Remotely assessing above-ground fresh biomass weight of wheat based on the combinations of pair vegetation indexes from HJ-CCD images

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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.

Keywords

Vegetation indexes computing HJ-CCD images processing Combination Above-ground fresh biomass weight Remote sensing assessment model 

Notes

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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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Changwei Tan
    • 1
  • Qing Zhang
    • 2
  • Jian Zhou
    • 1
  • Ying Du
    • 1
  • Dunliang Wang
    • 1
  • Ming Luo
    • 1
  • Haidong Zhang
    • 2
  • Wenshan Guo
    • 1
  1. 1.Jiangsu Key Laboratory of Crop Genetics and Physiology/Co-Innovation Center for Modern Production Technology of Grain CropsYangzhou UniversityYangzhouChina
  2. 2.Suzhou Academy of Agricultural SciencesSuzhouChina

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