Ссылки
1. Thenkabail, P. S., Enclona, E. A., Ashton, M. S. and Van Der
Meer, V., Accuracy assessments of hyperspectral waveband
performance for vegetation analysis applications. Remote Sensing
Environ., 2004, 91, 354–376.
2. Meerdink, S. K. et al., Linking seasonal foliar traits to VSWIR-
TIR spectroscopy across California ecosystems. Remote Sensing
Environ., 2016, 186, 322–338.
3. Gitelson, A., In Hyperspectral Remote Sensing of Vegetation (eds
Thenkabail, P. S., Lyon, G. J. and Huete, A.), CRC Press-Taylor
and Francis Group, Boca Raton, FL, USA, 2011, pp. 141–166.
4. Ozdogan, M. and Woodcock, C. E., Resolution dependent error in
remote sensing of cultivated areas. Remote Sensing Environ.,
2006, 103, 203–217.
5. Asner, G. P. and Martin, R. E., Spectral and chemical analysis of
tropical forests: scaling from leaf to canopy levels. Remote Sens-
ing Environ., 2008, 112, 3958–3970.
6. Jacquemoud, S., Baret, F., Andrieu, B., Danson, F. M. and
Jaggard, K., Extraction of vegetation biophysical parameters by
Inversion of the PROSPECT + SAIL models on sugar beet canopy
reflectance data. Application to TM and AVIRIS sensors. Remote
Sensing Environ., 1995, 52, 63–172.
7. Zarco-Tajada, P. J., Guillin-Climent, M. L., Hernandez-Clemente,
R. and Catalina, A., Estimating leaf carotenoid content in
vineyards using high resolution hyperspectral imaging acquired
from an unmanned aerial vehicle (UAV). Agric. For. Met., 2013,
17, 281–294.
8. Green, R. O. et al., Imaging spectroscopy and the airborne visi-
ble/infrared imaging spectrometer (AVIRIS). Remote Sensing
Environ., 1998, 65, 227–248.
9. Turner, D. P., Ollinger, S., Smith, M.-L., Krankina, O. and Gre-
gory, M., Scaling net primary production to a MODIS footprint in
support of Earth observing system product validation. Int. J.
Remote Sensing, 2004, 25, 1961–1979.
10. Bremner, J. M., Determination of nitrogen in soil using Kjeldahl
method. J. Agric. Sci., 1960, 55, 11–38.
11. Aguilar, M. A., Saldaña, M. M. and Aguilar, F. J., GeoEye-1 and
WorldView-2 pan-sharpened imagery for object-based classifica-
tion in urban environments. Int. J. Remote Sensing, 2012, 34,
2583–2606.
12. Yonezawa, C., Maximum likelihood classification combined with
spectral angle mapper algorithm for high resolution satellite
imagery. Int. J. Remote Sensing, 2007, 28, 3729–3737.
13. Paola, J. D. and Schowengerdt, R. A., A review and analysis of
backpropagationneural networks for classification of remotely
sensed multi-spectral imagery. Int. J. Remote Sensing, 1995, 16,
3033–3058.
14. Vapnik, V. N., The Nature of Statistical Learning Theory,
Springer Verlag, New York, USA, 1995.
15. Melgani, F. and Bruzzone, L., Classification of hyperspectral
remote sensing images with support vector machine. IEEE Trans.
Geosci. Remote Sensing, 2004, 8, 1778–1790.
16. Krebel, U., Pairwise classification and support vector machines. In
Advances in Kernel Methods-Support Vector Learning (eds
Schӧlkopf, B., Burges, C. J. C. and Smola, A. J.), The MIT Press,
Cambridge, MA, USA, 1999, pp. 255–268.
17. Green, A. A., Berman, M., Switzer, P. and Craig, M. D., A trans-
formation for ordering multispectral data in terms of image quality
with implications for noise removal. IEEE Trans. Geosci. Remote
Sensing, 1988, 26, 65–74.
18. Singh, A. and Harrison, A., Standardized principal components.
Int. J. Remote Sensing, 1985, 6, 883–896.
19. Friedl, M. A. and Brodley, C. E., Decision tree classification of
land cover from remotely sensed data. Remote Sensing Environ.,
1997, 61, 399–409.
20. Clark, R. N. et al., Imaging spectroscopy: earth and planetary re-
mote sensing with the USGS tetracorder and expert systems. J.
Geophys. Res., 2003, 108, 5131; doi:10.1029/2002JE001847.
21. Chen, Y., Lin, Z., Zhao, X., Wang, G. and Gu, Y., Deep learning-
based classification of hyperspectral data. IEEE J. Appl. Earth
Obs. Remote Sensing, 2014, 6, 2094–2107.
22. Kussul, N., Lavreniuk, M., Skakun, S. and Shelestov, A., Deep
learning classification of land cover and crop types using
remote sensing data. IEEE Geosci. Remote Sensing Lett., 2017,
doi:10.1109/LGRS.
23. Jacquemoud, S. et al., PROSPECT + SAIL models: areview of
use for vegetation characterization. Remote Sensing Environ.,
2009, 113, S56–S66.
24. Barker, D. M., Huang, W., Guo, Y. R., Bourgeois, A. J. and Xiao,
Q. N., A three-dimensional variational data assimilation system
for MM5: implementation and initial results. Mon. Weather Rev.,
2004, 132, 897–914.
25. Rouse, J. W., Has, R. H., Schell, J. A., Deering, D. W. and Harlan,
J. C., Monitoring the vernal advancement of retrodegradation of
natural vegetation, NASA/GSFC, Type III, Final report, Green-
belt, MD, 1974, p. 371; Rouse, J. W., Haas, R. S., Schell, J. A. and
Deering, D. W., Monitoring vegetation systems in the Great Plains
with ERTS. In Proceedings, 3rd Earth Resources Technology Sa-
tellite Symposium, 1973, vol. 1, pp. 48–623.
26. Peñuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J. and Field,
C. B., Reflectance indices associated with physiological changes
in nitrogen and water limited sunflower leaves. Remote Sensing
Environ., 1994, 48, 135–146.
27. Hardisky, M. A., Klemas, V. and Smart, R. M., The influences of
soil salinity, growth form, and leaf moisture on the spectral reflec-
tance of Spartina alterniflora canopies. Photogramm. Eng. Remote
Sensing, 1983, 49, 77–83.
28. Gao, B.-C., NDWI – a normalized difference water index for re-
mote sensing of vegetation liquid water from space. Remote Sens-
ing Environ., 1996, 58, 257–266.
29. Chen, D., Huang, J. and Jackson, T. T., Vegetation water content
estimation for corn and soybeans using spectral indices derived
from MODIS near- and short-wave infrared bands. Remote Sens-
ing Environ., 2005, 98, 225–236.
30. Ashourloo, D., Mobasheri, M. R. and Huete, A., Developing two
spectral disease indices for detection of wheat leaf rust (Puccinia
triticina). Remote Sensing, 2014, 6, 4723–4740.
31. Bolstad, P. V. and Lillesand, T. M., Semi-automated training
approaches for spectral class definition. Int. J. Remote Sensing,
1991, 13, 3157–3166.
32. Clark, M. L., Comparison of simulated hyperspectral HyspIRI and
multispectral Landsat 8 and Sentinel-2 imagery for multi-seasonal
1. Thenkabail, P. S., Enclona, E. A., Ashton, M. S. and Van Der
Meer, V., Accuracy assessments of hyperspectral waveband
performance for vegetation analysis applications. Remote Sensing
Environ., 2004, 91, 354–376.
2. Meerdink, S. K. et al., Linking seasonal foliar traits to VSWIR-
TIR spectroscopy across California ecosystems. Remote Sensing
Environ., 2016, 186, 322–338.
3. Gitelson, A., In Hyperspectral Remote Sensing of Vegetation (eds
Thenkabail, P. S., Lyon, G. J. and Huete, A.), CRC Press-Taylor
and Francis Group, Boca Raton, FL, USA, 2011, pp. 141–166.
4. Ozdogan, M. and Woodcock, C. E., Resolution dependent error in
remote sensing of cultivated areas. Remote Sensing Environ.,
2006, 103, 203–217.
5. Asner, G. P. and Martin, R. E., Spectral and chemical analysis of
tropical forests: scaling from leaf to canopy levels. Remote Sens-
ing Environ., 2008, 112, 3958–3970.
6. Jacquemoud, S., Baret, F., Andrieu, B., Danson, F. M. and
Jaggard, K., Extraction of vegetation biophysical parameters by
Inversion of the PROSPECT + SAIL models on sugar beet canopy
reflectance data. Application to TM and AVIRIS sensors. Remote
Sensing Environ., 1995, 52, 63–172.
7. Zarco-Tajada, P. J., Guillin-Climent, M. L., Hernandez-Clemente,
R. and Catalina, A., Estimating leaf carotenoid content in
vineyards using high resolution hyperspectral imaging acquired
from an unmanned aerial vehicle (UAV). Agric. For. Met., 2013,
17, 281–294.
8. Green, R. O. et al., Imaging spectroscopy and the airborne visi-
ble/infrared imaging spectrometer (AVIRIS). Remote Sensing
Environ., 1998, 65, 227–248.
9. Turner, D. P., Ollinger, S., Smith, M.-L., Krankina, O. and Gre-
gory, M., Scaling net primary production to a MODIS footprint in
support of Earth observing system product validation. Int. J.
Remote Sensing, 2004, 25, 1961–1979.
10. Bremner, J. M., Determination of nitrogen in soil using Kjeldahl
method. J. Agric. Sci., 1960, 55, 11–38.
11. Aguilar, M. A., Saldaña, M. M. and Aguilar, F. J., GeoEye-1 and
WorldView-2 pan-sharpened imagery for object-based classifica-
tion in urban environments. Int. J. Remote Sensing, 2012, 34,
2583–2606.
12. Yonezawa, C., Maximum likelihood classification combined with
spectral angle mapper algorithm for high resolution satellite
imagery. Int. J. Remote Sensing, 2007, 28, 3729–3737.
13. Paola, J. D. and Schowengerdt, R. A., A review and analysis of
backpropagationneural networks for classification of remotely
sensed multi-spectral imagery. Int. J. Remote Sensing, 1995, 16,
3033–3058.
14. Vapnik, V. N., The Nature of Statistical Learning Theory,
Springer Verlag, New York, USA, 1995.
15. Melgani, F. and Bruzzone, L., Classification of hyperspectral
remote sensing images with support vector machine. IEEE Trans.
Geosci. Remote Sensing, 2004, 8, 1778–1790.
16. Krebel, U., Pairwise classification and support vector machines. In
Advances in Kernel Methods-Support Vector Learning (eds
Schӧlkopf, B., Burges, C. J. C. and Smola, A. J.), The MIT Press,
Cambridge, MA, USA, 1999, pp. 255–268.
17. Green, A. A., Berman, M., Switzer, P. and Craig, M. D., A trans-
formation for ordering multispectral data in terms of image quality
with implications for noise removal. IEEE Trans. Geosci. Remote
Sensing, 1988, 26, 65–74.
18. Singh, A. and Harrison, A., Standardized principal components.
Int. J. Remote Sensing, 1985, 6, 883–896.
19. Friedl, M. A. and Brodley, C. E., Decision tree classification of
land cover from remotely sensed data. Remote Sensing Environ.,
1997, 61, 399–409.
20. Clark, R. N. et al., Imaging spectroscopy: earth and planetary re-
mote sensing with the USGS tetracorder and expert systems. J.
Geophys. Res., 2003, 108, 5131; doi:10.1029/2002JE001847.
21. Chen, Y., Lin, Z., Zhao, X., Wang, G. and Gu, Y., Deep learning-
based classification of hyperspectral data. IEEE J. Appl. Earth
Obs. Remote Sensing, 2014, 6, 2094–2107.
22. Kussul, N., Lavreniuk, M., Skakun, S. and Shelestov, A., Deep
learning classification of land cover and crop types using
remote sensing data. IEEE Geosci. Remote Sensing Lett., 2017,
doi:10.1109/LGRS.
23. Jacquemoud, S. et al., PROSPECT + SAIL models: areview of
use for vegetation characterization. Remote Sensing Environ.,
2009, 113, S56–S66.
24. Barker, D. M., Huang, W., Guo, Y. R., Bourgeois, A. J. and Xiao,
Q. N., A three-dimensional variational data assimilation system
for MM5: implementation and initial results. Mon. Weather Rev.,
2004, 132, 897–914.
25. Rouse, J. W., Has, R. H., Schell, J. A., Deering, D. W. and Harlan,
J. C., Monitoring the vernal advancement of retrodegradation of
natural vegetation, NASA/GSFC, Type III, Final report, Green-
belt, MD, 1974, p. 371; Rouse, J. W., Haas, R. S., Schell, J. A. and
Deering, D. W., Monitoring vegetation systems in the Great Plains
with ERTS. In Proceedings, 3rd Earth Resources Technology Sa-
tellite Symposium, 1973, vol. 1, pp. 48–623.
26. Peñuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J. and Field,
C. B., Reflectance indices associated with physiological changes
in nitrogen and water limited sunflower leaves. Remote Sensing
Environ., 1994, 48, 135–146.
27. Hardisky, M. A., Klemas, V. and Smart, R. M., The influences of
soil salinity, growth form, and leaf moisture on the spectral reflec-
tance of Spartina alterniflora canopies. Photogramm. Eng. Remote
Sensing, 1983, 49, 77–83.
28. Gao, B.-C., NDWI – a normalized difference water index for re-
mote sensing of vegetation liquid water from space. Remote Sens-
ing Environ., 1996, 58, 257–266.
29. Chen, D., Huang, J. and Jackson, T. T., Vegetation water content
estimation for corn and soybeans using spectral indices derived
from MODIS near- and short-wave infrared bands. Remote Sens-
ing Environ., 2005, 98, 225–236.
30. Ashourloo, D., Mobasheri, M. R. and Huete, A., Developing two
spectral disease indices for detection of wheat leaf rust (Puccinia
triticina). Remote Sensing, 2014, 6, 4723–4740.
31. Bolstad, P. V. and Lillesand, T. M., Semi-automated training
approaches for spectral class definition. Int. J. Remote Sensing,
1991, 13, 3157–3166.
32. Clark, M. L., Comparison of simulated hyperspectral HyspIRI and
multispectral Landsat 8 and Sentinel-2 imagery for multi-seasonal
1. Thenkabail, P. S., Enclona, E. A., Ashton, M. S. and Van Der
Meer, V., Accuracy assessments of hyperspectral waveband
performance for vegetation analysis applications. Remote Sensing
Environ., 2004, 91, 354–376.
2. Meerdink, S. K. et al., Linking seasonal foliar traits to VSWIR-
TIR spectroscopy across California ecosystems. Remote Sensing
Environ., 2016, 186, 322–338.
3. Gitelson, A., In Hyperspectral Remote Sensing of Vegetation (eds
Thenkabail, P. S., Lyon, G. J. and Huete, A.), CRC Press-Taylor
and Francis Group, Boca Raton, FL, USA, 2011, pp. 141–166.
4. Ozdogan, M. and Woodcock, C. E., Resolution dependent error in
remote sensing of cultivated areas. Remote Sensing Environ.,
2006, 103, 203–217.
5. Asner, G. P. and Martin, R. E., Spectral and chemical analysis of
tropical forests: scaling from leaf to canopy levels. Remote Sens-
ing Environ., 2008, 112, 3958–3970.
6. Jacquemoud, S., Baret, F., Andrieu, B., Danson, F. M. and
Jaggard, K., Extraction of vegetation biophysical parameters by
Inversion of the PROSPECT + SAIL models on sugar beet canopy
reflectance data. Application to TM and AVIRIS sensors. Remote
Sensing Environ., 1995, 52, 63–172.
7. Zarco-Tajada, P. J., Guillin-Climent, M. L., Hernandez-Clemente,
R. and Catalina, A., Estimating leaf carotenoid content in
vineyards using high resolution hyperspectral imaging acquired
from an unmanned aerial vehicle (UAV). Agric. For. Met., 2013,
17, 281–294.
8. Green, R. O. et al., Imaging spectroscopy and the airborne visi-
ble/infrared imaging spectrometer (AVIRIS). Remote Sensing
Environ., 1998, 65, 227–248.
9. Turner, D. P., Ollinger, S., Smith, M.-L., Krankina, O. and Gre-
gory, M., Scaling net primary production to a MODIS footprint in
support of Earth observing system product validation. Int. J.
Remote Sensing, 2004, 25, 1961–1979.
10. Bremner, J. M., Determination of nitrogen in soil using Kjeldahl
method. J. Agric. Sci., 1960, 55, 11–38.
11. Aguilar, M. A., Saldaña, M. M. and Aguilar, F. J., GeoEye-1 and
WorldView-2 pan-sharpened imagery for object-based classifica-
tion in urban environments. Int. J. Remote Sensing, 2012, 34,
2583–2606.
12. Yonezawa, C., Maximum likelihood classification combined with
spectral angle mapper algorithm for high resolution satellite
imagery. Int. J. Remote Sensing, 2007, 28, 3729–3737.
13. Paola, J. D. and Schowengerdt, R. A., A review and analysis of
backpropagationneural networks for classification of remotely
sensed multi-spectral imagery. Int. J. Remote Sensing, 1995, 16,
3033–3058.
14. Vapnik, V. N., The Nature of Statistical Learning Theory,
Springer Verlag, New York, USA, 1995.
15. Melgani, F. and Bruzzone, L., Classification of hyperspectral
remote sensing images with support vector machine. IEEE Trans.
Geosci. Remote Sensing, 2004, 8, 1778–1790.
16. Krebel, U., Pairwise classification and support vector machines. In
Advances in Kernel Methods-Support Vector Learning (eds
Schӧlkopf, B., Burges, C. J. C. and Smola, A. J.), The MIT Press,
Cambridge, MA, USA, 1999, pp. 255–268.
17. Green, A. A., Berman, M., Switzer, P. and Craig, M. D., A trans-
formation for ordering multispectral data in terms of image quality
with implications for noise removal. IEEE Trans. Geosci. Remote
Sensing, 1988, 26, 65–74.
18. Singh, A. and Harrison, A., Standardized principal components.
Int. J. Remote Sensing, 1985, 6, 883–896.
19. Friedl, M. A. and Brodley, C. E., Decision tree classification of
land cover from remotely sensed data. Remote Sensing Environ.,
1997, 61, 399–409.
20. Clark, R. N. et al., Imaging spectroscopy: earth and planetary re-
mote sensing with the USGS tetracorder and expert systems. J.
Geophys. Res., 2003, 108, 5131; doi:10.1029/2002JE001847.
21. Chen, Y., Lin, Z., Zhao, X., Wang, G. and Gu, Y., Deep learning-
based classification of hyperspectral data. IEEE J. Appl. Earth
Obs. Remote Sensing, 2014, 6, 2094–2107.
22. Kussul, N., Lavreniuk, M., Skakun, S. and Shelestov, A., Deep
learning classification of land cover and crop types using
remote sensing data. IEEE Geosci. Remote Sensing Lett., 2017,
doi:10.1109/LGRS.
23. Jacquemoud, S. et al., PROSPECT + SAIL models: areview of
use for vegetation characterization. Remote Sensing Environ.,
2009, 113, S56–S66.
24. Barker, D. M., Huang, W., Guo, Y. R., Bourgeois, A. J. and Xiao,
Q. N., A three-dimensional variational data assimilation system
for MM5: implementation and initial results. Mon. Weather Rev.,
2004, 132, 897–914.
25. Rouse, J. W., Has, R. H., Schell, J. A., Deering, D. W. and Harlan,
J. C., Monitoring the vernal advancement of retrodegradation of
natural vegetation, NASA/GSFC, Type III, Final report, Green-
belt, MD, 1974, p. 371; Rouse, J. W., Haas, R. S., Schell, J. A. and
Deering, D. W., Monitoring vegetation systems in the Great Plains
with ERTS. In Proceedings, 3rd Earth Resources Technology Sa-
tellite Symposium, 1973, vol. 1, pp. 48–623.
26. Peñuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J. and Field,
C. B., Reflectance indices associated with physiological changes
in nitrogen and water limited sunflower leaves. Remote Sensing
Environ., 1994, 48, 135–146.
27. Hardisky, M. A., Klemas, V. and Smart, R. M., The influences of
soil salinity, growth form, and leaf moisture on the spectral reflec-
tance of Spartina alterniflora canopies. Photogramm. Eng. Remote
Sensing, 1983, 49, 77–83.
28. Gao, B.-C., NDWI – a normalized difference water index for re-
mote sensing of vegetation liquid water from space. Remote Sens-
ing Environ., 1996, 58, 257–266.
29. Chen, D., Huang, J. and Jackson, T. T., Vegetation water content
estimation for corn and soybeans using spectral indices derived
from MODIS near- and short-wave infrared bands. Remote Sens-
ing Environ., 2005, 98, 225–236.
30. Ashourloo, D., Mobasheri, M. R. and Huete, A., Developing two
spectral disease indices for detection of wheat leaf rust (Puccinia
triticina). Remote Sensing, 2014, 6, 4723–4740.
31. Bolstad, P. V. and Lillesand, T. M., Semi-automated training
approaches for spectral class definition. Int. J. Remote Sensing,
1991, 13, 3157–3166.
32. Clark, M. L., Comparison of simulated hyperspectral HyspIRI and
multispectral Landsat 8 and Sentinel-2 imagery for multi-seasonal
1. Thenkabail, P. S., Enclona, E. A., Ashton, M. S. and Van Der
Meer, V., Accuracy assessments of hyperspectral waveband
performance for vegetation analysis applications. Remote Sensing
Environ., 2004, 91, 354–376.
2. Meerdink, S. K. et al., Linking seasonal foliar traits to VSWIR-
TIR spectroscopy across California ecosystems. Remote Sensing
Environ., 2016, 186, 322–338.
3. Gitelson, A., In Hyperspectral Remote Sensing of Vegetation (eds
Thenkabail, P. S., Lyon, G. J. and Huete, A.), CRC Press-Taylor
and Francis Group, Boca Raton, FL, USA, 2011, pp. 141–166.
4. Ozdogan, M. and Woodcock, C. E., Resolution dependent error in
remote sensing of cultivated areas. Remote Sensing Environ.,
2006, 103, 203–217.
5. Asner, G. P. and Martin, R. E., Spectral and chemical analysis of
tropical forests: scaling from leaf to canopy levels. Remote Sens-
ing Environ., 2008, 112, 3958–3970.
6. Jacquemoud, S., Baret, F., Andrieu, B., Danson, F. M. and
Jaggard, K., Extraction of vegetation biophysical parameters by
Inversion of the PROSPECT + SAIL models on sugar beet canopy
reflectance data. Application to TM and AVIRIS sensors. Remote
Sensing Environ., 1995, 52, 63–172.
7. Zarco-Tajada, P. J., Guillin-Climent, M. L., Hernandez-Clemente,
R. and Catalina, A., Estimating leaf carotenoid content in
vineyards using high resolution hyperspectral imaging acquired
from an unmanned aerial vehicle (UAV). Agric. For. Met., 2013,
17, 281–294.
8. Green, R. O. et al., Imaging spectroscopy and the airborne visi-
ble/infrared imaging spectrometer (AVIRIS). Remote Sensing
Environ., 1998, 65, 227–248.
9. Turner, D. P., Ollinger, S., Smith, M.-L., Krankina, O. and Gre-
gory, M., Scaling net primary production to a MODIS footprint in
support of Earth observing system product validation. Int. J.
Remote Sensing, 2004, 25, 1961–1979.
10. Bremner, J. M., Determination of nitrogen in soil using Kjeldahl
method. J. Agric. Sci., 1960, 55, 11–38.
11. Aguilar, M. A., Saldaña, M. M. and Aguilar, F. J., GeoEye-1 and
WorldView-2 pan-sharpened imagery for object-based classifica-
tion in urban environments. Int. J. Remote Sensing, 2012, 34,
2583–2606.
12. Yonezawa, C., Maximum likelihood classification combined with
spectral angle mapper algorithm for high resolution satellite
imagery. Int. J. Remote Sensing, 2007, 28, 3729–3737.
13. Paola, J. D. and Schowengerdt, R. A., A review and analysis of
backpropagationneural networks for classification of remotely
sensed multi-spectral imagery. Int. J. Remote Sensing, 1995, 16,
3033–3058.
14. Vapnik, V. N., The Nature of Statistical Learning Theory,
Springer Verlag, New York, USA, 1995.
15. Melgani, F. and Bruzzone, L., Classification of hyperspectral
remote sensing images with support vector machine. IEEE Trans.
Geosci. Remote Sensing, 2004, 8, 1778–1790.
16. Krebel, U., Pairwise classification and support vector machines. In
Advances in Kernel Methods-Support Vector Learning (eds
Schӧlkopf, B., Burges, C. J. C. and Smola, A. J.), The MIT Press,
Cambridge, MA, USA, 1999, pp. 255–268.
17. Green, A. A., Berman, M., Switzer, P. and Craig, M. D., A trans-
formation for ordering multispectral data in terms of image quality
with implications for noise removal. IEEE Trans. Geosci. Remote
Sensing, 1988, 26, 65–74.
18. Singh, A. and Harrison, A., Standardized principal components.
Int. J. Remote Sensing, 1985, 6, 883–896.
19. Friedl, M. A. and Brodley, C. E., Decision tree classification of
land cover from remotely sensed data. Remote Sensing Environ.,
1997, 61, 399–409.
20. Clark, R. N. et al., Imaging spectroscopy: earth and planetary re-
mote sensing with the USGS tetracorder and expert systems. J.
Geophys. Res., 2003, 108, 5131; doi:10.1029/2002JE001847.
21. Chen, Y., Lin, Z., Zhao, X., Wang, G. and Gu, Y., Deep learning-
based classification of hyperspectral data. IEEE J. Appl. Earth
Obs. Remote Sensing, 2014, 6, 2094–2107.
22. Kussul, N., Lavreniuk, M., Skakun, S. and Shelestov, A., Deep
learning classification of land cover and crop types using
remote sensing data. IEEE Geosci. Remote Sensing Lett., 2017,
doi:10.1109/LGRS.
23. Jacquemoud, S. et al., PROSPECT + SAIL models: a review of
use for vegetation characterization. Remote Sensing Environ.,
2009, 113, S56–S66.
24. Barker, D. M., Huang, W., Guo, Y. R., Bourgeois, A. J. and Xiao,
Q. N., A three-dimensional variational data assimilation system
for MM5: implementation and initial results. Mon. Weather Rev.,
2004, 132, 897–914.
25. Rouse, J. W., Has, R. H., Schell, J. A., Deering, D. W. and Harlan,
J. C., Monitoring the vernal advancement of retrodegradation of
natural vegetation, NASA/GSFC, Type III, Final report, Green-
belt, MD, 1974, p. 371; Rouse, J. W., Haas, R. S., Schell, J. A. and
Deering, D. W., Monitoring vegetation systems in the Great Plains
with ERTS. In Proceedings, 3rd Earth Resources Technology Sa-
tellite Symposium, 1973, vol. 1, pp. 48–623.
26. Peñuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J. and Field,
C. B., Reflectance indices associated with physiological changes
in nitrogen and water limited sunflower leaves. Remote Sensing
Environ., 1994, 48, 135–146.
27. Hardisky, M. A., Klemas, V. and Smart, R. M., The influences of
soil salinity, growth form, and leaf moisture on the spectral reflec-
tance of Spartina alterniflora canopies. Photogramm. Eng. Remote
Sensing, 1983, 49, 77–83.
28. Gao, B.-C., NDWI – a normalized difference water index for re-
mote sensing of vegetation liquid water from space. Remote Sens-
ing Environ., 1996, 58, 257–266.
29. Chen, D., Huang, J. and Jackson, T. T., Vegetation water content
estimation for corn and soybeans using spectral indices derived
from MODIS near- and short-wave infrared bands. Remote Sens-
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