Abstract

High-quality vegetation index time series are crucial for timely and accurate phenological mapping. However, vegetation index time series with high temporal resolution (such as daily resolution), can be influenced by cloudiness, shadows, and atmospheric effect and affects the interpretation of vegetation growth. We proposed a method for computing a daily 250m two-band enhanced vegetation index (EVI2) time series using MOD09GQ surface reflectance products through a self-supervised neural network autoencoders model. We tested the consistency of EVI2 with conventional three-band EVI, its temporal stability, and sensitivity to aerosol. The results indicated this method is suitable to be applied to areas where atmospheric effects are not prominent. (tel: +86 15229367141, e-mail: rsliuxk@whu.edu.cn).

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.