Integrating chlorophyll fAPAR and nadir photochemical reflectance index from EO-1/Hyperion to predict cornfield daily gross primary production

Remote Sensing of Environment

Volumn 186, Issue , 2016, Pages 311-321

  Zhang, Qingyuan ^a,b   Middleton, Elizabeth M ^b   Cheng, Yen Ben ^b,c   Huemmrich, K Fred ^b,d   Cook, Bruce D ^b   Corp, Lawrence A ^b,e   Kustas, William P ^f   Russ, Andrew L ^f   Prueger, John H ^g   Yao, Tian ^a,b  

^a Unversities Space Research Association   (United States)

^b NASA GODDARD SPACE FLIGHT CENTER   (United States)

^c SIGMA SPACE CORPORATION   (United States)

^d UNIVERSITY OF MARYLAND BALTIMORE COUNTY   (United States)

^e Science System and Applications Inc   (United States)

^f AGRICULTURAL RESEARCH SERVICE   (United States)

^g AGRICULTURAL RESEARCH SERVICE   (United States)

Author keywords

Chlorophyll; Cornfield; Daily GPP; EO 1 Hyperion; fAPARchl; HyspIRI; PRI

Indexed keywords

EFFICIENCY; HYPERSPECTRAL IMAGING; MARKOV PROCESSES; MEAN SQUARE ERROR; NASA; PHOTOSYNTHESIS; RADIATIVE TRANSFER; REFLECTION; VEGETATION;

CORNFIELD; DAILY GPP; EO-1/HYPERION; FAPARCHL; HYSPIRI;

CHLOROPHYLL;

AGRICULTURAL LAND; CHLOROPHYLL; EDDY COVARIANCE; HYPERION; LIGHT USE EFFICIENCY; MAIZE; MARKOV CHAIN; MONTE CARLO ANALYSIS; NADIR; NET PRIMARY PRODUCTION; NUMERICAL MODEL; PHOTOCHEMISTRY; PHOTOSYNTHESIS; PHOTOSYNTHETICALLY ACTIVE RADIATION; RADIATIVE TRANSFER; REFLECTANCE; SATELLITE IMAGERY; VEGETATION COVER;

MARYLAND; UNITED STATES;

EID: 84983738808     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2016.08.026     Document Type: Article

References (108)

1. 2 exchange to the canopy: a generalized model of forest photosynthesis compared with measurements by eddy correlation. Oecologia 106 (1996), 257–265.
2. Asner, G.P., Wessman, C.A., Archer, S., Scale dependence of absorption of photosynthetically active radiation in terrestrial ecosystems. Ecol. Appl. 8 (1998), 1003–1021.
3. Baker, N.R., Chlorophyll fluorescence: A probe of photosynthesis in vivo. Annu. Rev. Plant Biol. 59 (2008), 89–113.
4. Baret, F., Fourty, T., Radiometric Estimates of Nitrogen Status in Leaves and Canopies. Lemaire, G., (eds.) Diagnosis of the Nitrogen Status in Crops, 1997, Springer, Berlin, 201–227.
5. Barry, P.S., Mendenhall, J., Jarecke, P., Folkman, M., Pearlman, J., Markham, B., EO-1 Hyperion Hyperspectral Aggregation and Comparison with EO-1 Advanced Land Imager and Landsat 7 ETM +. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Toronto Canada, 2002.
6. Barton, C.V.M., North, P.R.J., Remote sensing of canopy light use efficiency using the photochemical reflectance index: Model and sensitivity analysis. Remote Sens. Environ. 78 (2001), 264–273.
7. Bishop, C., Neural Networks for Pattern Recognition. 1995, Oxford University Press, New York.
8. Braswell, B.H., Sacks, W.J., Linder, E., Schimel, D.S., Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with Eddy covariance net ecosystem exchange observations. Glob. Chang. Biol. 11 (2005), 335–355.
9. Braswell, B.H., Schimel, D.S., Privette, J.L., Moore, B., Emery, W.J., Sulzman, E.W., Hudak, A.T., Extracting ecological and biophysical information from AVHRR optical data: An integrated algorithm based on inverse modeling. J. Geophys. Res. 101 (1996), 23335–23348.
10. Cheng, Y.-B., Middleton, E.M., Zhang, Q., Corp, L.A., Dandois, J., Kustas, W.P., The photochemical reflectance index from directional cornfield reflectances: observations and simulations. Remote Sens. Environ. 124 (2012), 444–453.
11. Cheng, Y.-B., Zhang, Q., Lyapustin, A.I., Wang, Y., Middleton, E.M., Impacts of light use efficiency and fPAR parameterization on gross primary production modeling. Agric. For. Meteorol. 189-190 (2014), 187–197.
12. Cook, B.D., Corp, L.W., Nelson, R.F., Middleton, E.M., Morton, D.C., McCorkel, J.T., Masek, J.G., Ranson, K.J., Ly, V., Montesano, P.M., NASA Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager. Remote Sens. 5 (2013), 4045–4066.
13. Cook, B.D., Davis, K.J., Wang, W., Desai, A.R., Berger, B.W., Teclaw, R.M., Martin, J.M., Bolstad, P., Bakwin, P., Yi, C., Heilman, W., Carbon exchange and venting anomalies in an upland deciduous forest in northern Wisconsin, USA. Agric. For. Meteorol. 126 (2004), 271–295.
14. Corp, L.A., Cook, B.D., Middleton, E.M., Cheng, Y.-B., Huemmrich, K.F., Campbell, P.K.E., FUSION: A fully Ultraportable System for Imaging Objects in Nature. IEEE International Geoscience & Remote Sensing Symposium (IGARSS). Honolulu, HI, 2010.
15. Croft, H., Chen, J.M., Froelich, N.J., Chen, B., Staebler, R.M., Seasonal controls of canopy chlorophyll content on forest carbon uptake: implications for GPP modeling. J. Geophys. Res. Biogeosci., 2015, 1–11.
16. Dong, J., Xiao, X., Wagle, P., Zhang, G., Zhou, Y., Jin, C., Torn, M.S., Meyers, T.P., Suyker, A.E., Wang, J., Yan, H., Biradar, C., Moore, B. III, 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. 162 (2015), 154–168.
17. Drolet, G.G., Huemmrich, K.F., Hall, F.G., Middleton, E.M., Black, T.A., Barr, A.G., Margolis, H.A., A MODIS-derived photochemical reflectance index to detect interannual variations in the photosynthetic light use efficiency of a boreal deciduous forest. Remote Sens. Environ. 98 (2005), 212–224.
18. Drolet, G.G., Middleton, E.M., Huemmrich, K.F., Hall, F.G., Amiro, B.D., Barr, A.G., Black, T.A., McCaughey, J.H., Margolis, H.A., Regional mapping of gross light-use efficiency using MODIS spectral indices. Remote Sens. Environ. 112 (2008), 3064–3078.
19. Evans, J., Photosynthesis and nitrogen relationships in leaves of C3 plants. Oecologia 78 (1989), 9–19.
20. Field, C.B., Randerson, J.T., Malmstrom, C.M., Global net primary production - combining ecology and remote-sensing. Remote Sens. Environ. 51 (1995), 74–88.
21. Flanagan, L.B., Sharp, E.J., Gamon, J.A., Application of the photosynthetic light-use efficiency model in a northern Great Plains grassland. Remote Sens. Environ. 168 (2015), 239–251.
22. Gamon, J.A., Berry, J., Facultative and constitutive pigment effects on the Photochemical Reflectance Index (PRI) in sun and shade conifer needles. Isr. J. Plant Sci. 60 (2012), 85–95.
23. Gamon, J.A., Bond, B., Effects of irradiance and photosynthetic downregulation on the photochemical reflectance index in Douglas-fir and ponderosa pine. Remote Sens. Environ. 135 (2013), 141–149.
24. Gamon, J.A., Field, C.B., Bilger, W., Björkman, O., Fredeen, A.L., Peñuelas, J., Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies. Oecologia 85 (1990), 1–7.
25. Gamon, J.A., Field, C.B., Goulden, M.L., Griffin, K.L., Hartley, A.E., Joel, G., Peñuelas, J., Valentini, R., Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecol. Appl. 5 (1995), 28–41.
26. Gamon, J.A., Kovalchuk, O., Wong, C.Y.S., Harris, A., Garrity, S.R., Monitoring seasonal and diurnal changes in photosynthetic pigments with automated PRI and NDVI sensors. Biogeosciences 12 (2015), 4149–4159.
27. Gamon, J.A., Penuelas, J., Field, C.B., A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 41 (1992), 35–44.
28. Gamon, J., Serrano, L., Surfus, J., The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 112 (1997), 492–501.
29. Gao, B.-C., Davis, C.O., Development of a line-by-line-based atmosphere removal algorithm for airborne and spaceborne imaging spectrometers. SPIE 3118 (1997), 132–141.
30. Gao, B.-C., Heidebrecht, K.H., Goetz, A.F.H., Derivation of scaled surface reflectances from AVIRIS data. Remote Sens. Environ. 44 (1993), 165–178.
31. Garbulsky, M.F., Peñuelas, J., Gamon, J., Inoue, Y., Filella, I., The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: a review and meta-analysis. Remote Sens. Environ. 115 (2011), 281–297.
32. Garbulsky, M.F., PeÑUelas, J., Papale, D., Filella, I., Remote estimation of carbon dioxide uptake by a Mediterranean forest. Glob. Chang. Biol. 14 (2008), 2860–2867.
33. Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B., Markov Chain Simulation. Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B., (eds.) Bayesian Data Analysis, 2000, Champman & Hall/CRC, New York.
34. Gillon, D., Houssard, C., Joffre, R., Using near-infrared reflectance spectroscopy to predict carbon, nitrogen and phosphorus content in heterogeneous plant material. Oecologia 118 (1999), 173–182.
35. Gitelson, A.A., Kaufman, Y.J., Merzlyak, M.N., Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58 (1996), 289–298.
36. Gitelson, A.A., Viña, A., Verma, S.B., Rundquist, D.C., Arkebauer, T.J., Keydan, G., Leavitt, B., Ciganda, V., Burba, G.G., Suyker, A.E., Relationship between gross primary production and chlorophyll content in crops: implications for the synoptic monitoring of vegetation productivity. J. Geophys. Res., 111, 2006, D08S11.
37. Goetz, S.J., Prince, S.D., Modelling terrestrial carbon exchange and storage: evidence and implications of functional convergence in light-use efficiency. Adv. Ecol. Res. 28 (1999), 57–92.
38. Green, D.S., Erickson, J.E., Kruger, E.L., Foliar morphology and canopy nitrogen as predictors of light-use efficiency in terrestrial vegetation. Agric. For. Meteorol. 115 (2003), 163–171.
39. Hall, F.G., Hilker, T., Coops, N.C., PHOTOSYNSAT, photosynthesis from space: theoretical foundations of a satellite concept and validation from tower and spaceborne data. Remote Sens. Environ. 115 (2011), 1918–1925.
40. Hall, F.G., Hilker, T., Coops, N.C., Lyapustin, A., Huemmrich, K.F., Middleton, E., Margolis, H., Drolet, G., Black, T.A., Multi-angle remote sensing of forest light use efficiency by observing PRI variation with canopy shadow fraction. Remote Sens. Environ. 112 (2008), 3201–3211.
41. 2 flux measurements for estimation of canopy PAR absorption. Glob. Chang. Biol. 8 (2002), 563–574.
42. Hanan, N.P., Kabat, P., Dolman, A.J., Elbers, J.A., Photosynthesis and carbon balance of a Sahelian fallow savanna. Glob. Chang. Biol. 4 (1998), 523–538.
43. Hilker, T., Coops, N.C., Hall, F.G., Black, T.A., Wulder, M.A., Nesic, Z., Krishnan, P., Separating physiologically and directionally induced changes in PRI using BRDF models. Remote Sens. Environ. 112 (2008), 2777–2788.
44. Hilker, T., Hall, F.G., Coops, N.C., Lyapustin, A., Wang, Y., Nesic, Z., Grant, N., Black, T.A., Wulder, M.A., Kljun, N., Hopkinson, C., Chasmer, L., Remote sensing of photosynthetic light-use efficiency across two forested biomes: spatial scaling. Remote Sens. Environ. 114 (2010), 2863–2874.
45. Hmimina, G., Dufrêne, E., Soudani, K., Relationship between photochemical reflectance index and leaf ecophysiological and biochemical parameters under two different water statuses: towards a rapid and efficient correction method using real-time measurements. Plant Cell Environ. 37 (2014), 473–487.
46. Hmimina, G., Merlier, E., Dufrêne, E., Soudani, K., Deconvolution of pigment and physiologically related photochemical reflectance index variability at the canopy scale over an entire growing season. Plant Cell Environ. 38 (2015), 1578–1590.
47. Houborg, R., Anderson, M.C., Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at regional scales. J. Appl. Remote. Sens. 3 (2009), 1–29.
48. Houborg, R., Cescatti, A., Migliavacca, M., Kustas, W.P., Satellite retrievals of leaf chlorophyll and photosynthetic capacity forimproved modeling of GPP. Agric. For. Meteorol. 117 (2013), 10–23.
49. Houborg, R., McCabe, M.F., Cescatti, A., Gitelson, A.A., Leaf chlorophyll constraint on model simulated gross primary productivity in agricultural systems. Int. J. Appl. Earth Obs. Geoinf. 43 (2015), 160–176.
50. Hurtt, G.C., Armstrong, R.A., A pelagic ecosystem model calibrated with BATS data. Deep-Sea Res. II Top. Stud. Oceanogr. 43 (1996), 653–683.
51. Jacquemoud, S., Baret, F., PROSPECT - a model of Leaf Optical-properties spectra. Remote Sens. Environ. 34 (1990), 75–91.
52. Kergoat, L., Lafont, A., Arneth, V.L.D., Saugier, B., Nitrogen controls plant canopy light-use efficiency in temperate and boreal ecosystems. J. Geophys. Res. 113 (2008), 1–19.
53. Lee, C.M., Cable, M.L., Hook, S.J., Green, R.O., Ustin, S.L., Mandl, D.J., Middleton, E.M., An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities. Remote Sens. Environ. 167 (2015), 6–19.
54. Magney, T.S., Vierling, L.A., Eitel, J.U.H., Huggins, D.R., Garrity, S.R., Response of high frequency Photochemical Reflectance Index (PRI) measurements to environmental conditions in wheat. Remote Sens. Environ. 173 (2016), 84–97.
55. Malmstrom, C.M., Thompson, M.V., Juday, G.P., Los, S.O., Randerson, J.T., Field, C.B., Interannual variation in global-scale net primary production: testing model estimates. Glob. Biogeochem. Cycles 11 (1997), 367–392.
56. Mänd, P., Hallik, L., Peñuelas, J., Nilson, T., Duce, P., Emmett, B.A., Beier, C., Estiarte, M., Garadnai, J.n., Kalapos, T., Schmidt, I.K., Kovács-Láng, E., Prieto, P., Tietema, A., Westerveld, J.W., Kull, O., Responses of the reflectance indices PRI and NDVI to experimental warming and drought in European shrublands along a north–south climatic gradient. Remote Sens. Environ. 114 (2010), 626–636.
57. Merlier, E., Hmimina, G., Dufrêne, E., Soudani, K., Explaining the variability of the photochemical reflectance index (PRI) at the canopy-scale: disentangling the effects of phenological and physiological changes. J. Photochem. Photobiol. B Biol. 151 (2015), 161–171.
58. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E., Equations of state calculations by fast computing machines. J. Chem. Phys. 21 (1953), 1087–1092.
59. Middleton, E.M., Cheng, Y.-B., Corp, L.A., Huemmrich, K.F., Campbell, P.K.E., Zhang, Q., Kustas, W.P., Russ, A.L., Diurnal and Seasonal Dynamics of Canopy-Level Solar-Induced Chlorophyll Fluorescence and Spectral Reflectance Indices in a Cornfield. 6th European Association of Remote Sensing Laboratories (EARSeL) SIG Imaging Spectroscopy Workshop. Tel Aviv, Israel, 2009.
60. Middleton, E.M., Cheng, Y.-B., Corp, L., Huemmrich, K.F., Zhang, Q., Campbell, P.K.E., Kustas, W., Diurnal and Directional Responses of Chlorophyll Fluorescence and the PRI in a Cornfield. 4th International Workshop on Remote Sensing of Vegetation Fluorescence. Valencia, Spain, 2010.
61. Middleton, E.M., Cheng, Y.-B., Hilker, T., Black, T.A., Krishnan, P., Coops, N.C., Huemmrich, K.F., Linking foliage spectral responses to canopy level ecosystem photosynthetic light use efficiency at a Douglas-fir forest in Canada. Can. J. Remote. Sens. 35 (2009), 166–188.
62. Monteith, J.L., Solar-radiation and productivity in tropical ecosystems. J. Appl. Ecol. 9 (1972), 747–766.
63. Moorthy, I., Miller, J.R., Noland, T.L., Estimating chlorophyll concentration in conifer needles with hyperspectral data: an assessment at the needle and canopy level. Remote Sens. Environ. 112 (2008), 2824–2838.
64. Myneni, R.B., Williams, D.L., On the relationship between Fapar and Ndvi. Remote Sens. Environ. 49 (1994), 200–211.
65. Myneni, R.B., Hoffman, S., Knyazikhin, Y., Privette, J.L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G.R., Lotsch, A., Friedl, M., Morisette, J.T., Votava, P., Nemani, R.R., Running, S.W., Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 83 (2002), 214–231.
66. Myneni, R.B., Nemani, R.R., Running, S.W., Estimation of global leaf area index and absorbed PAR using radiative transfer models. IEEE Trans. Geosci. Remote Sens. 35 (1997), 1380–1393.
67. Nichol, C.J., Grace, J., Determination of leaf pigment content in Calluna vulgaris shoots from spectral reflectance. Int. J. Remote Sens. 31 (2010), 5409–5422.
68. Nichol, C.J., Huemmrich, K.F., Black, T.A., Jarvis, P.G., Walthall, C.L., Grace, J., Hall, F.G., Remote sensing of photosynthetic-light-use efficiency of boreal forest. Agric. For. Meteorol. 101 (2000), 131–142.
69. Ollinger, S.V., Sources of variability in canopy reflectance and the convergent properties of plants. New Phytol. 189 (2011), 375–394.
70. 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. 115 (2011), 978–989.
71. 2 uptake. New Phytol. 191 (2011), 596–599.
72. Pierce, L.L., Running, S.W., Walker, J., Regional-scale relationships of leaf area index to specific leaf area and leaf nitrogen content. Ecol. Appl. 4 (1994), 313–321.
73. 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. Glob. Biogeochem. Cycles 7 (1993), 811–841.
74. Prince, S.D., Goward, S.N., Evaluation of the NOAA/NASA pathfinder AVHRR land data set for global primary production modelling. Int. J. Remote Sens. 17 (1996), 217–221.
75. Raich, J.W., Rastetter, E.B., Melillo, J.M., Kicklighter, D.W., Steudler, P.A., Peterson, B.J., Grace, A.L., Moore, B., Vorosmarty, C.J., Potential net primary productivity in south-America - application of a global-model. Ecol. Appl. 1 (1991), 399–429.
76. Richardson, A.D., Duigan, S.P., Berlyn, G.P., An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytol. 153 (2002), 185–194.
77. Running, S., Nemani, R., Heinsch, F., Zhao, M., Reeves, M., Hashimoto, H., A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54 (2004), 547–560.
78. Running, S.W., Thornton, P.E., Nemani, R., Glassy, J.M., Global Terrestrial Gross and Net Primary Productivity from the Earth Observing System. Sala, O.E., Jackson, R.B., Mooney, H.A., Howarth, R.W., (eds.) Methods in Ecosystem Science, 2000, Springer Verlag, New York, 44–57.
79. Sage, R.F., Pearcy, R.W., Seeman, J.R., The nitrogen use efficiency of C3 and C4 plants. Plant Physiol. 85 (1987), 355–359.
80. Scanlon, T.M., Kustas, W.P., Partitioning carbon dioxide and water vapor fluxes using correlation analysis. Agric. For. Meteorol. 150 (2010), 89–99.
81. Sellers, P., Canopy reflectance, photosynthesis and transpiration. II. The role of biophysics in the linearity of their interdependence. Remote Sens. Environ. 21 (1987), 143–183.
82. Sellers, P.J., Randall, D.A., Collatz, G.J., Berry, J.A., Field, C.B., Dazlich, D.A., Zhang, C., Collelo, G.D., Bounoua, L., A revised land surface parameterization (SiB2) for atmospheric GCMs.I: Model formulation. J. Clim. 9 (1996), 676–705.
83. Serbin, S.P., Dillaway, D.N., Kruger, E.L., Townsend, P.A., Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature. J. Exp. Bot., 2011, 1–14.
84. 2 exchange in response to drought in a Southern California chaparral ecosystem. Remote Sens. Environ. 103 (2006), 289–303.
85. Smith, M.L., Ollinger, S.V., Martin, M.E., Aber, J.D., Hallett, R.A., Goodale, C.L., Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen. Ecol. Appl. 12 (2002), 1286–1302.
86. Soudani, K., Hmimina, G., Dufrêne, E., Berveiller, D., Delpierre, N., Ourcival, J.-M., Rambal, S., Joffre, R., Relationships between photochemical reflectance index and light-use efficiency in deciduous and evergreen broadleaf forests. Remote Sens. Environ. 144 (2014), 73–84.
87. Suárez, L., Zarco-Tejada, P.J., Berni, J.A.J., González-Dugo, V., Fereres, E., Modelling PRI for water stress detection using radiative transfer models. Remote Sens. Environ. 113 (2009), 730–744.
88. Sun, L., Neville, R., Staenz, K., White, H.P., Automatic destriping of Hyperion imagery based on spectral moment matching. Can. J. Remote. Sens. 34 (2008), S68–S81.
89. Twine, T.E., Kustas, W.P., Norman, J.M., Cook, D.R., Houser, P.R., Meyers, T.P., Prueger, J.H., Starks, P.J., Wesely, M.L., Correcting eddy-covariance flux underestimates over a grassland. Agric. For. Meteorol. 103 (2000), 279–300.
90. Verhoef, W., Light-scattering by leaf layers with application to canopy reflectance modeling - the SAIL model. Remote Sens. Environ. 16 (1984), 125–141.
91. Verhoef, W., Earth observation modeling based on layer scattering matrices. Remote Sens. Environ. 17 (1985), 165–178.
92. Verhoef, W., Theory of Radiative Transfer Models Applied in Optical Remote Sensing of Vegetation Canopies. Remote Sensing Department, 1998, National Aerospace Lab, NLR, Wageningen, 310.
93. Vermote, E.F., Tanre, D., Deuze, J.L., Herman, M., Morcrette, J.J., Second simulation of the satellite signal in the solar Spectrum, 6S: an overview. IEEE Trans. Geosci. Remote Sens. 35 (1997), 675–686.
94. Wu, C., Chen, J.M., Desai, A.R., Hollinger, D.Y., Arain, M.A., Margolis, H.A., Gough, C.M., Staebler, R.M., Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery. Remote Sens. Environ. 118 (2012), 60–72.
95. Wu, C., Huang, W., Yang, Q., Xie, Q., Improved estimation of light use efficiency by removal of canopy structural effect from the photochemical reflectance index (PRI). Agric. Ecosyst. Environ. 199 (2014), 333–338.
96. Wu, M., Muhammad, S., Chen, F., Niu, Z., Wang, C., Combining remote sensing and eddy covariance data to monitor the gross primary production of an estuarine wetland ecosystem in East China. Environ. Sci.: Processes Impacts 17 (2015), 753–762.
97. Xiao, X.M., Hollinger, D., Aber, J., Goltz, M., Davidson, E.A., Zhang, Q., Moore, B., Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens. Environ. 89 (2004), 519–534.
98. Yebra, M., van Dijk, A.I.J.M., Leuning, R., Guerschman, J.P., Global vegetation gross primary production estimation using satellite-derived light-use efficiency and canopy conductance. Remote Sens. Environ. 163 (2015), 206–216.
99. Zarco-Tejada, P.J., González-Dugo, V., Berni, J.A.J., Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens. Environ. 117 (2012), 322–337.
100. Zhang, Q., Cheng, Y.-B., Lyapustin, A., Wang, Y., Zhang, X., Suyker, A., Verma, S., Shuai, Y., Middleton, E., Estimation of crop gross primary production (GPP): II. Do the scaled vegetation indices improve performance?. Agric. For. Meteorol. 200 (2015), 1–8.
101. Zhang, Q., Ju, W., Chen, J.M., Wang, H., Yang, F., Fan, W., Huang, Q., Zheng, T., Feng, Y., Zhou, Y., He, M., Qiu, F., Wang, X., Wang, J., Zhang, F., Chou, S., Ability of the photochemical reflectance index to track light use efficiency for a sub-tropical planted coniferous forest. Remote Sens. 7:12 (2015), 16938–16962.
102. chl versus MOD15A2 FPAR. Remote Sens. Environ. 153 (2014), 1–6.
103. non-chl) at the Harvard Forest. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6 (2013), 2254–2264.
104. chl) and Leaf Water Content (LWC). IEEE Trans. Geosci. Remote Sens. 50 (2012), 1844–1852.
105. chl) improve predictions of light-use efficiency and ecosystem photosynthesis for a boreal aspen forest?. Remote Sens. Environ. 113 (2009), 880–888.
106. Zhang, Q., Xiao, X.M., Braswell, B., Linder, E., Baret, F., Moore, B., Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model. Remote Sens. Environ. 99 (2005), 357–371.
107. Zhang, Q., Xiao, X., Braswell, B., Linder, E., Ollinger, S., Smith, M.L., Jenkins, J.P., Baret, F., Richardson, A.D., Moore, B., Minocha, R., Characterization of seasonal variation of forest canopy in a temperate deciduous broadleaf forest, using daily MODIS data. Remote Sens. Environ. 105 (2006), 189–203.
108. Zhao, M., Running, S., Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329 (2010), 940–943.