Abstract
Abstract. The objective of this contribution is to monitor rice (Oryza sativa L., irrigated lowland rice) growth with multitemporal hyperspectral data during different phenological stages in Northeast China (Sanjiang Plain). Multitemporal hyperspectral data were measured with field spectroradiometers (ASD Inc.: QualitySpec and FieldSpec3) for two field experiments and nine farmers' fields. The field measurements were carried out together with corresponding measurements of agronomic data (aboveground biomass [AGB], Leaf Area Index [LAI], number of tillers). Eight selected standard hyperspectral vegetation indices (VIs), proved in several studies to be highly correlated with AGB or LAI, were calculated on the measured experimental field data. Additionally, the best two-band combinations for the Normalized Ratio Index (NRI) were determined. The results indicate that the NRI performed better than the selected standard VIs at the stages of stem elongation, booting and heading and also across all stages. Especially during the stem elongation stage (R2 = 0.76) and across all stages (R2 = 0.70), the NRI performed best. When applying the NRI on the farmers' field data, the performance was lower (R2 < 0.60). Overall, the sensitive individual wavelengths (±10 nm) for the best two-band combinations were detected at 711 and 799 nm (for tillering stage), 1575 and 1678 nm (for stem elongation stage), 515 and 695 nm (for booting stage), and 533 and 713 nm (for all stages). The results suggest that hyperspectral-based methods can estimate paddy rice AGB with a satisfying accuracy. In the context of precision agriculture, the findings are useful for future development of new hyperspectral devices such as scanners or cameras which could be fixed on tractors or unmanned aerial vehicles (UAVs).
Highlights
Rice is a staple grain and accounts for over 40 % of the grain protein production in China
The high number of measurements covering a wide range of aboveground biomass (AGB) values provides an ideal basis for the spectral analysis
Many studies revealed that various vegetation indices (VIs), especially standard VIs such as Normalized Difference Vegetation Index (NDVI) or Ratio Vegetation Indices (RVI), tend to asymptotically saturate in response to high AGB or Leaf Area Index (LAI) (Thenkabail et al, 2000; Chen et al 2009)
Summary
Rice is a staple grain and accounts for over 40 % of the grain protein production in China. For securing food production and quality, the estimation of agronomic parameters is an important task for decision support in rice cultivation. Agronomic parameters such as crop aboveground biomass (AGB) or Leaf Area Index (LAI) are considered as the major factors for the determination of the final yield because of their influence on the grain production at each growth stage (Shibayama & Munakata, 1986). High resolution hyperspectral sensors offer valuable information in the UV, Visible and NIR/SWIR region of the electromagnetic spectrum Their continuous acquisition of all reflectance values in a spectral range has a major advantage over multispectral sensors collecting broad band (Milton et al, 2009). They are applied more and more to estimate plant AGB (Shibayama & Munataka, 1986; Serrano et al, 2000; Osborne et al, 2002; Hansen & Schjoerring, 2003; Chen et al, 2009; Psomas et al, 2011)
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