Modeling grassland spring onset across the Western United States using climate variables and MODIS-derived phenology metrics

Remote Sensing of Environment

Volumn 161, Issue , 2015, Pages 63-77

  Xin, Qinchuan ^a   Broich, Mark ^b   Zhu, Peng ^a   Gong, Peng ^a,c,d  

^a TSINGHUA UNIVERSITY   (China)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d JOINT CENTER FOR GLOBAL CHANGE STUDIES   (China)

Author keywords

Climate variability; Flux tower; Phenology model; Remote sensing

Indexed keywords

ARID REGIONS; BIOLOGY; CLIMATE CHANGE; ECOLOGY; ECOSYSTEMS; RADIOMETERS; REMOTE SENSING; SATELLITE IMAGERY; SATELLITES; VEGETATION;

CLIMATE VARIABILITY; FLUX TOWERS; INTERANNUAL VARIATION; PHOTOSYNTHETIC ACTIVITY; SATELLITE OBSERVATIONS; SPATIOTEMPORAL PATTERNS; TERRESTRIAL ECOSYSTEMS; WESTERN UNITED STATES;

CLIMATE MODELS;

ANNUAL VARIATION; CLIMATE VARIATION; ECOSYSTEM MODELING; GRASSLAND; MODIS; PHENOLOGY; PHOTOPERIOD; PHOTOSYNTHESIS; SEMIARID REGION; SPATIOTEMPORAL ANALYSIS; SPRING (SEASON); WATER AVAILABILITY;

UNITED STATES;

EID: 85027924638     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2015.02.003     Document Type: Article

References (93)

1. Allen R.G., Pereira L.S., Raes D., Smith M. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56 1998, 300:6541. FAO, Rome.
2. Baldocchi D.D., Black T., Curtis P., Falge E., Fuentes J., Granier A., Gu L., Knohl A., Pilegaard K., Schmid H. Predicting the onset of net carbon uptake by deciduous forests with soil temperature and climate data: A synthesis of FLUXNET data. International Journal of Biometeorology 2005, 49:377-387.
3. Baldocchi D., Falge E., Gu L., Olson R., Hollinger D., Running S., Anthoni P., Bernhofer C., Davis K., Evans R. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society 2001, 82:2415-2434.
4. Bale J.S., Masters G.J., Hodkinson I.D., Awmack C., Bezemer T.M., Brown V.K., Butterfield J., Buse A., Coulson J.C., Farrar J. Herbivory in global climate change research: Direct effects of rising temperature on insect herbivores. Global Change Biology 2002, 8:1-16.
5. 2 flux measurements and phenological observations in Finland. Remote Sensing of Environment 2014, 140:625-638.
6. Broich M., Huete A., Tulbure M.G., Ma X., Xin Q., Paget M., Restrepo-Coupe N., Davies K., Devadas R., Held A. Land surface phenological response to decadal climate variability across Australia using satellite remote sensing. Biogeosciences 2014, 11:5181-5198.
7. Cesaraccio C., Spano D., Snyder R.L., Duce P. Chilling and forcing model to predict bud-burst of crop and forest species. Agricultural and Forest Meteorology 2004, 126:1-13.
8. Choler P., Sea W., Briggs P., Raupach M., Leuning R. A simple ecohydrological model captures essentials of seasonal leaf dynamics in semi-arid tropical grasslands. Biogeosciences 2010, 7:907-920.
9. Chuine I., Cour P., Rousseau D.D. Fitting models predicting dates of flowering of temperate-zone trees using simulated annealing. Plant, Cell and Environment 1998, 21:455-466.
10. Chuine I., Cour P., Rousseau D.D. Selecting models to predict the timing of flowering of temperate trees: Implications for tree phenology modelling. Plant, Cell and Environment 1999, 22:1-13.
11. Churkina G., Schimel D., Braswell B.H., Xiao X.M. Spatial analysis of growing season length control over net ecosystem exchange. Global Change Biology 2005, 11:1777-1787.
12. Cleland E.E., Chuine I., Menzel A., Mooney H.A., Schwartz M.D. Shifting plant phenology in response to global change. Trends in Ecology & Evolution 2007, 22:357-365.
13. Coops N.C., Hilker T., Bater C.W., Wulder M.A., Nielsen S.E., McDermid G., Stenhouse G. Linking ground-based to satellite-derived phenological metrics in support of habitat assessment. Remote Sensing Letters 2012, 3:191-200.
14. Coulter R.L., Pekour M.S., Cook D.R., Klazura G.E., Martin T.J., Lucas J.D. Surface energy and carbon dioxide fluxes above different vegetation types within ABLE. Agricultural and Forest Meteorology 2006, 136:147-158.
15. 2 and climate change: Results from six dynamic global vegetation models. Global Change Biology 2001, 7:357-373.
16. De Michele C., Vezzoli R., Pavlopoulos H., Scholes R.J. A minimal model of soil water-vegetation interactions forced by stochastic rainfall in water-limited ecosystems. Ecological Modelling 2008, 212:397-407.
17. De Réaumur R.A.F. Observations du thermometer, faites à Paris pendant l'année 1735, comparées avec celles qui ont été faites sous la ligne, à l'Isle de France, à Alger et en quelques-unes de nos isles de l'Amérique. Mémoires de l'Académie des Sciences 1735, 545-584.
18. Ding R., Kang S., Du T., Hao X., Zhang Y. Scaling up stomatal conductance from leaf to canopy using a dual-leaf model for estimating crop evapotranspiration. PLoS ONE 2014, 9:e95584.
19. 2 exchange of a Ponderosa pine forest. Global Change Biology 2008, 14:1801-1820.
20. Dragoni D., Schmid H.P., Wayson C.A., Potter H., Grimmond C.S.B., Randolph J.C. Evidence of increased net ecosystem productivity associated with a longer vegetated season in a deciduous forest in south-central Indiana, USA. Global Change Biology 2011, 17:886-897.
21. Duan Q., Gupta V.K., Sorooshian S. Shuffled complex evolution approach for effective and efficient global minimization. Journal of Optimization Theory and Applications 1993, 76:501-521.
22. Duan Q.Y., Sorooshian S., Gupta V. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research 1992, 28:1015-1031.
23. 2O, and sensible heat in agricultural fields of the Southern Great Plains. Earth Interactions 2007, 11:1-21.
24. Fisher J.I., Mustard J.F. Cross-scalar satellite phenology from ground, Landsat, and MODIS data. Remote Sensing of Environment 2007, 109:261-273.
25. Fisher J.I., Richardson A.D., Mustard J.F. Phenology model from surface meteorology does not capture satellite-based greenup estimations. Global Change Biology 2007, 13:707-721.
26. Friedl M.A., Gray J.M., Melaas E.K., Richardson A.D., Hufkens K., Keenan T.F., Bailey A., O'Keefe J. A tale of two springs: Using recent climate anomalies to characterize the sensitivity of temperate forest phenology to climate change. Environmental Research Letters 2014, 9:054006.
27. Friedl M.A., McIver D.K., Hodges J.C.F., Zhang X.Y., Muchoney D., Strahler A.H., Woodcock C.E., Gopal S., Schneider A., Cooper A., Baccini A., Gao F., Schaaf C. Global land cover mapping from MODIS: Algorithms and early results. Remote Sensing of Environment 2002, 83:287-302.
28. Friedl M.A., Sulla-Menashe D., Tan B., Schneider A., Ramankutty N., Sibley A., Huang X. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment 2010, 114:168-182.
29. Ganguly S., Friedl M.A., Tan B., Zhang X., Verma M. Land surface phenology from MODIS: Characterization of the Collection 5 global land cover dynamics product. Remote Sensing of Environment 2010, 114:1805-1816.
30. 2 flux and remotely-sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: Potential for quantitative spatial extrapolation. Global Ecology and Biogeography 2005, 14:271-292.
31. Hanninen H., Kramer K. A framework for modelling the annual cycle of trees in boreal and temperate regions. Silva Fennica 2007, 41:167-205.
32. Hastings D.A., Dunbar P. Development & assessment of the Global Land One-km Base Elevation digital elevation model (GLOBE). Group 1998, 4.
33. Heumann B.W., Seaquist J., Eklundh L., Jönsson P. AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982-2005. Remote Sensing of Environment 2007, 108:385-392.
34. Huete A., Didan K., Miura T., Rodriguez E.P., Gao X., Ferreira L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 2002, 83:195-213.
35. Hufkens K., Friedl M., Sonnentag O., Braswell B.H., Milliman T., Richardson A.D. Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sensing of Environment 2012, 117:307-321.
36. Jarvis P.G., McNaughton K. Stomatal control of transpiration: Scaling up from leaf to region. Advances in Ecological Research 1986, 15:1-49.
37. Jeong S.J., Medvigy D., Shevliakova E., Malyshev S. Uncertainties in terrestrial carbon budgets related to spring phenology. Journal of Geophysical Research, Biogeosciences 2012, 117:G01030.
38. Ji L., Peters A.J. A spatial regression procedure for evaluating the relationship between AVHRR-NDVI and climate in the northern Great Plains. International Journal of Remote Sensing 2004, 25:297-311.
39. Jin Z.N., Zhuang Q.L., He J.S., Luo T.X., Shi Y. Phenology shift from 1989 to 2008 on the Tibetan Plateau: An analysis with a process-based soil physical model and remote sensing data. Climatic Change 2013, 119:435-449.
40. Jolly W.M., Nemani R., Running S.W. A generalized, bioclimatic index to predict foliar phenology in response to climate. Global Change Biology 2005, 11:619-632.
41. Jönsson P., Eklundh L. TIMESAT-A program for analyzing time-series of satellite sensor data. Computers & Geosciences 2004, 30:833-845.
42. Julien Y., Sobrino J.A. Global land surface phenology trends from GIMMS database. International Journal of Remote Sensing 2009, 30:3495-3513.
43. Justice C., Townshend J., Vermote E., Masuoka E., Wolfe R., Saleous N., Roy D., Morisette J. An overview of MODIS Land data processing and product status. Remote Sensing of Environment 2002, 83:3-15.
44. Kaduk J.D., Los S.O. Predicting the time of green up in temperate and boreal biomes. Climatic Change 2011, 107:277-304.
45. 2 flux tower data. Agricultural and Forest Meteorology 2011, 151:1514-1528.
46. Koerner C., Basler D. Phenology under global warming. Science 2010, 327:1461-1462.
47. Koster R.D., Suarez M.J. Modeling the land surface boundary in climate models as a composite of independent vegetation stands. Journal of Geophysical Research: Atmospheres 1992, 97:2697-2715.
48. 2O vapor exchange. Ecological Modelling 2006, 196:1-31.
49. 2 rise leads to reduced maximum stomatal conductance in Florida vegetation. Proceedings of the National Academy of Sciences 2011, 108:4035-4040.
50. Leuning R. A critical appraisal of a combined stomatal-photosynthesis model for C3 plants. Plant, Cell & Environment 1995, 18:339-355.
51. Levis S., Bonan G.B. Simulating springtime temperature patterns in the community atmosphere model coupled to the community land model using prognostic leaf area. Journal of Climate 2004, 17:4531-4540.
52. Liang X., Lettenmaier D.P., Wood E.F. One-dimensional statistical dynamic representation of subgrid spatial variability of precipitation in the two-layer variable infiltration capacity model. Journal of Geophysical Research, [Atmospheres] 1996, 101:21403-21422.
53. Liu H., Tian F., Hu H., Hu H., Sivapalan M. Soil moisture controls on patterns of grass green-up in Inner Mongolia: An index based approach. Hydrology and Earth System Sciences 2013, 17:805-815.
54. Lobell D.B., Roberts M.J., Schlenker W., Braun N., Little B.B., Rejesus R.M., Hammer G.L. Greater sensitivity to drought accompanies maize yield increase in the U.S. Midwest. Science 2014, 344:516-519.
55. Lotsch A., Friedl M.A., Anderson B.T., Tucker C.J. Coupled vegetation-precipitation variability observed from satellite and climate records. Geophysical Research Letters 2003, 30:1774.
56. Luo L., Robock A., Mitchell K.E., Houser P.R., Wood E.F., Schaake J.C., Lohmann D., Cosgrove B., Wen F., Sheffield J. Validation of the North American Land Data Assimilation System (NLDAS) retrospective forcing over the southern Great Plains. Journal of Geophysical Research, [Atmospheres] 2003, 1984-2012:108.
57. Maurer E., Wood A., Adam J., Lettenmaier D., Nijssen B. A long-term hydrologically based dataset of land surface fluxes and States for the Conterminous United States. Journal of Climate 2002, 15:3237-3251.
58. Melaas E.K., Friedl M.A., Zhu Z. Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data. Remote Sensing of Environment 2013, 132:176-185.
59. Melaas E.K., Richardson A.D., Friedl M.A., Dragoni D., Gough C.M., Herbst M., Montagnani L., Moors E. Using FLUXNET data to improve models of springtime vegetation activity onset in forest ecosystems. Agricultural and Forest Meteorology 2013, 171:46-56.
60. Metropolis N., Rosenbluth A.W., Rosenbluth M.N., Teller A.H., Teller E. Equation of state calculations by fast computing machines. The Journal of Chemical Physics 1953, 21:1087-1092.
61. Myneni R.B., Hall F.G., Sellers P.J., Marshak A.L. The interpretation of spectral vegetation indexes. IEEE Transactions on Geoscience and Remote Sensing 1995, 33:481-486.
62. Nijland W., de Jong R., de Jong S.M., Wulder M.A., Bater C.W., Coops N.C. Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras. Agricultural and Forest Meteorology 2014, 184:98-106.
63. Peng S., Piao S., Zeng Z., Ciais P., Zhou L., Li L.Z., Myneni R.B., Yin Y., Zeng H. Afforestation in China cools local land surface temperature. Proceedings of the National Academy of Sciences 2014, 111:2915-2919.
64. Piao S., Cui M., Chen A., Wang X., Ciais P., Liu J., Tang Y. Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau. Agricultural and Forest Meteorology 2011, 151:1599-1608.
65. Piao S., Fang J., Zhou L., Ciais P., Zhu B. Variations in satellite-derived phenology in China's temperate vegetation. Global Change Biology 2006, 12:672-685.
66. Piao S., Friedlingstein P., Ciais P., Viovy N., Demarty J. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Global Biogeochemical Cycles 2007, 21.
67. Richardson A.D., Anderson R.S., Arain M.A., Barr A.G., Bohrer G., Chen G.S., Chen J.M., Ciais P., Davis K.J., Desai A.R., Dietze M.C., Dragoni D., Garrity S.R., Gough C.M., Grant R., Hollinger D.Y., Margolis H.A., McCaughey H., Migliavacca M., Monson R.K., Munger J.W., Poulter B., Raczka B.M., Ricciuto D.M., Sahoo A.K., Schaefer K., Tian H.Q., Vargas R., Verbeeck H., Xiao J.F., Xue Y.K. Terrestrial biosphere models need better representation of vegetation phenology: Results from the North American Carbon Program Site Synthesis. Global Change Biology 2012, 18:566-584.
68. Richardson A.D., Bailey A.S., Denny E.G., Martin C.W., O'Keefe J. Phenology of a northern hardwood forest canopy. Global Change Biology 2006, 12:1174-1188.
69. Richardson A.D., Black T.A., Ciais P., Delbart N., Friedl M.A., Gobron N., Hollinger D.Y., Kutsch W.L., Longdoz B., Luyssaert S. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philosophical Transactions of the Royal Society, B: Biological Sciences 2010, 365:3227-3246.
70. Richardson A.D., Keenan T.F., Migliavacca M., Ryu Y., Sonnentag O., Toomey M. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agricultural and Forest Meteorology 2013, 169:156-173.
71. Roy D., Jin Y., Lewis P., Justice C. Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data. Remote Sensing of Environment 2005, 97:137-162.
72. Schaake J.C., Duan Q., Koren V., Mitchell K.E., Houser P.R., Wood E.F., Robock A., Lettenmaier D.P., Lohmann D., Cosgrove B. An intercomparison of soil moisture fields in the North American Land Data Assimilation System (NLDAS). Journal of Geophysical Research, [Atmospheres] 2004, 1984-2012:109.
73. Scott R.L. Using watershed water balance to evaluate the accuracy of eddy covariance evaporation measurements for three semiarid ecosystems. Agricultural and Forest Meteorology 2010, 150:219-225.
74. Shen M., Tang Y., Chen J., Zhu X., Zheng Y. Influences of temperature and precipitation before the growing season on spring phenology in grasslands of the central and eastern Qinghai-Tibetan Plateau. Agricultural and Forest Meteorology 2011, 151:1711-1722.
75. Tan B., Morisette J.T., Wolfe R.E., Feng G., Ederer G.A., Nightingale J., Pedelty J.A. An enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2011, 4:361-371.
76. Tateishi R., Ebata M. Analysis of phenological change patterns using 1982-2000 Advanced Very High Resolution Radiometer (AVHRR) data. International Journal of Remote Sensing 2004, 25:2287-2300.
77. Thornton P., Thornton M., Mayer B., Wilhelmi N., Wei Y., Cook R. Daymet: Daily surface weather on a 1km grid for North America, 1980-2008 2012, Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, T, N, (doi, 10).
78. Wang X., Piao S., Ciais P., Li J., Friedlingstein P., Koven C., Chen A. Spring temperature change and its implication in the change of vegetation growth in North America from 1982 to 2006. Proceedings of the National Academy of Sciences 2011, 108:1240-1245.
79. White M.A., BEURS D., Kirsten M., Didan K., Inouye D.W., Richardson A.D., Jensen O.P., O'keefe J., Zhang G., Nemani R.R. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006. Global Change Biology 2009, 15:2335-2359.
80. White M.A., Thornton P.E., Running S.W. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochemical Cycles 1997, 11:217-234.
81. Willmott C.J., Ackleson S.G., Davis R.E., Feddema J.J., Klink K.M., Legates D.R., O'Donnell J., Rowe C.M. Statistics for the evaluation and comparison of models. Journal of Geophysical Research, Oceans 1985, 90:8995-9005.
82. Wu C.Y., Gonsamo A., Gough C.M., Chen J.M., Xu S.G. Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS. Remote Sensing of Environment 2014, 147:79-88.
83. Wu X., Liu H. Consistent shifts in spring vegetation green-up date across temperate biomes in China, 1982-2006. Global Change Biology 2013, 19:870-880.
84. Xia Y., Mitchell K., Ek M., Sheffield J., Cosgrove B., Wood E., Luo L., Alonge C., Wei H., Meng J. Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. Journal of Geophysical Research, [Atmospheres] 2012, 117.
85. Xiao J., Zhuang Q., Law B.E., Chen J., Baldocchi D.D., Cook D.R., Oren R., Richardson A.D., Wharton S., Ma S. A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data. Remote Sensing of Environment 2010, 114:576-591.
86. Xin Q., Broich M., Suyker A.E., Yu L., Gong P. Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States. Agricultural and Forest Meteorology 2015, 201:111-119.
87. Xin Q., Olofsson P., Zhu Z., Tan B., Woodcock C.E. Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data. Remote Sensing of Environment 2013, 135:234-247.
88. Xin Q., Woodcock C.E., Liu J., Tan B., Melloh R.A., Davis R.E. View angle effects on MODIS snow mapping in forests. Remote Sensing of Environment 2012, 118:50-59.
89. Yang X., Mustard J.F., Tang J., Xu H. Regional-scale phenology modeling based on meteorological records and remote sensing observations. Journal of Geophysical Research, Biogeosciences 2012, 2005-2012:117.
90. Yu F., Price K.P., Ellis J., Shi P. Response of seasonal vegetation development to climatic variations in eastern central Asia. Remote Sensing of Environment 2003, 87:42-54.
91. Zhang X., Friedl M.A., Schaaf C.B., Strahler A.H., Hodges J.C., Gao F., Reed B.C., Huete A. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 2003, 84:471-475.
92. Zhang G., Zhang Y., Dong J., Xiao X. Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011. Proceedings of the National Academy of Sciences 2013, 110:4309-4314.
93. Zhu Z., Bi J., Pan Y., Ganguly S., Anav A., Xu L., Samanta A., Piao S., Nemani R.R., Myneni R.B. Global data sets of vegetation leaf area index (LAI) 3g and Fraction of Photosynthetically Active Radiation (FPAR) 3g derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the period 1981 to 2011. Remote Sensing 2013, 5:927-948.