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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: Estimating spatial veriability in atmospheric properties over remotely sensed land-surface conditions

Authors
item Bertoldi, Giacomo - DUKE UNIVERSITY
item Kustas, William
item Albertson, John - DUKE UNIVERSITY

Submitted to: Journal of Applied Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 21, 2008
Publication Date: August 12, 2008
Citation: Bertoldi, G., Kustas, W.P., Albertson, J.D. 2008. Estimating spatial veriability in atmospheric properties over remotely sensed land-surface conditions. Journal of Applied Meteorology. 47:2147-2165.

Interpretive Summary: The estimation of evapotranspiration and heat/energy exchange typically requires knowledge of land cover characteristics, surface states, and near-surface air properties. Remote sensing can provide high- resolution information (~10-100 meters) of land surface states and properties, such as radiometric surface temperature (Ts), land use/land cover, and canopy cover or fractional density (fc), and, potentially at coarser resolution, surface soil moisture. This information can be used in a land surface scheme for computing spatially-distributed ET/energy fluxes. Atmospheric properties (AP), however, are often assumed constant at the regional scale (~10-100 kilometers), and are typically obtained from a single nearby weather station which is often in a location that is not necessarily representative of the entire study domain (e.g., airports, nearby to urban areas). An inaccurate estimation of the regional mean and spatial variability of the AP can be a source of error for the estimation of the surface fluxes in areas with heterogeneous land cover, as shown in previous studies. In this paper, we investigate the variability of AP and fluxes over sparsely vegetated agricultural locations, characterized by large contrasts in Ts, fc and roughness between vegetated and bare soil patches, with different background wetness conditions and scale of the land cover variability. The spatial distribution of AP is simulated by a Large Eddy Simulation (LES) model given by a two-source (soil and vegetation) energy balance approach having Ts and fc as key boundary conditions. The combination of the land-surface model and the LES provides dynamic heat flux prediction in space and time and it takes into account the feedback effect between ET/heat fluxes and near-surface AP. The results indicate a significant local correlation of the spatial distributions of the AP and the heat fluxes These relationships can be described by a general linear form, suggesting that a simple regression relation may be applicable for most agricultural landscapes to estimate spatially variable AP fields. A simple yet practical method is proposed using remotely sensed observations and the land surface scheme, based on a general linear expressions derived between AP and surface heat flux, ET and roughness. The method is shown to reproduce the main spatial patterns of AP, to reduce potential local errors in heat flux estimation by more than 50%, and to reduce biases in regionally-averaged fluxes. This approach is recommended when only local weather station observations are available.

Technical Abstract: This paper investigates the spatial relationships between land-surface fluxes and near-surface atmospheric properties (AP), and the potential errors in flux estimation due to homogeneous atmospheric inputs over heterogeneous landscapes. A Large Eddy Simulation (LES) model is coupled to a surface energy balance scheme with remotely sensed surface temperature (Ts) as a key boundary condition. Simulations were performed for different agricultural regions having major contrasts in Ts, canopy cover, and surface roughness (z0) between vegetated/irrigated and bare soil areas. If AP from a single weather station in a not representative location are applied uniformly over the domain, significant differences in surface flux estimation with respect to the LES are observed. The spatial correlations of AP with the fluxes, the land cover properties, and surface states were examined and the spatial scaling of these fields is analyzed using a two-dimensional wavelet technique. The results indicate a significant local correlation of the spatial distributions of the air temperature (Ta) with the sensible heat flux (H), of the specific humidity (q) with the latent heat flux (LE), and of the wind speed (U) with z0. These relationships can be described by a general linear form, suggesting that a simple regression relation may be applicable for most agricultural landscapes to estimate spatially variable AP fields. A simple yet practical method is proposed using remotely sensed observations and the land surface scheme, based on a general linear expressions derived between Ta and H, q and LE, and U and z0. The method is shown to reproduce the main spatial patterns of AP, to reduce potential local errors in heat flux estimation by more than 50%, and to reduce biases in regionally-averaged fluxes. This approach is recommended when only local weather station observations are available.

Last Modified: 12/21/2014
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