Predicting Rice Yield Using Canopy Reflectance Measured at Booting Stage

Abstract

Abilities to estimate rice (Oryza sativa L.) yields within fields from remote sensing images is not only fundamental to applications of precision agriculture, but can also be very useful to food provisions management. Major objectives of this study were to identify spectral characteristics associated with rice yield and to establish their quantitative relationships. Field experiments were conducted at Shi‐Ko experimental farm of TARI's Chiayi Station during 1999–2001. Rice cultivar Tainung 67, the major cultivar grown in Taiwan, was used in the study. Various levels of rice yield were obtained via N application treatments. Canopy reflectance spectra were measured during entire growth period, and dynamic changes of characteristic spectrum were analyzed. Relationships among rice yields and characteristic spectrum were studied to establish yield estimation models suitable for remote sensing purposes. Spectrum analysis indicated that the changes of canopy reflectance spectrum were least during booting stages. Therefore, the canopy reflectance spectra during this period were selected for model development. Two multiple regression models, constituting of band ratios (NIR/RED and NIR/GRN), were then constructed to estimate rice yields for first and second crops separately. Results of the validation experiments indicated that the derived regression equations successfully predicted rice yield using canopy reflectance measured at booting stage unless other severe stresses occurred afterward.