Leaf nitrogen estimation in potato based on spectral data and on simulated bands of the VENμS satellite

Precision Agriculture

Volumn 11, Issue 5, 2010, Pages 520-537

  Cohen, Y ^a   Alchanatis, V ^a   Zusman, Y ^a,b   Dar, Z ^c   Bonfil, D J ^a   Karnieli, A ^d   Zilberman, A ^c   Moulin, A ^e   Ostrovsky, V ^a   Levi, A ^a   Brikman, R ^a   Shenker, M ^b  

^a VOLCANI CENTER   (Israel)

^b HEBREW UNIVERSITY OF JERUSALEM   (Israel)

^c EXTENSION SERVICE   (Israel)

^d BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

^e BRANDON RESEARCH CENTRE   (Canada)

Author keywords

Nitrogen; Partial least squares regression (PLSR); Potato; Spectral reflectance; Transformed chlorophyll absorption in reflectance index (TCARI); VEN S satellite

Indexed keywords

ACCURACY ASSESSMENT; CHLOROPHYLL; DATA SET; FERTILIZER APPLICATION; LEAF; NITROGEN; NUMERICAL MODEL; PHYTOCHEMISTRY; POTATO; REGRESSION ANALYSIS; SATELLITE; SATELLITE IMAGERY; SPECTRAL ANALYSIS; SPECTRAL REFLECTANCE;

ISRAEL;

SOLANUM TUBEROSUM;

EID: 77956679959     PISSN: 13852256     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11119-009-9147-8     Document Type: Article

References (37)

1. Al-Abbas, A. H., Barr, R., Hall, J. D., Crane, F. L., & Baumgardner, M. F. (1974). Spectra of normal and nutrient-deficient maize leaves. Agronomy Journal, 66, 16-20.
2. Alchanatis, V., Schmilovitch, Z., & Meron, M. (2005). In-field assessment of single leaf nitrogen status by spectral reflectance measurements. Precision Agriculture, 6, 25-39.
3. Alva, K. A. (2008). Water management and water uptake efficiency by potatoes: A review. Archives of Agronomy and Soil Science, 54, 53-68.
4. Bonfil, D. J., Karnieli, A., Raz, M., Mufradi, I., Asido, S., Egozi, H., et al. (2005). Rapid assessing of water and nitrogen status in wheat flag leaves. Journal of Food Agriculture and Environment, 3, 148-153.
5. Botha, E. J., Zebarth, B. J., & Leblon, B. (2006). Non-destructive estimation of potato leaf chlorophyll and protein contents from hyperspectral measurements using the PROSPECT radiative transfer model. Canadian Journal of Plant Science, 86, 279-291.
6. Coops, N. C., Smith, M. L., Martin, M. E., & Ollinger, S. V. (2003). Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 41, 1338-1346.
7. Dar, Z. (2002). Potato crop protocol. Bet Dagan, Israel: Ministry of Agriculture Extension Service. in Hebrew.
8. Daughtry, C. S. T., Walthall, C. L., Kim, M. S., Brown de Colstoun, E., & McMurtrey, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of the Environment, 74, 229-239.
9. Errebhi, M., Rosen, C. J., Gupta, S. C., & Birong, D. E. (1998). Potato yield response and nitrate leaching as influenced by nitrogen management. Agronomy Journal, 90, 10-15.
10. Ferguson, R. B., Hergert, G. W., Schepers, J. S., Gotway, C. A., Cahoon, J. E., & Peterson, T. A. (2002). Site-specific nitrogen management of irrigated maize: Yield and soil residual nitrate effects. Soil Science Society of America Journal, 66, 544-553.
11. Gates, D. M., Keegan, H. J., Schleter, J. C., & Weidner, V. R. (1965). Spectral properties of plants. Applied Optics, 4, 11-20.
12. Gitelson, A. A., Gritz, Y., & Merzlyak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160, 271-282.
13. Gitelson, A. A., & Merzlyak, M. N. (1997). Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18, 2691-2697.
14. Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. B. (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.
15. Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81, 416-426.
16. Herrmann, I., Karnieli, A., Bonfil, D. J., Cohen, Y., and Alchanatis, V. (2009). SWIR-based spectral indices for assessing nitrogen content in potato fields. International Journal of Remote Sensing, (In press).
17. Hu, B. X., Qian, S. E., Haboudane, D., Miller, J. R., Hollinger, A. B., Tremblay, N., et al. (2004). Retrieval of crop chlorophyll content and leaf area index from decompressed hyperspectral data: The effects of data compression. Remote Sensing of Environment, 92, 139-152.
18. Jain, N., Ray, S. S., Singh, J. P., & Panigrahy, S. (2007). Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop. Precision Agriculture, 8, 225-239.
19. Meyer, R. D., & Marcum, D. B. (1998). Potato yield, petiole nitrogen, and soil nitrogen response to water and nitrogen. Agronomy Journal, 90, 420-429.
20. National Potato Council (NPC). (2006). Potato statistical yearbook, 2006-2007. Washington, DC: National Potato Council.
21. Ollinger, S. V., Smith, M. L., Martin, M. E., Hallett, R. A., Goodale, C. L., & Aber, J. D. (2002). Regional variation in foliar chemistry and N cycling among forests of diverse history and composition. Ecology, 83, 339-355.
22. Pimstein, A., Karnieli, A., & Bonfil, D. J. (2007). Wheat and maize monitoring based on ground spectral measurements and multivariate data analysis. Journal of Applied Remote Sensing, 1, 013530 (On-line journal).
23. Rondeaux, G., Steven, M. D., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, 95-107.
24. Shenk, J., & Westerhaus, M. (1991). Population structuring of near infrared spectra and modified partial least squares regression. Crop Science, 31, 1548-1555.
25. Shock, C. C., Pereira, A. B., & Eldredge, E. B. (2007). Irrigation best management practices for potato. American Journal of Potato Research, 84, 29-37.
26. Smith, M. L., Ollinger, S. V., Martin, M. E., Aber, J. D., Hallett, R. A., & Goodale, C. L. (2002). Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen. Ecological Applications, 12, 1286-1302.
27. Strachan, I. B., Pattey, E., & Boisvert, J. B. (2002). Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Remote Sensing of Environment, 80, 213-224.
28. Thomas, J. R., & Gausman, H. W. (1977). Leaf reflectance vs. leaf chlorophyll and carotenoid concentrations for eight crops. Agronomy Journal, 69, 799-802.
29. Townsend, P. A., Foster, J. R., Chastain, R. A., & Currie, W. S. (2003). Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the central Appalachian Mountains using hyperion and AVIRIS. IEEE Transactions on Geoscience and Remote Sensing, 41, 1347-1354.
30. Tso, B., & Mather, P. (2001). Classification methods for remotely sensed data (356 pp). Boca Raton, FL: CRC.
31. Van-Alphen, B. J., & Stoorvogel, J. J. (2000). A functional approach to soil characterization in support of precision agriculture. Soil Science Society of America Journal, 64, 1706-1713.
32. Vincini, M., & Frazzi, E. (2009). Sensitivity of narrow and broad-band vegetation indices to leaf chlorophyll concentration in planophile crops canopies. In E. J. van Henten, D. Goense, & C. Lokhorst (Eds.), Precision agriculture '09 (pp. 39-45). Wageningen: Wageningen Academic Publishers.
33. Vincini, M., Frazzi, E., & D'Alessio, P. (2008). A broad-band leaf chlorophyll vegetation index at the canopy scale. Precision Agriculture, 9, 303-319.
34. Westermann, D. T., & Kleinkopf, G. E. (1985). Nitrogen requirements of potatoes. Agronomy Journal, 77, 616-621.
35. Zakaluk, R., & Ranjan, R. S. (2007). Artificial neural network modelling of leaf water potential for potatoes using RGB digital images: a greenhouse study. Potato Research, 49, 255-272.
36. Zarco-Tejada, P. J., Berjon, A., Lopez-Lozano, R., Miller, J. R., Martin, P., Cachorro, V., et al. (2005). Assessing vineyard condition with hyperspectral indices: leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sensing of Environment, 99, 271-287.
37. Zebarth, B. J., & Rosen, C. J. (2007). Research perspective on nitrogen BMP development for potato. American Journal of Potato Research, 84, 3-18.