Concluding Remarks

All the methods and models reviewed in this chapter have potential for operational evaluation of the spatial distribution of evaporation for agricultural and hydrological applications. Toward that goal, relatively simple methods using one-time-of-day remote sensing observations for quantifying daily ET have been applied operationally (Seguin et al., 1989, 1991). However, for many regions of Earth's land surface, meteorological data (primarily wind speed and air temperature) essential for driving

6 CONCLUDING REMARKS 483

model computations are not available. Approaches using remotely sensed data for estimating the variation of these quantities are being developed and tested (Bastiaanssen et al., 1998; Gao et al., 1998). How reliable the algorithms are for different climatic regimes needs to be evaluated. For air temperature, another approach is in the utilization of radiometric temperature observations from significantly different view angles in a dual-source model (Kustas and Norman, 1997). SVAT models using remote-sensing observations and linked to operational climate and hydrologic models (Ottle and Vidal-Madjar, 1994; Gillies and Carlson, 1995; Mecikalski et al., 1999; Nouvellon et al., 2001) probably have the greatest potential for operational, regional application. This is because both the surface boundary conditions and atmospheric variables are simulated over time. For heterogenous and mountainous landscapes, further work should be focused on the development of robust aggregation techniques (e.g., Shuttleworth, 1998).

One of the greatest obstacles to the assimilation of remotely sensed information in physical models has been the inherent limitations of currently available sensors. Satellite-based sensors have the advantages of good geometric and radiometric integrity; the disadvantages include fixed spectral bands that may be inappropriate for a given application, spatial resolutions too coarse or too fine for the application, long time periods between image acquisition and delivery to user, and inadequate repeat coverage due to sensor or weather limitations. With the exception of the limitations due to weather, many of the existing limitations may be resolved with the newly launched Terra, Landsat-7, and Space Imaging satellites (Table 2).

Regarding the effects of clouds on image acquisitions, more work should be directed toward utilizing microwave remote sensing, which has some critical advantages over the use of optical data, including little atmospheric attenuation, cloud penetration, high spatial resolution, and day/night acquisitions. Microwave data have been used to derive soil moisture and other vegetation properties (Jackson et al., 1995; Moran et al., 1997b). Microwave data have also been used for estimating the partitioning of available energy into H and IE, for estimating soil evaporation, and in determining soil surface temperatures (Kustas et al., 1993b; Chanzy and Kustas, 1995; Troufleau et al., 1994). More recently, the dual-source model of Norman et al. (1995b) was revised to use remotely sensed near-surface moisture from a passive microwave sensor for estimating the soil surface energy balance (Kustas et al., 1998). With remotely sensed images of near-surface soil moisture, land cover classification and LAI, the model was applied over a semiarid area in southern Arizona. Comparison of model-predicted fluxes simulated over the daytime period with ground observations showed good results, with 15% differences in evaporation estimates, on average. It is also shown that it may be possible to simulate the daytime fluxes with only a single microwave observation.

The development of methods for combining microwave and optical data with SVAT schemes will likely produce the greatest advancement in the quantification of spatially distributed evaporation. This requires collection of remote-sensing data in concert with ground observations as part of large-scale field projects conducted in different climatic regions. This is a critical part in the further development and validation of model algorithms. Thus the conventional approaches for estimating evaporation outlined in this chapter play a key role in this effort.

Was this article helpful?

0 0
Renewable Energy 101

Renewable Energy 101

Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable. The usage of renewable energy sources is very important when considering the sustainability of the existing energy usage of the world. While there is currently an abundance of non-renewable energy sources, such as nuclear fuels, these energy sources are depleting. In addition to being a non-renewable supply, the non-renewable energy sources release emissions into the air, which has an adverse effect on the environment.

Get My Free Ebook


Post a comment