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

Global warming induced by atmospheric CO 2  has attracted increasing attention of researchers all over the world. Although space-based technology provides the ability to map atmospheric CO 2  globally, the number of valid CO 2  measurements is generally limited for certain instruments owing to the presence of clouds, which in turn constrain the studies of global CO 2  sources and sinks. Thus, it is a potentially promising work to combine the currently available CO 2  measurements. In this study, a strategy for fusing SCIAMACHY and GOSAT CO 2  measurements is proposed by fully considering the CO 2  global bias, averaging kernel, and spatiotemporal variations as well as the CO 2  retrieval errors. Based on this method, a global CO 2  map with certain UTC time can also be generated by employing the pattern of the CO 2  daily cycle reflected by Carbon Tracker (CT) data. The results reveal that relative to GOSAT, the global spatial coverage of the combined CO 2  map increased by 41.3% and 47.7% on a daily and monthly scale, respectively, and even higher when compared with that relative to SCIAMACHY. The findings in this paper prove the effectiveness of the combination method in supporting the generation of global full-coverage XCO 2  maps with higher temporal and spatial sampling by jointly using these two space-based XCO 2  datasets.

Combining XCO2 Measurements Derived from SCIAMACHY and GOSAT for Potentially Generating Global CO2 Maps with High Spatiotemporal Resolution