Land Surface Temperature from GOES-East and GOES-West

Abstract

Land surface temperature (LST) is an important climate parameter that controls the surface energy budget. For climate applications, information is needed at global scale with representation of the diurnal cycle. To achieve global coverage there is a need to merge about five independent geostationary satellites (GEO) that have different observing capabilities. An issue of practical importance is the merging of independent satellite observations in areas of overlap. An optimal approach in such areas could eliminate the need for redundant computations by differently viewing satellites. We use a previously developed approach to derive information on LST from GOES east (GOES-E), modify it for application to GOES west (GOES-W) and implement it simultaneously across areas of overlap at 5-km spatial resolution. We evaluate the GOES based LST against in-situ observations and an independent MODIS product for the period of 2004–2009. The methodology proposed minimizes differences between satellites in areas of overlap. The mean and median values of the differences in monthly mean LST retrieved from GOES-E and GOES-W at UTC 06 for July are 0.01° and 0.11° K, respectively. Similarly, at UTC 18 the respective mean and median value of the differences were 0.15° and 1.33° K. These findings can provide guidelines for potential users to decide whether the reported accuracy based on one satellite alone, meets their needs in area of overlap. Since the six year record of LST was produced at hourly time scale, the data are well suited to address scientific issues that require the representation of LST diurnal cycle or the diurnal temperature range (DTR).

Modeling primary production from carbon flux and satellite data

“…With an advancements of new generation geostationary satellites, such as Himawari-8, Geostationary Operational Environmental Satellite R-Series (GOES-R), Geostationary Korea Multi-Purpose Satellite-2A (GEO-KOMPSAT-2A), and FengYun-4A, possibility to monitor sub-daily GPP is increasing (e.g., Bessho et al, 2016;Wang et al, 2020). The optical sensors on these satellites have been added multiple solar reflective and thermal infrared bands and improved spatiotemporal resolution, enabling them to provide land surface parameters such as downwelling shortwave radiation (SRd), vegetation indices, and land surface temperature (LST) that are useful for estimating sub-daily GPP (Cheng et al, 2020;Chen et al, 2021). Xiao et al (2021) demonstrated that a semi-empirical light use efficiency (LUE) model driven by the Himawari-8 data has a possibility to represent the variation in diurnal GPP due to a heatwave at one site in Australia.…”