# Partitioning Evapotranspirationiration in a Cotton Field under
## 2 Materials and Methods
### EC System  
#### 2.4.1 Correct Fluxes Data  
The flux data were processed by EddyPro Software 4.2.1(LI-COR, Lincoln, NE, USA) with Webb-Pearman-Leuning corrections and coordinate rotation corrections. Generally, The scalar gas density of the ascending gas flow is lower than that of the descending gas flow due to thermal expansion and other reasons, and the transmission of water and heat flux will affect the density of scalar gas, causing the vertical velocity change of dry air that has no effect on flux transmission. Therefore, the effect of water and heat transmission on scalar gas flux should be considered and corrected in the actual calculation [29; 30; 31]. The effect of density fluctuation caused by heat flux on the measurement of water vapor can be eliminated by \(WPL\) corrections to the raw high-frequency data. \(WPL\) corrections are an important correction procedure since they can correct scalar fluxes up to 50% [32]. On the other hand, the CSAT3 and LI-7500 sensors were installed within specification, i.e., 10 cm from center to center, and the cotton fields were mostly homogeneous with small spatial variability of cotton leave area index and crop height. However, because the duration of the experiment was long, it was difficult for CSAT3 to maintain absolute vertical due to the influence of gravity during long-term operation, which affected the reliability of flux observation results. Therefore, it was necessary to convert the Cartesian coordinate system of the anemometer into a streamlined coordinate system and use coordinate rotation corrections to correct the original data measured by CSAT3 [33; 34]. After corrections, the data with a time interval of 30 min were finally output, including \(NE\), \(R_{n}\), \(T_{air}\), \(RH\), vapor pressure deficit (\(VPD\)), etc.  
In order to ensure the accuracy of the data, the output flux data needs to be adjusted to control the quality of the flux data because bad weather, instrument failure, power failure, and other factors will also lead to data abnormalities or even data dearth during the experiment. To ensure the integrity of the data group, it is necessary to delete the abnormal data and interpolate some missing data. The specific processes are as follows:  
(1) Delete abnormal data.  
Delete night data (total solar radiation < 10 W\(\cdot\)m\({}^{-2}\)). Delete the abnormal value that obviously does not conform to the actual situation [35]. Fluxes data collected on rainy days were not used for analysis; delete them because \(ET\) cannot represent the actual situation during rainfall [36]. Delete the data when the friction wind speed (\(u^{*}\)) is less than 0.15 m\(\cdot\)s\({}^{-1}\)[37].  
(2) Interpolate missing data.  
Different methods are used for interpolation according to the different time lengths of missing data. The linear interpolation method is used to interpolate missing data when the time length of the missing data is less than 3 h [38]. The Mean Diurnal Variation (MDV) [39] method is used to interpolate missing data when the time length of missing data is between 3 h and 24 h. If the time length of missing data is more than 24 h, the data will be regarded as invalid data without interpolation and deleted.