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

Xu, Tong; He, Xinlei; Bateni, Sayed M.; Auligné, Thomas; Liu, Shaomin; Xu, Ziwei; Zhou, Ji; Mao, Kebiao

doi:10.1016/j.rse.2018.11.023

Cited by 57 publications

(64 citation statements)

References 77 publications

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“…According to Xu, He, et al (2019), the a priori EF estimate is obtained by According to Xu, He, et al (2019), the a priori EF estimate is obtained by…”

Section: System Modelmentioning

confidence: 99%

“…Following Xu, He, et al (2019), τ is obtained from a leaf area index (LAI) (for vegetated land) or apparent thermal inertia (ATI) (for barren land). ); φ is the calibration coefficient, and τ is the environmental index.…”

Section: System Modelmentioning

confidence: 99%

“…Detailed information on the 12 EC flux towers in the HRB can be found in Xu, He, et al (2019). The barren land consists of barren or sparsely vegetated lands.…”

Section: 1029/2019ea000705mentioning

confidence: 99%

“…The readers are referred to Xu, He, et al (2019) for detailed information on the VDA approach. The two key unknown parameters of the EnKS approach are the neutral heat transfer coefficient (C HN ) and evaporative fraction (EF).…”

Section: Introductionmentioning

confidence: 99%

“…Monthly C HN estimates from the EnKS approach (first row), and LAI values (second row) over the HRB.corresponding EF estimates are chosen as optimum values, Herein, C HN is varied from 0.001 to 0.05 with an increment of 0.001He et al, 2018;Xu, Bateni, et al, 2018;Xu, He, et al, 2019).…”

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confidence: 99%

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Mapping Regional Turbulent Heat Fluxes via Assimilation of MODIS Land Surface Temperature Data into an Ensemble Kalman Smoother Framework

Bateni

et al. 2019

Earth and Space Science

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Estimation of turbulent heat fluxes via variational data assimilation (VDA) approaches has been the subject of several studies. The VDA approaches need an adjoint model that is difficult to derive. In this study, remotely sensed land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are assimilated into the heat diffusion equation within an ensemble Kalman smoother (EnKS) approach to estimate turbulent heat fluxes. The EnKS approach is tested in the Heihe River Basin (HRB) in northwest China. The results show that the EnKS approach can estimate turbulent heat fluxes by assimilating low temporal resolution LST data from MODIS. The findings indicate that the EnKS approach performs fairly well in various hydrological and vegetative conditions. The estimated sensible (H) and latent (LE) heat fluxes are compared with the corresponding observations from large aperture scintillometer systems at three sites (namely, Arou, Daman, and Sidaoqiao) in the HRB. The turbulent heat flux estimates from EnKS agree reasonably well with the observations, and are comparable to those of the VDA approach. The EnKS approach also provides statistical information on the H and LE estimates. It is found that the uncertainties of H and LE estimates are higher over wet and/or densely vegetated areas (grassland and forest) compared to the dry and/or slightly vegetated areas (cropland, shrubland, and barren land).

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“…According to Xu, He, et al (2019), the a priori EF estimate is obtained by According to Xu, He, et al (2019), the a priori EF estimate is obtained by…”

Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

“…Detailed information on the 12 EC flux towers in the HRB can be found in Xu, He, et al (2019). The barren land consists of barren or sparsely vegetated lands.…”

Section: 1029/2019ea000705mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Mapping Regional Turbulent Heat Fluxes via Assimilation of MODIS Land Surface Temperature Data into an Ensemble Kalman Smoother Framework

Bateni

et al. 2019

Earth and Space Science

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Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Conditions in the Heihe River Basin

Zhang

Liu

et al. 2020

Chin. Geogr. Sci.

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Land surface hydrothermal conditions (LSHCs) reflect land surface moisture and heat conditions, and play an important role in energy and water cycles in soil-plant-atmosphere continuum. Based on comparison of four evaluation methods (namely, the classic statistical method, geostatistical method, information theory method, and fractal method), this study proposed a new scheme for evaluating the spatial heterogeneity of LSHCs. This scheme incorporates diverse remotely sensed surface parameters, e.g., leaf area index-LAI, the normalized difference vegetation index-NDVI, net radiation-Rn, and land surface temperature-LST. The LSHCs can be classified into three categories, namely homogeneous, moderately heterogeneous and highly heterogeneous based on the remotely sensed LAI data with a 30 m spatial resolution and the combination of normalized information entropy (S') and coefficient of variation (CV). Based on the evaluation scheme, the spatial heterogeneity of land surface hydrothermal conditions at six typical flux observation stations in the Heihe River Basin during the vegetation growing season were evaluated. The evaluation results were consistent with the land surface type characteristics exhibited by Google Earth imagery and spatial heterogeneity assessed by high resolution remote sensing evapotranspiration data. Impact factors such as precipitation and irrigation events, spatial resolutions of remote sensing data, heterogeneity in the vertical direction, topography and sparse vegetation could also affect the evaluation results. For instance, short-term changes (precipitation and irrigation events) in the spatial heterogeneity of LSHCs can be diagnosed by energy factors, while long-term changes can be indicated by vegetation factors. The spatial heterogeneity of LSHCs decreases when decreasing the spatial resolution of remote sensing data. The proposed evaluation scheme would be useful for the quantification of spatial heterogeneity of LSHCs over flux observation stations toward the global scale, and also contribute to the improvement of the accuracy of estimation and validation for remotely sensed (or model simulated) evapotranspiration.

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Regional and Global Land Data Assimilation Systems: Innovations, Challenges, and Prospects

et al. 2019

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

Cited by 57 publications

References 77 publications

Mapping Regional Turbulent Heat Fluxes via Assimilation of MODIS Land Surface Temperature Data into an Ensemble Kalman Smoother Framework

Mapping Regional Turbulent Heat Fluxes via Assimilation of MODIS Land Surface Temperature Data into an Ensemble Kalman Smoother Framework

Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Conditions in the Heihe River Basin

Regional and Global Land Data Assimilation Systems: Innovations, Challenges, and Prospects

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