Estimation of surface turbulent heat fluxes via variational assimilation of sequences of land surface temperatures from Geostationary Operational Environmental Satellites

Abstract

Recently, a number of studies have focused on estimating surface turbulent heat fluxes via assimilation of sequences of land surface temperature (LST) observations into variational data assimilation (VDA) schemes. Using the full heat diffusion equation as a constraint, the surface energy balance equation can be solved via assimilation of sequences of LST within a VDA framework. However, the VDA methods have been tested only in limited field sites that span only a few climate and land use types. Hence, in this study, combined-source (CS) and dual-source (DS) VDA schemes are tested extensively over six FluxNet sites with different vegetation covers (grassland, cropland, and forest) and climate conditions. The CS model groups the soil and canopy together as a single source and does not consider their different contributions to the total turbulent heat fluxes, while the DS model considers them to be different sources. LST data retrieved from the Geostationary Operational Environmental Satellites are assimilated into these two VDA schemes. Sensible and latent heat flux estimates from the CS and DS models are compared with the corresponding measurements from flux tower stations. The results indicate that the performance of both models at dry, lightly vegetated sites is better than that at wet, densely vegetated sites. Additionally, the DS model outperforms the CS model at all sites, implying that the DS scheme is more reliable and can characterize the underlying physics of the problem better.

Uncertainty Quantification in Land Surface Hydrologic Modeling: Toward an Integrated Variational Data Assimilation Framework

“…The uncertainty analysis suggests that the VDA method retrieves the land surface heat fluxes with a higher degree of uncertainty: 1) in wet land surfaces than dry, 2) over densely vegetated land surfaces than lightly, and 3) when the difference between land surface and air temperature is low. These results correspond well with the results of previous works conducted on testing the performance of the VDA method in retrieving land surface fluxes over different field sites (e.g., [11], [21], [32]). Thus, the UQ framework proposed in this manuscript provides a calibration-free tool (i.e., not reliant on measurements of surface fluxes) to evaluate the influence of land surface and climatic factors on the performance of the VDA method.…”

“…The results of the uncertainty analysis are corresponding well with the results of previous work on the test of the performance of the VDA method [11], [19], [21], [32].…”

“…In summary, three common hypotheses that have been raised regarding the performance of the VDA technique in retrieving land surface turbulent fluxes in literature (e.g., [11], [32], [61], [21]) are: 1) the VDA approach has less satisfactory performance in retrieving surface fluxes in wet than dry land surfaces, 2) the VDA approach has less satisfactory performance in densely than lightly vegetated land surfaces, and 3) the simultaneous retrieval of unknown parameters of turbulent heat fluxes (i.e., C HN and EF) by the VDA approach is sensitive to even small perturbations in T over sites with small and nonvariable T − T a differences, which prevents robust retrieval of the land surface fluxes.…”

“…Similar observations were reported by other researchers (e.g., [11], [60]). Xu et al [21] tested the performance of the VDA model developed by Bateni et al [19] for retrieving fluxes over six AmeriFlux sites. They also conclude that the performance of the VDA method at dry, lightly vegetated sites is better than that at wet, densely vegetated sites.…”

Temporal Upscaling and Reconstruction of Thermal Remotely Sensed Instantaneous Evapotranspiration

“…have been developed during the last two decades [35][36][37][38]. Data assimilation methods have been used to generate temporal continuous soil moisture, soil temperature, surface fluxes, NDVI and LAI, by assimilating remote sensing data into land surface models [39][40][41][42][43][44][45][46]. For surface flux gap filling, a data assimilation scheme has been developed to reconstruct surface flux data from eddy covariance measurements [47].…”