Estimation of Turbulent Heat Fluxes and Gross Primary Productivity via Variational Assimilation of Remotely Sensed Land Surface Temperature and Leaf Area Index
U of Hawaii
8 Oct, Noon, in 2155
Assimilation of land surface temperature (LST) observations into variational data assimilation (VDA) frameworks to estimate turbulent heat ?uxes has been the subject of several studies. However, current VDAs neglect the role of leaf area index (LAI) in the simulation of vegetation dynamics and gross primary productivity (GPP). In this study, remotely sensed LST and LAI measurements are assimilated into a coupled surface energy balance-vegetation dynamic model (SEB-VDM) within a VDA system to estimate both turbulent heat fluxes and GPP. The SEB and VDM are coupled by relating photosynthesis in the VDM to transpiration in the SEB equation. The unknown parameters of the VDA system are neutral bulk heat transfer coefficient (CHN), soil evaporative fraction (EFS), canopy evaporative fraction (EFC), and specific leaf area (SLA). The performance of the VDA approach is tested over the Heihe River Basin (HRB) in northwest China, which covers an area of approximately 1.43×106 km2. The spatio-temporal patterns in the EFS estimates are consistent with those of SMAP soil moisture data. The estimated turbulent heat fluxes and GPP agree well with the corresponding eddy covariance measurements at eight sites in the HRB. The specific leaf area (SLA) retrievals show a physically reasonable response to changes in rainfall and irradiance. Overall, the results show that the developed VDA approach can extract the implicit information in the sequences of space-borne LAI and LST measurements to estimate sensible and latent heat fluxes, and GPP.