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246 Remote Sensing – Applications

to quantify and validate estimates of the C balance at these intermediate scales (Lin et al., 2004; Chen et al., 2008; Bakwin et al., 2004; Matross et al., 2006). Observations of CO2 over the continent within the PBL reflect exchange processes occurring at the surface at a regional scale (102 – 105 km2). The flux information contained in CO2 concentration data represents footprints of up to 105 km2 (Gloor et al., 2001; Lin et al., 2004), which are several orders of magnitude larger than the direct EC-flux footprint. This information is therefore needed in our effort to upscale from site to region. Moreover, the number of CO2 mixing ratio measurements above the land surface, made by either tower or aircraft, is steadily increasing. Previous efforts to interpret the signal of regional CO2 exchange making use of tower concentration data have focused on simple one-dimensional PBL budgets that rely on gradients in CO2 concentrations between the PBL and the free troposphere (Bakwin et al., 2004; Helliker et al., 2004). These methods are limited to monthly resolution because of the

A synthetic research framework is needed to strength the less well researched areas as reviewed in Section 5: bottom-up and top-down approaches integrating scalable (footprint and ecosystem) models and a spatially nested hierarchy of observations which include multispectral RS, inventories, existing regional clusters of eddy-covariance flux towers and

The current research trends and the future directions in this field include: (i) A synthesis aggregation method --- integrating ecohydrological and isotopic models, remote sensing and component flux data, is becoming a pragmatic approach towards a better understanding of the coupled C, N and water dynamics at landscape/watershed scales; and (ii) The landscape- and regional-scale C fluxes are being estimated using an integrated approach involving direct land surface measurements, RS measurements, and ecosystem-,

After comprehensive reviewing of a variety of approaches being used in research on the

Research gaps in this field are (i) The coupled terrestrial C and hydrological dynamics are far from well understood, especially at landscape (watershed) and regional scales; (2) Much progresses have been achieved at the extreme ends of the spatial-scale spectrum, either large regions/continents or small vegetation stands. Because of the heterogeneity of the land surface and the nonlinearity inherent in ecophysiological and ecohydrological processes in response to their driving forces, it is difficult to upscale stand level results to regions and the globe by extrapolation. Budgets of C and water at landscape intermediate regional scales

A coupled spatially-explicit ecohydrological model is a powerful tool for quantitative and predictive understanding of the coupled C and water mechanism. This modeling framework can be used to infer aspects of the land surface system that are difficult to measure, and will be critical to improving the accuracy of forecasts of landscape change and C dynamics in the

C/water cycles, the concluding remarks are summed the following:

need to smooth and average over several synoptic events (Matross et al., 2006).

**6. Future research directions** 

CO2 mixing ratio towers and chambers.

footprint- and inversion- modeling.

(102–105 km2) have large uncertainties.

**7. Summary** 

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**10** 

*USA* 

**Oceanic Evaporation: Trends and Variability** 

*1Department of Atmospheric, Oceanic and Earth Sciences, George Mason University* 

The global water and energy cycles are strongly coupled as two essential components of

Oceanic evaporation, or sea surface latent heat flux (LHF) divided by latent heat of vaporization (*L*v), is a key component of global water and energy cycle. In a bulk aerodynamic formulation, it is determined by the transfer coefficient of evaporation, *C*E, and bulk parameters such as surface wind speed (*U*), surface saturated and near-surface air

> ( ) *LHF L C U Q Q*

where sea surface saturated humidity is determined by sea surface temperature (SST) and salinity, and *ρ* is density of moist air. The transfer coefficient is dependent on the stability of the atmosphere and the sea state (Liu et al., 1979; Zeng et al., 1988). Historically, marine surface observations have provided the basis for estimating these oceanic turbulent fluxes (e.g. Bunker, 1976; Cayan, 1992; da Silva et al., 1994; Esbensen & Kushnir, 1981; Hastenrath, 1980; Hsiung, 1985; Isemer & Hasse, 1985, 1987; Josey et al., 1998; Oberhuber, 1988; Renfrew et al., 2002; Weare et al., 1981). The advent of remote sensing techniques offers means to retrieve a number of surface bulk variables. Microwave radiation interacts directly with water molecules and hence is effective in providing water vapor information. The sea surface emissivity is affected by the sea state and foam conditions, which is related to surface wind. For instance, global microwave measurement of the Special Sensor Microwave Imager (SSM/I) on board a series of Defense Meteorological Satellite Program (DMSP) satellites has been used to retrieve near-surface air humidity and winds over the ocean.

At present there are several remote sensing products of global ocean surface latent heat flux. They include the NASA/Goddard Satellite-based Surface Turbulent Flux (GSSTF) dataset version 1 (Chou et al., 1997) and version 2 (GSSTF2, Chou et al., 2003), the Japanese Ocean Flux utilizing Remote Sensing Observations (J-OFURO) dataset (Kubota et al., 2002) and the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) dataset (Grassl et al., 2000). Chiu et al. (2008) examined "trends" and variations in these global oceanic evaporation products for the period 1988–2000. They found a long-term increase in global average LHF that started around 1990 in GSSTF2. They argued that the dominant patterns may be related to an enhancement of Hadley circulation and El Niño-Southern Oscillation

earth system. They play important roles in altering the Earth's climate.

**1. Introduction** 

specific humidity (*Q*s and *Q*a),

Long S. Chiu1, Si Gao1 and Chung-Lin Shie2

*vE s a* (1)

*2UMBC/JCET, NASA/GSFC* 

