REMOTE SENSING AS AN INDIRECT WAY TO ESTIMATE BIOPHYSICAL AND BIOCHEMICAL PROPERTIES OF BEANS CROP | ||||
| Misr Journal of Agricultural Engineering | ||||
| Article 15, Volume 31, Issue 1, January 2014, Page 285-298 PDF (709.71 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjae.2014.100002 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Adel H. Elmetwalli | ||||
| Assi. Prof., Agric. Eng. Dept., Fac. of Agric., Tanta Univ., Egypt. | ||||
| Abstract | ||||
| Non-destructive monitoring of agricultural crops becomes more important to improve crop productivity. In site specific management, in-situ remotely sensed data is of significant importance for quantifying nitrogen deficiency and salinity stress effects on crops. In the reported research, the visible and near infrared portions of the electromagnetic spectrum were used to derive vegetation indices sensitive to nitrogen deficiency and salinity stress in beans (Phaseolus Vulgaris, L). Four nitrogen fertilization rates (0, 30, 60 and 100 kg/ha) and three water salinity levels (1.5, 3 and 5 dS/m) were used to subject plants to both stressors. Reflectance measurements were collected from beans plants under artificial illumination conditions at different growth stages and used to calculate 45 commonly used vegetation indices for predicting beans properties. Strong significant correlations between beans properties and different vegetation indices were observed. Crededge and R750/R700 ratio were found to be the optimum indices for predicting beans chlorophyll content (r = 0.657). R710/R760 ratio was also found to be the optimum index for predicting beans biomass (r = -0.582). PSNDb was found to be sensitive to beans grain yield (r > 0.595). The correlations with grain yield were found to be strongest at the R6 growth stage. | ||||
| Keywords | ||||
| beans; remote; sensing; nitrogen; salinity; reflectance | ||||
| References | ||||
|
Bao, H. and Li, Yinxin (2010). Effect of stage-specific saline irrigation on greenhouse tomato production. Irrigation science, 28: 421-430.
Eitel, J. U. H.; Long, D.S.; Gessler, P.E. and Hunt, E.R. (2008). Combined spectral index to improve ground based estimates of nitrogen status in dryland wheat. Agronomy J., 100: 1694-1702.
Elmetwalli, A.H. (2008). Remote sensing as a precision farming tool in the Nile Valley, Egypt.
Elmetwalli, A.H. Tyler, A.N., Hunter, P.D. and Salt, C.A. (2012). Detecting and distinguishing moisture and salinity induced stress in wheat and maize through in situ spectroradiometery measurements. Remote sensing Letters, 3(5): 363-372.
Galvao, L.S.; Roberts, D.A.; Formaggio, A.R.; Numata, I. and Breuing, F.M. (2009). View angle effects on the discrimination of soybean varieties and on the relationship between the vegetation indices and yield using off nadir hyperion data. Remote Sensing of Environment, 113: 846-856.
Kirda, C.; Cetin, M.; Dasgan, Y.; Topcu, S.; Kaman, H.; Ekici, B.; Derici, M.I. and Ozguven, A.I. (2004). Yield response of greenhouse grown tomato to partial root drying and conventional deficit irrigation. Agricultural Water Management, 69: 191-201.
Lichtenthaler, H. K. (1987). Chlorophylls and carotenoids: Pigments of photosynthetic biomembranes. Methods in Enzymology 148: 349-382. Monteiro, P. F. C.; Filho, R. A.; Xavier, A. C. and Monteiro, R. O. C. (2012). Assessing biophysical variable parameters of bean crop with hyperspectral measurements. Scientia Agricola, 69(2): 87-94.
Ndegwa, A. M.; Muchui, M. N.; Wachiuri, S. M. and Kimamira, J. N. (2006). Evaluation of snap bean varieties for adaptability and pod quality. In: Proceeding of the 10th KARI Biennial Conferenec. KARI HQs, Nairobi, Kenya. 13th – 17th Nov., 2006.
Qadir, M.; Sharma, B.R.; Bruggeman, A.; Choukr-Allah, R. and Karajeh, F. (2007). Non-conventional water resources and opportunities for water augmentation to achieve water security in water scarce countries. Agricultural water management, 87: 2-22.
Rudorff, B. F. T., Mulchi, C. L., Daughtry, C. S. T. and Lee, E. H. (1996). Growth, radiation use efficiency, and canopy reflectance of wheat and corn grown under elevated ozone and carbon dioxide atmospheres. Remote sensing of Environment 55: 163-173. Schlemmer, M.R., Francis, D.D., Shanahan, J.F. and Schepers, J.S. (2005). Remotely measuring chlorophyll content in corn leaves with different nitrogen levels and relative water content. Agronomy Journal, 97: 106-112. Stagakis, S.; Markos, N.; Sykioti, O. and Kyparissis, A. (2010). Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: an application on a phlomis fruticosa Mediterranean ecosystem using multi angular CHRIS/PROBA observations. Remote Sensing of Environment, 114: 977-994.
Steinmetz, S., Guerif, M., Delecolle, R. and Baret, F. (1990). Spectral estimates of the absorbed photosynthetically active radiation and light-use efficiency of a winter wheat crop subjected to N and water deficiencies. International Journal of Remote Sensing 11: 1797-1808. Thenkabail, P.S.; Smith, R.B. and Pauw, E. (2000). Hyperspectral vegetation indices and their relationships with agricultural crops characteristics. Remote Sensing of Environment, 71: 158-182.
Xavier, A.C.; Rundorff, B.F.T.; Moreira, M.A.; Alvarenga, B.S.; Freitas, J.G. and Salmon, M.V. (2006). Hyperspectral field reflectance measurements to estimate wheat grain yield and plant height. Sci. Agric. (Piracicaba, Braz.), 63(2): 130-138. | ||||
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