Uncertainties in fPAR estimation of grass canopies under different stress situations and differences in architecture

International Journal of Remote Sensing

Volumn 31, Issue 15, 2010, Pages 4095-4109

  Cristiano, P M ^a   Posse, G ^a   Di Bella, C M ^a   Jaimes, F R ^a  

^a INSTITUTO NACIONAL DE TECNOLOGÍA AGROPECUARIA INTA   (Argentina)

Author keywords

[No Author keywords available]

Indexed keywords

ANTENNAS; ECOSYSTEMS; FORESTRY; LIGHT REFLECTION; NITROGEN; NUTRIENTS; REMOTE SENSING; SEED; VEGETATION;

CANOPY ARCHITECTURES; NET PRIMARY PRODUCTIVITY; NITROGEN AVAILABILITY; NORMALIZED DIFFERENCE VEGETATION INDEX; NUTRIENT DEFICIENCY; NUTRIENT TREATMENTS; PHOTOSYNTHETIC ACTIVE RADIATIONS; VISIBLE ATMOSPHERICALLY RESISTANT INDICES;

WATER TREATMENT;

EID: 77956637808     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431160903229192     Document Type: Article

References (52)

1. Asrar, G., Kanemasu, E.T., Jackson, R.D. and Pinter, P.J., 1985, Estimation of total aboveground phytomass production using remotely sensed data. Remote Sensing of Environment, 17, pp. 211-220.
2. Awaya, Y., Kodani, E., Tanaka, K., Liu, I., Zhuang, D. and Meng, Y., 2004, Estimation of the global net primary productivity using NOAA images and meteorological data: changes between 1988 and 1993. International Journal of Remote Sensing, 25, pp. 1597-1613.
3. Cho, M.A. and Skidmore, A.K., 2006, A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sensing of Environment, 101, pp. 181-193.
4. Choudhury, B.J., 1987, Relationships between vegetation indices, radiation absorption and net photosynthesis evaluated by sensitivity analysis. Remote Sensing of Environment, 22, pp. 209-233.
5. Collino, D.J., Dardanelli, J.L., Sereno, R. and Racca, R.W., 2001, Physiological responses of Argentine peanut varieties to water stress. Light interception, light use efficiency and partitioning of assimilates. Field Crops Research, 70, 177-184.
6. Colwell, J.E., 1974, Vegetation canopy reflectance. Remote Sensing of Environment, 3, pp. 175-183.
7. Daughtry, C.S.T., Gallo, K.P. and Bauer, M.E., 1983, Spectral estimates of solar radiation intercepted by corn canopies. Agronomy Journal, 75, pp. 527-531.
8. Dawson, T.P. and Curran, P.J., 1998, A new technique for interpolating red edge position. International Journal of Remote Sensing, 19, pp. 2133-2139.
9. Di Bella, C., Rebella, C. and Paruelo, J., 2000, Evapotranspiration estimates using NOAA AVHRR imagery in the Pampa Region. International Journal of Remote Sensing, 21, pp. 791-797.
10. Di Bella, C., Paruelo, J., Becerra, J., Bacour, C. and Baret, F., 2004, Effect of senescent leaves on NDVI-based estimates of fPAR: experimental and modelling evidences. International Journal of Remote Sensing, 25, pp. 5415-5427.
11. Evans, J.R. and Edwards, E.J., 2001, Nutrient uptake and use in plant growth. In Net Ecosystem Exchange CRC Workshop Proceedings, pp. 75-81.
12. Fensholt, R., Sandholt, I. and Schultz Rasmussen, M., 2004, Evaluation of MODIS LAI, fPAR and the relation between fPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sensing of Environment, 91, pp. 490-507.
13. Gamon, J.A., Field, C.B., Goulden, M.L., Griffin, K.L., Hartley, A.E., Joel, G. and Valentini, R., 1995, Relationships between NDVI, canopy structure and photosynthesis in three Californian vegetation types. Ecological Applications, 5, pp. 28-41.
14. Gitelson, A., Kaufman, Y. and Merzlyak, M., 1996, Use of a Green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58, pp. 289-298.
15. Gitelson, A. and Merzlyak, M., 1998, Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18, pp. 291-298.
16. Gitelson, A.A., Kaufman, Y.J., Stark, R. and Rundquist, D., 2002, Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80, pp. 76-87.
17. Goel, N.S. and Qin, W., 1996, From leaf to scene: scaling problems in remote sensing of vegetation. Geoscience and Remote Sensing Symposium. IGARSS '96. Remote Sensing for a Sustainable Future International, 1, pp. 526-528.
18. Goward, S.N. and Huemmrich, K.F., 1992, Vegetation canopy PAR absorbance and the Normalize Difference Vegetation Index: An assessment using the SAIL model. Remote Sensing of Environment, 39, pp. 119-140.
19. Guyot, G. and Baret, F., 1988, Utilisation de la haute resolution spectral pour suivre ĺetat des couverts vegetaux. In Proceedings of 4th International Symposium on Spectral Signatures of Objects in Remote Sensing, European Space Agency, Noordwijk, ESA SP-287, pp. 279-286.
20. Huete, A.R., 1988, A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, pp. 295-309.
21. Huete, A., Justice, C. and Van Leeuwen, W., 1999, MODIS vegetation index (MOD13). Algorithm Theoretical Basis Document (ATBD). http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf.
22. Jackson, R.D. and Ezra, C.E., 1985, Spectral response of cotton to suddenly induced water stress. International Journal of Remote Sensing, 6, pp. 177-185.
23. Joel, G., Gamon, J.A. and Field, C.B., 1997, Production efficiency in sunflower: the role of water and nitrogen stress. Remote Sensing of Environment, 62, pp. 176-188.
24. Kerr, J.T. and Ostrovsky, M., 2003, From space to species: ecological applications for remote sensing. Trends in Ecology and Evolution, 18, pp. 299-305.
25. Landsberg, J.J., 1986, Physiological Ecology of Forest Production, p. 198 (London: Academic Press).
26. Leamer, R.W., Noriega, J.R. and Wiegand, C.L., 1978, Seasonal changes in reflectance of two wheat cultivars. Agronomical Journal, 70, pp. 113-118.
27. Monteith, J., 1972, Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology, 9, pp. 747-66.
28. Monteith, J.L., 1977, Climate and efficiency of crop production in Britain. Philosophical Transactions of the Royal Society of London, Series B, 281, pp. 277-294.
29. Myneni, R.B. and Williams, D.L., 1994, On the relationship between FPAR and NDVI. Remote Sensing of Environment, 49, pp. 200-211.
30. Nagendra, H., 2001, Using remote sensing to assess biodiversity. International Journal of Remote Sensing, 22, pp. 2377-2400.
31. Oesterheld, M., Di Bella, C. and Kerdiles, H., 1998, Relation between NOAAA/VHRR satellite data and stocking rate of rangelands. Ecological Applications, 8, pp. 207-212.
32. Olofsson, P. and Eklundh, L., 2007, Estimation of absorbed PAR across Scandinavia from satellite measurements. Part II: Modeling and evaluating the fractional absorption. Remote Sensing of Environment 110, pp. 240-251.
33. Oppelt, N. and Mauser, W., 2004, Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data. International Journal of Remote Sensing, 25, pp. 145-156.
34. Paruelo, J.M., Epstein, H.E., Laurenroth, W.K. and Burke, I.C., 1997, ANPP estimates from NDVI for the central grassland region of the United States. Ecology, 78, pp. 3953-3958.
35. Paruelo, J.M., Lauenroth, W.K., Burke, I.C. and Sala, O.E., 1999, Grassland precipitationuse efficiency varies across a resource gradient. Ecosystems, 2, pp. 64-68.
36. Posse, G. and Cingolani, A.M., 2000, Environmental controls on NDVI and sheep production in the Tierra del Fuego steppe of Argentina. Applied Vegetation Science, 3, pp. 253-260.
37. Quattrochi, D.A. and Pelletier, R., 1991, Remote sensing for analysis of landscapes: an introduction. In Quantitative Methods in Landscape Ecology, M.G. Turner and R.H. Gardner (Eds), pp. 51-76 (New York: Springer).
38. R 2.4.1 Development Core Team, 2006, R: A language and environment for statistical computing. RFoundation for Statistical Computing, Vienna, Austria. Available online at: http://www.R-project.org (accessed 10 December 2006).
39. Rondeaux, G., Steven, M. and Baret, F., 1996, Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, pp. 95-107.
40. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. and Harlan, J.C., 1974, Monitoring the vernal advancement of retrogradation of natural vegetation. NASA/GSFC Type III Final report. Maryland, p. 371.
41. Ruimy, A., Saugier, B. and Dedieu, G., 1994, Methodology for the estimation of terrestrial net primary production from remote sensed data. Journal of Geophysical Research, 99, pp. 5263-5283.
42. Scholander, P.F., Hammel, H.T., Bradstreeet, E.D. and Hemmingsen, E.A., 1965. Sap pressure in vascular plants. Science, 48, pp. 339-346.
43. Sellers, P.J., 1985, Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing, 6, pp. 1335-1372.
44. Serrano, L., Filella, I. and Penñ uelas, J., 2000, Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Science, 40, pp. 723-731.
45. Tucker, C.J., 1980, Remote sensing of leaf water content in the near infrared. Remote Sensing of Environment, 10, pp. 23-32.
46. Tucker, C.J. and Sellers, P.J., 1986, Satellite remote sensing of primary production. International Journal of Remote Sensing, 7, pp. 1395-1416.
47. Turner, D.P., Gower, S.T., Cohen, W.B., Gregory, M. and Maiesperger, T.K., 2002, Effect of spatial variability in light use efficiency on satellite-based NPP monitoring. Remote Sensing of Environment, 80, pp. 397-405.
48. Vaesen, K., Gilliams, S., Nackaerts, K. and Coppin, P., 2001, Ground-measured spectral signatures as indicators of ground cover and leaf area index: the case of paddy rice. Field Crops Research, 69, pp. 13-25.
49. Vinñ a, A. and Gitelson, A.A., 2005. New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops. Geophysical Research Letters, 32, p. L17403.
50. Weber, D.C. and Skillings, J.H., 2000, A First Course in the Design of Experiments (Boca Raton, FL: CRC Press).
51. Wylie, B.K., Mannel, S., Meyer, D.J. and Tieszen, L.L., 2002, Satellite mapping of surface biophysical parameters at the biome scale over North American grasslands. A case study. Remote Sensing of Environment, 79, pp. 266-278.
52. Xavier, A.C. and Vettorazzi, C.A., 2004, Mapping leaf area index through spectral vegetation indices in a subtropical watershed. International Journal of Remote Sensing, 25, pp. 1661-1672.