Analysis of crop reflectance for estimating biomass in rice canopies at different phenological stages

Martin Leon Gnyp, Kang Yu, Helge Aasen, Yinkun Yao, Shanyu Huang, Yuxin Miao, Georg Bareth

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

This paper contribLltes an assessment for estimating rice (OI)IZa sativa L., irrigated lowland rice) biomass by canopy reflectance in the Sanjiang Plain, China. Hyperspectral data were captured with field spectroradiometers in experimental field plots and farmers' fields and then accompanied by destructive aboveground biomass (AGB) sampling at different phenological growth stages. Best single bands, best two band-combinations, optimised simple ratio (SR), and optimised normalized ratio index (NRI), as well as multiple linear regression (MLR) were calculated from the reflectance for the non-destructive estimation of rice AGB. Experimental field data were used as the calibration dataset and farmers' field data a the validation dataset. Reflectance analy es di play several sensitive bands correlated to rice AGB, e.g. 550, 670, 708, 936, 1125, and J 670 nm, which changed depending on the phenological growth stages. Thesc bands were detected by correlograms for SR, NRI, and MLR with an off et of approximately ± 10 nm The assessment of the three methods showed clear advantage of MLR over SR and NRI in estimating rice AGB at fhe fillering and stem elongation stages by fitting and evaluating the models. he optimal band number for MLR was set to four to avoid overfitti ng. The best validated MLR model (R' - 0.82) at the tillering stage was using four bands at 672, 696, 814 and 707 nm, Overall, the optimized SR, NRI, and MLR have a great potential in non-de tructive estimation of rice AGB at different phenological stages, The performance against the validation dataset showed R2 of 0.69 for SR and R' of 0.70 for NRI, respecfively.

Original languageEnglish (US)
Pages (from-to)351-365
Number of pages15
JournalPhotogrammetrie, Fernerkundung, Geoinformation
Volume2013
Issue number4
DOIs
StatePublished - Aug 2013

Keywords

  • Biomass
  • Fice spectral indices
  • Hyperspectral
  • MLR

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