English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Cloud coverage hinders the effective range of Earth observation by optical remote sensing satellites. Rapid and accurate cloud detection is an important step in the product generation process of remote sensing applications. Given the lack of suitable and high-quality cloud detection models in the Google Earth Engine cloud platform, this study takes tropical cloudy Sri Lanka as the study area and constructs a Sentinel-2 image cloud detection model coupled with support vector machines and Cloud-Score algorithm. Through experiments, the cloud detection accuracy of this method was compared to the QA60 method, Cloud-Score algorithm, and Function of mask (Fmask) from the point of view of visual interpretation and quantitative analysis. Compared with the other three cloud detection methods, the cloud detection model proposed in this study has the highest overall accuracy, reaching 98.21%, with an extremely low omission and commission errors. The model can accurately identify the cloud boundary and meet the cloud detection pre-processing requirements of Sentinel-2 remote sensing products.

An automatic cloud detection model for Sentinel-2 imagery based on Google Earth Engine