HYPERSPECTRAL ANALYSIS OF RICE PHENOLOGICAL STAGES IN NORTHEAST CHINA
- 1Institute of Geography (GIS & RS Group), University of Cologne, 50923 Cologne, Germany
- 2College of Resources and Environmental Sciences, China Agricultural University, Beijing, 100094, China
- 3ICASD – International Center for Agro-Informatics and Sustainable Development
Keywords: Agriculture, Crop, Hyperspectral, Estimation, Infrared, Multitemporal
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).