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Comparison of Vegetation Indices from Two Ground Based Sensors

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Abstract

The best and commonly used ground-based sensor to monitor crop growth, ASD FieldSpecPro Spectroradiometer (Analytical Spectral Devices, Boulder, CO, USA) is a passive sensor, which can be used under adequate light condition. However, now-a-days active sensors such as GreenSeeker™ (GS) handheld crop response (Trimble Agriculture division, USA) are used for monitoring crop growth and are flexible in terms of timeliness and illumination conditions besides being cheaper than the ASD. Before its wide use, the suitability and accuracy of GS should be assessed by comparing the NDVI measured by this instrument with that by ASD, under diverse wheat growing conditions of India. Keeping this in view, the present experiment was undertaken with the following objectives: (1) to find out the temporal variation of NDVI measured both by ASD and GS treatments, (2) to find out relationship between the NDVI measured through ASD and GS and, (3) to evaluate the suitability of GS for NDVI measurements. It was observed that the numerical value of NDVI as measured by GS was always significantly (P < 0.05) lower than that measured by ASD for all the experiments under study. The NDVI-ASD and NDVI-GS were significantly positively correlated (P < 0.01) with the correlation coefficients being +0.94, +0.88 and +0.87 for irrigation and nitrogen experiment, irrigation and cultivars experiment, and tillage, residue and nitrogen experiments, respectively. Further, the regression equation developed between the NDVI-ASD and NDVI-GS: [NDVI-GS = 1.070 × (NDVI-ASD − 0.292] can be successfully used to compute the NDVI of ASD from that computed by GS.

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References

  • Dorigo, W., Zurita-Milla, R., De Wit, A., Brazile, J., Singh, R., & Schaepman, M. (2007). A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation, 9, 165–193.

    Article  Google Scholar 

  • Erdle, K., Mistele, B., & Schmidhalter, U. (2011). Comparision of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Research, 124, 74–84.

    Article  Google Scholar 

  • Gitelson, A. A., Vina, A., Arkebauer, J. J., Rundquist, D. C., Keydan, G., & Leavitt, B. (2003). Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 30(5), 1248.

    Article  Google Scholar 

  • Gomez, K. A., & Gomez, A. A. (1984). Statistical procedures for agricultural research. New York: John Wiley and Sons.

    Google Scholar 

  • Mohanty, S. K., Singh, A. K., Jat, S. L., Parihar, C. M., Pooniya, V., & Singh, B. (2015). Precision nitrogen management practices influences growth and yield of wheat (Triticum aestivum) under conservation agriculture. Indian Journal of Agronomy, 60, 617–621.

    Google Scholar 

  • Pradhan, S., Bandyopadhyay, K. K., Sahoo, R. N., Sehgal, V. K., Singh, R., Joshi, D. K., et al. (2013). Prediction of wheat (Triticum aestivum L.) grain and biomass yield under different irrigation and nitrogen management practices using canopy reflectance spectra model. Indian Journal of Agricultural Sciences, 83, 1136–1143.

    Google Scholar 

  • Pradhan, S., Bandyopadhyay, K. K., Sahoo, R. N., Sehgal, V. K., Singh, R., Gupta, V. K., et al. (2014). Predicting wheat grain and biomass yield using canopy reflectance of booting stage. Journal of Indian Society of Remote Sensing, 42, 711–718.

    Article  Google Scholar 

  • Prasad, B., Carver, B. F., Stone, M. L., Babar, M. A., Raun, W. R., & Klatt, A. R. (2007). Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under great plains conditions. Crop Science, 47, 426–1440.

    Article  Google Scholar 

  • Tremblay, N., Wang, Z., Ma, B. L., Belee, C., & Vigneault, P. (2009). A comparision of crop data measured by two commercial sensors for variable-rate nitrogen application. Precision Agriculture, 10, 145–161.

    Article  Google Scholar 

  • Yao, X., Yao, X., Jia, W., Tian, Y., Ni, J., Cao, W., et al. (2013). Comparison and intercalibration of vegetation indices from different sensors for monitoring above-ground plant nitrogen uptake in winter wheat. Sensors, 13, 3109–3130.

    Article  Google Scholar 

Download references

Acknowledgements

Funding was provided by Indian Council of Agricultural Research.

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Correspondence to S. Pradhan.

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Pradhan, S., Sehgal, V.K., Bandyopadhyay, K.K. et al. Comparison of Vegetation Indices from Two Ground Based Sensors. J Indian Soc Remote Sens 46, 321–326 (2018). https://doi.org/10.1007/s12524-017-0671-0

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  • DOI: https://doi.org/10.1007/s12524-017-0671-0

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