Abstract
To improve the reliability of remote sensing assessment model of above-ground fresh biomass weight (AFBW) in wheat, we investigated the relationships of AFBW at different critical stages with their corresponding vegetation indexes obtained from HJ-CCD images, established and evaluated new AFBW models based on paired vegetation indexes. The results showed that combination of normalized difference vegetation index (NDVI) and structure intensive pigment index (SIPI), namely N(NDVI, SIPI), could be used to accurately assess AFBW at jointing stage with R2 and RMSE of 0.84 and 379.14 kg ha−1, respectively, and more feasible than the single vegetation index model with accuracy increased by 10.95%. Moreover, the ratio combination of NDVI and green normalized difference vegetation index (GNDVI), namely R(NDVI, GNDVI), could be used to accurately assess AFBW at booting stage with R2 of 0.87 and RMSE of 987.64 kg ha−1 and accuracy increased by 12.56%. The difference combination of NDVI and nitrogen reflectance index (NRI), namely D(NDVI, NRI), could be used to accurately assess AFBW at anthesis with R2 of 0.86 and RMSE of 1786.37 kg ha−1 and accuracy increased by 13.37%. In totally, N(NDVI, SIPI), R(NDVI, GNDVI) and D(NDVI, NRI) are potential indicators of AFBW at different stages and can be applied as a new method for more accurate assessment of wheat growth.
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Helmisaari, H., Makkonen, K., Kellomaki, S., et al.: Below- and above-ground biomass, production and nitrogen use in scots pine stands in eastern finland. For. Ecol. Manage. 165(1–3), 317–326 (2002)
Harmoney, K., Moore, K., George, R., et al.: Determination of pasture biomass using four indirect methods. Agron. J. 89(3), 665–672 (1997)
Martin, R., Astatkie, T., Cooper, J., et al.: A comparison of methods used to determine biomass on naturalized swards. J. Agron. Crop Sci. 191(2), 151–160 (2005)
Whitbeck, M., Grace, J.: Evaluation of non-destructive methods for estimating biomass in marshes of the upper texas. USA Coast. Wetl. 26(1), 278–282 (2006)
Radloff, F., Mucina, L.: A quick and robust method for biomass estimation in structurally diverse vegetation. J. Veg. Sci. 18(5), 719–724 (2007)
Todd, S.W., Hoffer, R.M., Milchunas, D.G.: Biomass estimation on grazed and ungrazed rangelands using spectral indexes. Int. J. Remote Sens. 19(3), 427–438 (1998)
Mutanga, O., Skidmore, A.K.: Narrow band vegetation indexes overcome the saturation problem in biomass estimation. Int. J. Remote Sens. 25(19), 3999–4014 (2004)
Gnyp, M.L., Bareth, G., Li, F., et al.: Development and implementation of a multiscale biomass model using hyperspectral vegetation indexes for wheat in the north china plain. Int. J. Appl. Earth Obs. Geoinf. 33(12), 232–242 (2014)
Casanova, D., Epema, G.F., Goudriaan, J.: Assessing rice reflectance at field level for estimating biomass and LAI. Field Crops Research 55(1–2), 83–92 (1998)
Hansen, P.M., Schjoerring, J.K.: Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indexes and partial least square regression. Remote Sens. Environ. 86(4), 542–553 (2003)
Bai, J., Li, S., Wang, K., et al.: Estimation models of cotton above-ground fresh biomass based on field hyperspectral remote sensing. Acta Agron. Sin. 33(1), 311–316 (2007)
Perbandt, D., Fricke, T., Wachendorf, M.: Off-nadir hyperspectral measurements in maize to predict dry matter yield, protein content and metabolisable energy in total biomass. Precis. Agric. 12(2), 249–265 (2010)
Pittman, J., Arnall, D.B., Interrante, S.M., et al.: Bermudagrass, wheat, and tall fescue crude protein forage estimation using mobile-platform, active-spectral and canopy-height data. Crop Sci. 56(2), 870–881 (2016)
Claverie, M., Demarez, V., Duchemin, B., et al.: Maize and sunflower biomass estimation in southwest france using high spatial and temporal resolution remote sensing data. Remote Sens. Environ. 124(6), 844–857 (2012)
Gnyp, M.L., Miao, Y., Yuan, F., et al.: Hyperspectral canopy sensing of paddy rice above-ground biomass at different growth stages. Field Crops Research 155(155), 42–55 (2014)
Munoz, J.D., Finley, A.O., Gehl, R.J., et al.: Nonlinear hierarchical models for predicting cover crop biomass using normalized difference vegetation index. Remote Sens. Environ. 114(12), 2833–2840 (2010)
Liu, J., Pattey, E., Miller, J.R., et al.: Estimating crop stresses, above-ground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model. Remote Sens. Environ. 114(6), 1167–1177 (2010)
Tan, C.W., Zhou, J., Luo, M., et al.: Using combined vegetation indexes to monitor leaf chlorophyll content in winter wheat based on HJ-CCD images. Int. J. Agric. Biol. 19(9), 1576–1584 (2017)
Coops, N.C., Smith, M., Martin, M.E., et al.: Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data. IEEE Trans. Geosci. Remote Sens. 41(6), 1338–1346 (2003)
Vigneau, N., Ecarnot, M., Rabatel, G., et al.: Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in wheat. Field Crops Res. 122(1), 25–31 (2011)
Mahajan, G.R., Sahoo, R.N., Pandey, R.N., et al.: Using hyperspectral remote sensing techniques to assess nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.). Precis. Agric. 15(5), 499–522 (2014)
Doraiswamy, P.C., Hatfield, J.L., Jackson, T.J., et al.: Crop condition and yield simulations using Landsat and MODIS. Remote Sens. Environ. 92(4), 548–559 (2004)
Bannari, A., Pacheco, A., Staenz, K., et al.: Estimating and mapping crop residues cover on agricultural lands using hyperspectral and IKONOS data. Remote Sens. Environ. 104(4), 447–459 (2006)
Estornell, J., Ruiz, L.A., Velazquezmarti, B., et al.: Estimation of shrub biomass by airborne lidar data in small forest stands. For. Ecol. Manag. 262(9), 1697–1703 (2011)
Zhao, D., Raja, R.K., Gopal, K.V., et al.: Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum. Eur. J. Agron. 22(4), 391–403 (2005)
Erdle, K., Mistele, B., Schmidhalter, U.: Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Res. 124(1), 74–84 (2011)
Duncanson, L.I., Niemann, K.O., Wulder, M.A.: Integration of GLAS and Landsat TM data for above-ground biomass estimation. Can. J. Remote Sens. 36(2), 129–141 (2010)
Acknowledgements
Financial assistance for this research was provided by the National Natural Science Foundation of China (41271415), a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Fund for Independent Innovation of Agricultural Science and Technology in Jiangsu Province (CX (16)1042), Yangzhou City Science and Technology Project (YZ2016242) and Agricultural Science and Technology Innovation Project of Suzhou City (SNG201643).
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Changwei Tan and Qing Zhang have contributed equally to this work.
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Tan, C., Zhang, Q., Zhou, J. et al. Remotely assessing above-ground fresh biomass weight of wheat based on the combinations of pair vegetation indexes from HJ-CCD images. Cluster Comput 22 (Suppl 6), 15417–15427 (2019). https://doi.org/10.1007/s10586-018-2614-0
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DOI: https://doi.org/10.1007/s10586-018-2614-0