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

In general, the type of atmospheric aerosols can be efficiently identified with the characteristics of optical properties, such as\udÅngström exponent (AE) and single scattering albedo (SSA). However, the retrieval of SSA is not frequently available to global\udarea which may cause the difficulty in the identification of aerosol type. Since aerosol optical depth (AOD) can be easily requested,\uda novel index in terms of AOD, Normalized Gradient Aerosol Index (NGAI), is proposed to get over the constraint on SSA\udproviding. With the NGAI derived from MODIS AOD products, the type of atmospheric aerosols can be clearly categorized\udbetween mineral dusts, biomass burning and anthropogenic pollutants. The results of aerosol type categorization show the well\udagreement with the ground-based observations (AERONET) in AE and SSA properties, implying that the proposed index equips\udhighly practical for the application of aerosols type categorization by means of remote sensing. In addition, the fraction of AOD\udcompositions can be potentially determined according to the value of index after compared with the products of CALIPSO Aerosol\udSubtype

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

A NOVEL INDEX FOR ATMOSPHERIC AEROSOL TYPES CATEGORIZATION WITH SPECTRAL OPTICAL DEPTHS FROM SATELLITE RETRIEVAL