13 JUN 2025 (FRI) 10:30-11:30

GEOG HKU
Jun 13
2 min read

Geography Distinguished Seminars Series

Coupled Retrieval of Turbulent Heat Fluxes and Gross Primary Productivity via the Assimilation of Land Surface Temperature Data from Geostationary Satellites

Date: 13 JUN 2025 (Friday)

Time: 10:30-11:30 (HKT)

Mode: Hybrid

Venue: CLL, Department of Geography, 10/F, The Jockey Club Tower, Centennial Campus, HKU

Via Zoom: Zoom link will be provided upon successful registration

Registration link: https://hkuems1.hku.hk/hkuems/ec_hdetail.aspx?guest=Y&ueid=100641

Abstract:

Compared to polar-orbiting satellites/sensors (e.g., Landsat/MODIS), new-generation geostationary satellites, such as Himawari-8 and Geostationary Operational Environmental Satellite-R series (GOES-R), offer temporally continuous and more frequent observations of the land surface over the course of the diurnal cycle. In this study, Himawari-8 land surface temperature (LST) data and GLASS leaf area index (LAI) were assimilated into a coupled two-source surface energy balance–vegetation dynamics model (TSEB-VDM) via the variational data assimilation (VDA) method to retrieve the regional sensible heat flux (H), latent heat flux (LE), and gross primary productivity (GPP) (hereafter referred to as the VDAHimawari-8 scheme). Regional H, LE, and GPP values were estimated across the Heihe River Basin (HRB) in northwestern China, with a spatial resolution of 0.02° × 0.02°. Moreover, LST data from the MODIS onboard polar-orbiting satellites (i.e., Terra and Aqua), were assimilated into the TSEB-VDM model (hereafter referred to as the VDAMODIS scheme) for comparison with the VDAHimawari-8 scheme. Four unknown parameters of the TSEB-VDM model, i.e., the neutral bulk heat transfer coefficient (CHN), the evaporative fractions of the soil and canopy (EFS and EFC, respectively), and the specific leaf area, were optimized via the VDA approach. The estimated H and LE values were validated against ground measurements from the large-aperture scintillator, and the GPP estimates were validated against eddy covariance data at three sites (Arou, Daman, and Sidaoqiao) in the HRB. The results indicated that the assimilation of geostationary satellite-based LST data significantly improved the performance of the VDA model. The three-site-average root mean square errors (RMSEs) of the H, LE, and GPP estimates from the VDAMODIS scheme were 45.27 W m-2, 83.77 W m-2, and 3.30 g C m-2 d-1, respectively. Compared with the VDAMODIS scheme, the VDAHimawari-8 scheme notably enhanced the performance of the VDA framework and decreased the RMSE by 15.0% for H, 22.5% for LE, and 38.5% for the GPP, with an especially notable enhancement in humid (or vegetated) areas. The primary factors contributing to the enhancement in the performance of the VDA framework were the availability of much more frequent LST observations and the diurnal sampling capability of the Himawari-8 satellite.

Professor Sayed M. Bateni

Professor of Civil, Environmental and Construction Engineering, University of Hawaii at Manoa

Professor Sayed Bateni has dedicated his career to advancing research in natural hazards, terrestrial remote sensing, machine learning, data assimilation, and coupled land-atmosphere systems. He earned his Bachelor of Science in Civil and Environmental Engineering from Isfahan University of Technology (2002) and his Master of Science from Sharif University of Technology (2005). Later, he completed his Ph.D. at the Massachusetts Institute of Technology (2011). Currently, Dr. Bateni is a professor in the Department of Civil, Environmental, and Construction Engineering and the Water Resources Research Center at the University of Hawaiʻi at Mānoa. He has published over 150 peer-reviewed journal articles and has been recognized with numerous awards and fellowships.