Credit
We at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging.
This work was undertaken under an academic collaboration between the Australian National University (ANU) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO). We thank the continued support of the TERN Landscapes Observatory (https://www.tern.org.au/tern-observatory/tern-landscapes/), a sensing platform of the Terrestrial Ecosystem Research Network (TERN; https://www.tern.org.au/), which is supported and enabled by the Australian Government through the National Collaborative Research Infrastructure Strategy (NCRIS). We acknowledge the resources and services provided by the National Computational Infrastructure (NCI), which is also supported by the Australian Government through NCRIS. This work was supported by resources and expertise provided by CSIRO IMT Scientific Computing.
Purpose
The Himawari-8/9 satellites offer a unique opportunity to monitor sub-daily thermal dynamics over Asia and Oceania, and several operational LST retrieval algorithms have been developed for this purpose. However, studies have reported inconsistency between LST data obtained from geostationary and polar-orbiting platforms, particularly for daytime LST, which usually shows directional artifacts and can be strongly impacted by viewing and illumination geometries and shadowing effects. To overcome this challenge, we developed this collection, which utilised Solar Zenith Angle (SZA) as an ideal physical variable to quantify systematic differences between platforms. This LST collection demonstrated better consistency with LST obtained from both polar-orbiting platforms and ground-based measurements.
Lineage
This LST collection was developed using a split-window algorithm, with its daytime component operationally adjusted using an SZA-based Calibration (SZAC) method. SZAC describes the spatial heterogeneity and magnitude of diurnal LST discrepancies from different products. The SZAC coefficient was spatiotemporally optimised against highest-quality assured (error <1Â K) pixels from the MODIS daytime LST between 01/Jan/2016 and 31/Dec/2020.
See the method documentation for details.