Derivation of Daily Evaporative Fraction Based on Temporal Variations in Surface Temperature, Air Temperature, and Net Radiation
<p>Relationships among the values of <span class="html-italic">P</span><sub>rad</sub> for different atmospheres under clear days but for the same wind speed observations from the measurements at the Yucheng station on 24 April 2010 (Data are from the simulation by ALEX under different vegetation cover and soil water content conditions for each atmosphere, and atmosphere forcing on three representative days was measured at the Yucheng station on 24 April (DOY 114), 29 May (DOY 149), and 21 July (DOY 202) 2010, which can be found in <a href="#app1" class="html-app">Appendix</a>. <span class="html-italic">P</span><sub>rad</sub> is calculated by simulated <span class="html-italic">H</span> and <span class="html-italic">T</span><sub>s</sub> − <span class="html-italic">T</span><sub>a</sub>.)</p> ">
<p>Variation of <span class="html-italic">P</span><sub>rad</sub> in a diurnal cycle (<b>a</b>) for the same atmosphere and surface conditions but with various wind speeds (Atmosphere forcing data, including air temperature, water vapor pressure, incoming solar radiation, <span class="html-italic">etc.</span>, are from the measurements at the Yucheng station on 24 April 2010 (DOY 114). Fractional vegetation cover (<span class="html-italic">f</span><sub>c</sub>) is 0.5, and soil water content (SWC) is 0.21 m<sup>3</sup>/m<sup>3</sup>. WH denotes the high wind speed condition, which is from the measurement at the Yucheng station on 24 April 2010, and the maximum wind speed during the day is 6.1 m/s; WM represents the medium wind speed condition from the measurement on 29 May 2010 (DOY 149), and the maximum wind speed is 3.6 m/s; WL denotes the low wind speed condition measured on 21 July 2010 (DOY 202), and the maximum wind speed is 1.3 m/s) and (<b>b</b>) for the same atmosphere and wind speed but with different fractional vegetation cover (atmosphere and wind speed are from the measurements at the Yucheng station on 24 April 2010).</p> ">
<p>Relationship between <span class="html-italic">P</span><sub>rad</sub> and <span class="html-italic">f</span><sub>c</sub> for three atmosphere conditions at the Yucheng station on 24 April (DOY 114), 29 May (DOY 149) and 21 July (DOY 202) 2010 (<span class="html-italic">P</span><sub>rad</sub> s calculated by simulated <span class="html-italic">H</span> and <span class="html-italic">T</span><sub>s</sub> − <span class="html-italic">T</span><sub>a</sub> at 10:30 A.M.)</p> ">
<p>Diurnal cycle of EF driven by atmospheric forcing obtained at the Yucheng station (<b>a</b>) on 24 April (DOY114), and (<b>b</b>) on 21 July (DOY 202) 2010 (The horizontal lines with different color correspond to daily average EF for each case).</p> ">
<p>Relationships between daily average EF and EF at different times in one day for (<b>a</b>) ALEX-simulated data driven by the atmospheric forcing at the Yucheng station in 2010, and (<b>b</b>) <span class="html-italic">in situ</span> measurements at the Yucheng station during the wheat growth period in 2012.</p> ">
<p>Variations of (<b>a</b>) <span class="html-italic">T</span><sub>s</sub> and <span class="html-italic">T</span><sub>a</sub>, and (<b>b</b>) <span class="html-italic">R</span><sub>n</sub> during a diurnal cycle for an ALEX-simulated case given fractional vegetation cover (<span class="html-italic">f</span><sub>c</sub>) of 0.5 and soil water content (SWC) of 0.21 m<sup>3</sup>/m<sup>3</sup> and the atmospheric forcing at the Yucheng station on 24 April (DOY 114) 2010. The bold red, green, and blue lines denote the approximately linear variations of <span class="html-italic">T</span><sub>s</sub>, <span class="html-italic">T</span><sub>a</sub>, and <span class="html-italic">R</span><sub>n</sub> during the morning about from 1.5 h–4.5 h after sunrise. delt_<span class="html-italic">T</span><sub>s</sub>, delt_<span class="html-italic">T</span><sub>a</sub>, and delt_<span class="html-italic">R</span><sub>n</sub> are the differences of <span class="html-italic">T</span><sub>s</sub>, <span class="html-italic">T</span><sub>a</sub>, and <span class="html-italic">R</span><sub>n</sub> between 1:30 P.M. and 1:30 A.M., respectively. The sunrise is 5:30 A.M., and the sunset is 6:30 P.M. on 24 April.</p> ">
<p>Relationship between the values of (<span class="html-italic">T</span><sub>s</sub>′(<span class="html-italic">t</span>) − <span class="html-italic">T</span><sub>a</sub>′(<span class="html-italic">t</span>))/<span class="html-italic">R</span><sub>n</sub>′(<span class="html-italic">t</span>) and (Δ<span class="html-italic">T</span><sub>s</sub> − Δ<span class="html-italic">T</span><sub>a</sub>)/ Δ<span class="html-italic">R</span><sub>n</sub> for (<b>a</b>) ALEX-simulated data driven by the atmospheric forcing at the Yucheng station in 2010, and (<b>b</b>) <span class="html-italic">in situ</span> measurements at the Yucheng station during the wheat growth period in 2012.</p> ">
<p>Flow chart for obtaining the coefficients in <a href="#FD13" class="html-disp-formula">Equation (13)</a> using the SVAT-simulated data.</p> ">
<p>Variations of coefficients <span class="html-italic">A</span>, <span class="html-italic">B</span>, and <span class="html-italic">C</span> in the developed parameterization with atmospheric conditions for five different input schemes.</p> ">
Abstract
:1. Introduction
2. Method
2.1. Background of Theory
2.1.1. Radiometric Heat Conductance Prad
2.1.2. Diurnal Cycle of EF
2.2. Parameterization for EF Based on SVAT Modeling
3. Data
3.1. In situ Measurements
3.2. Remotely Sensed Data
4. Results and Discussions
4.1. Daily EF Estimates with In situ Measurements as Inputs
4.2. Application to Satellite Data
4.3. Comparison with SEBS-Estimated EF
5. Conclusion
Acknowledgments
Conflict of Interest
References
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Appendix
(a) | ||||
---|---|---|---|---|
Quantities | Units | Values | ||
Site | Yucheng | |||
Longitude | 116.5703 | |||
Latitude | 36.8291 | |||
Year | 2010 | |||
Date | 26 Mar, 24 Apr, 29 May, 28 June, 21 July | 17 Aug, 11 Sep, 4 Oct. | ||
Measure height | m | 2.93 | 4.2 | |
Vegetation type | C3 grass | corn | ||
Vegetation height | m | 0–0.6 | 0–2.4 | |
Leaf area index | m2/m2 | 0–10 | ||
Rooting depth | m | 0.5 | ||
Soil texture | loam | |||
sand | 0.42 | |||
silt | 0.4 | |||
clay | 0.18 | |||
Bulk density | g/cm3 | 1.5 | ||
Moisture release parameter | 4.5 | |||
Air entry potential | J/kg | −1.1 | ||
Saturated hydraulic conductivity | K·g·s/m3 | 3.7 × 10−4 | ||
soil water content (0–2 m) | m3/m3 | 0.09–0.21, 0.43 |
(b) | ||||
---|---|---|---|---|
Quantities | Units | Values | ||
Site | Goodwin | Cottonwood | Audubon | |
Longitude | −89.7735 | −101.8466 | −110.5092 | |
Latitude | 34.2547 | 43.95 | 31.5907 | |
Year | 2006 | 2008 | 2006 | |
Date | 9 Feb, 14 Mar, 12 Apr, 12 May, 15 June, 17 July, 17 Aug, 13 Sep, 7 Oct | 10 Mar, 14 Apr, 17 May, 14 June, 13 July, 17 Aug, 14 Sep, 7 Oct | 19 Feb, 21 Mar, 15 Apr, 4 May, 10 June, 20 July, 27 Aug, 16 Sep, 20 Oct, 2 Nov | |
Measure height | m | 4 | 5 | 4 |
Vegetation type | C3 grass | soybean | desert C3-type shrubs | |
Vegetation height | m | 0–1 | 0–1 | 0–0.6 |
Leaf area index | m2/m2 | 0–10 | 0–10 | 0–10 |
Rooting depth | m | 1 | 2 | 0.5 |
Soil texture | clay loam | clay loam | silt loam | |
sand | 0.32 | 0.32 | 0.2 | |
silt | 0.34 | 0.34 | 0.65 | |
clay | 0.34 | 0.34 | 0.15 | |
Bulk density | g/cm3 | 1.5 | 1.5 | 1.5 |
Moisture release parameter | 5.2 | 5.2 | 4.7 | |
Air entry potential | J/kg | −2.6 | −2.6 | −2.1 |
Saturated hydraulic conductivity | K·g·s/m3 | 6.4 × 10−4 | 6.4 × 10−4 | 1.9 × 10−4 |
soil water content (0–2 m) | m3/m3 | 0.13–0.25, 0.43 | 0.13–0.25, 0.43 | 0.11–0.23, 0.43 |
Data | Inputs | Linear Relationships | R2 | RMSE |
---|---|---|---|---|
ALEX-simulated data | Aqua daytime-Aqua nighttime | Y = 0.6619X − 0.0003 | 0.808 | 0.0047 |
Terra daytime-Terra nighttime | Y = 1.3435X − 0.0046 | 0.874 | 0.0038 | |
Terra daytime-Aqua nighttime | Y = 1.0790X − 0.0017 | 0.882 | 0.0037 | |
Aqua daytime-Terra nighttime | Y = 0.7858X − 0.0023 | 0.821 | 0.0045 | |
In situ measurements | Aqua daytime-Aqua nighttime | Y = 0.6066X − 0.0021 | 0.891 | 0.0029 |
Terra daytime-Terra nighttime | Y = 0.7854X − 0.0001 | 0.827 | 0.0036 | |
Terra daytime-Aqua nighttime | Y = 0.8130X − 0.0006 | 0.853 | 0.0033 | |
Aqua daytime-Terra nighttime | Y = 0.5996X − 0.0017 | 0.894 | 0.0028 |
Sites | Inputs | R2 | RMSE | BIAS |
---|---|---|---|---|
Yucheng | Change rate during the morning | 0.817 | 0.107 | 0.020 |
Aqua daytime-Aqua nighttime | 0.878 | 0.083 | −0.002 | |
Terra daytime-Terra nighttime | 0.810 | 0.103 | −0.001 | |
Terra daytime-Aqua nighttime | 0.869 | 0.086 | 0.007 | |
Aqua daytime-Terra nighttime | 0.835 | 0.098 | −0.009 | |
Goodwind | Change rate during the morning | 0.806 | 0.106 | 0.023 |
Aqua daytime-Aqua nighttime | 0.847 | 0.093 | 0.019 | |
Terra daytime-Terra nighttime | 0.787 | 0.129 | 0.045 | |
Terra daytime-Aqua nighttime | 0.826 | 0.106 | 0.029 | |
Aqua daytime-Terra nighttime | 0.840 | 0.098 | 0.023 | |
Cottonwood | Change rate during the morning | 0.787 | 0.124 | 0.029 |
Aqua daytime-Aqua nighttime | 0.877 | 0.087 | 0.008 | |
Terra daytime-Terra nighttime | 0.839 | 0.100 | 0.009 | |
Terra daytime-Aqua nighttime | 0.850 | 0.111 | 0.033 | |
Aqua daytime-Terra nighttime | 0.860 | 0.093 | −0.007 | |
Audubon | Change rate during the morning | 0.827 | 0.101 | 0.001 |
Aqua daytime-Aqua nighttime | 0.858 | 0.092 | −0.005 | |
Terra daytime-Terra nighttime | 0.737 | 0.137 | 0.034 | |
Terra daytime-Aqua nighttime | 0.837 | 0.104 | 0.019 | |
Aqua daytime-Terra nighttime | 0.823 | 0.103 | −0.005 |
Inputs | Equations |
---|---|
Change rate during the morning | |
Aqua daytime-Aqua nighttime | |
Terra daytime-Terra nighttime | |
Terra daytime-Aqua nighttime | |
Aqua daytime-Terra nighttime |
EC | RE | BR | |||||||
---|---|---|---|---|---|---|---|---|---|
R2 | RMSE | BIAS | R2 | RMSE | BIAS | R2 | RMSE | BIAS | |
New method | 0.139 | 0.215 | 0.051 | 0.394 | 0.242 | 0.093 | 0.382 | 0.210 | 0.066 |
SEBS | 0.125 | 0.268 | 0.053 | 0.292 | 0.275 | 0.096 | 0.296 | 0.249 | 0.069 |
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Lu, J.; Tang, R.; Tang, H.; Li, Z.-L. Derivation of Daily Evaporative Fraction Based on Temporal Variations in Surface Temperature, Air Temperature, and Net Radiation. Remote Sens. 2013, 5, 5369-5396. https://doi.org/10.3390/rs5105369
Lu J, Tang R, Tang H, Li Z-L. Derivation of Daily Evaporative Fraction Based on Temporal Variations in Surface Temperature, Air Temperature, and Net Radiation. Remote Sensing. 2013; 5(10):5369-5396. https://doi.org/10.3390/rs5105369
Chicago/Turabian StyleLu, Jing, Ronglin Tang, Huajun Tang, and Zhao-Liang Li. 2013. "Derivation of Daily Evaporative Fraction Based on Temporal Variations in Surface Temperature, Air Temperature, and Net Radiation" Remote Sensing 5, no. 10: 5369-5396. https://doi.org/10.3390/rs5105369