The climate system is controlled by a number of complex coupled physical, chemical and biological processes (Figure 1). The terrestrial biosphere plays a crucial role in the climate system, providing both positive and negative feedbacks to climate change through biogeophysical and biogeochemical processes [1]. Couplings between the climate system and biogeochemistry are mainly through tightly linked dynamics of C and water cycles. The importance of coupled C and water dynamics for the climate system has been increasingly recognized [2-8]; however the mechanisms behind these coupled cycles are still far from well understood.

One of the crucial issues in the prognosis of future climate change is the global budget of atmospheric CO₂. The growth rate of atmospheric CO₂ is increasing rapidly. Three processes contribute to this rapid increase: fossil fuel emission, land use change (deforestation), and ocean and terrestrial uptake. As shown in Figure 2, terrestrial C budgets have large uncertainties and interannual variability.

Terrestrial ecosystems mediate a large part of CO₂ flux between the Earth's surface and the atmosphere, with ∼120 Pg C yr^-1 taken up by photosynthesis and roughly the same amount released back to the atmosphere by respiration annually [1,15]. Imbalances between gross ecosystem photosynthesis or gross primary productivity (GPP) and ecosystem respiration (R_e) lead to land surfaces being either CO₂ sinks or sources. C sequestration by global terrestrial ecosystems is estimated to be about 1-2 Gt C yr^-1 [1,15]. A detailed understanding of the interactive relationships in atmosphere-biosphere exchange is relevant to ecosystem-scale analysis and is needed to improve our knowledge of the global C cycle [16]. The metabolism of terrestrial ecosystems is complex and highly dynamic because ecosystems consist of coupled, non-linear processes that possess many positive and negative feedbacks [17,18]. Complex features of ecosystem metabolism are relatively unknown and how C budget of major ecosystems will respond to changes in climate is not quantitatively well understood [19-23].

Thermodynamically, a terrestrial ecosystem is an open system. Therefore, hydrological and C cycles are closely coupled at various temporal and spatial scales [7,24-30]. C uptake for example, is closely coupled to water loss by ecosystems mainly through leaf stomatal pathway governed principally through leaf conductance [31-33]. Soil organic C decomposition is very sensitive to soil moisture content via microbial activity and other processes [8,25,30,34,35]. The flux of terrestrial organic C by river runoff to the ocean and wetland discharge is an important component of the global organic C cycle [36,37]. It is estimated that 0.25 × 10¹⁵ g dissolved organic carbon (DOC) is discharged to the ocean by the world rivers each year [38]. The land surface hydrological processes (in particular the terrestrial river systems) play an important role in transport of dissolved and particulate organic C from terrestrial to marine ecosystems [37]. However, the interactions between C and water cycles and the mechanisms how these interactions will shape future climatic and biosphere conditions are far from well understood.

N is an essential element that can limit the growth of living organisms across a wide range of ecosystems [39,40]. However, global inputs to the terrestrial N cycle have doubled in the past century due to anthropogenic activities, particularly fertilizer use and fossil fuel burning [41,42]. High atmospheric N and N depositions have changed the dynamics of N and C in many ecosystems of developed countries (e.g., in Europe and North America) [43], with the impact in other Asian developing countries projected to increase over the next few decades [44]. N addition to forest and other ecosystems will affect the health and vitality of ecosystems and the terrestrial C cycle [45,46]. There is a continuing discussion on whether N deposition has positive (e.g., increases in forest growth and C sequestration) or negative (e.g., increases in nitrate leaching, reduction in forest growth and C sequestration) effects on an ecosystem depends on the N status of the system and the rate and duration of N deposition [45,46]. Bauer et al. [47] summarized that (1) if the remaining available N does not exceed the capacity for N uptake by vegetation net primary productivity (NPP), C sequestration may be enhanced; (2) if deposition rates exceed the capacity for N uptake, nutrient imbalances can lead to forest decline due to N saturation [48,49]. Aber et al. [45,46] hypothesized that forest ecosystems positively respond to N addition in the short-term, while negatively respond to N addition over the long-term. N fertilization in northern temperate zones has been estimated to enhance C storage by 0.3–0.5 Pg C per year [50] with Pregitzer et al. [51] reporting that simulated chronic N deposition has increased C storage in northern temperate forests. Olsson et al. [52] found that fertilization of a boreal Norway spruce stand led to a three-fold increase in aboveground productivity, possibly due to decreased C allocation to roots in response to higher nutrient availability. Leggett and Kelting [53] found that N fertilization of Loblolly pine plantations not only increased aboveground and belowground biomass but also increased soil C pools. However, other estimates suggest that N loading on ecosystems only makes a minor contribution to C sequestration [54] or does not likely account for significant C storage [55,56] or in some cases may actually reduce ecosystem productivity and C storage [46,57].

N fertilisation may affect the pool of soil organic carbon (SOC) through the changes of litterfall. Both negative [58] and positive [59] effects of N fertilisation on root biomass have been observed. In a detailed review Nadelhoffer [60] found it is likely that N deposition will decrease fine-root biomass, however, stimulate fine-root turnover and production. N addition/fertilization to soils has also shown variable effects on SOC storage by enhanced, reduced or unchanged heterotrophic and/or autotrophic respiration as a response to litter C/N ratio [61-68]. With increasing rates of anthropogenic N deposition [41,42], there is a strong need to understand links between ecosystem N inputs and C sequestration.

The EC technique is commonly used to directly measure the CO₂, water vapor and energy exchange between the atmosphere and terrestrial ecosystems [9]. A global network of collaborating regional networks, FLUXNET, began in 1997 [9] and today, there exist more than 400 EC-flux towers across the globe. EC measurements are a rich source of information on temporal variability and environmental controls of CO₂ exchange between the atmosphere and terrestrial ecosystems [69]. These global EC datasets allow us to (1) explore emergent-scale properties by quantifying how the metabolism of complex ecosystems respond to perturbations in climate variables on diurnal, seasonal, interannual and decadal time scales and elucidate physical and biological controlling factors [9,69]; (2) examine carry-over effects that may be introduced by either favorable or deleterious conditions during antecedent years [70]; (3) observe a disturbance and the recovery from it or to span a natural sequence of ecological development coupled with fluctuations in climate [71,72]; and (4) test and validate ecosystem process models [73,74], since most of these models span timescales from hours to decades. Although the available EC data have been rapidly accumulating, there are some issues associated with its use due to difficulties/uncertainties in (i) assessing/interpreting the associated measuring biases of EC data, and (ii) upscaling of the EC fluxes at the ecosystem (typically less than 1–3 km² for each site) to larger scales, e.g., landscape and regional scales.

Observations of CO₂ over the continent within the atmospheric boundary layer 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 [75-77], which is several orders of magnitude larger than the direct EC-flux footprint. These measurements form a record of integrated net CO₂ exchange from multiple processes, geographic areas, and times [78]. CO₂ concentration measurements are made from tall towers [79-81] or balloons that reach the top of the planetary boundary layer (PBL) of the Earth. These measurements therefore provide significant information to upscale from site to region. Moreover, the number of CO₂ concentration measurements above the land surface, made by either tower or aircraft, is steadily increasing. Data are collected by numerous agencies around the world, for instance, the National Oceanic and Atmospheric Administration's (NOAA's) Earth System Research Laboratory (ESRL) monitors CO₂ in the atmosphere as a contribution to the North American Carbon Program (NACP) [82]. In addition, Peters et al. [78] suggest that direct satellite observations of CO₂ are available already for the upper troposphere, whereas near-surface CO₂ from space will become available within several years to augment the current efforts.

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 [77,79,83-85]. These methods are limited to monthly resolution because of the need to smooth and average over several synoptic events [86].

The atmosphere integrates surface fluxes over many temporal and spatial scales and links scalar sources and sinks with concentrations and fluxes. This principle has been successfully used to develop inverse models to estimate annual carbon budgets [87-90]. However, due to model limitations and paucity of continental CO₂ observations these studies have yielded carbon fluxes only at coarse resolution, over large spatial regions (i.e., at continental scale, [91]). Recently, Stephens [89] and Yang [90] showed that a large set of atmospheric inverse model results were inconsistent with free troposphere CO₂ concentration. Transport biases are one of the largest unknown sources of error in flux inversions [88].

A powerful way to use all these CO₂ data is in a data assimilation system, which combines diverse data and models into a unified description of a physical/biogeochemical system consistent with observations [13]. Such a new data assimilation system called CarbonTracker, has been built in the NOAA ESRL, using a state-of-the-art atmospheric transport model coupled to an ensemble Kalman filter [78]. The first release of CarbonTracker marks a significant step in our ability to monitor month-by-month surface sources and sinks of CO₂ [78], with all results, data and code of CarbonTracker freely available from NOAA ERSL's CarbonTracker web site, http://carbontracker.noaa.gov.

The information on the biological and physical processes that exchange CO₂ between terrestrial ecosystems and the atmosphere is recorded by the signals of ¹³C /¹²C ratio in the atmosphere CO₂. The stable isotope ratio of CO₂ (δ¹³C) in the atmosphere contains unique information to study the overall balance of surface CO₂ fluxes [92,93]. As reviewed by Suits et al. [94], C isotopes can be helpful in investigations of the following four aspects at ecosystem and local scales: (i) plant water-use efficiency and the response of plants to changes in precipitation and relative humidity [95-100], (ii) variation in light distribution and stand structure [101-104], (iii) recycling of respired CO₂ [105-109], and (iv) determining the relative contributions of photosynthesis and respiration to the total net ecosystem exchange [110-114].

Multiple efforts to measure stable C isotopes at both flux towers and flask stations around the world have been achieved. Several new techniques (e.g., automated measurement systems, tunable diode laser (TDL) spectrometer and pulsed quantum cascade laser spectrometer) have also been applied to this investigation [115-117], isotope measurements, however, are still lacking considering the land surface diversity/heterogeneity. This shortage of long-term measurements and of sampling frequency still limits isotopic studies and applications to various spatial/temporal scales.

Efforts to expand CarbonTracker to assimilate observations of C isotopes (¹³CO₂, ¹⁴CO₂) and other observation types (eddy-flux measurements, satellite radiances) are underway. Such observations could facilitate attribution of carbon fluxes to specific processes such as fossil fuel burning, biomass burning, or agricultural food and biofuel production [78].

Satellite-borne remote sensing offers unique opportunities to parameterize land surface characteristics over large spatial extents at variable spatial and temporal resolutions. For example, the Moderate Resolution Imaging Spectroradiometer (MODIS) provides a global dataset every 1–2 days with 36 bands. The spatial resolution of MODIS (pixel size at nadir) is 250 m for channels 1 and 2 (0.6–0.9 μm), 500 m for channels 3 to 7 (0.4–2.1 μm) and 1,000 m for channels 8 to 36 (0.4–14.4 μm), respectively. Data from the satellite-borne MODIS are currently used in the calculation of global weekly GPP at 1 km spatial resolution [10]. Other sensors, the Landsat Thematic Mapper sensors carried onboard the Landsat series of satellites, acquire images at a 30 m spatial resolution with a 16 day interval.

In general, water evapotranspired from ecosystems into the atmosphere will reduce the land surface temperature (T_a). Reduction in soil moisture will decrease plant transpiration and evaporation from soil and plant surfaces. Reduction in ET will increase T_a. T_a can be derived from remotely-sensed thermal-infrared (TIR) band (8-14 microns) from various operational satellites. Based on the relationship between T_a and ET, remotely sensed T_a has been used to estimate regional ET [118-121]. The existing thermal imaging sensors provide adequate coverage of thermal dynamics that are useful for operational monitoring applications of ET. For example, thermal images at 15 minutes intervals and at a spatial resolution of 5 kilometers can be obtained from the NOAA Geostationary Operational Environmental Satellites (GOES), and TIR data at a fine spatial resolution (60 m or 120 m) with a much longer time interval (16 days) have been provided by the Thematic Mapper (TM) and ETM+ instruments on Landsat 5 and Landsat 7.

ET, the largest component of water loss from ecosystems, plays an important role in affecting soil moisture, vegetation productivity, C cycle, and water budgets in terrestrial ecosystems [122-124]. Verstraeten et al. [12] provided a comprehensive review of remote sensing methods for assessing ET and soil moisture content across different scales and Kalma et al. [11] reviewed satellite-based algorithms for estimating ET and land surface temperatures at local, regional and continental scales, with particular emphasis on studies published since the early 1990s.

In addition, as Marquis and Tans [13] reviewed, satellite-based instruments can also provide information about CO₂ concentration in the atmosphere, but no current satellite-borne instrument comes close to providing the accuracy, precision, and continuity required to determine regional CO₂ concentrations and local fluxes. Future satellites, including the Greenhouse Gases Observing Satellite (GOSAT) [125], are expected to provide more accurate CO₂ measurements than do today's satellites [13,126].

Besides satellite monitoring, other airborne observation techniques (e.g., aircraft, airplane and land surface remote sensing) have been developed rapidly since the latest decade. For instance, a new approach is LiDAR (Light Detection and Ranging), which is a remote sensing technology that determines distances to an object or surface using laser pulses. LiDAR data have proved to be highly effective for the determination of three dimensional forest attributes. The suitability of airborne LiDAR for the determination of forest stand attributes including leaf area index (LAI) and the probability of canopy gaps within different layers of canopy has been widely acknowledged by various studies [127,128]. The interpreted LiDAR data have been further used for landscape C modeling and scaling [129,130].

Global climate and the global carbon cycle are controlled by exchanges of water, carbon, and energy between the terrestrial biosphere and atmosphere. Thus land surface models (LSMs) are essential for the purpose of developing predictive capability for the Earth's climate on all time scales [135]. Most current LSMs can be associated with three broad types [136]: soil-vegetation-atmosphere transfer schemes (SVATS), potential vegetation models (PVMs), and terrestrial biogeochemistry models (TBMs).

The first generation of SVATS evolved from simple bucket schemes focusing on soil water availability [137], through the schemes of Deardorff [138]. Marked improvements of the second generation (e.g., BATS [136], SiB [139,140], and CLASS [141,142]) from the first generation are the separation of vegetation from soil and the inclusion of multiple soil layers for dynamic heat and moisture-flow simulations [143]. The second generation SVATS firstly modeled plant physiology in an explicit manner in GCMs (General Circulation Model or Global Climate Model) [144]. For most second-generation SVATS, land cover was fixed, with seasonally-varying prescriptions of parameters such as reflectance, leaf area index or rooting depth [145-148]. Some SVATS incorporated satellite data to characterize more realistically the seasonal dynamics in vegetation function [146,149]. The latest (third generation) SVATS used more recent theories relating photosynthesis and plant water relations to provide a consistent description of energy exchange, ET, and C exchange by plants [143,150]. In our effort in understanding the impact of climate change on terrestrial ecosystems, energy, water, and C cycles need to be modelled simultaneously [150,151]. Recently, most of SVATS have thus been enhanced to include the CO₂ flux between the land surface and the atmosphere, such as SiB2 [150], IBIS [152], NCAR-LSM [153], BATS [134], CLASS-C [154] and EASS [143].

The earlier generation of PVMs comprised a suite of schemes that focus on modeling distributions of vegetation as a function of climate [155,156] without influences of anthropogenic or natural disturbance. The second generation of PVMs included more sophisticated modules to account for factors controlling vegetation distributions, such as competition, varying combinations of plant functional types, and physiological and ecological constraints [157].

TBMs developed from scaling up local ecological models, are process-based models that simulate dynamics of energy, water, and C and N exchange among biospheric pools and the atmosphere [136]. Few of the existing TBMs incorporate PVMs. These models are not applicable to transient climate change experiments without coupling with PVMs.

In recent decades, the interactions among soil, vegetation and climate have been studied intensively and modeled successfully on the basis of water and energy transfer in the soil-vegetation-atmosphere system [136,140,142,158]. Also the construction and refinement of LSMs have received increasing attention [150,159,160]. Combination of these three different LSMs and utilization of remotely sensed land surface parameters are critical in the future LSM development, because of (1) the tight coupling of exchanges of water, energy and C between the land surface and the atmosphere; (2) the sophisticated impact/feedback mechanisms between climate change and terrestrial ecosystems; and (3) increasingly strong anthropogenic alterations to land cover. On-line coupling of a LSM with a GCM is needed for studying interannual to multi-decadal climate variations.

Several model intercomparisons have focused on evaluating SVATS and TBMs with particular objectives. For instance, the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) was initiated to evaluate an array of LSMs existing in GCMs [144]; while the AMMA (African Monsoon Multidisciplinary Analysis) Land Surface Model Intercomparison Project (ALMIP) is being conducted to get a better understanding of the role of soil moisture in land surface processes in West Africa [161]. Coordinated land surface modeling activities have improved our understanding of land surface processes [161].

Hydrology and ecosystem have, for the most part, been studied independently. Most LSMs and ecosystem models make an assumption of “flat Earth” with the absence of lateral redistribution of soil moisture. On the other hand, hydrological models have mostly been concerned with runoff production. Spatially-distributed models are needed, especially for hydrological simulation objective, because of heterogeneity of land surface and non-linearity of hydrological processes. Spatially-distributed hydrological models are not only able to account for spatial variability of hydrological processes, but enable computation of internal fluxes and state variables. Such kinds of models are increasingly applied to simulate spatial variability of forcing variables (e.g., precipitation), physiographic characteristics, detailed processes and internal fluxes within a catchment [162-167].

It is recognized that the atmospheric measurements are still too sparse, relative to its spatial variability, to be used for inferring the surface flux at high spatial resolution [168]. The use of the isotope ratio as an additional constraint to identify various C sources and sinks can contribute to a significant reduction in the uncertainty. Though available isotopic datasets are being accumulated quickly [117,169-171], isotope measurements are still lacking considering land surface diversity and heterogeneity. This shortage of long-term measurements and of sampling frequency still limits C isotopic studies.

Mechanistic ecosystem models that couple micrometeorological and eco-physiological theories have the potential to shed light on how to extend efforts and applications of stable isotopes of CO₂ to global C budgeting, because biophysical models have the capacities of simulating isotope discrimination in response to environmental perturbations and can produce information on its diurnal, seasonal and interannual dynamics. Few biophysical models, however, have been developed to assess stable C discrimination between a plant canopy and the atmosphere [94,87,135,]. Most existing biophysical models are based on individual leaf level discrimination equations given by Farquhar et al. [173,174] and only focus on the land surface layer (ignoring vertical and horizontal advection effects beyond 50∼100 m above the ground [172]. However, in nature, the convective boundary layer (CBL) integrates the effects of photosynthesis, respiration, and turbulent transport of CO₂ over the landscape [107,175]. The influence of the CBL cannot be ignored when using isotope composition of CO₂ to investigate biological processes [176], because the effect of atmospheric stability on turbulent mixing/diffusion has an important impact on scalar fluxes and concentration fields within and above canopies [177,178]. Few such models considering the CBL effects on isotope fractionation have been developed to date [107,178-182].

C and N dynamics and hydrological processes are closely linked. The stomatal conductance (g_s) is the key linkage between C assimilation (photosynthesis) and transpiration. An empirical equation is used in the second-generation LSMs to calculate g_s, which is hypothesized to be controlled by the environmental conditions [31]. While field and laboratory studies have documented that leaf photosynthesis also affects g_s. Therefore, Ball et al. [24] proposed a semi-empirical stomatal conductance formulation (Ball-Woodrow-Berry model), in which g_s is controlled by both photosynthesis and the environmental conditions. Most of third-generation LSMs (Ecological models, e.g., SiB2 [139,150]; CN-CLASS [28]; Ecosys [183-184; and EASS [143]) fully couple photosynthesis and transpiration processes by employing the Ball-Woodrow-Berry stamatal conductance formulation.

In addition to the coupling of hydrological condition and C assimilation through the linkage of g_s, C assimilation is also coupling with N dynamics through another biochemical parameter, V c max 25 — maximum carboxylation rate at 25 °C. In the photosynthesis model proposed by Farquhar et al.[174], the net photosynthetic rate A_net at leaf level is a function of two tightly-correlated parameters V c max 25 and J c max 25 (the maximum electron transport rate at 25 °C), and is calculated as: (1) A net = min ( A c , A j ) − R dwhere A_c and A_j are Rubiso-limited and light-limited gross photosynthesis rates, respectively, and R_d is the daytime leaf dark respiration and computed as R_d = 0.015 V_{c max}. A_c and A_j are expressed as: (2a) A c = V c max C c − Γ ∗ C c + K c ( 1 + O c / K o )and: (2b) A j = J max C c − Γ ∗ 4 ( C c + 2 Γ ∗ )where C_c and O_c are the intercellular CO₂ and O₂ mole fractions (mol mol⁻¹), respectively; Γ* is the CO₂ compensation point without dark respiration (mol mol⁻¹); K_c and K_o are Michaelis-Menten constants for CO₂ and O₂ (mol mol⁻¹), respectively. In the nutrient-limited stands, A_net is generally limited by A_c, while A_c is dominantly controlled by a parameter V_{c max} (see Eqn. 2a). Many research results showed V c max 25 is very sensitive to leaf N status (more specifically leaf Rubisco-N) [134,185-187]. As a result in some ecosystem models (i.e., C&N-CLASS [23]), V c max 25 is calculated as a nonlinear function of Rubisco-N following observations made by Warren and Adams [134]: (3) V c max 25 ( N ) = α [ 1 − exp ( − 1·8 N r 0 ]where α is the maximum value of V c max 25 and N_r0 is the leaf Rubisco-N (g N m⁻² leaf area) in the top canopy.

The coupled C, N and water processes have been carefully considered in most of the third-generation LSMs (e.g., SiB2 [139,140,150]); CN-CLASS [28] and Ecosys [183,184]), the models' grids, however, are isolated from their neighboring grids mainly due to the availability of input data. Vertical soil hydrological processes are hard to be realistically simulated if the lateral flows are ignored by assuming that the Earth is “flat”. However, simulations of the topographically-driven lateral water flows are important components in most of spatially-distributed models, while the detailed ecophsiological processes are weakly represented [188]. Much effort to bridge these two different models has been increasingly made [33,35,188-194]. However, a model coupling approach—a full combination of ecosystem model and hydrological model, i.e., ecohydrological modeling, is still lacking.

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 Wigmosta 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]. Koster 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]. Kite and Pietroniro [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].

Coupled modeling will help refine the experimental and instrumental design and generate cross-disciplinary hypotheses that can be tested in the experiment. Ecohydrological models are powerful tools for quantitative and predictive understanding the coupled C, N and water mechanisms. Spatially-explicit ecohydrological modeling can be used to infer aspects of the land surface system that are difficult to measure by mass and energy balance, and will be critical to improving the accuracy of forecasts of landscape change and C dynamics in the real world. While developing a process-based, coupled-system model is a significant task, the coupling to existing models may provide a relatively easy way forward. For example, a spatially explicit, process-based ecohydrological model, EASS-TerrainLab, is developed to improve the representation of the coupled C, N and hydrological processes by integrating of two existing models (an ecosystem LSM model—EASS and a distributed hydrological model—TerrainLab).

Figure 5 schematically shows a synthetic upscaling framework for estimating landscape and regional C and water budgets with a reasonable accuracy. Using a data-model fusion with footprint weighting method to optimize the ecohydrological model's parameters is strongly suggested because the land surface is normally heterogeneous. This is a key component in the upscaling framework. Model parameters are optimized by minimizing the ‘cost’ function [240]: (4) J ( x ) = 1 2 [ ( O − Y ( x ) ) T C o − 1 ( O − Y ( x ) + ( x − x b ) T P b − 1 ( x − x b ) ]where x is the vector of unknown parameters and x_b is a vector of a priori values of x; O is the vector of observations and Y is the nonlinear model results, C_o is the covariance matrix of observations and P_b is the covariance matrix of a priori parameters. Different from general data-fusion method, Y is weighting averaged by footprint f_j for each pixel (j) within the footprint domain as Y = ∑Y_jf_j.

We also note that the dynamics of the PBL is tightly coupled with the land surface processes (e.g., energy / water / C fluxes). But for simplification, we proposed off-line modeling instead of the on-line coupling with regional climate model or the GCMs. The meteorological driving inputs for the ecohydrological model, therefore, are required for model runs. These inputs can either acquire from spatially extrapolation from climate station (including EC flux towers) measurements, or from the re-analysis data (e.g., the data provided by National Centers for Environmental Prediction (NCEP)), or from the regional climate modeling outputs.