This study investigates the use of two different optical sensors, the multispectral imager (MSI) onboard the RapidEye satellites and the operational land imager (OLI) onboard the Landsat-8 for mapping within-field variability of crop growth conditions and tracking the seasonal growth dynamics. The study was carried out in southern Ontario, Canada, during the 2013 growing season for three annual crops, corn, soybeans, and winter wheat. Plant area index (PAI) was measured at different growth stages using digital hemispherical photography at two corn fields, two winter wheat fields, and two soybean fields. Comparison between several conventional vegetation indices derived from concurrently acquired image data by the two sensors showed a good agreement. The two-band enhanced vegetation index (EVI2) and the normalized difference vegetation index (NDVI) were derived from the surface reflectance of the two sensors. The study showed that EVI2 was more resistant to saturation at high biomass range than NDVI. A linear relationship could be used for crop green effective PAI estimation from EVI2, with a coefficient of determination (R2) of 0.85 and root-mean-square error of 0.53. The estimated multitemporal product of green PAI was found to be able to capture the seasonal dynamics of the three crops.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.