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

The study focused on deriving the LST of the Raipur City of India and generating the relationships of LST with six selected remote sensing indices, like MNDWI, NDBaI, NDBI, NDVI, NDWI, and NMDI. The entire study was performed by using 210 cloud-free Landsat data of different months from 1988 to 2019. The LST retrieval mono-window algorithm was applied in the study. Based on Pearson's linear correlation coefficient ( r ), the study finds that LST builds a strong positive correlation ( r  = 0.65) with NDBI, a moderate positive correlation ( r  = 0.30) with NDBaI, a weak positive correlation with NDWI ( r  = 0.19), a strong negative relation with NMDI ( r  = −0.54), and a moderate negative correlation ( r  = −0.38) with MNDWI and NDVI. These relationships were consistent and stronger in earlier years. The LST-NDBI correlation is the most consistent ( CV  = 9.09), while the LST-NDBaI correlation is the most variable ( CV  = 60.21).

Annual assessment on the relationship between land surface temperature and six remote sensing indices using landsat data from 1988 to 2019