Establishment of Rice Yield Prediction Model Using Canopy Reflectance | Chapter 01 | Recent Advances in Biological Research Vol. 6
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.
We also integrated multiple regression models, derived from reflectance
spectrum measurements and using band ratios (NIR/RED and NIR/GRN) as
independent variables, with SPOT 5 multispectral images taken at booting stage
to predict rice yield before harvest. A 4.8-ha paddy rice field was used as
testing ground for the accuracy of prediction with the rice yield prediction
model. Within the site, different rice yield scenarios were produced by using
combinations of rice varieties, Japonica and Indica type, nitrogen rate and
drought treatments. Rice yields harvested in 10m X 10m mesh were used as ground
truth data for comparison. The regional rice yield map is produced with the
rice yield prediction model using SPOT 5 images taken at booting stage in this
study. The results from the regional rice yield map shows that the relative
errors between actual yield and predicted yield in the first season and second
season in 2014 are lower than 5%. Those have demonstrated its potential for
using SPOT 5 images to estimate the regional rice yield with the rice yield
prediction model derived from reflectance spectrum measurements and using band
ratios (NIR/RED and NIR/GRN) as independent variables.
Author(s) Details
K. W. Chang
College of Tourism
Management, Baise University, Guangxi, 533000, China.
K. X. Li
College of Tourism
Management, Baise University, Guangxi, 533000, China.
L. H. Xie
College of Tourism
Management, Baise University, Guangxi, 533000, China.
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