Skip to main content
SpringerLink
Log in

Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

Remote sensing-based nitrogen (N) management has been evaluated in many crops. The water background and wide range of varieties in rice (Oryza sativa), are unique features that require additional consideration when using sensor technology. The commonly calculated normalized difference vegetation index is of limited use when the crop has reached complete canopy closure. The objective of this research was to evaluate mid-season agronomic parameter and grain yield prediction models along with the effect of water background and of different varieties using a red- and red-edge-based vegetation index. Varieties × N trials were established at the LSU AgCenter Rice Research Station located in Crowley, Louisiana in 2011 and 2012. Canopy spectral reflectance under clear and turbid water, biomass yield, N content, plant coverage, and water depth were collected each week for three consecutive weeks beginning 2 weeks before panicle differentiation. Grain yield was also determined. Water turbidity had an influence on spectral reflectance when canopy coverage was less than 50 %. While water depth influenced red reflectance, this was not carried over when reflectance was transformed to vegetation indices. The red-edge-based vegetation indices, especially those computed by ratio, had stronger relationships with measured agronomic parameters as compared with red-based indices. Furthermore, the effect of variety on the yield prediction model was observed using derivative-based red-edge indices but not with other ratio-based indices. Future researches should focus on developing a generalized yield prediction model using ratio-based red-edge indices across different varieties to extend its applicability in production fields.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Abd-Elrahman, A., Croxton, M., Pande-Chettri, R., Toor, G. S., & Smith, S. (2011). In situ estimation of water quality parameters in freshwater aquaculture ponds using hyperspectral imaging system. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 463–472.

    Article  Google Scholar 

  • Boochs, F., Kupfer, G., Dockter, K., & Kuhbauch, W. (1990). Shape of the red-edge as vitality indicator for plants. International Journal of Remote Sensing, 11, 1741–1753.

    Article  Google Scholar 

  • Cao, Q., Miao, Y., Shen, J., Yu, W., Yuan, F., Cheng, et al. (2015). Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor. Precision Agriculture. doi:10.1007/s11119-015-9412-y.

  • Cao, Q., Miao, Y., Wang, H., Huang, S., Cheng, S., Khosla, R., et al. (2013). Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor. Field Crops Research, 154, 133–144.

    Article  Google Scholar 

  • Chang, K., Shen, Y., & Lo, J. (2005). Predicting rice yield using canopy reflectance measured at booting stage. Agronomy Journal, 97, 872–878.

    Article  Google Scholar 

  • Cho, M. A., & 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, 181–193.

    Article  Google Scholar 

  • Cho, M. A., Skidmore, A. K., & Atzberger, C. (2008). Towards red-edge positions less sensitive to canopy biophysical parameters for leaf chlorophyll estimation using properties optique spectrales des feuilles (PROSPECT) and scattering by arbitratily inclined leaves (SAILH) simulated data. International Journal of Remote Sensing, 29, 2241–2255.

    Article  Google Scholar 

  • Curran, P. J., Dungan, J. L., & Gholz, H. L. (1990). Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. Tree Physiology, 15, 203–206.

    Article  Google Scholar 

  • Curran, P. J., Windham, W. R., & Gholz, H. L. (1995). Exploring the relationship between reflectance red edge and chlorophyll concentration in slash pine leaves. Tree Physiology, 15, 203–206.

    Article  PubMed  Google Scholar 

  • Darvishsefat, A. A., Abbasi, M., & Schaepman, M. E. (2011). Evaluation of spectral reflectance of seven Iranian rice varieties canopies. Journal of Agricultural Science and Technology, 13, 1091–1104.

    CAS  Google Scholar 

  • Daughtry, C. S. T., Walthall, C. L., Kim, M. S., Brown de Colstoun, E., & McMurtrey, J. E., I. I. I. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, 229–239.

    Article  Google Scholar 

  • Dawson, T. P., & Curran, P. J. (1998). A new technique for interpolation the reflectance red edge position. International Journal of Remote Sensing, 19, 2133–2139.

    Article  Google Scholar 

  • Dobermann, A., Witt, C., Dawe, D., Gines, G. C., Nagarajan, R., Satawathananont, S., et al. (2002). Site-specific nutrient management for intensive rice cropping systems in Asia. Field Crops Research, 74, 37–66.

    Article  Google Scholar 

  • Elvidge, C. D., & Chen, Z. (1995). Comparison of broad-band and narrow-band red and near-infrared vegetation in dices. Remote Sensing of Environment, 54, 38–48.

    Article  Google Scholar 

  • Evri, M., Akiyama, T., & Kawamura, K. (2008). Spectrum analysis of hyperspectral red edge position to predict rice biophysical parameters and grain yield. Journal of the Japan Society of photogrammetry and Remote Sensing, 47, 4–15.

    Article  Google Scholar 

  • Galvão, L. S., Formaggio, A. R., & Tisot, D. A. (2005). Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 hyperion data. Remote Sensing of Environment, 94, 523–534.

    Article  Google Scholar 

  • Gitelson, A. A. (2004). Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology, 161, 165–173.

    Article  CAS  PubMed  Google Scholar 

  • Gitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. C. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80, 76–87.

    Article  Google Scholar 

  • Gitelson, A. A., Vina, A., Ciganda, V., Rundquist, C. D., & Arkebauer, J. T. (2005). Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32, L08403. doi:10.1029/2005GL022688.

    Article  Google Scholar 

  • Guyot, G., & Baret, F. (1988). Utilisation de la haute résolution spectrale pour suivre l’état des couverts végétaux (Use of high spectral resolution to monitor the status of plant coverage). In Proceedings of the 4th International Colloquium on Spectral Signatures of Objects in Remote Sensing, European Space Sgency (ESA) SP-287) Paris, France, pp. 279–286.

  • Guyot, G., Baret, F., & Jacquemoud, S. (1992). Imaging spectroscopy for vegetation studies. In F. Toselli & J. Bodechtel (Eds.), Imaging spectroscopy: Fundamentals and prospective application (pp. 145–165). The Netherland: Kluwer Academic Publishers.

    Google Scholar 

  • Han, L. (1997). Spectral reflectance with varying suspended sediment concentrations in clear and algae-laden waters. Photogrammetric Engineering & Remote Sensing, 63, 701–705.

    Google Scholar 

  • Harrell, D. L., Tubaña, B. S., Walker, T. M., & Phillips, S. B. (2011). Estimating rice grain yield potential using normalized difference vegetation index. Agronomy Journal, 103, 1717–1723.

    Article  Google Scholar 

  • Hoshi., T., Ishida, T., & Nakayama, K. (1984). Experimental study on the spectral reflectance of paddy soils in the field survey. ISE-TR-84-47. Institute of Information Science and Electronics, University of Tukuba, Japan.

  • Huete, A. R. (1988). A soil adjusted vegetation index. Remote Sensing of Environment, 25, 295–309.

    Article  Google Scholar 

  • Jackson, R. D., & Pinter, P. J. (1986). Spectral response of architecturally different wheat canopies. Remote Sensing of Environment, 20, 43–56.

    Article  Google Scholar 

  • Malingreau, J. P. (1989). The vegetation index and the study of vegetation dynamics. In F. Toselli (Ed.), Application of remote sensing to agrometeorology (pp. 285–303). Netherland: Kluwer Academic Publishers.

    Chapter  Google Scholar 

  • Mutanga, O., & Skidmore, A. K. (2004). Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing, 25, 3999–4014.

    Article  Google Scholar 

  • Norman, R. J., Wilson, C. E., Jr., Slaton, N. A., Moldenhauer, K. A. K., Boothe, D. L., Clark, S. D., et al. (2000). Grain yield response of new rice cultivars. In R. J. Norman & C. A. Beyrouty (Eds.), Arkansas rice research studies 1999 (Vol. 476, pp. 267–271). Fayetteville, USA: Arkansas Agriculture Experiment Station.

    Google Scholar 

  • Peng, Y., & Gitelson, A. A. (2012). Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content. Remote Sensing of Environment, 117, 440–448.

    Article  Google Scholar 

  • Pu, R., Gong, P., Biging, G. S., & Larrieu, M. R. (2003). Extraction of red edge optical parameters from hyperion data for estimation of forest leaf area index. IEEE Transactions on Geoscience and Remote Sensing, 41, 916–921.

    Article  Google Scholar 

  • R Development Core Team. (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org. Accessed 15 Nov 2015.

  • Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Lukina, E. V., Thomason, W. E., et al. (1999). In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal, 93, 131–138.

    Article  Google Scholar 

  • Raun, W. R., Solie, J. B., Stone, M. L., Martin, K. L., Freeman, K. W., Mullen, R. W., et al. (2005). Optical sensor-based algorithm for crop nitrogen fertilization. Communications in Soil Science and Plant Analysis, 36, 2759–2781.

    Article  CAS  Google Scholar 

  • Saichuk., J., Harrell, D., Fontenot, K., Groth, D., Hollier, C., Hummel, N., et al. (2012). Rice varieties & management tips. Pub. 227. Louisiana State University AgCenter, Baton Rouge, LA, USA.

  • SAS. (2009). The SAS system for Windows. Version 9.0. Cary, NC, USA: SAS Institute.

  • Sen, L. T. H., Ranamukhaarachchi1, S. L., Zoebisch, M. A., Hasan, M. M., & Meskuntavon, W. (2002). Effects of early-inundation and water depth on weed competition and grain yield of rice in the Central Plains of Thailand. Tropical and Subtropical Agricultural and Natural Resource Management (TROPENTAG). http://www.tropentag.de/2002/abstracts/full/242.pdf. Accessed 7 Dec 2015.

  • Shafri, H. Z. M., Shalleh, M. A. M., & Ghiyamat, A. (2006). Hyperspectral remote sensing of vegetation using red edge position techniques. American Journal of Applied Sciences, 6, 1864–1871.

    Google Scholar 

  • Shibayama, M., & Akiyama, T. (1991). Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements. Remote Sensing of Environment, 36, 45–53.

    Article  Google Scholar 

  • Sims, D. A., & Gamon, J. A. (2002). Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81, 337–354.

    Article  Google Scholar 

  • Stroppiana, D., Boschetti, M., Brivio, P. A., & Bocchi, S. (2009). Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Research, 111, 119–129.

    Article  Google Scholar 

  • Thenkabail, P. S., Smith, R. B., & DePauw, E. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71, 158–182.

    Article  Google Scholar 

  • Tian, Y., Yao, X., Yang, J., Cao, W., & Zhu, Y. (2011). Extracting red-edge position parameters from ground and space based hyperspectral data for estimation of canopy leaf nitrogen concentration in rice. Plant Production Science, 14, 270–281.

    Article  Google Scholar 

  • Tubaña, B. S., Arnall, D. B., Walsh, O., Chung, B., Solie, J. B., Girma, K., et al. (2008). Adjusting midseason nitrogen rate using a sensor-based optimization algorithm to increase use efficiency in corn. Journal of Plant Nutrition, 31, 1393–1419.

    Article  Google Scholar 

  • Tubaña, B. S., Harrell, D., Walker, T., Teboh, J., Lofton, J., Kanke, Y., et al. (2011). Relationships of spectral vegetation indices with rice biomass and grain yield at different sensor view angles. Agronomy Journal, 103, 1405–1413.

    Article  Google Scholar 

  • Vaesen, K., Gilliams, S., Nackaerts, K., & Coppin, P. (2000). Ground-measures spectral signatures as indicators of ground cover and leaf area index: The case of paddy rice. Field Crops Research, 69, 13–25.

    Article  Google Scholar 

  • Van der Meer, F., & De Jong, S. M. (2006). Imaging spectrometry for agriculture applications imaging spectrometry for agriculture applications. In J. G. P. W. Clevers & R. Jongschaap (Eds.), Imaging spectrometry: Basic principal and prospective application (pp. 157–197). Dordrecht, The Netherlands: Springer.

    Google Scholar 

  • Vincikova, H., Hanus, J., & Pechar, Li. (2015). Spectral reflectance is a reliable water-quality estimator for small, highly turbid wetlands. Wetlands Ecology and Management,. doi:10.1007/s11273-015-9431-5.

    Google Scholar 

  • Walker, T. W., & Street, J. E. (2003). Rice fertilization. Mississippi State University extension service publication, Starkville, USA. Information Sheet: No. 1341.

  • Wells, B. R., Norman, R. J., Helms, R. S., & Baser, R. E. (1989). Use of plant measurement as an indication of midseason nitrogen fertilization. In W. E. Sabbe (Ed.), Arkansas soil fertility studies (pp. 45–48). Fayetteville, USA: Arkansas Agriculture Experiment Station.

    Google Scholar 

  • Wu, J. L., Ho, C. R., Haung, C. C., Srivastav, A. L., Tzeng, J. H., & Lin, Y. T. (2014). Hyperspectral sensing for turbid water quality monitoring in freshwater rivers: Empirical relationship between reflectance and turbidity and total solids. Sensors, 12, 22670–22688.

    Article  Google Scholar 

  • Xue, L., & Yang, W. (2008). Recommendations for nitrogen fertilizer topdressing rates in rice using canopy reflectance spectra. Biosystems Engineering, 100, 524–534.

    Article  Google Scholar 

  • Zhang, J. H., Wang, K., Bailey, J. S., & Wang, R. C. (2006). Predicting nitrogen status of rice using multispectral data at canopy scale. Pedosphere, 16, 108–117.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This study was supported in part by the Louisiana Rice Research Board.

Author information

Authors and Affiliations

  1. School of Plant, Environmental, and Soil Sciences LSU AgCenter, 104 Sturgis Hall, Baton Rouge, LA, 70803, USA

    Yumiko Kanke, Brenda Tubaña & Marilyn Dalen

  2. LSU AgCenter H. Rouse Caffey Rice Research Station, 1373 Caffey Road, Rayne, LA, 70578, USA

    Dustin Harrell

Authors
  1. Yumiko Kanke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brenda Tubaña
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marilyn Dalen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dustin Harrell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Brenda Tubaña.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kanke, Y., Tubaña, B., Dalen, M. et al. Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields. Precision Agric 17, 507–530 (2016). https://doi.org/10.1007/s11119-016-9433-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-016-9433-1

Keywords

Search

Navigation