Using Vegetative Indices to Quantify Agricultural Crop Characteristics

• Autorzy: Kokhan Svitlana , Vostokov Anatoliy

Identyfikatory

DOI

10.12911/22998993/119808

• Języki publikacji: EN

Abstrakty

EN

In this study, the winter wheat aboveground biomass (AGB), leaf area index (LAI) and leaf nitrogen concentration (LNC) were estimated using the vegetation indices, derived from a high spatial resolution Pleiades imagery. The AGB, LAI and LNC estimation equations were established between the selected VIs, such as NDVI, EVI and SAVI. Regression models (linear and exponential) were examined to determine the best empirical regression equations for estimating the crop characteristics. The results showed that all three vegetation indices provide the AGB, LAI and LNC estimations. The application of NDVI showed the smallest value of RMSE for the aboveground biomass estimation at stem elongation and heading of winter wheat. EVI gave the best significant estimation of LNC and showed better results to quantify winter wheat vegetation characteristics at stem elongation phase. This study demonstrated that Pleiades high spatial resolution imagery provides in-situ crop monitoring.

Słowa kluczowe

EN

vegetation indice; aboveground biomass; leaf area index; leaf nitrogen concentration

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2020
• Tom: Vol. 21, nr 4
• Strony: 120--127
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Kokhan Svitlana

• Department of Geoinformatics and Aerospace Research of the Earth, National University of Life and Environmental Sciences of Ukraine, 17 Vasylkivska St., 03040 Kyiv, Ukraine

autor

Vostokov Anatoliy

• Department of Geoinformatics and Aerospace Research of the Earth, National University of Life and Environmental Sciences of Ukraine, 17 Vasylkivska St., 03040 Kyiv, Ukraine

Bibliografia

• 1. Baret, F., Hagollec, O., Geiger, B., Bickering, P., Miras, B., Huc, M., Berthelot, B., Niño, F., Weiss, M., Samain, O., Roujean, J.L., Leroy, M. 2007. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm,” Remote Sens. Environ, vol. 110, no. 3, 275–286. doi.org/10.1016/j.rse.2007.02.018
• 2. Berger J.D., Ludwig C., 2014. Contrasting adaptive strategies to terminal drought-stress gradients in Mediterranean legumes: phenology, productivity, and water relations in wild and domesticated Lupinus luteus L. J. Exp. Bot., 65, 6219–6229.
• 3. Bsaibes, A., Courault, D., Baret, F., Weiss, M., Olioso, A., Jacob, F., Hagolle, O., Marloi, O., Bertrand, N., Desfond, V., Kzemipour, F., 2009. Albedo and LAI estimates from FORMOSAT-2 data for crop monitoring. Remote Sens. Environ. vol. 113, no. 4, 716–729. doi.org/10.1016/j.rse.2008.11.014
• 4. Boissard, P., Pointel, J.G., Tranchefort, J., 1992. Estimation of the ground cover ratio of a wheat canopy using radiometry, Int. J. Remote Sens. 13(9), 1681–1692.
• 5. Chang, L., Peng-Sen, S., Shi-Rong, L., 2016. A review of plant spectral reflectance response to water physiological changes. Chinese Journal of Plant Ecology, vol. 40 (1), 80–91.
• 6. Cho, M.A., Skidmore, A.K., 2006. A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sens. Environ, 101, 181–193.
• 7. Ecarnot, M., Compan, F., Roumet, F.P., 2013. Assessing leaf nitrogen content and leaf mass per unit area of wheat in the field throughout plant cycle with a portable spectrometer. Field Crop. Res. 140, 44–50.
• 8. Filella, I, Penuelas, J., 1994. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. Int. J. of Remote Sensing, 15(7): 1459−1470.
• 9. Fitzgerald, G.J., Rodriguez, D., O’Leary, G., 2010. Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index–the canopy chlorophyll content index (CCCI). Field Crop. Res. 116, 318–324.
• 10. Gitelson, A.A., Kaufman, Y.J., Starkc, R., Rundquist. D., 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ, 80, 76–87.
• 11. Goetz, S.J., 1997. Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site. Int. J. Remote Sens., 18, 71–94.
• 12. Glenn E.P., Huete, A.R., Nagler, P.L., Nelson S.G., 2008. Relationship between remotely sensed vegetation indices, canopy attributes and plant physiological processes: What vegetation indices can and cannot tell us about the landscape. Sensors, 8, 2136–2160.
• 13. Hansen, P.M., Schjoerring, J.K., 2003. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens. Environ, 86, 542–553.
• 14. He, Y.H., Guo, X.L., and Wilmshurst, J., 2006. Studying mixed grassland ecosystems: suitable hyperspectral vegetation indices. Canadian J. Remote Sens., 32, 98–107.
• 15. Hinzman, L.D., Bauer, M.E., Daughtry, C.S.T., 1986. Effects of nitrogen fertilization on growth and reflectance characteristics of winter wheat, Remote Sens. Environ, 19, 47–61.
• 16. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ, 83, 195–213.
• 17. Kokhan S.S., 2011. Application of vegetation indexes derived from satellite images IRS-1D LISSIII for determination of crop status (In Ukrainian). Space Sci. & Technol., 17(5):58–63.
• 18. Mulla D.J., 2013. Twenty-five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosystems Engineering, vol. 114(4), 358–371
• 19. Myneni R.B., Maggion S., Iaquinta J., Privette J.L., Gobron N., Pinty B., Kimes D.S., Verstraete M.M., William D.L., 1995. Optical remote sensing of vegetation: Modeling, caveates, and algorithms. Remote Sens. Environ, 51, 169–188. Doi: 10.1016/0034–4257(94)00073-V
• 20. Niu, Z., Chen, Y., Sui, Z., Zhang, Q.Y., Zhao, C.J., 2000. Mechanism Analysis of Leaf Biochemical Concentration by High Spectral Remote Sensing. J. Remote Sens. 4, 125–130.
• 21. Post Launch Commissioning and Testing of Pleiades 1, 2012. ASTRIUM GEO-Information
• 22. Rahman, A.F., Gamon, J.A., Sims, D.A., Schmidts, M., 2003. Optimum pixel size for hyperspectral studies of ecosystem function in southern California chaparral and grassland. Remote Sens. Environ, 84, 192–207.
• 23. Schlerf, M., Alzberger, C., Hill, J., 2005. Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sens. Environ. 95 (2005), 177–194.
• 24. Tian, Y.C., Yao X., Yang, J., Cao, W.X., Hannaway D.B., Zhu, Y., 2011. Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground-and spacebased hyperspectral reflectance. Fuel Energy Abstr. 120, 299–310.
• 25. Tucker, C.J., Holben, B.N., Elgin J.H., McMurtrey G.J. III, 1981. Remote sensing of total dry-matter accumulation in winter wheat, Remote Sens. Environ, 11, 171–189.
• 26. Turner, D.P., Cohen, W.D., Kennedy, R.E., Fassnacht, K.S., Briggs, J.M., 1999. Relationships between Leaf Area Index and Landsat TM Spectral Vegetation Indices across Three Temperate Zone Sites. Remote Sens. Environ, 70, 52–68.
• 27. Verrelst J., Romijn E., Kooistra L., 2012. Mapping vegetation density in a heterogeneous river fl31-oodplain ecosystem using pointable CHRIS/ PROBA data. Remote Sens. 4, 2866–2889.
• 28. Vin˜a1 A., Gitelson, A.A., 2005. Geophysical Research Letters, vol. 32, L17403.
• 29. Wuest, S.B., Cassman, K.J., 1992. Fertilizer-nitrogen use efficiency of irrigated wheat: I. uptake efficiency of preplant versus late-season application. J. Argon, 84, 682–688.
• 30. Xue, J., Su, B., 2017. Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Journal of Sensors Volume 2017, Article ID 1353691, 17 pages.
• 31. Zhang, C., Kovacs. J.M., 2012. The application of smallunmanned aerial systems for precision agriculture: a review. Precision Agriculture, vol. 13 (6), 693–712.
• 32. Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., Le Moine, J., Ryu, S., 2004. Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sens. Environ, 93(3), 402–411.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).