Assessment of clear and cloudy sky parameterizations for daily downwelling longwave radiation over different land surfaces in Florida, USA

Clear sky downwelling longwave radiation (Rldc) and cloudy sky downwelling longwave radiation (Rld) formulas were tested across eleven sites in Florida. The Brunt equation, using air vapor pressure and temperature measurements, provides the best Rldc estimates with a root mean square error of less than around 12 Wm−2 across all sites. The Crawford and Duchon\u27s cloudiness factor with Brunt equation is recommended for Rld calculations. This combined approach requires no local calibration and estimates Rld with a root mean square error of less than around 13 Wm−2 and squared correlation coefficients that typically exceed 0.9

Aerodynamic Methods for Estimating Turbulent Fluxes

The exchange of energy and mass between a surface and the lowest region of the troposphere is a complex process that governs many hydrological, agricultural, and atmospheric processes. The layer of air directly affected by surface– atmosphere exchanges is strongly influenced by turbulent processes at the surface–atmosphere boundary and extends upward into the atmosphere to a height of approximately 1 km. This region is commonly referred to as the atmospheric boundary layer (ABL) that is uniquely characterized by turbulence resulting from mechanical (wind shear) and buoyancy (thermal) forces at or near the surface. Methods have been developed to evaluate energy/mass (heat, water vapor, trace gases, and pollutants) exchanges between the ABL and the underlying surface. In this chapter, we describe the flux gradient approach for estimating mass and energy fluxes under the rubric of aerodynamic methods. We provide some historical perspective, present fundamental equations in the context of Monin-Obukhov similarity theory and introduce recent developments of an alternative method to compute heat and water vapor fluxes using turbulence variance statistics

Aerodynamic Methods for Estimating Turbulent Fluxes

The exchange of energy and mass between a surface and the lowest region of the troposphere is a complex process that governs many hydrological, agricultural, and atmospheric processes. The layer of air directly affected by surface– atmosphere exchanges is strongly influenced by turbulent processes at the surface–atmosphere boundary and extends upward into the atmosphere to a height of approximately 1 km. This region is commonly referred to as the atmospheric boundary layer (ABL) that is uniquely characterized by turbulence resulting from mechanical (wind shear) and buoyancy (thermal) forces at or near the surface. Methods have been developed to evaluate energy/mass (heat, water vapor, trace gases, and pollutants) exchanges between the ABL and the underlying surface. In this chapter, we describe the flux gradient approach for estimating mass and energy fluxes under the rubric of aerodynamic methods. We provide some historical perspective, present fundamental equations in the context of Monin-Obukhov similarity theory and introduce recent developments of an alternative method to compute heat and water vapor fluxes using turbulence variance statistics

The effect of land-atmosphere feedbacks on the spatial structure of land surface fluxes over heterogeneous terrain

The ability to understand and accurately map land surface fluxes at the spatial resolutions of human activity can support efforts to define the impact of anthropogenic induced land cover changes on hydrological and ecological processes. While remote sensors can map the surface states, the scientific problem arises from an incomplete knowledge of how heterogeneous surface states excite heterogeneity in the states of the lower atmosphere, which feedback on the exchange rates of mass, energy, and momentum across these heterogeneous land surfaces. Through the development and implementation of a framework for merging remotely sensed land surface data into a Large Eddy Simulation (LES) model of the atmospheric boundary layer, a procedure now exists for evaluating the typical ecohydrological modeling assumption of homogeneous atmospheric variables (i.e. decoupled from surface heterogeneity) over a study region. The strength of the feedback effects (or surface-air state coupling), with particular attention to the effect of variability of surface states on atmospheric properties in the surface layer, has been shown in our previous work to depend on both the length scales of the surface features [Albertson et al., 2001] and the magnitude of the contrast in surface states across the features [Kustas and Albertson, 2003]. Ignoring consideration of the feedback effects can lead to erroneous flux estimation since most landscapes are inherently heterogeneous. In this talk we examine new results and present a simple scale-dependent means to account for surface-atmosphere coupling in the estimation of land surface fluxes from remotely sensed data over complex terrain

Determining a robust indirect measurement of leaf area index in California vineyards for validating remote sensing-based retrievals

Accurate ground-based measurements of leaf area index (LAI) are needed for validation of remote sensing-based retrievals used in models estimating plant water use, stress, carbon assimilation and other land surface processes. Several methods for indirect LAI estimation with the Plant Canopy Analyzer (PCA, LAI-2200C, LI-COR, Lincoln, NE, USA) were evaluated using destructive (direct) leaf area measurements in three split-canopy vineyards and one double-vertical vineyard in California, as part of the Grape Remote sensing and Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). A method with the sensor facing the canopy, and four readings occurring evenly across the interrow space, had a coefficient of determination (R2) of 0.87 and relative root mean square error (RRMSE) of 16%, when compared to direct LAI measurements via destructive sampling. A previously used method, with the sensor facing down-row, showed lower correlation to direct LAI (R2 = 0.75, RRMSE = 33%) and underestimation which was mitigated by removing the outer sensor rings from analysis. A PCA method is recommended for rapid and accurate LAI estimation in split-canopy vineyards, though local calibration may be required. The method was tested within small units of ground surface area, which compliments high-resolution datasets such as those acquired by small unmanned aerial vehicles. The utility of ground-based LAI measurements to validate remote sensing products is discussed.info:eu-repo/semantics/acceptedVersio

Utility of thermal sharpening over Texas high plains irrigated agricultural fields

Irrigated crop production in the Texas high plains (THP) is dependent on water extracted from the Ogallala Aquifer, an area suffering from sever water shortage. Water management in this area is therefore highly important. Thermal satellite imagery at high temporal (~daily) and high spatial (~100 m) resolutions could provide important surface boundary conditions for vegetation stress and water use monitoring, mainly through energy balance models such as DisALEXI. At present, however, no satellite platform collects such high spatiotemporal resolution data. The objective of this study is to examine the utility of an image sharpening technique (TsHARP) for retrieving land surface temperature at high spatial resolution (down to 60 m) from moderate spatial resolution (1 km) imagery, which is typically available at higher (~daily) temporal frequency. A simulated sharpening experiment was applied to Landsat 7 imagery collected over the THP in September 2002 to examine its utility over both agricultural and natural vegetation cover. The Landsat thermal image was aggregated to 960 m resolution and then sharpened to its native resolution of 60 m and to various intermediate resolutions. The algorithm did not provide any measurable improvement in estimating high-resolution temperature distributions over natural land cover. In contrast, TsHARP was shown to retrieve high-resolution temperature information with good accuracy over much of the agricultural area within the scene. However, in recently irrigated fields, TsHARP could not reproduce the temperature patterns. Therefore we conclude that TsHARP is not an adequate substitute for 100-m-scale observations afforded by the current Landsat platforms. Should the thermal imager be removed from follow-on Landsat platforms, we will lose valuable capacity to monitor water use at the field scale, particularly in many agricultural regions where the typical field size is ~100 X 100 m. In this scenario, sharpened thermal imagery from instruments like MODIS or VIIRS would be the suboptimal alternative

Modeling Surface Energy Fluxes over a Dehesa (Oak Savanna) Ecosystem Using a Thermal Based Two-Source Energy Balance Model (TSEB) I

Savannas are among the most variable, complex and extensive biomes on Earth, supporting livestock and rural livelihoods. These water-limited ecosystems are highly sensitive to changes in both climatic conditions, and land-use/management practices. The integration of Earth Observation (EO) data into process-based land models enables monitoring ecosystems status, improving its management and conservation. In this paper, the use of the Two-Source Energy Balance (TSEB) model for estimating surface energy fluxes is evaluated over a Mediterranean oak savanna (dehesa). A detailed analysis of TSEB formulation is conducted, evaluating how the vegetation architecture (multiple layers) affects the roughness parameters and wind profile, as well as the reliability of EO data to estimate the ecosystem parameters. The results suggest that the assumption of a constant oak leaf area index is acceptable for the purposes of the study and the use of spectral information to derive vegetation indices is sufficiently accurate, although green fraction index may not reflect phenological conditions during the dry period. Although the hypothesis for a separate wind speed extinction coefficient for each layer is partially addressed, the results show that taking a single oak coefficient is more precise than using bulk system coefficient. The accuracy of energy flux estimations, with an adjusted Priestley–Taylor coefficient (0.9) reflecting the conservative water-use tendencies of this semiarid vegetation and a roughness length formulation which integrates tree structure and the low fractional cover, is considered adequate for monitoring the ecosystem water use (RMSD ~40W m-2)

Utility of thermal sharpening over Texas high plains irrigated agricultural fields

Irrigated crop production in the Texas high plains (THP) is dependent on water extracted from the Ogallala Aquifer, an area suffering from sever water shortage. Water management in this area is therefore highly important. Thermal satellite imagery at high temporal (~daily) and high spatial (~100 m) resolutions could provide important surface boundary conditions for vegetation stress and water use monitoring, mainly through energy balance models such as DisALEXI. At present, however, no satellite platform collects such high spatiotemporal resolution data. The objective of this study is to examine the utility of an image sharpening technique (TsHARP) for retrieving land surface temperature at high spatial resolution (down to 60 m) from moderate spatial resolution (1 km) imagery, which is typically available at higher (~daily) temporal frequency. A simulated sharpening experiment was applied to Landsat 7 imagery collected over the THP in September 2002 to examine its utility over both agricultural and natural vegetation cover. The Landsat thermal image was aggregated to 960 m resolution and then sharpened to its native resolution of 60 m and to various intermediate resolutions. The algorithm did not provide any measurable improvement in estimating high-resolution temperature distributions over natural land cover. In contrast, TsHARP was shown to retrieve high-resolution temperature information with good accuracy over much of the agricultural area within the scene. However, in recently irrigated fields, TsHARP could not reproduce the temperature patterns. Therefore we conclude that TsHARP is not an adequate substitute for 100-m-scale observations afforded by the current Landsat platforms. Should the thermal imager be removed from follow-on Landsat platforms, we will lose valuable capacity to monitor water use at the field scale, particularly in many agricultural regions where the typical field size is ~100 X 100 m. In this scenario, sharpened thermal imagery from instruments like MODIS or VIIRS would be the suboptimal alternative

Utility of thermal image sharpening for monitoring field-scale evapotranspiration over rainfed and irrigated agricultural regions

The utility of a thermal image sharpening algorithm (TsHARP) in providing fine resolution land surface temperature data to a Two-Source-Model for mapping evapotranspiration (ET) was examined over two agricultural regions in the U.S. One site is in a rainfed corn and soybean production region in central Iowa. The other lies within the Texas High Plains, an irrigated agricultural area. It is concluded that in the absence of fine (sub-field scale) resolution thermal data, TsHARP provides an important tool for monitoring ET over rainfed agricultural areas. In contrast, over irrigated regions, TsHARP applied to kilometer-resolution thermal imagery is unable to provide accurate fine resolution land surface temperature due to significant sub-pixel moisture variations that are not captured in the sharpening procedure. Consequently, reliable estimation of ET and crop stress requires thermal imagery acquired at high spatial resolution, resolving the dominant length-scales of moisture variability present within the landscape

Soil profile method for soil thermal diffusivity, conductivity and heat flux: Comparison to soil heat flux plates

Diffusive heat flux at the soil surface is commonly determined as a mean value over a time period using heat flux plates buried at some depth (e.g., 5–8 cm) below the surface with a correction to surface flux based on the change in heat storage during the corresponding time period in the soil layer above the plates. The change in heat storage is based on the soil temperature change in the layer over the time period and an estimate of the soil thermal heat capacity that is based on soil water content, bulk density and organic matter content. One- or multiple-layer corrections using some measure of mean soil temperature over the layer depth are common; and in some cases the soil water content has been determined, although rarely. Several problems with the heat flux plate method limit the accuracy of soil heat flux values. An alternative method is presented and this flux gradient method is compared with soil heat flux plate measurements. The method is based on periodic (e.g., half-hourly) water content and temperature sensing at multiple depths within the soil profile and a solution of the Fourier heat flux equation. A Fourier sine series is fit to the temperature at each depth and the temperature at the next depth below is simulated with a sine series solution of the differential heat flux equation using successive approximation of the best fit based on changing the thermal diffusivity value. The best fit thermal diffusivity value is converted to a thermal conductivity value using the soil heat capacity, which is based on the measured water content and bulk density. A statistical analysis of the many data resulting from repeated application of this method is applied to describe the thermal conductivity as a function of water content and bulk density. The soil heat flux between each pair of temperature measurement depths is computed using the thermal conductivity function and measured water contents. The thermal gradient method of heat flux calculation compared well to values determined using heat flux plates and calorimetric correction to the soil surface; and it provided better representation of the surface spatiotemporal variation of heat flux and more accurate heat flux values. The overall method resulted in additional important knowledge including the water content dynamics in the near-surface soil profile and a soil-specific function relating thermal conductivity to soil water content and bulk density