Abstract

We present a physical-based atmospheric correction algorithm for land surface reflectance retrieval based on radiative transfer model MODTRAN 5, with which the aerosol optical thickness @550 nm (AOT@550nm), columnar water vapor (CWV) could also be estimated from the hyperspectral data collected over UAV platform. Then, the method was tested on both the synthetic and field campaign–collected hyperspectral data by an UAV-VNIRIS (UAV visible/near-infrared imaging hyperspectrometer) with the spectral range covering from 400 to 1000 nm. The retrieval results were validated with theoretical values from synthetic data and truth values from field campaign measurements. The results show that the averaged MAE (mean absolute error) and RMSE (root mean squared error) of measured and retrieved surface reflectance based on estimated AOT@550nm and CWV is 0.0134 and 0.0130. Meanwhile, the averaged MAE and RMSE of measured and retrieved surface reflectance based on ground measured AOT@550nm and CWV is 0.0101 and 0.0112. The results show that our introduced method has good agreement with the method based on ground-measured AOT@550nm and CWV. These encouraging results also indicate that the introduced physical-based atmospheric approach provides a quick and reliable way to acquire the land surface reflectance from UAV platform–observed hyperspectral data for further quantitative remote sensing applications.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2018 (1)

T. Ishida, J. Kurihara, F. A. Viray, S. B. Namuco, E. C. Paringit, G. J. Perez, Y. Takahashi, and J. J. Marciano, “A novel approach for vegetation classification using UAV-based hyperspectral imaging,” Comput. Electron. Agric. 144, 80–85 (2018).
[Crossref]

2017 (2)

T. Sankey, J. Donager, J. McVay, and J. B. Sankey, “UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA,” Remote Sens. Environ. 195(15), 30–43 (2017).
[Crossref]

H. Yang, L. Zhang, C. Ong, A. Rodger, J. Liu, X. Sun, H. Zhang, X. Jian, and Q. Tong, “Improved aerosol optical thickness, columnar water vapor, and surface reflectance retrieval from combined CASI and SASI airborne hyperspectral sensors,” Remote Sens. 9(3), 217 (2017).
[Crossref]

2016 (1)

M. Pérez-Ortiz, J. M. Peña, P. A. Gutiérrez, J. Torres-Sánchez, C. Hervás-Martínez, and F. López-Granados, “Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery,” Expert Syst. Appl. 47(1), 85–94 (2016).
[Crossref]

2015 (2)

C. Bassani, C. Manzo, F. Braga, M. Bresciani, C. Giardino, and L. Alberotanza, “The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters,” Atmos. Meas. Tech. 8(3), 1593–1604 (2015).
[Crossref]

O. Hagolle, M. Huc, D. Villa Pascual, and G. Dedieu, “A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images,” Remote Sens. 7(3), 2668–2691 (2015), doi:.
[Crossref]

2014 (3)

Y. Liu, T. Wang, L. Ma, and N. Wang, “Spectral Calibration of Hyperspectral Data Observed From a Hyperspectrometer Loaded on an Unmanned Aerial Vehicle Platform,” IEEE J. Sel. Top. Appl. 7(6), 2630–2638 (2014).

I. Colomina and P. Molina, “Unmanned aerial systems for photogrammetry and remote sensing: a review,” ISPRS J. Photogramm. 92, 79–97 (2014).
[Crossref]

S. Hu, X. She, and Q. Tong, “Design and interpolation of a general look-up table for remote sensing image atmospheric correction,” Yaogan Xuebao 18, 45–60 (2014).

2013 (1)

S. B. Duan, Z. L. Li, B. H. Tang, H. Wu, L. Ma, E. Zhao, and C. Li, “Land surface reflectance retrieval from hyperspectral data collected by an unmanned aerial vehicle over the Baotou test site,” PLoS One 8(6), e66972 (2013), doi:.
[Crossref] [PubMed]

2012 (1)

F. D. van der Meer, H. M. A. van der Werff, F. J. A. Van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai, “Multi– and hyperspectral geologic remote sensing: A review,” Int. J. Appl. Earth Obs. 14(1), 112–128 (2012).
[Crossref]

2011 (2)

S. S. Li, L. F. Chen, J. H. Tao, D. Han, Z. T. Wang, and B. H. He, “[Retrieval and validation of the surface reflectance using HJ-1-CCD data],” Guangpuxue Yu Guangpu Fenxi 31(2), 516–520 (2011).
[PubMed]

S. Sterckx, E. Knaeps, and K. Ruddick, “Detection and correction of adjacency effects in hyperspectral airborne data of coastal and inland waters: the use of the near infrared similarity spectrum,” Yaogan Xuebao 32(21), 6479–6505 (2011).
[Crossref]

2010 (1)

C. Bassani, R. M. Cavalli, and S. Pignatti, “Aerosol optical retrieval and surface reflectance from airborne remote sensing data over land,” Sensors (Basel) 10(7), 6421–6438 (2010).
[Crossref] [PubMed]

2009 (4)

J. Franke, D. Roberts, K. Halligan, and G. Menz, “Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments,” Remote Sens. Environ. 113(8), 1712–1723 (2009).
[Crossref]

L. Guanter, R. Richter, and H. Kaufmann, “On the application of the MODTRAN4 atmospheric radiative transfer code to optical remote sensing,” Int. J. Remote Sens. 30(6), 1407–1424 (2009).
[Crossref]

B. Gao, M. Montes, C. Davis, and A. Goetz, “Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean,” Remote Sens. Environ. 113, S17–S24 (2009).
[Crossref]

Z. Wang, Q. Li, J. Tao, S. Li, Q. Wang, and L. Chen, “Monitoring of aerosol optical depth over land surface using CCD camera on HJ-2 satellite,” Guangpuxue Yu Guangpu Fenxi 29, 902–907 (2009).

2007 (1)

L. Guanter, V. Estellés, and J. Moreno, “Spectral calibration and atmospheric correction of ultra-fine spectral and spatial resolution remote sensing data. Application to CASI-1500 data,” Remote Sens. Environ. 109(1), 54–65 (2007).
[Crossref]

2006 (3)

R. Richter, D. Schläpfer, and A. Müller, “An automatic atmospheric correction algorithm for visible/NIR imagery,” Int. J. Remote Sens. 27(10), 2077–2085 (2006).
[Crossref]

M. L. Whiting, S. L. Ustin, P. Zarco-Tejada, A. Palacios-Orueta, and V. C. Vanderbilt, “Hyperspectral mapping of crop and soils for precision agriculture,” Proc. SPIE 6298, 62980B (2006), doi:.
[Crossref]

A. Filippi, K. Carder, and C. Davis, “Vicarious calibration of the Ocean PHILLS hyperspectral sensor using a coastal tree-shadow method,” Geophys. Res. Lett. 33(22), 22605 (2006).

2005 (2)

L. Guanter, L. Alonso, and J. Moreno, “A Method for the Surface Reflectance Retrieval from PROBA/CHRIS Data over land: Application to ESA SPARC Campaigns,” IEEE Trans. Geosci. Remote Sens. 43(12), 2908–2917 (2005).
[Crossref]

C. Miesch, L. Poutier, V. Achard, X. Briottet, X. Lenot, and Y. Boucher, “Direct and inverse radiative transfer solutions for visible and near-infrared hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 43(7), 1552–1562 (2005).
[Crossref]

2004 (3)

S. Liang and H. Fang, “An improved atmospheric correction algorithm for hyperspectral remotely sensed imagery,” IEEE Trans. Geosci. Remote Sens. 1(2), 112–117 (2004).
[Crossref]

E. Ben-Dor, B. Kindel, and A. Goetz, “Quality assessment of several methods to recover surface reflectance using synthetic imaging spectroscopy data,” Remote Sens. Environ. 90(3), 389–404 (2004).
[Crossref]

D. Haboudane, J. R. Miller, E. Pattey, P. J. Zarco-Tejada, and I. B. Strachan, “Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture,” Remote Sens. Environ. 90(3), 337–352 (2004).
[Crossref]

2003 (1)

P. Chylek, C. Borel, W. Clodius, P. Pope, and A. Rodger, “Satellite-based columnar water vapor retrieval with the multi-spectral thermal imager (MTI),” IEEE Trans. Geosci. Remote Sens. 41(12), 2767–2770 (2003).
[Crossref]

2002 (1)

R. Richter and D. Schläpfer, “Geo-atmospheric processing of airborne imaging spectrometry data, Part 2: atmospheric/topographic correction,” Int. J. Remote Sens. 23(13), 2631–2649 (2002).
[Crossref]

2001 (1)

R. Bennartz and J. Fischer, “Retrieval of columnar water vapour over land from backscattered solar radiation using the Medium Resolution Imaging Spectrometer,” Remote Sens. Environ. 78(3), 274–283 (2001).
[Crossref]

1998 (1)

D. Schläpfer, C. Borel, J. Keller, and K. Itten, “Atmospheric precorrected differential absorption technique to retrieve colummar water vapor,” Remote Sens. Environ. 65(3), 353–366 (1998).
[Crossref]

1997 (1)

Y. Kaufman, D. Tanre, L. Remer, E. Vermote, A. Chu, and B. Holben, “Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer,” J. Geophys. Res. 102(D14), 17051–17067 (1997).
[Crossref]

1992 (2)

C. Bruegge, J. Conel, R. Green, J. Margolis, R. Holm, and G. Toon, “Water-vapor column abundance retrievals during FIFE,” J. Geophys. Res. 97(D17), 18759–18768 (1992).
[Crossref]

Y. Kaufman and B. Gao, “Remote sensing of water vapor in the near IR from EOS/MODIS,” IEEE Trans. Geosci. Remote Sens. 30(5), 871–884 (1992).
[Crossref]

1990 (1)

B. Gao and A. Goetz, “Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data,” J. Geophys. Res. 95(D4), 3549–3564 (1990).
[Crossref]

1987 (1)

J. Conel, R. Green, G. Vane, C. Bruegge, R. Alley, and B. Curtiss, “Airborne imaging spectrometer-2: radiometric spectral characteristics and comparison of ways to compensate for the atmosphere,” Proc. SPIE 834, 140–157 (1987).
[Crossref]

1986 (1)

T. Lee and Y. Kaufman, “Non-Lambertian effects on remote sensing of surface reflectance and vegetation index,” IEEE Trans. Geosci. Remote Sens.,  GE-24(5), 699–708 (1986).
[Crossref]

Achard, V.

C. Miesch, L. Poutier, V. Achard, X. Briottet, X. Lenot, and Y. Boucher, “Direct and inverse radiative transfer solutions for visible and near-infrared hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 43(7), 1552–1562 (2005).
[Crossref]

Alberotanza, L.

C. Bassani, C. Manzo, F. Braga, M. Bresciani, C. Giardino, and L. Alberotanza, “The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters,” Atmos. Meas. Tech. 8(3), 1593–1604 (2015).
[Crossref]

Alley, R.

J. Conel, R. Green, G. Vane, C. Bruegge, R. Alley, and B. Curtiss, “Airborne imaging spectrometer-2: radiometric spectral characteristics and comparison of ways to compensate for the atmosphere,” Proc. SPIE 834, 140–157 (1987).
[Crossref]

Alonso, L.

L. Guanter, L. Alonso, and J. Moreno, “A Method for the Surface Reflectance Retrieval from PROBA/CHRIS Data over land: Application to ESA SPARC Campaigns,” IEEE Trans. Geosci. Remote Sens. 43(12), 2908–2917 (2005).
[Crossref]

Arnone, R.

Z. Lee, B. Casey, R. Parsons, W. Goode, A. Weidemann, and R. Arnone, “Bathymetry of shallow coastal regions derived from space-borne hyperspectral sensor,” in OCEANS, Proc. of MTS/IEEE, 2160–2170 (2005).

Bakker, W. H.

F. D. van der Meer, H. M. A. van der Werff, F. J. A. Van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai, “Multi– and hyperspectral geologic remote sensing: A review,” Int. J. Appl. Earth Obs. 14(1), 112–128 (2012).
[Crossref]

Bassani, C.

C. Bassani, C. Manzo, F. Braga, M. Bresciani, C. Giardino, and L. Alberotanza, “The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters,” Atmos. Meas. Tech. 8(3), 1593–1604 (2015).
[Crossref]

C. Bassani, R. M. Cavalli, and S. Pignatti, “Aerosol optical retrieval and surface reflectance from airborne remote sensing data over land,” Sensors (Basel) 10(7), 6421–6438 (2010).
[Crossref] [PubMed]

Ben-Dor, E.

E. Ben-Dor, B. Kindel, and A. Goetz, “Quality assessment of several methods to recover surface reflectance using synthetic imaging spectroscopy data,” Remote Sens. Environ. 90(3), 389–404 (2004).
[Crossref]

Bennartz, R.

R. Bennartz and J. Fischer, “Retrieval of columnar water vapour over land from backscattered solar radiation using the Medium Resolution Imaging Spectrometer,” Remote Sens. Environ. 78(3), 274–283 (2001).
[Crossref]

Borel, C.

P. Chylek, C. Borel, W. Clodius, P. Pope, and A. Rodger, “Satellite-based columnar water vapor retrieval with the multi-spectral thermal imager (MTI),” IEEE Trans. Geosci. Remote Sens. 41(12), 2767–2770 (2003).
[Crossref]

D. Schläpfer, C. Borel, J. Keller, and K. Itten, “Atmospheric precorrected differential absorption technique to retrieve colummar water vapor,” Remote Sens. Environ. 65(3), 353–366 (1998).
[Crossref]

Boucher, Y.

C. Miesch, L. Poutier, V. Achard, X. Briottet, X. Lenot, and Y. Boucher, “Direct and inverse radiative transfer solutions for visible and near-infrared hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 43(7), 1552–1562 (2005).
[Crossref]

Braga, F.

C. Bassani, C. Manzo, F. Braga, M. Bresciani, C. Giardino, and L. Alberotanza, “The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters,” Atmos. Meas. Tech. 8(3), 1593–1604 (2015).
[Crossref]

Bresciani, M.

C. Bassani, C. Manzo, F. Braga, M. Bresciani, C. Giardino, and L. Alberotanza, “The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters,” Atmos. Meas. Tech. 8(3), 1593–1604 (2015).
[Crossref]

Briottet, X.

C. Miesch, L. Poutier, V. Achard, X. Briottet, X. Lenot, and Y. Boucher, “Direct and inverse radiative transfer solutions for visible and near-infrared hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 43(7), 1552–1562 (2005).
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Bruegge, C.

C. Bruegge, J. Conel, R. Green, J. Margolis, R. Holm, and G. Toon, “Water-vapor column abundance retrievals during FIFE,” J. Geophys. Res. 97(D17), 18759–18768 (1992).
[Crossref]

J. Conel, R. Green, G. Vane, C. Bruegge, R. Alley, and B. Curtiss, “Airborne imaging spectrometer-2: radiometric spectral characteristics and comparison of ways to compensate for the atmosphere,” Proc. SPIE 834, 140–157 (1987).
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A. Filippi, K. Carder, and C. Davis, “Vicarious calibration of the Ocean PHILLS hyperspectral sensor using a coastal tree-shadow method,” Geophys. Res. Lett. 33(22), 22605 (2006).

Carranza, E. J. M.

F. D. van der Meer, H. M. A. van der Werff, F. J. A. Van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai, “Multi– and hyperspectral geologic remote sensing: A review,” Int. J. Appl. Earth Obs. 14(1), 112–128 (2012).
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Casey, B.

Z. Lee, B. Casey, R. Parsons, W. Goode, A. Weidemann, and R. Arnone, “Bathymetry of shallow coastal regions derived from space-borne hyperspectral sensor,” in OCEANS, Proc. of MTS/IEEE, 2160–2170 (2005).

Cavalli, R. M.

C. Bassani, R. M. Cavalli, and S. Pignatti, “Aerosol optical retrieval and surface reflectance from airborne remote sensing data over land,” Sensors (Basel) 10(7), 6421–6438 (2010).
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Chen, L.

Z. Wang, Q. Li, J. Tao, S. Li, Q. Wang, and L. Chen, “Monitoring of aerosol optical depth over land surface using CCD camera on HJ-2 satellite,” Guangpuxue Yu Guangpu Fenxi 29, 902–907 (2009).

Chen, L. F.

S. S. Li, L. F. Chen, J. H. Tao, D. Han, Z. T. Wang, and B. H. He, “[Retrieval and validation of the surface reflectance using HJ-1-CCD data],” Guangpuxue Yu Guangpu Fenxi 31(2), 516–520 (2011).
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Chen, Z.

H. Zhang, Z. Chen, B. Zhang, and D. Peng, “Comparison of two water vapor retrieval algorithm for HJ1A hyperspectral imagery,” IEEE International Geoscience and Remote Sensing Symposium (IGARSS), doi: (2011).
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Chu, A.

Y. Kaufman, D. Tanre, L. Remer, E. Vermote, A. Chu, and B. Holben, “Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer,” J. Geophys. Res. 102(D14), 17051–17067 (1997).
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Chylek, P.

P. Chylek, C. Borel, W. Clodius, P. Pope, and A. Rodger, “Satellite-based columnar water vapor retrieval with the multi-spectral thermal imager (MTI),” IEEE Trans. Geosci. Remote Sens. 41(12), 2767–2770 (2003).
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Clodius, W.

P. Chylek, C. Borel, W. Clodius, P. Pope, and A. Rodger, “Satellite-based columnar water vapor retrieval with the multi-spectral thermal imager (MTI),” IEEE Trans. Geosci. Remote Sens. 41(12), 2767–2770 (2003).
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Colomina, I.

I. Colomina and P. Molina, “Unmanned aerial systems for photogrammetry and remote sensing: a review,” ISPRS J. Photogramm. 92, 79–97 (2014).
[Crossref]

Conel, J.

C. Bruegge, J. Conel, R. Green, J. Margolis, R. Holm, and G. Toon, “Water-vapor column abundance retrievals during FIFE,” J. Geophys. Res. 97(D17), 18759–18768 (1992).
[Crossref]

J. Conel, R. Green, G. Vane, C. Bruegge, R. Alley, and B. Curtiss, “Airborne imaging spectrometer-2: radiometric spectral characteristics and comparison of ways to compensate for the atmosphere,” Proc. SPIE 834, 140–157 (1987).
[Crossref]

Curtiss, B.

J. Conel, R. Green, G. Vane, C. Bruegge, R. Alley, and B. Curtiss, “Airborne imaging spectrometer-2: radiometric spectral characteristics and comparison of ways to compensate for the atmosphere,” Proc. SPIE 834, 140–157 (1987).
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Davis, C.

B. Gao, M. Montes, C. Davis, and A. Goetz, “Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean,” Remote Sens. Environ. 113, S17–S24 (2009).
[Crossref]

A. Filippi, K. Carder, and C. Davis, “Vicarious calibration of the Ocean PHILLS hyperspectral sensor using a coastal tree-shadow method,” Geophys. Res. Lett. 33(22), 22605 (2006).

de Smeth, J. B.

F. D. van der Meer, H. M. A. van der Werff, F. J. A. Van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai, “Multi– and hyperspectral geologic remote sensing: A review,” Int. J. Appl. Earth Obs. 14(1), 112–128 (2012).
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Dedieu, G.

O. Hagolle, M. Huc, D. Villa Pascual, and G. Dedieu, “A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images,” Remote Sens. 7(3), 2668–2691 (2015), doi:.
[Crossref]

Donager, J.

T. Sankey, J. Donager, J. McVay, and J. B. Sankey, “UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA,” Remote Sens. Environ. 195(15), 30–43 (2017).
[Crossref]

Duan, S. B.

S. B. Duan, Z. L. Li, B. H. Tang, H. Wu, L. Ma, E. Zhao, and C. Li, “Land surface reflectance retrieval from hyperspectral data collected by an unmanned aerial vehicle over the Baotou test site,” PLoS One 8(6), e66972 (2013), doi:.
[Crossref] [PubMed]

Estellés, V.

L. Guanter, V. Estellés, and J. Moreno, “Spectral calibration and atmospheric correction of ultra-fine spectral and spatial resolution remote sensing data. Application to CASI-1500 data,” Remote Sens. Environ. 109(1), 54–65 (2007).
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Fang, H.

S. Liang and H. Fang, “An improved atmospheric correction algorithm for hyperspectral remotely sensed imagery,” IEEE Trans. Geosci. Remote Sens. 1(2), 112–117 (2004).
[Crossref]

Filippi, A.

A. Filippi, K. Carder, and C. Davis, “Vicarious calibration of the Ocean PHILLS hyperspectral sensor using a coastal tree-shadow method,” Geophys. Res. Lett. 33(22), 22605 (2006).

Fischer, J.

R. Bennartz and J. Fischer, “Retrieval of columnar water vapour over land from backscattered solar radiation using the Medium Resolution Imaging Spectrometer,” Remote Sens. Environ. 78(3), 274–283 (2001).
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Franke, J.

J. Franke, D. Roberts, K. Halligan, and G. Menz, “Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments,” Remote Sens. Environ. 113(8), 1712–1723 (2009).
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Gao, B.

B. Gao, M. Montes, C. Davis, and A. Goetz, “Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean,” Remote Sens. Environ. 113, S17–S24 (2009).
[Crossref]

Y. Kaufman and B. Gao, “Remote sensing of water vapor in the near IR from EOS/MODIS,” IEEE Trans. Geosci. Remote Sens. 30(5), 871–884 (1992).
[Crossref]

B. Gao and A. Goetz, “Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data,” J. Geophys. Res. 95(D4), 3549–3564 (1990).
[Crossref]

Giardino, C.

C. Bassani, C. Manzo, F. Braga, M. Bresciani, C. Giardino, and L. Alberotanza, “The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters,” Atmos. Meas. Tech. 8(3), 1593–1604 (2015).
[Crossref]

Goetz, A.

B. Gao, M. Montes, C. Davis, and A. Goetz, “Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean,” Remote Sens. Environ. 113, S17–S24 (2009).
[Crossref]

E. Ben-Dor, B. Kindel, and A. Goetz, “Quality assessment of several methods to recover surface reflectance using synthetic imaging spectroscopy data,” Remote Sens. Environ. 90(3), 389–404 (2004).
[Crossref]

B. Gao and A. Goetz, “Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data,” J. Geophys. Res. 95(D4), 3549–3564 (1990).
[Crossref]

Goode, W.

Z. Lee, B. Casey, R. Parsons, W. Goode, A. Weidemann, and R. Arnone, “Bathymetry of shallow coastal regions derived from space-borne hyperspectral sensor,” in OCEANS, Proc. of MTS/IEEE, 2160–2170 (2005).

Green, R.

C. Bruegge, J. Conel, R. Green, J. Margolis, R. Holm, and G. Toon, “Water-vapor column abundance retrievals during FIFE,” J. Geophys. Res. 97(D17), 18759–18768 (1992).
[Crossref]

J. Conel, R. Green, G. Vane, C. Bruegge, R. Alley, and B. Curtiss, “Airborne imaging spectrometer-2: radiometric spectral characteristics and comparison of ways to compensate for the atmosphere,” Proc. SPIE 834, 140–157 (1987).
[Crossref]

Guanter, L.

L. Guanter, R. Richter, and H. Kaufmann, “On the application of the MODTRAN4 atmospheric radiative transfer code to optical remote sensing,” Int. J. Remote Sens. 30(6), 1407–1424 (2009).
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L. Guanter, V. Estellés, and J. Moreno, “Spectral calibration and atmospheric correction of ultra-fine spectral and spatial resolution remote sensing data. Application to CASI-1500 data,” Remote Sens. Environ. 109(1), 54–65 (2007).
[Crossref]

L. Guanter, L. Alonso, and J. Moreno, “A Method for the Surface Reflectance Retrieval from PROBA/CHRIS Data over land: Application to ESA SPARC Campaigns,” IEEE Trans. Geosci. Remote Sens. 43(12), 2908–2917 (2005).
[Crossref]

Gutiérrez, P. A.

M. Pérez-Ortiz, J. M. Peña, P. A. Gutiérrez, J. Torres-Sánchez, C. Hervás-Martínez, and F. López-Granados, “Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery,” Expert Syst. Appl. 47(1), 85–94 (2016).
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Haboudane, D.

D. Haboudane, J. R. Miller, E. Pattey, P. J. Zarco-Tejada, and I. B. Strachan, “Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture,” Remote Sens. Environ. 90(3), 337–352 (2004).
[Crossref]

Hagolle, O.

O. Hagolle, M. Huc, D. Villa Pascual, and G. Dedieu, “A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images,” Remote Sens. 7(3), 2668–2691 (2015), doi:.
[Crossref]

Halligan, K.

J. Franke, D. Roberts, K. Halligan, and G. Menz, “Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments,” Remote Sens. Environ. 113(8), 1712–1723 (2009).
[Crossref]

Han, D.

S. S. Li, L. F. Chen, J. H. Tao, D. Han, Z. T. Wang, and B. H. He, “[Retrieval and validation of the surface reflectance using HJ-1-CCD data],” Guangpuxue Yu Guangpu Fenxi 31(2), 516–520 (2011).
[PubMed]

He, B. H.

S. S. Li, L. F. Chen, J. H. Tao, D. Han, Z. T. Wang, and B. H. He, “[Retrieval and validation of the surface reflectance using HJ-1-CCD data],” Guangpuxue Yu Guangpu Fenxi 31(2), 516–520 (2011).
[PubMed]

Hecker, C. A.

F. D. van der Meer, H. M. A. van der Werff, F. J. A. Van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai, “Multi– and hyperspectral geologic remote sensing: A review,” Int. J. Appl. Earth Obs. 14(1), 112–128 (2012).
[Crossref]

Hervás-Martínez, C.

M. Pérez-Ortiz, J. M. Peña, P. A. Gutiérrez, J. Torres-Sánchez, C. Hervás-Martínez, and F. López-Granados, “Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery,” Expert Syst. Appl. 47(1), 85–94 (2016).
[Crossref]

Holben, B.

Y. Kaufman, D. Tanre, L. Remer, E. Vermote, A. Chu, and B. Holben, “Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer,” J. Geophys. Res. 102(D14), 17051–17067 (1997).
[Crossref]

Holm, R.

C. Bruegge, J. Conel, R. Green, J. Margolis, R. Holm, and G. Toon, “Water-vapor column abundance retrievals during FIFE,” J. Geophys. Res. 97(D17), 18759–18768 (1992).
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Hu, S.

S. Hu, X. She, and Q. Tong, “Design and interpolation of a general look-up table for remote sensing image atmospheric correction,” Yaogan Xuebao 18, 45–60 (2014).

Huc, M.

O. Hagolle, M. Huc, D. Villa Pascual, and G. Dedieu, “A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images,” Remote Sens. 7(3), 2668–2691 (2015), doi:.
[Crossref]

Ishida, T.

T. Ishida, J. Kurihara, F. A. Viray, S. B. Namuco, E. C. Paringit, G. J. Perez, Y. Takahashi, and J. J. Marciano, “A novel approach for vegetation classification using UAV-based hyperspectral imaging,” Comput. Electron. Agric. 144, 80–85 (2018).
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Itten, K.

D. Schläpfer, C. Borel, J. Keller, and K. Itten, “Atmospheric precorrected differential absorption technique to retrieve colummar water vapor,” Remote Sens. Environ. 65(3), 353–366 (1998).
[Crossref]

Jian, X.

H. Yang, L. Zhang, C. Ong, A. Rodger, J. Liu, X. Sun, H. Zhang, X. Jian, and Q. Tong, “Improved aerosol optical thickness, columnar water vapor, and surface reflectance retrieval from combined CASI and SASI airborne hyperspectral sensors,” Remote Sens. 9(3), 217 (2017).
[Crossref]

Kaufman, Y.

Y. Kaufman, D. Tanre, L. Remer, E. Vermote, A. Chu, and B. Holben, “Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer,” J. Geophys. Res. 102(D14), 17051–17067 (1997).
[Crossref]

Y. Kaufman and B. Gao, “Remote sensing of water vapor in the near IR from EOS/MODIS,” IEEE Trans. Geosci. Remote Sens. 30(5), 871–884 (1992).
[Crossref]

T. Lee and Y. Kaufman, “Non-Lambertian effects on remote sensing of surface reflectance and vegetation index,” IEEE Trans. Geosci. Remote Sens.,  GE-24(5), 699–708 (1986).
[Crossref]

Kaufmann, H.

L. Guanter, R. Richter, and H. Kaufmann, “On the application of the MODTRAN4 atmospheric radiative transfer code to optical remote sensing,” Int. J. Remote Sens. 30(6), 1407–1424 (2009).
[Crossref]

Keller, J.

D. Schläpfer, C. Borel, J. Keller, and K. Itten, “Atmospheric precorrected differential absorption technique to retrieve colummar water vapor,” Remote Sens. Environ. 65(3), 353–366 (1998).
[Crossref]

Kindel, B.

E. Ben-Dor, B. Kindel, and A. Goetz, “Quality assessment of several methods to recover surface reflectance using synthetic imaging spectroscopy data,” Remote Sens. Environ. 90(3), 389–404 (2004).
[Crossref]

Knaeps, E.

S. Sterckx, E. Knaeps, and K. Ruddick, “Detection and correction of adjacency effects in hyperspectral airborne data of coastal and inland waters: the use of the near infrared similarity spectrum,” Yaogan Xuebao 32(21), 6479–6505 (2011).
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Kurihara, J.

T. Ishida, J. Kurihara, F. A. Viray, S. B. Namuco, E. C. Paringit, G. J. Perez, Y. Takahashi, and J. J. Marciano, “A novel approach for vegetation classification using UAV-based hyperspectral imaging,” Comput. Electron. Agric. 144, 80–85 (2018).
[Crossref]

Lee, T.

T. Lee and Y. Kaufman, “Non-Lambertian effects on remote sensing of surface reflectance and vegetation index,” IEEE Trans. Geosci. Remote Sens.,  GE-24(5), 699–708 (1986).
[Crossref]

Lee, Z.

Z. Lee, B. Casey, R. Parsons, W. Goode, A. Weidemann, and R. Arnone, “Bathymetry of shallow coastal regions derived from space-borne hyperspectral sensor,” in OCEANS, Proc. of MTS/IEEE, 2160–2170 (2005).

Lenot, X.

C. Miesch, L. Poutier, V. Achard, X. Briottet, X. Lenot, and Y. Boucher, “Direct and inverse radiative transfer solutions for visible and near-infrared hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 43(7), 1552–1562 (2005).
[Crossref]

Li, C.

S. B. Duan, Z. L. Li, B. H. Tang, H. Wu, L. Ma, E. Zhao, and C. Li, “Land surface reflectance retrieval from hyperspectral data collected by an unmanned aerial vehicle over the Baotou test site,” PLoS One 8(6), e66972 (2013), doi:.
[Crossref] [PubMed]

Li, Q.

Z. Wang, Q. Li, J. Tao, S. Li, Q. Wang, and L. Chen, “Monitoring of aerosol optical depth over land surface using CCD camera on HJ-2 satellite,” Guangpuxue Yu Guangpu Fenxi 29, 902–907 (2009).

Li, S.

Z. Wang, Q. Li, J. Tao, S. Li, Q. Wang, and L. Chen, “Monitoring of aerosol optical depth over land surface using CCD camera on HJ-2 satellite,” Guangpuxue Yu Guangpu Fenxi 29, 902–907 (2009).

Li, S. S.

S. S. Li, L. F. Chen, J. H. Tao, D. Han, Z. T. Wang, and B. H. He, “[Retrieval and validation of the surface reflectance using HJ-1-CCD data],” Guangpuxue Yu Guangpu Fenxi 31(2), 516–520 (2011).
[PubMed]

Li, Z. L.

S. B. Duan, Z. L. Li, B. H. Tang, H. Wu, L. Ma, E. Zhao, and C. Li, “Land surface reflectance retrieval from hyperspectral data collected by an unmanned aerial vehicle over the Baotou test site,” PLoS One 8(6), e66972 (2013), doi:.
[Crossref] [PubMed]

Liang, S.

S. Liang and H. Fang, “An improved atmospheric correction algorithm for hyperspectral remotely sensed imagery,” IEEE Trans. Geosci. Remote Sens. 1(2), 112–117 (2004).
[Crossref]

Liu, J.

H. Yang, L. Zhang, C. Ong, A. Rodger, J. Liu, X. Sun, H. Zhang, X. Jian, and Q. Tong, “Improved aerosol optical thickness, columnar water vapor, and surface reflectance retrieval from combined CASI and SASI airborne hyperspectral sensors,” Remote Sens. 9(3), 217 (2017).
[Crossref]

Liu, Y.

Y. Liu, T. Wang, L. Ma, and N. Wang, “Spectral Calibration of Hyperspectral Data Observed From a Hyperspectrometer Loaded on an Unmanned Aerial Vehicle Platform,” IEEE J. Sel. Top. Appl. 7(6), 2630–2638 (2014).

López-Granados, F.

M. Pérez-Ortiz, J. M. Peña, P. A. Gutiérrez, J. Torres-Sánchez, C. Hervás-Martínez, and F. López-Granados, “Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery,” Expert Syst. Appl. 47(1), 85–94 (2016).
[Crossref]

Ma, L.

Y. Liu, T. Wang, L. Ma, and N. Wang, “Spectral Calibration of Hyperspectral Data Observed From a Hyperspectrometer Loaded on an Unmanned Aerial Vehicle Platform,” IEEE J. Sel. Top. Appl. 7(6), 2630–2638 (2014).

S. B. Duan, Z. L. Li, B. H. Tang, H. Wu, L. Ma, E. Zhao, and C. Li, “Land surface reflectance retrieval from hyperspectral data collected by an unmanned aerial vehicle over the Baotou test site,” PLoS One 8(6), e66972 (2013), doi:.
[Crossref] [PubMed]

Manzo, C.

C. Bassani, C. Manzo, F. Braga, M. Bresciani, C. Giardino, and L. Alberotanza, “The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters,” Atmos. Meas. Tech. 8(3), 1593–1604 (2015).
[Crossref]

Marciano, J. J.

T. Ishida, J. Kurihara, F. A. Viray, S. B. Namuco, E. C. Paringit, G. J. Perez, Y. Takahashi, and J. J. Marciano, “A novel approach for vegetation classification using UAV-based hyperspectral imaging,” Comput. Electron. Agric. 144, 80–85 (2018).
[Crossref]

Margolis, J.

C. Bruegge, J. Conel, R. Green, J. Margolis, R. Holm, and G. Toon, “Water-vapor column abundance retrievals during FIFE,” J. Geophys. Res. 97(D17), 18759–18768 (1992).
[Crossref]

McVay, J.

T. Sankey, J. Donager, J. McVay, and J. B. Sankey, “UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA,” Remote Sens. Environ. 195(15), 30–43 (2017).
[Crossref]

Menz, G.

J. Franke, D. Roberts, K. Halligan, and G. Menz, “Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments,” Remote Sens. Environ. 113(8), 1712–1723 (2009).
[Crossref]

Miesch, C.

C. Miesch, L. Poutier, V. Achard, X. Briottet, X. Lenot, and Y. Boucher, “Direct and inverse radiative transfer solutions for visible and near-infrared hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 43(7), 1552–1562 (2005).
[Crossref]

Miller, J. R.

D. Haboudane, J. R. Miller, E. Pattey, P. J. Zarco-Tejada, and I. B. Strachan, “Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture,” Remote Sens. Environ. 90(3), 337–352 (2004).
[Crossref]

Molina, P.

I. Colomina and P. Molina, “Unmanned aerial systems for photogrammetry and remote sensing: a review,” ISPRS J. Photogramm. 92, 79–97 (2014).
[Crossref]

Montes, M.

B. Gao, M. Montes, C. Davis, and A. Goetz, “Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean,” Remote Sens. Environ. 113, S17–S24 (2009).
[Crossref]

Moreno, J.

L. Guanter, V. Estellés, and J. Moreno, “Spectral calibration and atmospheric correction of ultra-fine spectral and spatial resolution remote sensing data. Application to CASI-1500 data,” Remote Sens. Environ. 109(1), 54–65 (2007).
[Crossref]

L. Guanter, L. Alonso, and J. Moreno, “A Method for the Surface Reflectance Retrieval from PROBA/CHRIS Data over land: Application to ESA SPARC Campaigns,” IEEE Trans. Geosci. Remote Sens. 43(12), 2908–2917 (2005).
[Crossref]

Müller, A.

R. Richter, D. Schläpfer, and A. Müller, “An automatic atmospheric correction algorithm for visible/NIR imagery,” Int. J. Remote Sens. 27(10), 2077–2085 (2006).
[Crossref]

Namuco, S. B.

T. Ishida, J. Kurihara, F. A. Viray, S. B. Namuco, E. C. Paringit, G. J. Perez, Y. Takahashi, and J. J. Marciano, “A novel approach for vegetation classification using UAV-based hyperspectral imaging,” Comput. Electron. Agric. 144, 80–85 (2018).
[Crossref]

Noomen, M. F.

F. D. van der Meer, H. M. A. van der Werff, F. J. A. Van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai, “Multi– and hyperspectral geologic remote sensing: A review,” Int. J. Appl. Earth Obs. 14(1), 112–128 (2012).
[Crossref]

Ong, C.

H. Yang, L. Zhang, C. Ong, A. Rodger, J. Liu, X. Sun, H. Zhang, X. Jian, and Q. Tong, “Improved aerosol optical thickness, columnar water vapor, and surface reflectance retrieval from combined CASI and SASI airborne hyperspectral sensors,” Remote Sens. 9(3), 217 (2017).
[Crossref]

Palacios-Orueta, A.

M. L. Whiting, S. L. Ustin, P. Zarco-Tejada, A. Palacios-Orueta, and V. C. Vanderbilt, “Hyperspectral mapping of crop and soils for precision agriculture,” Proc. SPIE 6298, 62980B (2006), doi:.
[Crossref]

Paringit, E. C.

T. Ishida, J. Kurihara, F. A. Viray, S. B. Namuco, E. C. Paringit, G. J. Perez, Y. Takahashi, and J. J. Marciano, “A novel approach for vegetation classification using UAV-based hyperspectral imaging,” Comput. Electron. Agric. 144, 80–85 (2018).
[Crossref]

Parsons, R.

Z. Lee, B. Casey, R. Parsons, W. Goode, A. Weidemann, and R. Arnone, “Bathymetry of shallow coastal regions derived from space-borne hyperspectral sensor,” in OCEANS, Proc. of MTS/IEEE, 2160–2170 (2005).

Pattey, E.

D. Haboudane, J. R. Miller, E. Pattey, P. J. Zarco-Tejada, and I. B. Strachan, “Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture,” Remote Sens. Environ. 90(3), 337–352 (2004).
[Crossref]

Peña, J. M.

M. Pérez-Ortiz, J. M. Peña, P. A. Gutiérrez, J. Torres-Sánchez, C. Hervás-Martínez, and F. López-Granados, “Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery,” Expert Syst. Appl. 47(1), 85–94 (2016).
[Crossref]

Peng, D.

H. Zhang, Z. Chen, B. Zhang, and D. Peng, “Comparison of two water vapor retrieval algorithm for HJ1A hyperspectral imagery,” IEEE International Geoscience and Remote Sensing Symposium (IGARSS), doi: (2011).
[Crossref]

Perez, G. J.

T. Ishida, J. Kurihara, F. A. Viray, S. B. Namuco, E. C. Paringit, G. J. Perez, Y. Takahashi, and J. J. Marciano, “A novel approach for vegetation classification using UAV-based hyperspectral imaging,” Comput. Electron. Agric. 144, 80–85 (2018).
[Crossref]

Pérez-Ortiz, M.

M. Pérez-Ortiz, J. M. Peña, P. A. Gutiérrez, J. Torres-Sánchez, C. Hervás-Martínez, and F. López-Granados, “Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery,” Expert Syst. Appl. 47(1), 85–94 (2016).
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Pignatti, S.

C. Bassani, R. M. Cavalli, and S. Pignatti, “Aerosol optical retrieval and surface reflectance from airborne remote sensing data over land,” Sensors (Basel) 10(7), 6421–6438 (2010).
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Pope, P.

P. Chylek, C. Borel, W. Clodius, P. Pope, and A. Rodger, “Satellite-based columnar water vapor retrieval with the multi-spectral thermal imager (MTI),” IEEE Trans. Geosci. Remote Sens. 41(12), 2767–2770 (2003).
[Crossref]

Poutier, L.

C. Miesch, L. Poutier, V. Achard, X. Briottet, X. Lenot, and Y. Boucher, “Direct and inverse radiative transfer solutions for visible and near-infrared hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 43(7), 1552–1562 (2005).
[Crossref]

Remer, L.

Y. Kaufman, D. Tanre, L. Remer, E. Vermote, A. Chu, and B. Holben, “Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer,” J. Geophys. Res. 102(D14), 17051–17067 (1997).
[Crossref]

Richter, R.

L. Guanter, R. Richter, and H. Kaufmann, “On the application of the MODTRAN4 atmospheric radiative transfer code to optical remote sensing,” Int. J. Remote Sens. 30(6), 1407–1424 (2009).
[Crossref]

R. Richter, D. Schläpfer, and A. Müller, “An automatic atmospheric correction algorithm for visible/NIR imagery,” Int. J. Remote Sens. 27(10), 2077–2085 (2006).
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R. Richter and D. Schläpfer, “Geo-atmospheric processing of airborne imaging spectrometry data, Part 2: atmospheric/topographic correction,” Int. J. Remote Sens. 23(13), 2631–2649 (2002).
[Crossref]

Roberts, D.

J. Franke, D. Roberts, K. Halligan, and G. Menz, “Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments,” Remote Sens. Environ. 113(8), 1712–1723 (2009).
[Crossref]

Rodger, A.

H. Yang, L. Zhang, C. Ong, A. Rodger, J. Liu, X. Sun, H. Zhang, X. Jian, and Q. Tong, “Improved aerosol optical thickness, columnar water vapor, and surface reflectance retrieval from combined CASI and SASI airborne hyperspectral sensors,” Remote Sens. 9(3), 217 (2017).
[Crossref]

P. Chylek, C. Borel, W. Clodius, P. Pope, and A. Rodger, “Satellite-based columnar water vapor retrieval with the multi-spectral thermal imager (MTI),” IEEE Trans. Geosci. Remote Sens. 41(12), 2767–2770 (2003).
[Crossref]

Ruddick, K.

S. Sterckx, E. Knaeps, and K. Ruddick, “Detection and correction of adjacency effects in hyperspectral airborne data of coastal and inland waters: the use of the near infrared similarity spectrum,” Yaogan Xuebao 32(21), 6479–6505 (2011).
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Sankey, J. B.

T. Sankey, J. Donager, J. McVay, and J. B. Sankey, “UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA,” Remote Sens. Environ. 195(15), 30–43 (2017).
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Sankey, T.

T. Sankey, J. Donager, J. McVay, and J. B. Sankey, “UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA,” Remote Sens. Environ. 195(15), 30–43 (2017).
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Schläpfer, D.

R. Richter, D. Schläpfer, and A. Müller, “An automatic atmospheric correction algorithm for visible/NIR imagery,” Int. J. Remote Sens. 27(10), 2077–2085 (2006).
[Crossref]

R. Richter and D. Schläpfer, “Geo-atmospheric processing of airborne imaging spectrometry data, Part 2: atmospheric/topographic correction,” Int. J. Remote Sens. 23(13), 2631–2649 (2002).
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D. Schläpfer, C. Borel, J. Keller, and K. Itten, “Atmospheric precorrected differential absorption technique to retrieve colummar water vapor,” Remote Sens. Environ. 65(3), 353–366 (1998).
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She, X.

S. Hu, X. She, and Q. Tong, “Design and interpolation of a general look-up table for remote sensing image atmospheric correction,” Yaogan Xuebao 18, 45–60 (2014).

Sterckx, S.

S. Sterckx, E. Knaeps, and K. Ruddick, “Detection and correction of adjacency effects in hyperspectral airborne data of coastal and inland waters: the use of the near infrared similarity spectrum,” Yaogan Xuebao 32(21), 6479–6505 (2011).
[Crossref]

Strachan, I. B.

D. Haboudane, J. R. Miller, E. Pattey, P. J. Zarco-Tejada, and I. B. Strachan, “Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture,” Remote Sens. Environ. 90(3), 337–352 (2004).
[Crossref]

Sun, X.

H. Yang, L. Zhang, C. Ong, A. Rodger, J. Liu, X. Sun, H. Zhang, X. Jian, and Q. Tong, “Improved aerosol optical thickness, columnar water vapor, and surface reflectance retrieval from combined CASI and SASI airborne hyperspectral sensors,” Remote Sens. 9(3), 217 (2017).
[Crossref]

Takahashi, Y.

T. Ishida, J. Kurihara, F. A. Viray, S. B. Namuco, E. C. Paringit, G. J. Perez, Y. Takahashi, and J. J. Marciano, “A novel approach for vegetation classification using UAV-based hyperspectral imaging,” Comput. Electron. Agric. 144, 80–85 (2018).
[Crossref]

Tang, B. H.

S. B. Duan, Z. L. Li, B. H. Tang, H. Wu, L. Ma, E. Zhao, and C. Li, “Land surface reflectance retrieval from hyperspectral data collected by an unmanned aerial vehicle over the Baotou test site,” PLoS One 8(6), e66972 (2013), doi:.
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Tanre, D.

Y. Kaufman, D. Tanre, L. Remer, E. Vermote, A. Chu, and B. Holben, “Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer,” J. Geophys. Res. 102(D14), 17051–17067 (1997).
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Tao, J.

Z. Wang, Q. Li, J. Tao, S. Li, Q. Wang, and L. Chen, “Monitoring of aerosol optical depth over land surface using CCD camera on HJ-2 satellite,” Guangpuxue Yu Guangpu Fenxi 29, 902–907 (2009).

Tao, J. H.

S. S. Li, L. F. Chen, J. H. Tao, D. Han, Z. T. Wang, and B. H. He, “[Retrieval and validation of the surface reflectance using HJ-1-CCD data],” Guangpuxue Yu Guangpu Fenxi 31(2), 516–520 (2011).
[PubMed]

Tong, Q.

H. Yang, L. Zhang, C. Ong, A. Rodger, J. Liu, X. Sun, H. Zhang, X. Jian, and Q. Tong, “Improved aerosol optical thickness, columnar water vapor, and surface reflectance retrieval from combined CASI and SASI airborne hyperspectral sensors,” Remote Sens. 9(3), 217 (2017).
[Crossref]

S. Hu, X. She, and Q. Tong, “Design and interpolation of a general look-up table for remote sensing image atmospheric correction,” Yaogan Xuebao 18, 45–60 (2014).

Toon, G.

C. Bruegge, J. Conel, R. Green, J. Margolis, R. Holm, and G. Toon, “Water-vapor column abundance retrievals during FIFE,” J. Geophys. Res. 97(D17), 18759–18768 (1992).
[Crossref]

Torres-Sánchez, J.

M. Pérez-Ortiz, J. M. Peña, P. A. Gutiérrez, J. Torres-Sánchez, C. Hervás-Martínez, and F. López-Granados, “Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery,” Expert Syst. Appl. 47(1), 85–94 (2016).
[Crossref]

Ustin, S. L.

M. L. Whiting, S. L. Ustin, P. Zarco-Tejada, A. Palacios-Orueta, and V. C. Vanderbilt, “Hyperspectral mapping of crop and soils for precision agriculture,” Proc. SPIE 6298, 62980B (2006), doi:.
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F. D. van der Meer, H. M. A. van der Werff, F. J. A. Van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai, “Multi– and hyperspectral geologic remote sensing: A review,” Int. J. Appl. Earth Obs. 14(1), 112–128 (2012).
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F. D. van der Meer, H. M. A. van der Werff, F. J. A. Van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai, “Multi– and hyperspectral geologic remote sensing: A review,” Int. J. Appl. Earth Obs. 14(1), 112–128 (2012).
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van der Werff, H. M. A.

F. D. van der Meer, H. M. A. van der Werff, F. J. A. Van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai, “Multi– and hyperspectral geologic remote sensing: A review,” Int. J. Appl. Earth Obs. 14(1), 112–128 (2012).
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F. D. van der Meer, H. M. A. van der Werff, F. J. A. Van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai, “Multi– and hyperspectral geologic remote sensing: A review,” Int. J. Appl. Earth Obs. 14(1), 112–128 (2012).
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Vanderbilt, V. C.

M. L. Whiting, S. L. Ustin, P. Zarco-Tejada, A. Palacios-Orueta, and V. C. Vanderbilt, “Hyperspectral mapping of crop and soils for precision agriculture,” Proc. SPIE 6298, 62980B (2006), doi:.
[Crossref]

Vane, G.

J. Conel, R. Green, G. Vane, C. Bruegge, R. Alley, and B. Curtiss, “Airborne imaging spectrometer-2: radiometric spectral characteristics and comparison of ways to compensate for the atmosphere,” Proc. SPIE 834, 140–157 (1987).
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Vermote, E.

Y. Kaufman, D. Tanre, L. Remer, E. Vermote, A. Chu, and B. Holben, “Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer,” J. Geophys. Res. 102(D14), 17051–17067 (1997).
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Villa Pascual, D.

O. Hagolle, M. Huc, D. Villa Pascual, and G. Dedieu, “A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images,” Remote Sens. 7(3), 2668–2691 (2015), doi:.
[Crossref]

Viray, F. A.

T. Ishida, J. Kurihara, F. A. Viray, S. B. Namuco, E. C. Paringit, G. J. Perez, Y. Takahashi, and J. J. Marciano, “A novel approach for vegetation classification using UAV-based hyperspectral imaging,” Comput. Electron. Agric. 144, 80–85 (2018).
[Crossref]

Wang, N.

Y. Liu, T. Wang, L. Ma, and N. Wang, “Spectral Calibration of Hyperspectral Data Observed From a Hyperspectrometer Loaded on an Unmanned Aerial Vehicle Platform,” IEEE J. Sel. Top. Appl. 7(6), 2630–2638 (2014).

Wang, Q.

Z. Wang, Q. Li, J. Tao, S. Li, Q. Wang, and L. Chen, “Monitoring of aerosol optical depth over land surface using CCD camera on HJ-2 satellite,” Guangpuxue Yu Guangpu Fenxi 29, 902–907 (2009).

Wang, T.

Y. Liu, T. Wang, L. Ma, and N. Wang, “Spectral Calibration of Hyperspectral Data Observed From a Hyperspectrometer Loaded on an Unmanned Aerial Vehicle Platform,” IEEE J. Sel. Top. Appl. 7(6), 2630–2638 (2014).

Wang, Z.

Z. Wang, Q. Li, J. Tao, S. Li, Q. Wang, and L. Chen, “Monitoring of aerosol optical depth over land surface using CCD camera on HJ-2 satellite,” Guangpuxue Yu Guangpu Fenxi 29, 902–907 (2009).

Wang, Z. T.

S. S. Li, L. F. Chen, J. H. Tao, D. Han, Z. T. Wang, and B. H. He, “[Retrieval and validation of the surface reflectance using HJ-1-CCD data],” Guangpuxue Yu Guangpu Fenxi 31(2), 516–520 (2011).
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Weidemann, A.

Z. Lee, B. Casey, R. Parsons, W. Goode, A. Weidemann, and R. Arnone, “Bathymetry of shallow coastal regions derived from space-borne hyperspectral sensor,” in OCEANS, Proc. of MTS/IEEE, 2160–2170 (2005).

Whiting, M. L.

M. L. Whiting, S. L. Ustin, P. Zarco-Tejada, A. Palacios-Orueta, and V. C. Vanderbilt, “Hyperspectral mapping of crop and soils for precision agriculture,” Proc. SPIE 6298, 62980B (2006), doi:.
[Crossref]

Woldai, T.

F. D. van der Meer, H. M. A. van der Werff, F. J. A. Van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai, “Multi– and hyperspectral geologic remote sensing: A review,” Int. J. Appl. Earth Obs. 14(1), 112–128 (2012).
[Crossref]

Wu, H.

S. B. Duan, Z. L. Li, B. H. Tang, H. Wu, L. Ma, E. Zhao, and C. Li, “Land surface reflectance retrieval from hyperspectral data collected by an unmanned aerial vehicle over the Baotou test site,” PLoS One 8(6), e66972 (2013), doi:.
[Crossref] [PubMed]

Yang, H.

H. Yang, L. Zhang, C. Ong, A. Rodger, J. Liu, X. Sun, H. Zhang, X. Jian, and Q. Tong, “Improved aerosol optical thickness, columnar water vapor, and surface reflectance retrieval from combined CASI and SASI airborne hyperspectral sensors,” Remote Sens. 9(3), 217 (2017).
[Crossref]

Zarco-Tejada, P.

M. L. Whiting, S. L. Ustin, P. Zarco-Tejada, A. Palacios-Orueta, and V. C. Vanderbilt, “Hyperspectral mapping of crop and soils for precision agriculture,” Proc. SPIE 6298, 62980B (2006), doi:.
[Crossref]

Zarco-Tejada, P. J.

D. Haboudane, J. R. Miller, E. Pattey, P. J. Zarco-Tejada, and I. B. Strachan, “Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture,” Remote Sens. Environ. 90(3), 337–352 (2004).
[Crossref]

Zhang, B.

H. Zhang, Z. Chen, B. Zhang, and D. Peng, “Comparison of two water vapor retrieval algorithm for HJ1A hyperspectral imagery,” IEEE International Geoscience and Remote Sensing Symposium (IGARSS), doi: (2011).
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Zhang, H.

H. Yang, L. Zhang, C. Ong, A. Rodger, J. Liu, X. Sun, H. Zhang, X. Jian, and Q. Tong, “Improved aerosol optical thickness, columnar water vapor, and surface reflectance retrieval from combined CASI and SASI airborne hyperspectral sensors,” Remote Sens. 9(3), 217 (2017).
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H. Zhang, Z. Chen, B. Zhang, and D. Peng, “Comparison of two water vapor retrieval algorithm for HJ1A hyperspectral imagery,” IEEE International Geoscience and Remote Sensing Symposium (IGARSS), doi: (2011).
[Crossref]

Zhang, L.

H. Yang, L. Zhang, C. Ong, A. Rodger, J. Liu, X. Sun, H. Zhang, X. Jian, and Q. Tong, “Improved aerosol optical thickness, columnar water vapor, and surface reflectance retrieval from combined CASI and SASI airborne hyperspectral sensors,” Remote Sens. 9(3), 217 (2017).
[Crossref]

Zhao, E.

S. B. Duan, Z. L. Li, B. H. Tang, H. Wu, L. Ma, E. Zhao, and C. Li, “Land surface reflectance retrieval from hyperspectral data collected by an unmanned aerial vehicle over the Baotou test site,” PLoS One 8(6), e66972 (2013), doi:.
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Atmos. Meas. Tech. (1)

C. Bassani, C. Manzo, F. Braga, M. Bresciani, C. Giardino, and L. Alberotanza, “The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters,” Atmos. Meas. Tech. 8(3), 1593–1604 (2015).
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T. Ishida, J. Kurihara, F. A. Viray, S. B. Namuco, E. C. Paringit, G. J. Perez, Y. Takahashi, and J. J. Marciano, “A novel approach for vegetation classification using UAV-based hyperspectral imaging,” Comput. Electron. Agric. 144, 80–85 (2018).
[Crossref]

Expert Syst. Appl. (1)

M. Pérez-Ortiz, J. M. Peña, P. A. Gutiérrez, J. Torres-Sánchez, C. Hervás-Martínez, and F. López-Granados, “Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery,” Expert Syst. Appl. 47(1), 85–94 (2016).
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A. Filippi, K. Carder, and C. Davis, “Vicarious calibration of the Ocean PHILLS hyperspectral sensor using a coastal tree-shadow method,” Geophys. Res. Lett. 33(22), 22605 (2006).

Guangpuxue Yu Guangpu Fenxi (2)

Z. Wang, Q. Li, J. Tao, S. Li, Q. Wang, and L. Chen, “Monitoring of aerosol optical depth over land surface using CCD camera on HJ-2 satellite,” Guangpuxue Yu Guangpu Fenxi 29, 902–907 (2009).

S. S. Li, L. F. Chen, J. H. Tao, D. Han, Z. T. Wang, and B. H. He, “[Retrieval and validation of the surface reflectance using HJ-1-CCD data],” Guangpuxue Yu Guangpu Fenxi 31(2), 516–520 (2011).
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IEEE J. Sel. Top. Appl. (1)

Y. Liu, T. Wang, L. Ma, and N. Wang, “Spectral Calibration of Hyperspectral Data Observed From a Hyperspectrometer Loaded on an Unmanned Aerial Vehicle Platform,” IEEE J. Sel. Top. Appl. 7(6), 2630–2638 (2014).

IEEE Trans. Geosci. Remote Sens. (6)

L. Guanter, L. Alonso, and J. Moreno, “A Method for the Surface Reflectance Retrieval from PROBA/CHRIS Data over land: Application to ESA SPARC Campaigns,” IEEE Trans. Geosci. Remote Sens. 43(12), 2908–2917 (2005).
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C. Miesch, L. Poutier, V. Achard, X. Briottet, X. Lenot, and Y. Boucher, “Direct and inverse radiative transfer solutions for visible and near-infrared hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 43(7), 1552–1562 (2005).
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S. Liang and H. Fang, “An improved atmospheric correction algorithm for hyperspectral remotely sensed imagery,” IEEE Trans. Geosci. Remote Sens. 1(2), 112–117 (2004).
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P. Chylek, C. Borel, W. Clodius, P. Pope, and A. Rodger, “Satellite-based columnar water vapor retrieval with the multi-spectral thermal imager (MTI),” IEEE Trans. Geosci. Remote Sens. 41(12), 2767–2770 (2003).
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F. D. van der Meer, H. M. A. van der Werff, F. J. A. Van Ruitenbeek, C. A. Hecker, W. H. Bakker, M. F. Noomen, M. van der Meijde, E. J. M. Carranza, J. B. de Smeth, and T. Woldai, “Multi– and hyperspectral geologic remote sensing: A review,” Int. J. Appl. Earth Obs. 14(1), 112–128 (2012).
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Int. J. Remote Sens. (3)

L. Guanter, R. Richter, and H. Kaufmann, “On the application of the MODTRAN4 atmospheric radiative transfer code to optical remote sensing,” Int. J. Remote Sens. 30(6), 1407–1424 (2009).
[Crossref]

R. Richter, D. Schläpfer, and A. Müller, “An automatic atmospheric correction algorithm for visible/NIR imagery,” Int. J. Remote Sens. 27(10), 2077–2085 (2006).
[Crossref]

R. Richter and D. Schläpfer, “Geo-atmospheric processing of airborne imaging spectrometry data, Part 2: atmospheric/topographic correction,” Int. J. Remote Sens. 23(13), 2631–2649 (2002).
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ISPRS J. Photogramm. (1)

I. Colomina and P. Molina, “Unmanned aerial systems for photogrammetry and remote sensing: a review,” ISPRS J. Photogramm. 92, 79–97 (2014).
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C. Bruegge, J. Conel, R. Green, J. Margolis, R. Holm, and G. Toon, “Water-vapor column abundance retrievals during FIFE,” J. Geophys. Res. 97(D17), 18759–18768 (1992).
[Crossref]

Y. Kaufman, D. Tanre, L. Remer, E. Vermote, A. Chu, and B. Holben, “Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer,” J. Geophys. Res. 102(D14), 17051–17067 (1997).
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S. B. Duan, Z. L. Li, B. H. Tang, H. Wu, L. Ma, E. Zhao, and C. Li, “Land surface reflectance retrieval from hyperspectral data collected by an unmanned aerial vehicle over the Baotou test site,” PLoS One 8(6), e66972 (2013), doi:.
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Proc. SPIE (2)

M. L. Whiting, S. L. Ustin, P. Zarco-Tejada, A. Palacios-Orueta, and V. C. Vanderbilt, “Hyperspectral mapping of crop and soils for precision agriculture,” Proc. SPIE 6298, 62980B (2006), doi:.
[Crossref]

J. Conel, R. Green, G. Vane, C. Bruegge, R. Alley, and B. Curtiss, “Airborne imaging spectrometer-2: radiometric spectral characteristics and comparison of ways to compensate for the atmosphere,” Proc. SPIE 834, 140–157 (1987).
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Remote Sens. (2)

H. Yang, L. Zhang, C. Ong, A. Rodger, J. Liu, X. Sun, H. Zhang, X. Jian, and Q. Tong, “Improved aerosol optical thickness, columnar water vapor, and surface reflectance retrieval from combined CASI and SASI airborne hyperspectral sensors,” Remote Sens. 9(3), 217 (2017).
[Crossref]

O. Hagolle, M. Huc, D. Villa Pascual, and G. Dedieu, “A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images,” Remote Sens. 7(3), 2668–2691 (2015), doi:.
[Crossref]

Remote Sens. Environ. (8)

D. Haboudane, J. R. Miller, E. Pattey, P. J. Zarco-Tejada, and I. B. Strachan, “Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture,” Remote Sens. Environ. 90(3), 337–352 (2004).
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R. Bennartz and J. Fischer, “Retrieval of columnar water vapour over land from backscattered solar radiation using the Medium Resolution Imaging Spectrometer,” Remote Sens. Environ. 78(3), 274–283 (2001).
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Figures (12)

Fig. 1
Fig. 1 A subset of the RGB true-colour image from the UAV-VNIRIS airborne optical hyperspectral data collected at 06:42 UTC on September 3, 2011.
Fig. 2
Fig. 2 Retrieved AOT at 340nm, 380nm, 440nm, 500nm, 550nm 670nm, 870nm, 1020nm and 1640nm from the automatic sun tracking photometer CE318 measurements on September 3, 2011.
Fig. 3
Fig. 3 Retrieved CWV based on the automatic sun tracking photometer CE318 measured solar irradiance at 936nm.
Fig. 4
Fig. 4 The scatter plot between the effective apparent reflectance ratio and CWV for (a) 0.815μm water absorption at band 85 and (b) 0.82μm water absorption at band 86.
Fig. 5
Fig. 5 Flowchart of the land surface reflectance retrieval from UAV-VNIRIS hyperspectral.
Fig. 6
Fig. 6 Comparisons between 30 theoretical and retrieved AOT@550 nm with synthetic hyperspectral data sets.
Fig. 7
Fig. 7 Comparisons between theoretical and retrieved CWV with synthetic hyperspectral data sets: (a) without high humidity atmospheric conditions; (b) with high humidity atmospheric conditions.
Fig. 8
Fig. 8 The false colour RGB images of the retrieved land surface reflectance images: (a) the artificial target area; (b) the agricultural crop areas.
Fig. 9
Fig. 9 Comparison of the surface reflectance retrieved using estimated AOT@550 nm (0.142) and CWV(1.557g/cm2) as well as the surface reflectance retrieved using local CIMEL photometer measured AOT@550 nm (0.1724) and CWV(1.6964g/cm2) with the ground measured surface reflectance for the (a) H1, (b) H2, (c) H3 and (d) H4 artificial targets as shown in Fig. 1.
Fig. 10
Fig. 10 Comparison of the surface reflectance retrieved using estimated AOT@550nm (0.142) and CWV (1.557g/cm2) as well as the surface reflectance retrieved using local CIMEL photometer measured AOT@550 nm (0.1724) and CWV (1.6964g/cm2) with the ground measured surface reflectance for the (a) potato marked with black dash rectangle and (b) sorghum marked with solid rectangle as showed in Fig. 7 (b).
Fig. 11
Fig. 11 Differences between measured and retrieved surface reflectance from UAV-VNIRIS airborne optical hyperspectral data as a function of the wavelength for the (a) H1, (b) H2, (c) H3 and (d) H4 artificial targets.
Fig. 12
Fig. 12 Differences between measured and retrieved surface reflectance from UAV-VNIRIS airborne optical hyperspectral data as a function of the wavelength for the (a) potato agriculture crop and (b) sorghum agriculture crop.

Tables (5)

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Table 1 The specification of the UAV-VNIRIS sensor.

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Table 2 The breakpoint positions of the Look-up table (LUT) for the 6 input parameters.

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Table 3 Essential input parameters configured to drive MODTRAN 5 code to simulate the synthetic data set

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Table 4 Validation between retrieved and measured atmospheric parameters.

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Table 5 Validation between retrieved and measured land surface reflectance

Equations (12)

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L T O A ( λ ) = L p a t h ( λ ) + ρ s ( λ ) π ( 1 ρ s ( λ ) S ( λ ) ) μ s E s ( λ ) τ ( μ s ) τ ( μ v )
ρ s r e d = k ρ s b l u e
f ( τ 5 5 0 , k ) = i = 1 c h n ( ρ s r e d , i k ρ s b l u e , i ) 2
ρ s i = L T O A i L P a t h i ( L T O A i L P a t h i ) S i + μ s E s τ ( μ s ) τ ( μ v ) / π
ρ e f f = π ( L ( μ v ) L p ( μ v ) ) μ s E 0 = ρ s 1 ρ s S τ ( μ s ) τ ( μ v )
ρ e f f = ρ s τ ( μ s ) τ ( μ v )
R ( c w v ) = ρ e f f a w 1 * ρ e f f 1 + w 2 * ρ e f f 2
w 1 = λ 2 - λ a λ 2 - λ 1
w 2 = λ a - λ 1 λ 2 - λ 1
y = 0.00018 x 4 0.0045 x 3 + 0.044 x 2 0.2 x + 1
y = 0.00027 x 4 0.0055 x 3 + 0.048 x 2 0.2 x + 0.98
ρ s = π ( L T O A L p a t h ) π ( L T O A L p a t h ) S + μ s E s τ ( μ s ) τ ( μ v )

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