Evaluating Ecohydrological Impacts of Vegetation Activities on Climatological Perspectives Using MODIS Gross Primary Productivity and Evapotranspiration Products at Korean Regional Flux Network Site

Accurate assessments of spatio-temporal variations in gross primary productivity (GPP), evapotranspiration (ET), and water use efficiency (WUE) play a crucial role in the evaluation of carbon and water balance as well as have considerable effects on climate change. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to quantify the mean annual GPP and ET at Korean regional flux network site. We found that the seasonal mean values of WUE were 2.86 to 2.92 g·C·g·H2O in the dormant season and 1.81 to 1.88 g·C·g·H2O in the growing season during 2007 and 2008. The WUE was relatively stable during the growing season and tended to vary in the dormant season. Remote sensing data obtained by the MODIS satellite were appeared to be effective to improve our understanding of the spatio-temporal variation of ecohydrological parameters which have not yet been investigated in a number of previous articles. Based on the results of this study, we summarize the interactions between carbon and water circulation in terrestrial ecosystems and how their ecological procedures generated by the photosynthesis of vegetation influence in climatological perspectives.


Introduction
Vegetation growth is regarded as crucial portion of global carbon and water cycling, and its role has been receiving more attention due to global climate change including increases in the atmospheric carbon dioxide concentration [1][2][3][4].The continued increase in atmospheric concentrations of carbon dioxide due to anthropogenic emissions is predicted to lead significant changes in climate [5].Explicit changes in other factors such as land use, land cover types and atmospheric which influence the water budget, plant water use strategy, and the global carbon cycle are expected due to the result of global climate change [6,7].The monitoring of the carbon cycle has been emphasized in many ecological studies [1,8,9], particularly focused on the relationship between the carbon cycle and vegetation production in the ecosystem, and parameters for detecting vegetation production.
The gross primary productivity (GPP) and net primary productivity (NPP) have been widely used [10][11][12][13][14] in traditional methods to study the carbon cycle.The GPP is calculated using observations of vegetation at the beginning of the carbon cycle and is the primary indicator of vegetation carbon fluxes.The GPP is essential in order to control parameter estimations of global carbon variation.Running et al. [10] developed the theoretical basis for the global-scaled NPP algorithm.Tum and Gunther [13] noted that the NPP was based on the dynamic vegetation model of the Biosphere Energy Transfer Hydrology (BETHY/DLR) study in Germany and Austria during 2000 and 2001.Matsushita and Tamura [6] developed a method for estimation of the NPP in East Asia by implementing the Boreal Ecosystem Productivity Simulator (BEPS) model, which combines several sets of data for global applications of the algorithm.Cao et al. [15] and Singh et al. [16] used the Global Production Efficiency Model (GLO-PEM) to calculate the spatio-temporal variation of the NPP between 1981 and 2000 at global scale.Xiao et al. [17] developed satellite-based Vegetation Photosynthesis Model (VPM) to analyze the seasonal variation of GPP in evergreen needleleaf forests.Yuan et al. [11] conducted various spatial and temporal resolutions to quantify the global carbon cycle using EC-LUE (Eddy covariance Light Use Efficiency) model, generated by only four parameters: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the energy flux terms.
The parameters used in these models were limited because they did not consider water circulation.There are many methods for analyzing of the relationships between the water and carbon cycles.The WUE, ratio of carbon gain during plant photosynthesis to water loss during evapotranspiration (ET), is an imperative concept to study these interactive effects, because it couples the water and carbon cycles very effectively [18][19][20][21].The WUE can be defined in various ways, such as in different spatial scales, for different study objectives [18,22,23].Hwang et al. [22] used an eco-hydrological model, the Regional Hydro-Ecological Simulation System (RHESSys), as a scaling tool at regional catchment scale.The RHESSys represented the spatial variation of the water and carbon cycles in a forest ecosystem.Yu et al. [18] analyzed the seasonal variation of the WUE in an ecosystem and demonstrated spatial patterns of ecosystem variables such as GPP, ET, and WUE at 1 km spatial resolution over three local flux tower sites in China between 2003 and 2005.Despite the completion of many studies focused on flux tower measurements, only a limited number of flux tower field measurements for WUE have been available in recent decades, and for only a few ecosystems, in comparison to the development of eddy covariance systems [24][25][26] The elevation is 340 m, the slope ranges from 10 to 20°, and the canopy height is approximately 18 m.The annual mean temperature is 11.5 °C, the annual mean precipitation is 1,365 mm [31,32] and the maximum leaf area index (LAI) is approximately 6 (Table 1).The data was collected for the study period of 1 January 2007 to 31 December 2008.The MODIS products replaced meteorological observations, as the observed input data and the primary product measurements were used to validate satellite-based estimates.

Eddy Covariance System
In this study, two meteorological variables (i.e., vapor pressure deficit (VPD) and air temperature) measured by the eddy covariance system at every half hour interval were used to estimate the ET and gross primary productivity.The eddy covariance system is an essential part of the mass conservation equation, which provides the framework to estimate the net ecosystem exchange (NEE, in mgC•m -2 •d -1 ) between the photosynthetic carbon assimilation and releasing respiration.Negative NEE is expressed as another parameter, net ecosystem productivity (NEP, in mgC•m -2 •d -1 ).The GPP is calculated using the following equation: where R e is the total ecosystem respiration.The ground-measured GPP was validated with the MODIS-based GPP in this study.The eddy covariance system was applied to the flux observation.This method has been widely used for flux tower measurements worldwide, since the energy and water exchange between the surface and atmosphere can be directly determined: The first component on the right side of the equation is the amount of flux stored below the measurement height, second is the flux generated by vertical turbulent motions, third is the amount of flux advected by the mean vertical flow in the presence of the vertical gradient of concentrations, and last component is the fluxes generated by the horizontal flow divergence of the mean flows and the concentrations below the measurement height.Among these components, the conventional eddy covariance system depends on the measurement of the second term, eddy flux.This term is considered to be very useful and efficient tool under steady state conditions over flat terrain with an extended upwind fetch of the underlying vegetation [33].Due to the inherent complexity, heterogeneity, and variability of the eddy flux in nature, the eddy covariance method has been used over various landscapes and extended temporal scales, resulting in many problems and subsequent remedies [33].We selected flux tower data measured by the eddy covariance system in order to validate the MODIS ET product.

The Properties of the MODIS Satellite
The MODIS multispectral sensor of the NASA Earth Observing System (EOS) is being provided atmosphere, land, and ocean products.The overpass time of the Terra satellite is approximately 10:30 AM when descending and 10:30 PM when ascending.The Aqua satellite overpass time is approximately 1:30 AM when descending and 1:30 PM when ascending.The MODIS 17 GPP and 16 global ET products from the Terra satellite were employed to estimate the WUE during 2007 and 2008.The provided datasets in the Hierarchical Data Format (HDF)-EOS with Sinusoidal (SIN) projection was reprojected using Transverse Mercator (TM) coordinate system.

MODIS 17 GPP/NPP Algorithm and Estimated Products
The GPP is one of the parameters representing the material circulation of the terrestrial ecosystem and is used for detecting and measuring the change in carbon circulation [10,11,18,22,28].Primary production can be divided into the GPP, which represents the total amount of organic matter produced by vegetation through photosynthesis, and the NPP, which excludes the breathing quantity of the vegetation from total organic matter [19].These two indices are expressed as the amount of carbon per unit area.In this study, the 8-day GPP output with spatial resolution of 1 km from the MODIS primary product provided by the NASA was used.The MODIS 17 GPP algorithm is associated with carbon circulation.The GPP is a function of sunlight and active radiation-related parameters.The MODIS GPP algorithm is based on the radiation use efficiency of vegetation [10,19] as expressed in the following equations: where ε is the radiation use efficiency of vegetation, PAR is the photosynthetically available radiation (MJ•m -2 •d -1 ), ε max is the maximum radiation use efficiency, T min is the daily minimum temperature (°C), VPD is the vapor pressure deficit (the difference between the vapor pressure and actual pressure) (Pa), R s is the short wave radiant energy (MJ m -2 d -1 ), and FPAR is the fraction of the PAR absorbed by the plant with a value ranging from 0 to 1.When the PAR absorbed by the plant is large or the value of ε is high, then the GPP value increases.Equation (3) shows that the MODIS GPP algorithm assumes that the value of ε is determined according to the vegetation type [34].The PAR is assumed to be 45% of R s (Equation ( 3)), which expresses the change in use efficiency determined by the opening and closing of the stomata according to T min and VPD which have upper and lower limits ranging from ε max to 0. The MODIS 15 FPAR product is used due to its similarity with normalized difference vegetation index (NDVI) [10].The meteorological data of the MODIS GPP algorithm used the Data Assimilation Office (DAO) provided by the NASA, having a spatial resolution of 1.00° × 1.25° as input data.The upper and lower thresholds of ε max , T min and VPD determined the opening and closing of the stomata use constant, which were determined according to the vegetation type [34].

MODIS 16 Global ET Products
The MODIS 16 global ET product was developed centered on Cleugh et al.'s [29] Penman-Monteith based ET (RS-PM) method [30].The RS-PM method is based on the Penman-Monteith (P-M) equation [35]: where λE is the latent heat flux (W•m -2 ), e sat is the saturated water vapor pressure (Pa), s is the slope of the curve relating the saturated water vapor pressure to the temperature (kPa•K -1 ), A is the available energy (W•m -2 ), ρ is the air density (kg•m -3 ), C p is the specific heat capacity of the air (J•kg -1 •K -1 ), e is the actual water vapor pressure (Pa), r a and r s are the aerodynamic and surface resistances (s•m -1 ), respectively, and γ is the psychrometric constant (Pa•K -1 ).The RS-PM algorithm uses the Global Modeling and Assimilation Office (GMAO) meteorological data at a 1.00° × 1.25° resolution (Global Modeling and Assimilation Office, 2004).The algorithm also needs the Collection 4 MODIS land cover (MOD12Q1; [36]), MOD13A2 NDVI/EVI [37,38], MOD15A2 LAI [39], and the 0.05° albedo from the MOD43C1 [40][41][42][43] data as remote sensing inputs.In this study, the MODIS 16 global ET product was used to estimate the WUE.

Estimating the WUE Based on Remote Sensing Technology
The previous studies applied the GPP and NPP as parameters of the carbon cycle [10,18,19,22].These parameters have been widely used and applied to many studies as the amount of carbon produced from photosynthesis and the respiration generated from the stomata of the vegetation, respectively.This study introduced the new carbon cycle index to understand the change in vegetation due to climate change as a hydrological factor.The WUE is the amount of carbon included in 1 kg of water in the atmosphere and it can be used as an index representing the carbon and water cycles [19].The WUE can be defined in many ways at an ecosystem scale.The three mainly used definitions include the GPP based WUE: GPP/ET, the NPP based WUE: NPP/ET, and the net ecosystem carbon production (NEP) based WUE: NEP/ET.The ET value can be replaced by the annual rainfall and the photochemical reflectance index to calculate the rainfall use efficiency (RUE, [44,45]) and light use efficiency (LUE, [12,27]), respectively.In this study, the first definition (GPP-based WUE) was primarily used to deal with our objectives, since the GPP can reflect the annual net carbon fixation in the plant biomass and also examined how the WUE is distributed nationally.These res variation of Among thes VPD were s study site, G region [46,4 akes 2).In Hwang et al. [22] which compared MODIS GPP product with the products of GPP using the RHESSys model at same study site, we found that results of validation showed almost similar patterns.The distribution of the ET showed similar trends with GPP distributions [11,18,19].The ET near the coastline had higher value, while the ET distribution in the summer was different than the GPP distribution.Since the plateau with higher ET value has lower temperature and air pressure values, there was lower ET in that area [51].The global MOD16 terrestrial calculated ET was higher than in comparison with the ground measured ET at the GDK flux tower site as shown in Figure 10 [50].Validation of the MODIS 16 global ET product was performed using flux tower measurements registered by the AsiaFlux network [50] and the results are presented in Figure 11 and Table 2.The fitted lines between the MODIS-based ET (y) and the flux tower measurement ET (x) were: y = 1.07x + 17.
We found ranging from ranging from 2.92 g O − an WUE wa and the mea n (Figure 14 pattern wa the dorman d by both th how a stab rating.
relationship between the GPP and ET.The slope of the GPP-ET curvature in this figure represents WUE which was the ratio of GPP and ET.In dormant season, GPP and ET were correlated most significantly at GPP value less than 10 gC•m -2 •8-day −1 and at ET around the 0 to 5 g•m -2 •8-day −1 during 2007, whereas GPP and ET values were affected the WUE value in very little extent during 2008, with no correlation at higher values of GPP and ET during 2007 and 2008 (Figures13 (a,c)).In contrast, in growing season, GPP and ET showed the positive relationship for all ranges, particularly GPP ranging from 10 to 50 gC•m −2 •8-day −1 and ET from 10 to 40 g•m −2 •8-day −1 (Figures13(b,d)).
Figure 13(b) showed the dense effect of GPP and ET values on WUE during 2007, whereas Figure 13(d) showed the spatially scattered effect of GPP and ET on WUE in 2008.
. Due to the limited number of