Abstract
The 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 2001 to 2005. Rice cultivar Tainung 67 (Oryza sativa L.), the major cultivar grown in Taiwan, was used in the study. Various levels of rice yield were obtained via nitrogen 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. Key words: Rice yield, canopy reflectance, prediction model.
Highlights
Yield maps are the basis of making precision management decisions
Regions always having higher or lower yields can be delineated, which can be very useful for diagnosing the causes responsible for low yield
A remote sensed yield map would not be affected by the inaccuracies associated with combine yield monitors, as suggested by Lark et al (1997) and Arslan and Colvin (1999)
Summary
Yield maps are the basis of making precision management decisions. Through accumulated yield maps during past seasons, maps for field management can be produced. Regions always having higher or lower yields can be delineated, which can be very useful for diagnosing the causes responsible for low yield. Remotely sensed images showing spatial and spectral variations resulting from soil and crop characteristics are important source of data for making yield maps (NRC, 1997). A remote sensed yield map would not be affected by the inaccuracies (problems connected with grain flow dynamics and accurate logging of geographic position) associated with combine yield monitors, as suggested by Lark et al (1997) and Arslan and Colvin (1999). Difficulty results from a lack of valid regression models to convert imagery spectral information to a yield map
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