Mapping regional turbulent heat fluxes via variational assimilation of land surface temperature data from polar orbiting satellites

• Published in: Remote sensing of environment Vol. 221; pp. 444 - 461
• Main Authors: Xu, Tongren, He, Xinlei, Bateni, Sayed M., Auligne, Thomas, Liu, Shaomin, Xu, Ziwei, Zhou, Ji, Mao, Kebiao
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.02.2019; Elsevier BV
• Subjects: Apertures; China; Data assimilation; Data collection; Diurnal; Diurnal cycle; Diurnal variations; energy; equations; Geostationary satellites; Heat; Heat diffusion; Heat flux; Heat transfer; Hydrologic models; Hydrology; Land surface temperature; Latent heat; Latent heat flux; latitude; Leaf area; Leaf area index; Mapping; Meteorological satellites; moderate resolution imaging spectroradiometer; MODIS; Polar orbiting satellites; remote sensing; River basins; Rivers; Satellite imagery; Satellite observation; Satellites; Scintillation counters; Sensible and latent heat; Sensible heat flux; Spatial data; Spatial resolution; Spectroradiometers; Surface temperature; Synchronous satellites; Temperature data; Temporal resolution; Thermal inertia; Variational data assimilation; watersheds; China; Land surface temperature; Sensible heat flux; Latent heat flux; MODIS; Variational data assimilation
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2018.11.023

Estimation of turbulent heat fluxes by assimilating sequences of land surface temperature (LST) measurements into variational data assimilation (VDA) frameworks has been the subject of several studies. The VDA approaches estimate turbulent heat fluxes by minimizing the difference between LST observations and estimations from the heat diffusion equation. The VDA methods have been tested only with high temporal resolution LST observations (e.g., from geostationary satellites) when applied at regional scales. Geostationary satellites can capture the diurnal cycle of LST, but they have a relatively low spatial resolution and mainly focus on low latitudes. To overcome these shortcomings, this study assimilates high spatial resolution LST data from polar orbiting satellites (e.g., Moderate Resolution Imaging Spectroradiometer, MODIS) into the combined-source (CS) and dual-source (DS) VDA schemes. An expression is developed to obtain an a priori evaporative fraction (EF) estimate from leaf area index (LAI) or apparent thermal inertia (ATI). The a priori EF estimate is used as an initial guess in the VDA approach. The results indicate that the VDA method is able to find the optimal value of EF by assimilating the low-temporal resolution MODIS LST data. The predicted turbulent heat fluxes from VDA are compared with the measurements from the large-aperture scintillometer at three sites (Arou, Daman, and Sidaoqiao) in the Heihe River Basin (located in northwest China). The findings indicate that the CS and DS VDA models perform well in various hydrological and vegetative conditions. The three-site-average root mean square errors (RMSEs) of sensible and latent heat fluxes estimates from the CS scheme are 37.44 W m−2 and 94.30 W m−2, respectively. The DS model reduces the abovementioned RMSEs by 19.82% and 21.37%, respectively. Overall, the results show that using the a priori EF estimate from the proposed expression in the VDA approach eliminates the need for the high resolution LST data from geostationary satellites, and allows the VDA method to estimate turbulent heat fluxes by assimilating LST data from polar orbiting satellites. Finally, several numerical tests are conducted to assess the effect of LST temporal sampling on the turbulent heat fluxes estimates. The results show that the LST measurement at 1400 Local Time (LT) has the most amount of information for partitioning the available energy into sensible and latent heat fluxes. •An expression is developed to obtain a priori EF estimate in terms of LAI or ATI.•The VDA scheme can estimate heat fluxes by assimilating low-temporal resolution LST.•The effect of LST temporal sampling on turbulent heat fluxes estimates is examined.•LST at 2:00 p.m. has most amount of information on partitioning available energy.