## 4 Modeling of C and Water Dynamics in Terrestrial Ecosystems
### Applications of Remotely-sensed Data in Ecohydrological Modeling  
Remote sensing techniques, which inherently have the ability to provide spatially comprehensive and temporally repeatable information of the land surface, may be the only feasible way to obtaining data needed for land surface and ecological modeling [140,194-197]. The most common rationale for interfacing remote sensing and land surface-ecosystem models is using remotely sensed data as model inputs [198]. These input data, corresponding to forcing functions or state variables in ecological modeling, include LC, LAI, normalized difference vegetation index (NDVI), and the fraction of photosynthetically active radiation (\(f_{PAR}\)) [140,199-201]. Another effort is the direct estimation of GPP and NPP [202,203] of biomass [203-204] and of plant growth [205,206], by making use of \(f_{PAR}\) and NDVI. It has been shown that the direct estimation has lower accuracy than the integration of remotely sensed data with process based models [202].  
Remote sensing data have also been used to parameterize hydrological models [194,195,207]. For instance, a hydrological model (TerrainLab) was further developed using remote sensing as inputs [194]. TerrainLab is a spatially distributed, process-oriented hydrological model using the explicit routing scheme of [PERSON] _et al_. [208]. This model has been applied to flat areas (such as boreal and wet land region, [188, 195, 209]), but it has not yet been applied to mountainous areas.  
Different from traditional hydrological models, which have coarse spatial resolutions, the grid-based-distributed ecohydrological models have a high demand for spatial data [195, 210]. Some researchers highlight that the main obstacles in current distributed ecohydrological modeling is the lack of sufficient spatially distributed data for input and model validation [211]. Remote sensing can potentially fill in some of the gaps in data availability and produce means of spatial calibration and validation of distributed hydrological models. As a result the application of remote sensing techniques in hydrological studies and water resources management has progressed in the past decades (see review by [195]).  
In general, the applications of remotely sensed data in ecohydrological modeling can be in the two ways [194, 195, 207, 210-218]: (i) multispectral remote sensing data are used to quantify surface parameters, such as vegetation types and density. Although the usefulness of remote sensing data is widely recognized, there remain few cases where remote sensing data have been actually used in ecohydrological simulations. Difficulties still exist in choosing the most suitable spectral data for studying hydrological processes as well as in interpreting such data to extract useful information [194, 195, 219]; and (ii) processed remote sensing data are used to provide fields of hydrological parameters for calibration and validation of ecohydrological models, such as precipitation [195, 220], and soil moisture [221-224]. [PERSON] _et al_. [224] pointed out that remote sensing data take the form of emitted and reflected radiances and thus are not the type of data traditionally used to run and calibrate models. Hence, it is important to understand and develop relationships between the electromagnetic signals and hydrological parameters of interest [194]. [PERSON] and [PERSON] [195] stated that the use of remote sensing in hydrological modeling was limited. Even though a number of new sensors have been launched since then and research has documented that remote sensing data have promising perspectives, operational uses of satellite data in hydrological modeling still appear to be in its infancy [211].