Crop Growth Monitoring using Green Seeker Technology - A Case of NARC Field Station in Pothwar Region

Green Seeker Technology

Authors

  • Arshad Ashraf Climate change, Alternate Energy and Water Resources Institute, National Agricultural Research Centre (NARC), Park Road, Islamabad, Pakistan
  • Imtiaz Hussain International Maize and Wheat Improvement Center (CIMMYT) Office, Islamabad, Pakistan
  • Muhammad Munir Ahmad Climate change, Alternate Energy and Water Resources Institute, National Agricultural Research Centre (NARC), Park Road, Islamabad, Pakistan
  • Muhammad Bilal Iqbal Climate change, Alternate Energy and Water Resources Institute, National Agricultural Research Centre (NARC), Park Road, Islamabad, Pakistan
  • Mansoor Ali Climate change, Alternate Energy and Water Resources Institute, National Agricultural Research Centre (NARC), Park Road, Islamabad, Pakistan
  • Qurban Hussain Natural Resource Division, Pakistan Agricultural Research Council (PARC), Islamabad, Pakistan

Keywords:

Green seeker, spectral reflectance, vegetation index, crop monitoring, Pothwar

Abstract

The green seeker technology was applied to acquire Normalized Difference Vegetation Index (NDVI) and Red-Near Infrared (NIR) ratio of main crops i.e. wheat, rice and oats grown in the NARC field station, Pothwar region to provide repository for future monitoring of the crops through satellite imaging. The spectral data of different growth stages of the wheat crop indicated NDVI values ranging between 0.1 -0.88 during Rabi 2006-07 and Rabi 2015-16. The values of wheat and oat crops indicated a similar pattern during Rabi 2015-16, but with slight difference before the heading and near the harvesting stage of the crops. The NDVI values of rice crop grown in the irrigated fields during Kharif 2007 ranged between 0.03 - 0.61. The value was maximum during the heading followed by maturity stage. NDVI values observed in the rainfed fields indicated high variability due to heterogeneous crop cover resulting from variable moisture conditions, fertilizer use, soil type and sowing practices. A detail study needs to be carried out in different agro-ecological regions of the country to collect unique spectral characteristic of different crops and the surrounding land covers to support satellite based crop monitoring and yield forecasting in future.

References

Jago, R.A., E.J.C. Mark, & P.J. Curran. Estimating canopy chlorophyll concentration from field and airborne spectra. Remote Sensing of Environment 68: 217–224 (1999).

Broge, N.H., & E. Leblanc. 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 (2001).

Gitelson, A.A., Y.J. Kaufman, & R. D. Stark. Rundquist. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of

Environment 80: 76–87 (2002).

Sims, D.A. & J.A. Gamon. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment 81: 331–354 (2002).

Zhao, D.H., J.L. Li & J.G. Qi. Identification of red and NIR spectral regions and vegetative indices for discrimination of cotton nitrogen stress and growth stage. Computers and Electronics in Agriculture 48: 155–169 (2005).

Hayes, J.T., P.A. O’Rourke, W.E. Terjung & P.E. Todhunter. YIELD: A numerical crop yield model of irrigated and rainfed agriculture. Publications in Climatology, 35 pp. (1982).

Benedetti, R. & P. Rossinni. On the use of NDVI profiles as a tool for agricultural statistics: the case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sensing of Environment 45: 311–326 (1993).

Quarmby, N.A., M. Milnes, T.L. Hindle & N. Silicos. The use of multitemporal NDVI measurements from AVHRR data for crop yield estimation and prediction. International Journal of Remote Sensing 14: 199–210 (1993).

Swain, P.H. & S.M. Davis. (EDS.) Remote Sensing: The Quantitative Approach, Mc Graw-Hill, New York (1978).

Boegh, E., H. Soegaarda, et al. Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sensing of Environment 81: 179–193 (2002).

Hansen, P.M. & J.K. Schjoerring. Reflectance measurements of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment 86: 542–553 (2003).

Craig, J.C. Multi-scale remote sensing techniques for vegetation stress detection. PhD dissertation, University of Florida, Gainesville, Florida (2001).

Strachan, I.B., E. Pattey & J.B. Boisvert. Impact of nitrogen and environment conditions on corn as detected by hyperspectral reflectance. Remote Sensing of Environment 80: 213–224 (2002).

Tucker, C.T., Jr. J.H. Elgin, J.E. McMurtry III & C.J. Fan. Monitoring corn and soyabean crop development with handheld radiometer spectral data. Remote Sensing of Environment 8: 237–248 (1979).

Hochheim, K.P. & D.G. Barber. Spring Wheat Yield Estimation for western Canada Using NOAA NDVI data. Canadian Journal of Remote Sensing 24(91) (1998). http://webgrs.wur.nl/courses/grs10306/Clevers/RS%20Ch2%20Spectral%20Signatures/Cropscan-exercise.pdf

NARC (National Agricultural Research Center). A Guide for Physical Development 1990-2000, Vol. 2, Physical Master Plan. National agricultural Research Center, Pakistan Agricultural Research Council, Islamabad (1989).

PARC (Pakistan Agricultural Research Council). Management of gully eroded areas in Pothwar. Directorate of Publications, Pakistan Agricultural Research Council, Islamabad, 64 pp. (1986).

Adyasuren Ts. & Yu. Bayarjargal. Vegetation And Drought Monitoring Using Satellite And Ground Data, Space Informatics For Grassland Sustainable Development: Grassland Monitoring and Management, Proceedings of First International Seminar on Space Informatics for Sustainable Development: Ulan Bator, Mongolia, 216 pp. (1995).

Teal, R.K., B. Tubana, K. Girma, K.W. Freeman, D.B. Arnall, O. Walsh & W.R. Raun. In-season prediction of corn grain yield potential using normalized difference vegetation index. Agronomy Journal 98:1488–1494 (2006).

Raun, W.R., G.V. Johnson, M.L. Stone, J.B. Sollie, E.V. Lukina, W.E. Thomason & J.S. Schepers. Inseason prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal 93:131–138 (2001).

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Published

2016-09-17

How to Cite

Ashraf, A., Hussain, . I. ., Ahmad, M. M., Iqbal, M. B. ., Ali, M. ., & Hussain, Q. . (2016). Crop Growth Monitoring using Green Seeker Technology - A Case of NARC Field Station in Pothwar Region: Green Seeker Technology. Proceedings of the Pakistan Academy of Sciences: B. Life and Environmental Sciences, 53(3), 195–205. Retrieved from https://ppaspk.org/index.php/PPAS-B/article/view/331

Issue

Vol. 53 No. 3 (2016)

Section

Research Articles

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