S.D., Goward S.N. Global primary production: a remote sensing approach. J. Biogeogr. 1995, 22(4-5):815-835.
47. Qian T.T., Dai A., Trenberth K.E., Oleson K.W. Simulation of global land surface conditions from 1948 to 2004. Part I: Forcing data and evaluations. J. Hydrometeorol. 2006, 7(5):953-975.
48. Raich J.W., et al. Potential net primary productivity in South-America - application of a global-model. Ecol. Appl. 1991, 1(4):399-429.
49. 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. Cycle 2008, 22(1):GB1003.
50. Reda I., et al. Using a blackbody to calculate net longwave responsivity of shortwave solar pyranometers to correct for their thermal offset error during outdoor calibration using the component sum method. J. Atmos. Ocean Technol. 2005, 22(10):1531-1540.
51. Reichstein M., et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biol. 2005, 11(9):1424-1439.
52. Richardson A., et al. Uncertainty quantification. Eddy Covariance. Springer Atmospheric Sciences 2012, 173-209. Springer, Netherlands. M. Aubinet, T. Vesala, D. Papale (Eds.).
53. Rubio M.A., Lopez G., Tovar J., Pozo D., Batlles F.J. The use of satellite measurements to estimate photosynthetically active radiation. Phys. Chem. Earth 2005, 30(1-3):159-164.
54. Running S., Thornton P., Nemani R., Glassy J. Global terrestrial gross and net primary productivity from the earth observing system. Methods in Ecosystem Science 2000, 44-57. Springer, New York. O. Sala, R. Jackson, H. Mooney, R. Howarth (Eds.).
55. Running S.W., et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 2004, 54(6):547-560.
56. Sakamoto T., Gitelson A.A., Wardlow B.D., Verma S.B., Suyker A.E. Estimating daily gross primary production of maize based only on MODIS WDRVI and shortwave radiation data. Remote Sens. Environ. 2011, 115(12):3091-3101.
57. Schroeder T.A., Hember R., Coops N.C., Liang S.L. Validation of solar radiation surfaces from MODIS and reanalysis data over topographically complex terrain. J. Appl. Meteorol. Clim. 2009, 48(12):2441-2458.
58. Stanhill G., Cohen S. Global dimming: a review of the evidence for a widespread and significant reduction in global radiation with discussion of its probable causes and possible agricultural consequences. Agric. Forest Meteorol. 2001, 107(4):255-278.
59. Stoy P.C., et al. An evaluation of models for partitioning eddy covariance-measured net ecosystem exchange into photosynthesis and respiration. Agric. Forest Meteorol. 2006, 141(1):2-18.
60. Taylor K.E. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. Atmos. 2001, 106(D7):7183-7192.
61. Troy T.J., Wood E.F. Comparison and evaluation of gridded radiation products across northern Eurasia. Environ. Res. Lett. 2009, 4(4).
62. Verma S.B., et al. Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems. Agric. Forest Meteorol. 2005, 131(1-2):77-96.
63. Veroustraete F., Sabbe H., Eerens H. Estimation of carbon mass fluxes over Europe using the C-Fix model and Euroflux data. Remote Sens. Environ. 2002, 83(3):376-399.
64. Wagle P., Xiao X.M., Suyker A.E. Estimation and analysis of gross primary production of soybean under various management practices and drought conditions. ISPRS J. Photogramm. 2015, 99:70-83.
65. Walsh J.E. Arctic cloud fraction and radiative fluxes in atmospheric reanalyses. J. Clim. 2009, 22(9):2316-2334.
66. Wang X.H., et al. Spring temperature change and its implication in the change of vegetation growth in North America from 1982 to 2006. Proc. Natl. Acad. Sci. U. S. A. 2011, 108(4):1240-1245.
67. Wei Y., et al. The North American carbon program multi-scale synthesis and terrestrial model intercomparison project - Part 2: Environmental driver data. Geosci. Model Dev. Discuss. 2013, 6(4):5375-5422.
68. Wheeler T., von Braun J. Climate change impacts on global food security. Science 2013, 341(6145):508-513.
69. Wu C.Y., et al. Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices. Agric. Forest Meteorol. 2009, 149(6-7):1015-1021.
70. Xiao J., Davis K.J., Urban N.M., Keller K., Saliendra N.Z. Upscaling carbon fluxes from towers to the regional scale: influence of parameter variability and land cover representation on regional flux estimates. J. Geophys. Res. Biogeosci. 2011, 116(G3). G00J06.
71. Xiao J., et al. Data-driven diagnostics of terrestrial carbon dynamics over North America. Agric. Forest Meteorol. 2014, 197:142-157.
72. Xiao X.M., et al. Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens. Environ. 2004, 89(4):519-534.
73. Xiao X.M., et al. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sens. Environ. 2004, 91(2):256-270.
74. Yuan W., et al. Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database. Agric. Forest Meteorol. 2014, 192-193:108-120.
75. Yuan W.P., et al. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sens. Environ. 2010, 114(7):1416-1431.
76. Zhang F., et al. Evaluating spatial and temporal patterns of MODIS GPP over the conterminous U.S. against flux measurements and a process model. Remote Sens. Environ. 2012, 124:717-729.
77. Zhang K., et al. Sensitivity of pan-Arctic terrestrial net primary productivity simulations to daily surface meteorology from NCEP-NCAR and ERA-40 reanalyses. J. Geophys. Res. Biogeosci. 2007, 112(G1).
78. Zhang Q.Y., et al. Estimation of crop gross primary production (GPP): I. Impact of MODIS observation footprint and impact of vegetation BRDF characteristics. Agric. Forest Meteorol. 2014, 191:51-63.
79. Zhang Q.Y., et al. Estimation of crop gross primary production (GPP): II. Do scaled MODIS vegetation indices improve performance?. Agric. Forest Meteorol. 2015, 200:1-8.
80. Zhao L., Lee X.H., Liu S.D. Correcting surface solar radiation of two data assimilation systems against FLUXNET observations in North America. J. Geophys. Res. Atmos. 2013, 118(17):9552-9564.
81. 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(G1).
82. Zhao X., et al. The Global Land Surface Satellite (GLASS) remote sensing data processing system and products. Remote Sens. Basel 2013, 5(5):2436-2450.
83. Zib B.J., Dong X.Q., Xi B.K., Kennedy A. Evaluation and intercomparison of cloud fraction and radiative fluxes in recent reanalyses over the arctic using BSRN surface observations. J. Clim. 2012, 25(7):2291-2305.