Abstract

Accurate estimation of leaf nitrogen contents (LNCs) is essential for nutrition management in monitoring crop growth status. The aim of this study was to compare the potential of hyperspectral LiDAR (HSL) and laser-induced chlorophyll fluorescence (LIF) data in accurately predicting rice LNC. First of all, the intensity values of HSL at 694 and 742 nm and LIF at ~685 and ~740 nm were selected as the characteristic variables to analyze rice LNC using data collected in 2014 and 2015, respectively. Second, spectral indices derived from HSL (only) and LIF (only) were utilized to estimate LNC of rice, respectively. Third, a combined ratio indices (the ratio indices of reflectance to fluorescence and NDVI-based indices at the above four wavelengths) was developed and evaluated in estimating rice LNC. The statistical method of linking these spectral indices to rice LNC was the artificial neural network, which was to obtain the optimum performance in LNC estimation of rice. The results demonstrated that the combined ratio indices, especially the ratio of reflectance to fluorescence at ~740 nm, showed a moderate relationship with rice LNC (R2 = 0.736, 0.704, and 0.713 for the 2014 first experiment, 2014 second experiment, and 2015 experiment, respectively).

© 2017 Optical Society of America

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    [Crossref]
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2017 (1)

S. Nie, C. Wang, X. H. Xi, S. Z. Luo, and X. F. Sun, “Estimating the Biomass of Maize with Hyperspectral and LiDAR Data,” Remote Sens. 9, 11 (2017).

2016 (5)

L. Du, W. Gong, S. Shi, J. Yang, J. Sun, B. Zhu, and S. Song, “Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR,” Int. J. Appl. Earth Obs. Geoinf. 44, 136–143 (2016).
[Crossref]

L. Du, S. Shi, J. Yang, J. Sun, and W. Gong, “Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data,” Remote Sens. 8(6), 526 (2016).
[Crossref]

J. Yang, L. Du, J. Sun, Z. Zhang, B. Chen, S. Shi, W. Gong, and S. Song, “Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra,” Opt. Express 24(17), 19354–19365 (2016).
[Crossref] [PubMed]

J. Yang, S. Shi, W. Gong, L. Du, Y. Y. Ma, B. Zhu, and S. L. Song, “Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content,” Plant Soil Environ. 61(4), 182–188 (2016).
[Crossref]

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, and S. L. Song, “Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice,” Plant Soil Environ. 62(4), 178–183 (2016).
[Crossref]

2015 (1)

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, B. Zhu, Y.-y. Ma, and S. L. Song, “Vegetation identification based on characteristics of fluorescence spectral spatial distribution,” RSC Advances 5(70), 56932–56935 (2015).
[Crossref]

2014 (1)

Q. Chen, G. V. Laurin, J. A. Lindsell, D. A. Coomes, F. D. Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

2013 (2)

K. Yu, F. Li, M. L. Gnyp, Y. Miao, G. Bareth, and X. Chen, “Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain,” ISPRS J. Photogramm. Remote Sens. 78, 102–115 (2013).
[Crossref]

D. Valle, K. Zhao, S. Popescu, X. S. Zhang, and B. Mallick, “Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection,” Remote Sens. Environ. 132, 102–119 (2013).
[Crossref]

2009 (1)

M. Boschrtti, D. Stroppiana, P. A. Brivio, and S. Bocchi, “Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry,” Field Crops Res. 11, 119–129 (2009).

2008 (1)

S. Lafont, L. Kergoat, A. Arneth, V. Le Dantec, and B. Saugier, “Nitrogen controls plant canopy light‐use efficiency in temperate and boreal ecosystems,” J. Geophys. Res. Biogeosci. 113, 1–19 (2008).

2007 (4)

J. Grace, C. Nichol, M. Disney, P. Lewis, T. Quaife, and P. Bowyer, “Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence?” Glob. Change Biol. 13(7), 1484–1497 (2007).
[Crossref]

S. Apostol, A. A. Viau, and N. Tremblay, “A comparison of multiwavelength laser-induced fluorescence parameters for the remote sensing of nitrogen stress in field-cultivated corn,” Can. J. Rem. Sens. 33(3), 150–161 (2007).
[Crossref]

F. Van der Meer, J. Farifteh, C. Atzberger, and E. Carranza, “Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN),” Remote Sens. Environ. 110(1), 59–78 (2007).
[Crossref]

D. E. Knapp, G. P. Asner, T. Kennedy-Bowdoin, M. O. Jones, R. E. Martin, J. Boardman, and C. B. Field, “Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging (wLiDAR) for three-dimensional studies of ecosystems,” J. Appl. Remote Sens. 1, 013536 (2007).

2006 (1)

G. Camps-Valls, L. Gómez-Chova, J. Muñoz-Marí, J. Vila-Francés, J. Amorós-López, and J. Calpe-Maravilla, “Retrieval of oceanic chlorophyll concentration with relevance vector machines,” Remote Sens. Environ. 105(1), 23–33 (2006).
[Crossref]

2003 (1)

M. Pal and P. M. Mather, “An assessment of the effectiveness of decision tree methods for land cover classification,” Remote Sens. Environ. 86(4), 554–565 (2003).
[Crossref]

2001 (1)

P. J. Curran, J. L. Dungan, and D. L. Peterson, “Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: testing the Kokaly and Clark methodologies,” Remote Sens. Environ. 76(3), 349–359 (2001).
[Crossref]

2000 (3)

P. J. Zarco-Tejada, J. R. Miller, G. H. Mohammed, and T. L. Noland, “Chlorophyll fluorescence effects on vegetation apparent reflectance: I. Leaf-level measurements and model simulation,” Remote Sens. Environ. 74(3), 582–595 (2000).
[Crossref]

K. Maxwell and G. N. Johnson, “Chlorophyll fluorescence--a practical guide,” J. Exp. Bot. 51(345), 659–668 (2000).
[PubMed]

C. Daughtry, C. Walthall, M. Kim, E. B. De Colstoun, and J. McMurtrey Iii, “Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance,” Remote Sens. Environ. 74(2), 229–239 (2000).
[Crossref]

1997 (1)

H. K. Lichtenthaler and J. A. Miehé, “Fluorescence imaging as a diagnostic tool for plant stress,” Trends Plant Sci. 2(8), 316–320 (1997).
[Crossref]

1996 (1)

A. A. Gitelson, Y. J. Kaufman, and M. N. Merzlyak, “Use of a green channel in remote sensing of global vegetation from EOS-MODIS,” Remote Sens. Environ. 58(3), 289–298 (1996).
[Crossref]

1994 (1)

1993 (2)

J. Wu, M. S. Feld, and R. P. Rava, “Analytical model for extracting intrinsic fluorescence in turbid media,” Appl. Opt. 32(19), 3585–3595 (1993).
[Crossref] [PubMed]

F. Stober and H. K. Lichtenthaler, “Characterization of the laser‐induced blue, green and red fluorescence signatures of leaves of wheat and soybean grown under different irradiance,” Physiol. Plant. 88(4), 696–704 (1993).
[Crossref]

1991 (2)

J. E. McMurtrey, E. W. Chappelle, and M. S. Kim, “Identification of the pigment responsible for the blue fluorescence band in the laser induced fluorescence (LIF) spectra of green plants, and the potential use of this band in remotely estimating rates of photosynthesis,” Remote Sens. Environ. 36(3), 213–218 (1991).
[Crossref]

G. H. Krause and E. Weis, “Chlorophyll fluorescence and photosynthesis: the basics,” Annu. Rev. Plant Biol. 42(1), 313–349 (1991).
[Crossref]

1990 (1)

F. Baret and S. Jacquemoud, “PROSPECT: A model of leaf optical properties spectra,” Remote Sens. Environ. 34(2), 75–91 (1990).
[Crossref]

1988 (1)

H. K. Lichtenthaler and U. Rinderle, “The role of chlorophyll fluorescence in the detection of stress conditions in plants,” Crit. Rev. Anal. Chem. 19(sup1), S29–S85 (1988).
[Crossref]

1987 (1)

M. G. R. Cannell, R. Milne, L. J. Sheppard, and M. H. Unsworth, “Radiation interception and productivity of willow,” Appl. Ecol. 24(1), 261–278 (1987).
[Crossref]

1985 (1)

K. D. Wutzke and W. Heine, “[A century of Kjeldahl’s nitrogen determination],” Z. Med. Lab. Diagn. 26(7), 383–388 (1985).
[PubMed]

1974 (1)

J. W. Rouse, R. Haas, J. Schell, and D. Deering, “Monitoring vegetation systems in the Great Plains with ERTS,” NASA Spec. Publ. 351, 309 (1974).

Allen, S.

T. Stocker, D. Qin, G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia, B. Bex, and B. Midgley, “IPCC, 2013: climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change” (2013).

Amorós-López, J.

G. Camps-Valls, L. Gómez-Chova, J. Muñoz-Marí, J. Vila-Francés, J. Amorós-López, and J. Calpe-Maravilla, “Retrieval of oceanic chlorophyll concentration with relevance vector machines,” Remote Sens. Environ. 105(1), 23–33 (2006).
[Crossref]

Apostol, S.

S. Apostol, A. A. Viau, and N. Tremblay, “A comparison of multiwavelength laser-induced fluorescence parameters for the remote sensing of nitrogen stress in field-cultivated corn,” Can. J. Rem. Sens. 33(3), 150–161 (2007).
[Crossref]

Arneth, A.

S. Lafont, L. Kergoat, A. Arneth, V. Le Dantec, and B. Saugier, “Nitrogen controls plant canopy light‐use efficiency in temperate and boreal ecosystems,” J. Geophys. Res. Biogeosci. 113, 1–19 (2008).

Asner, G. P.

D. E. Knapp, G. P. Asner, T. Kennedy-Bowdoin, M. O. Jones, R. E. Martin, J. Boardman, and C. B. Field, “Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging (wLiDAR) for three-dimensional studies of ecosystems,” J. Appl. Remote Sens. 1, 013536 (2007).

Atzberger, C.

F. Van der Meer, J. Farifteh, C. Atzberger, and E. Carranza, “Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN),” Remote Sens. Environ. 110(1), 59–78 (2007).
[Crossref]

Baret, F.

F. Baret and S. Jacquemoud, “PROSPECT: A model of leaf optical properties spectra,” Remote Sens. Environ. 34(2), 75–91 (1990).
[Crossref]

Bareth, G.

K. Yu, F. Li, M. L. Gnyp, Y. Miao, G. Bareth, and X. Chen, “Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain,” ISPRS J. Photogramm. Remote Sens. 78, 102–115 (2013).
[Crossref]

Berger, M.

J. R. Miller, P. J. Zarco-Tejada, R. Pedrós, W. Verhoef, and M. Berger, “FluorMODgui: a graphic user interface for the spectral simulation of leaf and canopy fluorescence effects,” in 2nd International Workshop on Remote Sensing of Vegetation Fluorescence (2004).

Bex, B.

T. Stocker, D. Qin, G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia, B. Bex, and B. Midgley, “IPCC, 2013: climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change” (2013).

Boardman, J.

D. E. Knapp, G. P. Asner, T. Kennedy-Bowdoin, M. O. Jones, R. E. Martin, J. Boardman, and C. B. Field, “Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging (wLiDAR) for three-dimensional studies of ecosystems,” J. Appl. Remote Sens. 1, 013536 (2007).

Bocchi, S.

M. Boschrtti, D. Stroppiana, P. A. Brivio, and S. Bocchi, “Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry,” Field Crops Res. 11, 119–129 (2009).

Boschrtti, M.

M. Boschrtti, D. Stroppiana, P. A. Brivio, and S. Bocchi, “Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry,” Field Crops Res. 11, 119–129 (2009).

Boschung, J.

T. Stocker, D. Qin, G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia, B. Bex, and B. Midgley, “IPCC, 2013: climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change” (2013).

Bowyer, P.

J. Grace, C. Nichol, M. Disney, P. Lewis, T. Quaife, and P. Bowyer, “Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence?” Glob. Change Biol. 13(7), 1484–1497 (2007).
[Crossref]

Brivio, P. A.

M. Boschrtti, D. Stroppiana, P. A. Brivio, and S. Bocchi, “Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry,” Field Crops Res. 11, 119–129 (2009).

Calpe-Maravilla, J.

G. Camps-Valls, L. Gómez-Chova, J. Muñoz-Marí, J. Vila-Francés, J. Amorós-López, and J. Calpe-Maravilla, “Retrieval of oceanic chlorophyll concentration with relevance vector machines,” Remote Sens. Environ. 105(1), 23–33 (2006).
[Crossref]

Camps-Valls, G.

G. Camps-Valls, L. Gómez-Chova, J. Muñoz-Marí, J. Vila-Francés, J. Amorós-López, and J. Calpe-Maravilla, “Retrieval of oceanic chlorophyll concentration with relevance vector machines,” Remote Sens. Environ. 105(1), 23–33 (2006).
[Crossref]

Cannell, M. G. R.

M. G. R. Cannell, R. Milne, L. J. Sheppard, and M. H. Unsworth, “Radiation interception and productivity of willow,” Appl. Ecol. 24(1), 261–278 (1987).
[Crossref]

Carranza, E.

F. Van der Meer, J. Farifteh, C. Atzberger, and E. Carranza, “Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN),” Remote Sens. Environ. 110(1), 59–78 (2007).
[Crossref]

Chappelle, E. W.

J. E. McMurtrey, E. W. Chappelle, and M. S. Kim, “Identification of the pigment responsible for the blue fluorescence band in the laser induced fluorescence (LIF) spectra of green plants, and the potential use of this band in remotely estimating rates of photosynthesis,” Remote Sens. Environ. 36(3), 213–218 (1991).
[Crossref]

Chen, B.

Chen, Q.

Q. Chen, G. V. Laurin, J. A. Lindsell, D. A. Coomes, F. D. Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

Chen, X.

K. Yu, F. Li, M. L. Gnyp, Y. Miao, G. Bareth, and X. Chen, “Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain,” ISPRS J. Photogramm. Remote Sens. 78, 102–115 (2013).
[Crossref]

Coomes, D. A.

Q. Chen, G. V. Laurin, J. A. Lindsell, D. A. Coomes, F. D. Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

Curran, P. J.

P. J. Curran, J. L. Dungan, and D. L. Peterson, “Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: testing the Kokaly and Clark methodologies,” Remote Sens. Environ. 76(3), 349–359 (2001).
[Crossref]

Daughtry, C.

C. Daughtry, C. Walthall, M. Kim, E. B. De Colstoun, and J. McMurtrey Iii, “Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance,” Remote Sens. Environ. 74(2), 229–239 (2000).
[Crossref]

De Colstoun, E. B.

C. Daughtry, C. Walthall, M. Kim, E. B. De Colstoun, and J. McMurtrey Iii, “Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance,” Remote Sens. Environ. 74(2), 229–239 (2000).
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J. W. Rouse, R. Haas, J. Schell, and D. Deering, “Monitoring vegetation systems in the Great Plains with ERTS,” NASA Spec. Publ. 351, 309 (1974).

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J. Grace, C. Nichol, M. Disney, P. Lewis, T. Quaife, and P. Bowyer, “Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence?” Glob. Change Biol. 13(7), 1484–1497 (2007).
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J. Yang, L. Du, J. Sun, Z. Zhang, B. Chen, S. Shi, W. Gong, and S. Song, “Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra,” Opt. Express 24(17), 19354–19365 (2016).
[Crossref] [PubMed]

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, and S. L. Song, “Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice,” Plant Soil Environ. 62(4), 178–183 (2016).
[Crossref]

J. Yang, S. Shi, W. Gong, L. Du, Y. Y. Ma, B. Zhu, and S. L. Song, “Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content,” Plant Soil Environ. 61(4), 182–188 (2016).
[Crossref]

L. Du, W. Gong, S. Shi, J. Yang, J. Sun, B. Zhu, and S. Song, “Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR,” Int. J. Appl. Earth Obs. Geoinf. 44, 136–143 (2016).
[Crossref]

L. Du, S. Shi, J. Yang, J. Sun, and W. Gong, “Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data,” Remote Sens. 8(6), 526 (2016).
[Crossref]

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, B. Zhu, Y.-y. Ma, and S. L. Song, “Vegetation identification based on characteristics of fluorescence spectral spatial distribution,” RSC Advances 5(70), 56932–56935 (2015).
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Dungan, J. L.

P. J. Curran, J. L. Dungan, and D. L. Peterson, “Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: testing the Kokaly and Clark methodologies,” Remote Sens. Environ. 76(3), 349–359 (2001).
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F. Van der Meer, J. Farifteh, C. Atzberger, and E. Carranza, “Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN),” Remote Sens. Environ. 110(1), 59–78 (2007).
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Field, C. B.

D. E. Knapp, G. P. Asner, T. Kennedy-Bowdoin, M. O. Jones, R. E. Martin, J. Boardman, and C. B. Field, “Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging (wLiDAR) for three-dimensional studies of ecosystems,” J. Appl. Remote Sens. 1, 013536 (2007).

Frate, F. D.

Q. Chen, G. V. Laurin, J. A. Lindsell, D. A. Coomes, F. D. Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” J. Photogramm. Remote Sens. 89, 49–58 (2014).
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A. A. Gitelson, Y. J. Kaufman, and M. N. Merzlyak, “Use of a green channel in remote sensing of global vegetation from EOS-MODIS,” Remote Sens. Environ. 58(3), 289–298 (1996).
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K. Yu, F. Li, M. L. Gnyp, Y. Miao, G. Bareth, and X. Chen, “Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain,” ISPRS J. Photogramm. Remote Sens. 78, 102–115 (2013).
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Gómez-Chova, L.

G. Camps-Valls, L. Gómez-Chova, J. Muñoz-Marí, J. Vila-Francés, J. Amorós-López, and J. Calpe-Maravilla, “Retrieval of oceanic chlorophyll concentration with relevance vector machines,” Remote Sens. Environ. 105(1), 23–33 (2006).
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Gong, W.

L. Du, W. Gong, S. Shi, J. Yang, J. Sun, B. Zhu, and S. Song, “Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR,” Int. J. Appl. Earth Obs. Geoinf. 44, 136–143 (2016).
[Crossref]

J. Yang, S. Shi, W. Gong, L. Du, Y. Y. Ma, B. Zhu, and S. L. Song, “Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content,” Plant Soil Environ. 61(4), 182–188 (2016).
[Crossref]

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, and S. L. Song, “Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice,” Plant Soil Environ. 62(4), 178–183 (2016).
[Crossref]

J. Yang, L. Du, J. Sun, Z. Zhang, B. Chen, S. Shi, W. Gong, and S. Song, “Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra,” Opt. Express 24(17), 19354–19365 (2016).
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L. Du, S. Shi, J. Yang, J. Sun, and W. Gong, “Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data,” Remote Sens. 8(6), 526 (2016).
[Crossref]

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, B. Zhu, Y.-y. Ma, and S. L. Song, “Vegetation identification based on characteristics of fluorescence spectral spatial distribution,” RSC Advances 5(70), 56932–56935 (2015).
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Grace, J.

J. Grace, C. Nichol, M. Disney, P. Lewis, T. Quaife, and P. Bowyer, “Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence?” Glob. Change Biol. 13(7), 1484–1497 (2007).
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Guerriero, L.

Q. Chen, G. V. Laurin, J. A. Lindsell, D. A. Coomes, F. D. Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” J. Photogramm. Remote Sens. 89, 49–58 (2014).
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Haas, R.

J. W. Rouse, R. Haas, J. Schell, and D. Deering, “Monitoring vegetation systems in the Great Plains with ERTS,” NASA Spec. Publ. 351, 309 (1974).

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K. D. Wutzke and W. Heine, “[A century of Kjeldahl’s nitrogen determination],” Z. Med. Lab. Diagn. 26(7), 383–388 (1985).
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Jacquemoud, S.

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Johnson, G. N.

K. Maxwell and G. N. Johnson, “Chlorophyll fluorescence--a practical guide,” J. Exp. Bot. 51(345), 659–668 (2000).
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D. E. Knapp, G. P. Asner, T. Kennedy-Bowdoin, M. O. Jones, R. E. Martin, J. Boardman, and C. B. Field, “Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging (wLiDAR) for three-dimensional studies of ecosystems,” J. Appl. Remote Sens. 1, 013536 (2007).

Kaufman, Y. J.

A. A. Gitelson, Y. J. Kaufman, and M. N. Merzlyak, “Use of a green channel in remote sensing of global vegetation from EOS-MODIS,” Remote Sens. Environ. 58(3), 289–298 (1996).
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D. E. Knapp, G. P. Asner, T. Kennedy-Bowdoin, M. O. Jones, R. E. Martin, J. Boardman, and C. B. Field, “Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging (wLiDAR) for three-dimensional studies of ecosystems,” J. Appl. Remote Sens. 1, 013536 (2007).

Kergoat, L.

S. Lafont, L. Kergoat, A. Arneth, V. Le Dantec, and B. Saugier, “Nitrogen controls plant canopy light‐use efficiency in temperate and boreal ecosystems,” J. Geophys. Res. Biogeosci. 113, 1–19 (2008).

Kim, M.

C. Daughtry, C. Walthall, M. Kim, E. B. De Colstoun, and J. McMurtrey Iii, “Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance,” Remote Sens. Environ. 74(2), 229–239 (2000).
[Crossref]

Kim, M. S.

J. E. McMurtrey, E. W. Chappelle, and M. S. Kim, “Identification of the pigment responsible for the blue fluorescence band in the laser induced fluorescence (LIF) spectra of green plants, and the potential use of this band in remotely estimating rates of photosynthesis,” Remote Sens. Environ. 36(3), 213–218 (1991).
[Crossref]

Knapp, D. E.

D. E. Knapp, G. P. Asner, T. Kennedy-Bowdoin, M. O. Jones, R. E. Martin, J. Boardman, and C. B. Field, “Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging (wLiDAR) for three-dimensional studies of ecosystems,” J. Appl. Remote Sens. 1, 013536 (2007).

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G. H. Krause and E. Weis, “Chlorophyll fluorescence and photosynthesis: the basics,” Annu. Rev. Plant Biol. 42(1), 313–349 (1991).
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S. Lafont, L. Kergoat, A. Arneth, V. Le Dantec, and B. Saugier, “Nitrogen controls plant canopy light‐use efficiency in temperate and boreal ecosystems,” J. Geophys. Res. Biogeosci. 113, 1–19 (2008).

Laurin, G. V.

Q. Chen, G. V. Laurin, J. A. Lindsell, D. A. Coomes, F. D. Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

Le Dantec, V.

S. Lafont, L. Kergoat, A. Arneth, V. Le Dantec, and B. Saugier, “Nitrogen controls plant canopy light‐use efficiency in temperate and boreal ecosystems,” J. Geophys. Res. Biogeosci. 113, 1–19 (2008).

Lewis, P.

J. Grace, C. Nichol, M. Disney, P. Lewis, T. Quaife, and P. Bowyer, “Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence?” Glob. Change Biol. 13(7), 1484–1497 (2007).
[Crossref]

Li, F.

K. Yu, F. Li, M. L. Gnyp, Y. Miao, G. Bareth, and X. Chen, “Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain,” ISPRS J. Photogramm. Remote Sens. 78, 102–115 (2013).
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Lichtenthaler, H. K.

H. K. Lichtenthaler and J. A. Miehé, “Fluorescence imaging as a diagnostic tool for plant stress,” Trends Plant Sci. 2(8), 316–320 (1997).
[Crossref]

F. Stober and H. K. Lichtenthaler, “Characterization of the laser‐induced blue, green and red fluorescence signatures of leaves of wheat and soybean grown under different irradiance,” Physiol. Plant. 88(4), 696–704 (1993).
[Crossref]

H. K. Lichtenthaler and U. Rinderle, “The role of chlorophyll fluorescence in the detection of stress conditions in plants,” Crit. Rev. Anal. Chem. 19(sup1), S29–S85 (1988).
[Crossref]

Lindsell, J. A.

Q. Chen, G. V. Laurin, J. A. Lindsell, D. A. Coomes, F. D. Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

Luo, S. Z.

S. Nie, C. Wang, X. H. Xi, S. Z. Luo, and X. F. Sun, “Estimating the Biomass of Maize with Hyperspectral and LiDAR Data,” Remote Sens. 9, 11 (2017).

Ma, Y. Y.

J. Yang, S. Shi, W. Gong, L. Du, Y. Y. Ma, B. Zhu, and S. L. Song, “Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content,” Plant Soil Environ. 61(4), 182–188 (2016).
[Crossref]

Ma, Y.-y.

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, B. Zhu, Y.-y. Ma, and S. L. Song, “Vegetation identification based on characteristics of fluorescence spectral spatial distribution,” RSC Advances 5(70), 56932–56935 (2015).
[Crossref]

Mallick, B.

D. Valle, K. Zhao, S. Popescu, X. S. Zhang, and B. Mallick, “Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection,” Remote Sens. Environ. 132, 102–119 (2013).
[Crossref]

Martin, R. E.

D. E. Knapp, G. P. Asner, T. Kennedy-Bowdoin, M. O. Jones, R. E. Martin, J. Boardman, and C. B. Field, “Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging (wLiDAR) for three-dimensional studies of ecosystems,” J. Appl. Remote Sens. 1, 013536 (2007).

Mather, P. M.

M. Pal and P. M. Mather, “An assessment of the effectiveness of decision tree methods for land cover classification,” Remote Sens. Environ. 86(4), 554–565 (2003).
[Crossref]

Maxwell, K.

K. Maxwell and G. N. Johnson, “Chlorophyll fluorescence--a practical guide,” J. Exp. Bot. 51(345), 659–668 (2000).
[PubMed]

McMurtrey, J. E.

J. E. McMurtrey, E. W. Chappelle, and M. S. Kim, “Identification of the pigment responsible for the blue fluorescence band in the laser induced fluorescence (LIF) spectra of green plants, and the potential use of this band in remotely estimating rates of photosynthesis,” Remote Sens. Environ. 36(3), 213–218 (1991).
[Crossref]

McMurtrey Iii, J.

C. Daughtry, C. Walthall, M. Kim, E. B. De Colstoun, and J. McMurtrey Iii, “Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance,” Remote Sens. Environ. 74(2), 229–239 (2000).
[Crossref]

Merzlyak, M. N.

A. A. Gitelson, Y. J. Kaufman, and M. N. Merzlyak, “Use of a green channel in remote sensing of global vegetation from EOS-MODIS,” Remote Sens. Environ. 58(3), 289–298 (1996).
[Crossref]

Miao, Y.

K. Yu, F. Li, M. L. Gnyp, Y. Miao, G. Bareth, and X. Chen, “Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain,” ISPRS J. Photogramm. Remote Sens. 78, 102–115 (2013).
[Crossref]

Midgley, B.

T. Stocker, D. Qin, G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia, B. Bex, and B. Midgley, “IPCC, 2013: climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change” (2013).

Miehé, J. A.

H. K. Lichtenthaler and J. A. Miehé, “Fluorescence imaging as a diagnostic tool for plant stress,” Trends Plant Sci. 2(8), 316–320 (1997).
[Crossref]

Miller, J. R.

P. J. Zarco-Tejada, J. R. Miller, G. H. Mohammed, and T. L. Noland, “Chlorophyll fluorescence effects on vegetation apparent reflectance: I. Leaf-level measurements and model simulation,” Remote Sens. Environ. 74(3), 582–595 (2000).
[Crossref]

J. R. Miller, P. J. Zarco-Tejada, R. Pedrós, W. Verhoef, and M. Berger, “FluorMODgui: a graphic user interface for the spectral simulation of leaf and canopy fluorescence effects,” in 2nd International Workshop on Remote Sensing of Vegetation Fluorescence (2004).

Milne, R.

M. G. R. Cannell, R. Milne, L. J. Sheppard, and M. H. Unsworth, “Radiation interception and productivity of willow,” Appl. Ecol. 24(1), 261–278 (1987).
[Crossref]

Mohammed, G. H.

P. J. Zarco-Tejada, J. R. Miller, G. H. Mohammed, and T. L. Noland, “Chlorophyll fluorescence effects on vegetation apparent reflectance: I. Leaf-level measurements and model simulation,” Remote Sens. Environ. 74(3), 582–595 (2000).
[Crossref]

Muñoz-Marí, J.

G. Camps-Valls, L. Gómez-Chova, J. Muñoz-Marí, J. Vila-Francés, J. Amorós-López, and J. Calpe-Maravilla, “Retrieval of oceanic chlorophyll concentration with relevance vector machines,” Remote Sens. Environ. 105(1), 23–33 (2006).
[Crossref]

Nauels, A.

T. Stocker, D. Qin, G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia, B. Bex, and B. Midgley, “IPCC, 2013: climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change” (2013).

Nichol, C.

J. Grace, C. Nichol, M. Disney, P. Lewis, T. Quaife, and P. Bowyer, “Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence?” Glob. Change Biol. 13(7), 1484–1497 (2007).
[Crossref]

Nie, S.

S. Nie, C. Wang, X. H. Xi, S. Z. Luo, and X. F. Sun, “Estimating the Biomass of Maize with Hyperspectral and LiDAR Data,” Remote Sens. 9, 11 (2017).

Noland, T. L.

P. J. Zarco-Tejada, J. R. Miller, G. H. Mohammed, and T. L. Noland, “Chlorophyll fluorescence effects on vegetation apparent reflectance: I. Leaf-level measurements and model simulation,” Remote Sens. Environ. 74(3), 582–595 (2000).
[Crossref]

Pal, M.

M. Pal and P. M. Mather, “An assessment of the effectiveness of decision tree methods for land cover classification,” Remote Sens. Environ. 86(4), 554–565 (2003).
[Crossref]

Patterson, M. S.

Pedrós, R.

J. R. Miller, P. J. Zarco-Tejada, R. Pedrós, W. Verhoef, and M. Berger, “FluorMODgui: a graphic user interface for the spectral simulation of leaf and canopy fluorescence effects,” in 2nd International Workshop on Remote Sensing of Vegetation Fluorescence (2004).

Peterson, D. L.

P. J. Curran, J. L. Dungan, and D. L. Peterson, “Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: testing the Kokaly and Clark methodologies,” Remote Sens. Environ. 76(3), 349–359 (2001).
[Crossref]

Pirotti, F.

Q. Chen, G. V. Laurin, J. A. Lindsell, D. A. Coomes, F. D. Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

Plattner, G.

T. Stocker, D. Qin, G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia, B. Bex, and B. Midgley, “IPCC, 2013: climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change” (2013).

Pogue, B. W.

Popescu, S.

D. Valle, K. Zhao, S. Popescu, X. S. Zhang, and B. Mallick, “Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection,” Remote Sens. Environ. 132, 102–119 (2013).
[Crossref]

Qin, D.

T. Stocker, D. Qin, G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia, B. Bex, and B. Midgley, “IPCC, 2013: climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change” (2013).

Quaife, T.

J. Grace, C. Nichol, M. Disney, P. Lewis, T. Quaife, and P. Bowyer, “Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence?” Glob. Change Biol. 13(7), 1484–1497 (2007).
[Crossref]

Rava, R. P.

Rinderle, U.

H. K. Lichtenthaler and U. Rinderle, “The role of chlorophyll fluorescence in the detection of stress conditions in plants,” Crit. Rev. Anal. Chem. 19(sup1), S29–S85 (1988).
[Crossref]

Rouse, J. W.

J. W. Rouse, R. Haas, J. Schell, and D. Deering, “Monitoring vegetation systems in the Great Plains with ERTS,” NASA Spec. Publ. 351, 309 (1974).

Saugier, B.

S. Lafont, L. Kergoat, A. Arneth, V. Le Dantec, and B. Saugier, “Nitrogen controls plant canopy light‐use efficiency in temperate and boreal ecosystems,” J. Geophys. Res. Biogeosci. 113, 1–19 (2008).

Schell, J.

J. W. Rouse, R. Haas, J. Schell, and D. Deering, “Monitoring vegetation systems in the Great Plains with ERTS,” NASA Spec. Publ. 351, 309 (1974).

Sheppard, L. J.

M. G. R. Cannell, R. Milne, L. J. Sheppard, and M. H. Unsworth, “Radiation interception and productivity of willow,” Appl. Ecol. 24(1), 261–278 (1987).
[Crossref]

Shi, S.

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, and S. L. Song, “Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice,” Plant Soil Environ. 62(4), 178–183 (2016).
[Crossref]

J. Yang, L. Du, J. Sun, Z. Zhang, B. Chen, S. Shi, W. Gong, and S. Song, “Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra,” Opt. Express 24(17), 19354–19365 (2016).
[Crossref] [PubMed]

J. Yang, S. Shi, W. Gong, L. Du, Y. Y. Ma, B. Zhu, and S. L. Song, “Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content,” Plant Soil Environ. 61(4), 182–188 (2016).
[Crossref]

L. Du, W. Gong, S. Shi, J. Yang, J. Sun, B. Zhu, and S. Song, “Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR,” Int. J. Appl. Earth Obs. Geoinf. 44, 136–143 (2016).
[Crossref]

L. Du, S. Shi, J. Yang, J. Sun, and W. Gong, “Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data,” Remote Sens. 8(6), 526 (2016).
[Crossref]

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, B. Zhu, Y.-y. Ma, and S. L. Song, “Vegetation identification based on characteristics of fluorescence spectral spatial distribution,” RSC Advances 5(70), 56932–56935 (2015).
[Crossref]

Song, S.

L. Du, W. Gong, S. Shi, J. Yang, J. Sun, B. Zhu, and S. Song, “Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR,” Int. J. Appl. Earth Obs. Geoinf. 44, 136–143 (2016).
[Crossref]

J. Yang, L. Du, J. Sun, Z. Zhang, B. Chen, S. Shi, W. Gong, and S. Song, “Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra,” Opt. Express 24(17), 19354–19365 (2016).
[Crossref] [PubMed]

Song, S. L.

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, and S. L. Song, “Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice,” Plant Soil Environ. 62(4), 178–183 (2016).
[Crossref]

J. Yang, S. Shi, W. Gong, L. Du, Y. Y. Ma, B. Zhu, and S. L. Song, “Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content,” Plant Soil Environ. 61(4), 182–188 (2016).
[Crossref]

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, B. Zhu, Y.-y. Ma, and S. L. Song, “Vegetation identification based on characteristics of fluorescence spectral spatial distribution,” RSC Advances 5(70), 56932–56935 (2015).
[Crossref]

Stober, F.

F. Stober and H. K. Lichtenthaler, “Characterization of the laser‐induced blue, green and red fluorescence signatures of leaves of wheat and soybean grown under different irradiance,” Physiol. Plant. 88(4), 696–704 (1993).
[Crossref]

Stocker, T.

T. Stocker, D. Qin, G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia, B. Bex, and B. Midgley, “IPCC, 2013: climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change” (2013).

Stroppiana, D.

M. Boschrtti, D. Stroppiana, P. A. Brivio, and S. Bocchi, “Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry,” Field Crops Res. 11, 119–129 (2009).

Sun, J.

L. Du, S. Shi, J. Yang, J. Sun, and W. Gong, “Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data,” Remote Sens. 8(6), 526 (2016).
[Crossref]

L. Du, W. Gong, S. Shi, J. Yang, J. Sun, B. Zhu, and S. Song, “Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR,” Int. J. Appl. Earth Obs. Geoinf. 44, 136–143 (2016).
[Crossref]

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, and S. L. Song, “Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice,” Plant Soil Environ. 62(4), 178–183 (2016).
[Crossref]

J. Yang, L. Du, J. Sun, Z. Zhang, B. Chen, S. Shi, W. Gong, and S. Song, “Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra,” Opt. Express 24(17), 19354–19365 (2016).
[Crossref] [PubMed]

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, B. Zhu, Y.-y. Ma, and S. L. Song, “Vegetation identification based on characteristics of fluorescence spectral spatial distribution,” RSC Advances 5(70), 56932–56935 (2015).
[Crossref]

Sun, X. F.

S. Nie, C. Wang, X. H. Xi, S. Z. Luo, and X. F. Sun, “Estimating the Biomass of Maize with Hyperspectral and LiDAR Data,” Remote Sens. 9, 11 (2017).

Tignor, M.

T. Stocker, D. Qin, G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia, B. Bex, and B. Midgley, “IPCC, 2013: climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change” (2013).

Tremblay, N.

S. Apostol, A. A. Viau, and N. Tremblay, “A comparison of multiwavelength laser-induced fluorescence parameters for the remote sensing of nitrogen stress in field-cultivated corn,” Can. J. Rem. Sens. 33(3), 150–161 (2007).
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M. G. R. Cannell, R. Milne, L. J. Sheppard, and M. H. Unsworth, “Radiation interception and productivity of willow,” Appl. Ecol. 24(1), 261–278 (1987).
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Q. Chen, G. V. Laurin, J. A. Lindsell, D. A. Coomes, F. D. Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

Valle, D.

D. Valle, K. Zhao, S. Popescu, X. S. Zhang, and B. Mallick, “Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection,” Remote Sens. Environ. 132, 102–119 (2013).
[Crossref]

Van der Meer, F.

F. Van der Meer, J. Farifteh, C. Atzberger, and E. Carranza, “Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN),” Remote Sens. Environ. 110(1), 59–78 (2007).
[Crossref]

Verhoef, W.

J. R. Miller, P. J. Zarco-Tejada, R. Pedrós, W. Verhoef, and M. Berger, “FluorMODgui: a graphic user interface for the spectral simulation of leaf and canopy fluorescence effects,” in 2nd International Workshop on Remote Sensing of Vegetation Fluorescence (2004).

Viau, A. A.

S. Apostol, A. A. Viau, and N. Tremblay, “A comparison of multiwavelength laser-induced fluorescence parameters for the remote sensing of nitrogen stress in field-cultivated corn,” Can. J. Rem. Sens. 33(3), 150–161 (2007).
[Crossref]

Vila-Francés, J.

G. Camps-Valls, L. Gómez-Chova, J. Muñoz-Marí, J. Vila-Francés, J. Amorós-López, and J. Calpe-Maravilla, “Retrieval of oceanic chlorophyll concentration with relevance vector machines,” Remote Sens. Environ. 105(1), 23–33 (2006).
[Crossref]

Walthall, C.

C. Daughtry, C. Walthall, M. Kim, E. B. De Colstoun, and J. McMurtrey Iii, “Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance,” Remote Sens. Environ. 74(2), 229–239 (2000).
[Crossref]

Wang, C.

S. Nie, C. Wang, X. H. Xi, S. Z. Luo, and X. F. Sun, “Estimating the Biomass of Maize with Hyperspectral and LiDAR Data,” Remote Sens. 9, 11 (2017).

Weis, E.

G. H. Krause and E. Weis, “Chlorophyll fluorescence and photosynthesis: the basics,” Annu. Rev. Plant Biol. 42(1), 313–349 (1991).
[Crossref]

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Wutzke, K. D.

K. D. Wutzke and W. Heine, “[A century of Kjeldahl’s nitrogen determination],” Z. Med. Lab. Diagn. 26(7), 383–388 (1985).
[PubMed]

Xi, X. H.

S. Nie, C. Wang, X. H. Xi, S. Z. Luo, and X. F. Sun, “Estimating the Biomass of Maize with Hyperspectral and LiDAR Data,” Remote Sens. 9, 11 (2017).

Xia, Y.

T. Stocker, D. Qin, G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia, B. Bex, and B. Midgley, “IPCC, 2013: climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change” (2013).

Yang, J.

L. Du, S. Shi, J. Yang, J. Sun, and W. Gong, “Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data,” Remote Sens. 8(6), 526 (2016).
[Crossref]

L. Du, W. Gong, S. Shi, J. Yang, J. Sun, B. Zhu, and S. Song, “Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR,” Int. J. Appl. Earth Obs. Geoinf. 44, 136–143 (2016).
[Crossref]

J. Yang, S. Shi, W. Gong, L. Du, Y. Y. Ma, B. Zhu, and S. L. Song, “Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content,” Plant Soil Environ. 61(4), 182–188 (2016).
[Crossref]

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, and S. L. Song, “Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice,” Plant Soil Environ. 62(4), 178–183 (2016).
[Crossref]

J. Yang, L. Du, J. Sun, Z. Zhang, B. Chen, S. Shi, W. Gong, and S. Song, “Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra,” Opt. Express 24(17), 19354–19365 (2016).
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J. Yang, W. Gong, S. Shi, L. Du, J. Sun, B. Zhu, Y.-y. Ma, and S. L. Song, “Vegetation identification based on characteristics of fluorescence spectral spatial distribution,” RSC Advances 5(70), 56932–56935 (2015).
[Crossref]

Yu, K.

K. Yu, F. Li, M. L. Gnyp, Y. Miao, G. Bareth, and X. Chen, “Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain,” ISPRS J. Photogramm. Remote Sens. 78, 102–115 (2013).
[Crossref]

Zarco-Tejada, P. J.

P. J. Zarco-Tejada, J. R. Miller, G. H. Mohammed, and T. L. Noland, “Chlorophyll fluorescence effects on vegetation apparent reflectance: I. Leaf-level measurements and model simulation,” Remote Sens. Environ. 74(3), 582–595 (2000).
[Crossref]

J. R. Miller, P. J. Zarco-Tejada, R. Pedrós, W. Verhoef, and M. Berger, “FluorMODgui: a graphic user interface for the spectral simulation of leaf and canopy fluorescence effects,” in 2nd International Workshop on Remote Sensing of Vegetation Fluorescence (2004).

Zhang, X. S.

D. Valle, K. Zhao, S. Popescu, X. S. Zhang, and B. Mallick, “Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection,” Remote Sens. Environ. 132, 102–119 (2013).
[Crossref]

Zhang, Z.

Zhao, K.

D. Valle, K. Zhao, S. Popescu, X. S. Zhang, and B. Mallick, “Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection,” Remote Sens. Environ. 132, 102–119 (2013).
[Crossref]

Zhu, B.

L. Du, W. Gong, S. Shi, J. Yang, J. Sun, B. Zhu, and S. Song, “Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR,” Int. J. Appl. Earth Obs. Geoinf. 44, 136–143 (2016).
[Crossref]

J. Yang, S. Shi, W. Gong, L. Du, Y. Y. Ma, B. Zhu, and S. L. Song, “Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content,” Plant Soil Environ. 61(4), 182–188 (2016).
[Crossref]

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, B. Zhu, Y.-y. Ma, and S. L. Song, “Vegetation identification based on characteristics of fluorescence spectral spatial distribution,” RSC Advances 5(70), 56932–56935 (2015).
[Crossref]

Annu. Rev. Plant Biol. (1)

G. H. Krause and E. Weis, “Chlorophyll fluorescence and photosynthesis: the basics,” Annu. Rev. Plant Biol. 42(1), 313–349 (1991).
[Crossref]

Appl. Ecol. (1)

M. G. R. Cannell, R. Milne, L. J. Sheppard, and M. H. Unsworth, “Radiation interception and productivity of willow,” Appl. Ecol. 24(1), 261–278 (1987).
[Crossref]

Appl. Opt. (2)

Can. J. Rem. Sens. (1)

S. Apostol, A. A. Viau, and N. Tremblay, “A comparison of multiwavelength laser-induced fluorescence parameters for the remote sensing of nitrogen stress in field-cultivated corn,” Can. J. Rem. Sens. 33(3), 150–161 (2007).
[Crossref]

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H. K. Lichtenthaler and U. Rinderle, “The role of chlorophyll fluorescence in the detection of stress conditions in plants,” Crit. Rev. Anal. Chem. 19(sup1), S29–S85 (1988).
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M. Boschrtti, D. Stroppiana, P. A. Brivio, and S. Bocchi, “Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry,” Field Crops Res. 11, 119–129 (2009).

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J. Grace, C. Nichol, M. Disney, P. Lewis, T. Quaife, and P. Bowyer, “Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence?” Glob. Change Biol. 13(7), 1484–1497 (2007).
[Crossref]

Int. J. Appl. Earth Obs. Geoinf. (1)

L. Du, W. Gong, S. Shi, J. Yang, J. Sun, B. Zhu, and S. Song, “Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR,” Int. J. Appl. Earth Obs. Geoinf. 44, 136–143 (2016).
[Crossref]

ISPRS J. Photogramm. Remote Sens. (1)

K. Yu, F. Li, M. L. Gnyp, Y. Miao, G. Bareth, and X. Chen, “Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain,” ISPRS J. Photogramm. Remote Sens. 78, 102–115 (2013).
[Crossref]

J. Appl. Remote Sens. (1)

D. E. Knapp, G. P. Asner, T. Kennedy-Bowdoin, M. O. Jones, R. E. Martin, J. Boardman, and C. B. Field, “Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging (wLiDAR) for three-dimensional studies of ecosystems,” J. Appl. Remote Sens. 1, 013536 (2007).

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K. Maxwell and G. N. Johnson, “Chlorophyll fluorescence--a practical guide,” J. Exp. Bot. 51(345), 659–668 (2000).
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S. Lafont, L. Kergoat, A. Arneth, V. Le Dantec, and B. Saugier, “Nitrogen controls plant canopy light‐use efficiency in temperate and boreal ecosystems,” J. Geophys. Res. Biogeosci. 113, 1–19 (2008).

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Q. Chen, G. V. Laurin, J. A. Lindsell, D. A. Coomes, F. D. Frate, L. Guerriero, F. Pirotti, and R. Valentini, “Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data,” J. Photogramm. Remote Sens. 89, 49–58 (2014).
[Crossref]

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J. W. Rouse, R. Haas, J. Schell, and D. Deering, “Monitoring vegetation systems in the Great Plains with ERTS,” NASA Spec. Publ. 351, 309 (1974).

Opt. Express (1)

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F. Stober and H. K. Lichtenthaler, “Characterization of the laser‐induced blue, green and red fluorescence signatures of leaves of wheat and soybean grown under different irradiance,” Physiol. Plant. 88(4), 696–704 (1993).
[Crossref]

Plant Soil Environ. (2)

J. Yang, S. Shi, W. Gong, L. Du, Y. Y. Ma, B. Zhu, and S. L. Song, “Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content,” Plant Soil Environ. 61(4), 182–188 (2016).
[Crossref]

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, and S. L. Song, “Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice,” Plant Soil Environ. 62(4), 178–183 (2016).
[Crossref]

Remote Sens. (2)

L. Du, S. Shi, J. Yang, J. Sun, and W. Gong, “Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data,” Remote Sens. 8(6), 526 (2016).
[Crossref]

S. Nie, C. Wang, X. H. Xi, S. Z. Luo, and X. F. Sun, “Estimating the Biomass of Maize with Hyperspectral and LiDAR Data,” Remote Sens. 9, 11 (2017).

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F. Baret and S. Jacquemoud, “PROSPECT: A model of leaf optical properties spectra,” Remote Sens. Environ. 34(2), 75–91 (1990).
[Crossref]

G. Camps-Valls, L. Gómez-Chova, J. Muñoz-Marí, J. Vila-Francés, J. Amorós-López, and J. Calpe-Maravilla, “Retrieval of oceanic chlorophyll concentration with relevance vector machines,” Remote Sens. Environ. 105(1), 23–33 (2006).
[Crossref]

M. Pal and P. M. Mather, “An assessment of the effectiveness of decision tree methods for land cover classification,” Remote Sens. Environ. 86(4), 554–565 (2003).
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J. E. McMurtrey, E. W. Chappelle, and M. S. Kim, “Identification of the pigment responsible for the blue fluorescence band in the laser induced fluorescence (LIF) spectra of green plants, and the potential use of this band in remotely estimating rates of photosynthesis,” Remote Sens. Environ. 36(3), 213–218 (1991).
[Crossref]

F. Van der Meer, J. Farifteh, C. Atzberger, and E. Carranza, “Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN),” Remote Sens. Environ. 110(1), 59–78 (2007).
[Crossref]

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C. Daughtry, C. Walthall, M. Kim, E. B. De Colstoun, and J. McMurtrey Iii, “Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance,” Remote Sens. Environ. 74(2), 229–239 (2000).
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[Crossref]

D. Valle, K. Zhao, S. Popescu, X. S. Zhang, and B. Mallick, “Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection,” Remote Sens. Environ. 132, 102–119 (2013).
[Crossref]

RSC Advances (1)

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, B. Zhu, Y.-y. Ma, and S. L. Song, “Vegetation identification based on characteristics of fluorescence spectral spatial distribution,” RSC Advances 5(70), 56932–56935 (2015).
[Crossref]

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H. K. Lichtenthaler and J. A. Miehé, “Fluorescence imaging as a diagnostic tool for plant stress,” Trends Plant Sci. 2(8), 316–320 (1997).
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K. D. Wutzke and W. Heine, “[A century of Kjeldahl’s nitrogen determination],” Z. Med. Lab. Diagn. 26(7), 383–388 (1985).
[PubMed]

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T. Stocker, D. Qin, G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia, B. Bex, and B. Midgley, “IPCC, 2013: climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change” (2013).

J. R. Miller, P. J. Zarco-Tejada, R. Pedrós, W. Verhoef, and M. Berger, “FluorMODgui: a graphic user interface for the spectral simulation of leaf and canopy fluorescence effects,” in 2nd International Workshop on Remote Sensing of Vegetation Fluorescence (2004).

S. Xie, A. E. Profio, and H. K. Shu, “Diagnosis of tumors by fluorescence: quantification of photosensitizer concentration,” OE/LASE'90, 14–19 Jan., Los Angeles, CA. International Society for Optics and Photonics, 12–18 (1990).

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Figures (4)

Fig. 1
Fig. 1 Simple diagram of HSL and LID LiDAR system. M1 and M2 are plane mirrors which are to make the emitted laser be coaxial with the receiving module of LiDAR. The returned signals are collected by a grating spectrograph.
Fig. 2
Fig. 2 Spectrum collected with HSL at a range of 538–802 nm (blue line). The superimposed (red line) is the characteristic fluorescence spectrum gained with the LIF system with double peaks at ~685 and ~740 nm. Thin lines represent a nitrogen level of 3.4 mg/g, and the thick lines represent the lower nitrogen level of 3 mg/g.
Fig. 3
Fig. 3 Comparison of the determination coefficient (R2) and RMSE using separate ratio indices for rice LNC estimation based on the data collected on (a) July 15 2014, booting stage (b) August 1 2014, heading stage, and (c) 2015.
Fig. 4
Fig. 4 Correlation between the measured LNC and predicted LNC estimated with combined ratio indices and ANNs in different years. (a), (b), (d) and (e): 2014; (c), and (f): 2015; (a), (c) and (e): ratio of reflectance to fluorescence; (b), (d) and (f): ratio indices developed like NDVI using reflectance and fluorescence at ~685 and ~740 nm, respectively. The dotted red line represents the 1:1 line.

Equations (7)

Equations on this page are rendered with MathJax. Learn more.

R(λ)= R l (λ) R ref (λ)
RI( I reflectance , I fluorescence )= I reflectance I fluorescence
NDSI( I red , I near-irfrared )= I red I near-irfrared I red + I near-irfrared
u i = i=1 n w i x i
y i =f( u i + b i )
RMSE= i=1 N ( x predicted x measured ) N
PRI= I 531 I 570 I 531 + I 570

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