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Near Real-time Experiments
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An operational remote sensing ET estimation

The approach uses the most recent MODIS data. The data include MOD12Q1 (Land Cover Type Yearly L3 Global 1km), MOD09GQ (Surface Reflectance Daily L2G Global 250m), MOD09GA (MODIS Terra Surface Reflectance Daily L2G Global 1km and 500m), MOD11A1 (Land Surface Temperature/Emissivity Daily L3 Global 1km), MOD13Q1 (Vegetation Indices 16-Day L3 Global 250m), and MCD43A3 (Albedo 16-Day L3 Global 500m).

The landscape is assumed as a mixture of vegetation and bare soil. The fractional vegetation cover ($f_{veg}$) is estimated from the spectral vegetation index.

\begin{displaymath}f_{veg} = (NDVI-NDVI_{min})/(NDVI-NDVI_{min}) \end{displaymath}
where $NDVI$ is the normalized difference vegetation index.

\begin{displaymath}NDVI = (R_{nir}-R_{red})/(R_{nir}+R_{red}) \end{displaymath}

The $NDVI$ is calculated from MOD09GQ (Surface Reflectance Daily L2G Global 250m) data with 8-days composite. If NDVI is unavailable, then MOD13Q1 (Vegetation Indices 16-Day L3 Global 250m) is used.

The actual evapotranspiration (ET) is described from a pixel as a linear combination of ET from vegetation and bare soil.

\begin{displaymath}ET = f_{veg}ET_{veg} + (1 - f_{veg})ET_{soil} \end{displaymath}

The evaporation fraction (EF) is define as an index for ET.

\begin{displaymath}EF = ET / Q \end{displaymath}

where $Q$ is available energy (W m$^{-2}$). EF is nearly constant during most daytime in many cases. We assume the EF is a constant during daytime that is truth during most daytime in many cases. Therefore the daily average ET is calculated using the instantaneous EF and average available energy. According to the definition of EF, $ET_{veg}=Q_{veg}EF_{veg}$, and $ET_{soil}=Q_{soil}EF_{soil}$.

\begin{displaymath}EF = \frac{ET}{Q} = f_{veg}\frac{Q_{veg}}{Q}ET_{veg} + (1 - f_{veg})\frac{Q_{soil}}{Q}EF_{soil} \end{displaymath}

Estimation of Radiation Components

Extraterrest_carial radiation $Ra$ for different latitudes can be estimated from the solar constant, the solar declination and the time of the year. To estimate the instantaneous extraterrest_carial radiation, the solar time angle should be considered (Allen et al, 1998).

Downward short-wave radiation $Rd$ is calculated with the Angstrom formula which relates solar radiation to extraterrest_carial radiation and relative sunshine duration.

\begin{displaymath}Rd = ( a_s + b_s n/N ) R_a \end{displaymath}

where $a_s$ = 0.25, $b_s$ = 0.5. $n/N$ is estimate with cloud condition. $n/N = 1 -CLOUD $. $CLOUD$ is from MOD09GA Reflectance Data State QA cloud state data.

Downward short-wave radiation $Rd$ is taken from GEWEX Continental Scale International Project (GCIP) and GEWEX Americas Prediction Project (GAPP) Surface Radiation Budget (SRB) Data (New!).

Reflected short-wave radiation $Ru = albedo \times R_d $. $albedo$ is the albedo from MCD43A3 (Albedo 16-Day L3 Global 500m).

Downward long-wave radiation $L_d$ is calculated $ L_d = \varepsilon\sigma T_a^4 $.

Outgoing long-wave radiation $L_u$ is calculated $ L_u = \varepsilon\sigma T_s^4 + (1-\varepsilon) L_d $.

Ground heat flux $G$ of bare soil is estimated by $G = C_G R_n$. $G$ of full vegetation is assumed to be negligible.

Estimation of $EF_{soil}$

Net radiation $R_n$ is calculated as:

\begin{displaymath}R_n = (1- albedo) R_d + L_d - \varepsilon\sigma T_s^4 \end{displaymath}

where $albedo$ is the albedo from MCD43A3 (Albedo 16-Day L3 Global 500m). $R_d$ is the downward short-wave radiation (W m$^{-2}$), $L_d$ is the downward long-wave radiation (W m$^{-2}$), $\varepsilon$ is the emissivity from MOD11A1 (Land Surface Temperature/Emissivity Daily L3 Global 1km). and $\sigma$ is the Stefan-Boltzmann constant (W m$^{-2}$ K$^{-4}$).

\begin{displaymath}Q_{soil} = (1-C_G)R_n=\rho C_p(T_{soil}-T_a)/r_{asoil}+ET \end{displaymath}

where $C_G$ is an empirical coefficient. $C_G$ = 0.4. If set $R_{n0} = (1- albedo) R_d + L_d - 4\varepsilon\sigma T_a^3(T_{soil}-T_a) $ and $Q_{soil0}=(1-C_G)R_{n0}$. There is the relationship (Nishda et al, 2003):

\begin{displaymath}\frac{T_{soilmax}-T_{soil}}{T_{soilmax}-T_a} = \frac{ET_{soil}}{Q_{soil0}} = \frac{Q_{soil}}{Q_{soil0}}EF_{soil} \end{displaymath}

Estimation of $EF_{veg}$

Assuming the complementary relationship and the advection aridity, $EF_{veg}$ is calculated (Nishda et al, 2003):

\begin{displaymath}EF_{veg} = \frac{\alpha\Delta}{\Delta+\gamma(1+r_c/2r_a)} \end{displaymath}

where $\alpha$ is Priestley-Taylor's parameter. $\alpha$ = 1.26. $\Delta$ is derivative of the saturated vapor pressure in term of temperature (Pa K$-1$).

\begin{displaymath}\Delta = 2.504 \times10^6 \frac{exp{[\frac{17.27(T_a-273.15)}{T_a}}]}{T_a^2} \end{displaymath}

1/r_a = 0.008 U_{50m} & forest_ca canopy\\
1/r_a = 0.003 U_{1m} & grassland/cropland\\

where $U_{50m}$ and $U_{1m}$ are wind speed at 50 and 1 m heights (m s$^{-1}$). The wind speed is estimated from the logarithm profile of wind:

\begin{displaymath}U = u_* ln[(z-d)/z_0]/k \end{displaymath}

where $u_*$ is the shear velocity (m s$^{-1}$), $z$ is the height (m), $d$ is the surface displacement (m), $z_0$ is the roughness length (m) and $k$ is the von Karman's constant $k=0.4$.

\begin{displaymath}1/r_c=f_1{(Ta)}f_2{(PAR)}/r_{cMIN} + 1/r_{cuticle} \end{displaymath}

where $r_{cMIN}$ is minimum resistance (s m$^{-1}$). $r_{cMIN}$ = 50 for natural vegetation and $r_{cMIN}$ = 33 for crop. $r_{cuticle}$ is cuticle resistance. $r_{cuticle}$ = 100,000 s m$^{-1}$.

\begin{displaymath}f_1{(Ta)}= \left(\frac{T_a-T_n}{T_o-T_n} \right)\left(\frac{T_x-T_a}{T_x-T_o}\right)^{[(T_x-T_o)/(T_o-T_n)]} \end{displaymath}

\begin{displaymath}f_2{(PAR)}= \frac{PAR}{PAR+A}\end{displaymath}

where $T_n$, $T_o$, $T_x$ are minimum, optimal and maximum temperatures for stomatal activity, respectively. $T_n$, $T_o$, $T_x$ are set to 2.7, 31.1, 45.3 Celsius degree, respectively. $PAR$ is estimated from $Rd$ by multiplying a constant of 2.05 $\mu$mol W$^{-1}$. $A$ is related to light use efficiency. $A$ = 152 $\mu$mol m$^{-2}$s$^{-1}$.


Tang, Q., S. Peterson, R. Cuenca, Y. Hagimoto and D. P. Lettenmaier, 2009. Satellite-based near real-time estimation of irrigated crop water consumption. Journal of Geophysical Research, (in press) .
Tang, Q., A.W. Wood, and D.P. Lettenmaier, AGU Fall Meeting, San Francisco, CA, USA, Dec 2007, Near Real Time Evapotranspiration Estimation Using Remote Sensing Data
Nishida, K., R. R. Nemani, S. W. Running, and J. M. Glassy (2003), An operational remote sensing algorithm of land surface evaporation, J. Geophys. Res., 108(D9), 4270, doi:10.1029/2002JD002062.
Cleugh, Helen A., Leuning, R., Mu, Q., Running, S.W. (2007). Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sensing of the Environment, 106(3), 285-304.
Jiang, L., and S. Islam (2001), Estimation of surface evaporation map over southern Great Plains using remote sensing data, Water Resour. Res., 37(2), 329-340.