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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Processing of data recorded by MODIS sensors on board the polar orbiting satellite Terra and Aqua usually provides Aerosol Optical Thickness maps at a coarse spatial resolution. It is appropriate for applications of air pollution monitoring at the global scale but not adequate enough for monitoring at local scales. Different from the traditional approach based on physical algorithms to downscale the spatial resolution, in this article, we propose a methodology to derive AOT maps over land at 1 km2 of spatial resolution from MODIS data using support vector regression relied on domain knowledge. Experiments carried out on data recorded in three years over Europe areas show promising results on limited areas located around ground measurement sites where data are collected to make empirical data models as well as on large areas over satellite maps.

DOWNSCALING AEROSOL OPTICAL THICKNESS TO 1 KM2 SPATIAL RESOLUTION USING SUPPORT VECTOR REGRESSION REPLIED ON DOMAIN KNOWLEDGE