Using spectral distance, spectral angle and plant abundance derived from hyperspectral imagery to characterize crop yield variation

Precision Agriculture

Volumn 13, Issue 1, 2012, Pages 62-75

  Yang, Chenghai ^a   Everitt, James H ^a  

^a KIKA DE LA GARZA SUBTROPICAL AGRICULTURAL RESEARCH CENTER   (United States)

Author keywords

Hyperspectral imagery; Plant abundance; Spectral angle; Spectral distance; Spectral unmixing; Vegetation index; Yield

Indexed keywords

ABUNDANCE; CORRELATION; CROP YIELD; GROWTH RATE; INFRARED IMAGERY; REMOTE SENSING; SORGHUM; SPECTRAL ANALYSIS; SPECTRAL REFLECTANCE; VEGETATION INDEX; WAVELENGTH;

SORGHUM BICOLOR BICOLOR;

EID: 84862965436     PISSN: 13852256     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11119-011-9248-z     Document Type: Article

References (30)

1. Adams, J. B., Smith, M. O., & Johnson, P. E. (1986). Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 site. Journal of Geophysical Research, 91, 8098-8112.
2. Baret, F., & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35, 61-173.
3. Bateson, A., & Curtiss, B. (1996). A method for manual endmember and spectral unmixing selection. Remote Sensing of Environment, 55, 229-243.
4. Broge, N. H., & Leblanc, E. (2000). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76, 156-172.
5. Campbell, J. B. (2002). Introduction to remote sensing (3rd ed.). New York: The Guilford Press.
6. Chen, J. (1996). Evaluation of vegetation indices and modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 22, 229-242.
7. Daughtry, C. S. T., Walthall, C. L., Kim, M. S., Brown de Colstoun, E., & McMurtrey, J. E., I. I. I. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, 229-239.
8. Dennison, P. E., Halligan, K. Q., & Roberts, D. A. (2004). A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper. Remote Sensing of Environment, 93, 359-367.
9. Franke, J., Roberts, D. A., Halligan, K., & Menz, G. (2009). Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of hyperspectral imagery for urban environments. Remote Sensing of Environment, 113, 1712-1723.
10. Garcia-Haro, F. J., Gilabert, M. A., & Melia, J. (1996). Linear spectral mixture modelling to estimate vegetation amount from optical spectral data. International Journal of Remote Sensing, 17(17), 3373-3400.
11. Gitelson, A. A., Kaufman, Y., & Merzlyak, M. N. (1996). Use of green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58, 289-298.
12. Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90, 337-352.
13. Huete, A. R. (1988). A soil adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295-309.
14. Jordan, C. F. (1969). Derivation of leaf area index from quality of light on the forest floor. Ecology, 50(4), 663-666.
15. Kruse, F. A., Lefkoff, A. B., Boardman, J. W., Heidebrecht, K. B., Shapiro, A. T., Barloon, J. P., et al. (1993). The spectral image processing system (SIPS): Interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment, 44, 145-163.
16. Moran, M. S., Maas, S. J., & Pinter, P. J., Jr. (1995). Combining remote sensing and modeling for estimating surface evaporation and biomass production. Remote Sensing of Environment, 12, 335-353.
17. Qi, J., Chehbouni, A., Huete, A. R., Keer, Y. H., & Sorooshian, S. (1994). A modified soil vegetation adjusted index. Remote Sensing of Environment, 48, 119-126.
18. Ray, S. S., Das, G., Singh, J. P., & Panigrahy, S. (2006). Evaluation of hyperspectral indices for LAI estimation and discrimination of potato crop under different irrigation treatments. International Journal of Remote Sensing, 27(24), 5373-5387.
19. Rougean, J.-L., & Breon, F. M. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51, 375-384.
20. Rouse, J. W., Haas, R. H., Shell, J. A., Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of 3rd ERTS symposium (vol. 1, pp. 309-317). NASA SP-351. Washington, D. C.: U. S. Government Printing Office.
21. Thenkabail, P. S., Smith, R. B., & Pauw, E. D. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71, 158-182.
22. Wiegand, C. L., Richardson, A. J., Escobar, D. E., & Gerbermann, A. H. (1991). Vegetation indices in crop assessments. Remote Sensing of Environment, 35, 105-119.
23. Wu, C., Han, X., Niu, Z., & Dong, J. (2010). An evaluation of EO-1 hyperspectral Hyperion data for chlorophyll content and leaf area index estimation. International Journal of Remote Sensing, 31(4), 1079-1086.
24. Yang, C., & Anderson, G. L. (1999). Airborne videography to identify spatial plant growth variability for grain sorghum. Precision Agriculture, 1(1), 67-79.
25. Yang, C., & Everitt, J. H. (2002). Relationships between yield monitor data and airborne multidate multispectral digital imagery for grain sorghum. Precision Agriculture, 3(4), 373-388.
26. Yang, C., Everitt, J. H., & Bradford, J. M. (2004). Airborne hyperspectral imagery and yield monitor data for estimating grain sorghum yield variability. Transactions of the ASAE, 47(3), 915-924.
27. Yang, C., Everitt, J. H., & Bradford, J. M. (2007). Airborne hyperspectral imagery and linear spectral unmixing for mapping variation in crop yield. Precision Agriculture, 8(6), 279-296.
28. Yang, C., Everitt, J. H., & Bradford, J. M. (2008). Yield estimation from hyperspectral imagery using spectral angle mapper (SAM). Transactions of the ASABE, 51(2), 729-737.
29. Yang, C., Everitt, J. H., Davis, M. R., & Mao, C. (2003). A CCD camera-based hyperspectral imaging system for stationary and airborne applications. Geocarto International, 18(2), 71-80.
30. Zarco-Tejada, P. J., Ustin, S. L., & Whiting, M. L. (2005). Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agronomy Journal, 97, 641-653.