## 3 Results and Discussion
### Spatial and Temporal Variability of Evaportranspiration at the Field Scale  
Spatial distribution of daily ET at the field-scale is essential to understand the water consumption pattern over a whole field region to irrigate rationally. Figure 10a,b show the distribution of irrigated fields in Daman, where plants mainly include maize, winter wheat, and orchards. Water consumption via the ET of individual fields was captured by the MPDI-integrated model using HJ-1 images on 24 May and 3 July 2015 (DOY 144). To understand the variability of water consumption via ET for a specific crop, we overlaid the field boundaries of cropland provided by \"Heihe Plan Science Data Center, National Natural Science Foundation of China\" ([[http://www.heihedata.org](http://www.heihedata.org)]([http://www.heihedata.org](http://www.heihedata.org))). There was high variability of evapotranspiration over agricultural fields, which was strongly linked to the crop planting structure. Most of the pixels of high ET value (ET > 4 mm d\({}^{-1}\)) presented well-irrigated wheat parcels when winter wheat was in the heading stage, with LAI > 3 on 24 May 2015. The maize pixels maintained a low ET value of less than 2 mm, which was still in the tillering stage with LAI < 2. When it came to 3 July, the high ET values (ET > 5 mm d\({}^{-1}\)) occurred at well-irrigated maize parcels in the heading stages with LAI > 4. Wheat harvesting in July caused the field's ET to decline to less than 2 mm. Meanwhile, the orchard field maintained a relatively high daily ET through the growing season. Based on the high spatial variability of ET at the field scale, an individual farmer could use water  
Figure 9: Spatial distribution and frequency distribution of ET\({}_{\text{d}}\) using the SEBS model (**a**) and the MPDI-integrated SEBS model (**b**) on 4 July 2015.  
consumption information for his/her field to understand the relationship between water application and yield for improved water management. Irrigation management requires continuous daily ET, especially during growth seasons, so ET losses can be compensated for by applying the proper quantity of water at the right time to meet the plant water demands. Here, based on HJ-1 data, the complete daily time series of ET estimates were obtained by using the cubic spline interpolation method to fill in the gaps of daily ET during the 33 cloudless days [54]. The areal statistical means of daily ET for the main land-cover types in Ganzhou through the whole growing season in 2015 are presented in Figure 9. Obviously, the high temporal resolution of HJ-1 promised more satellite images to capture the dynamic changes of the daily ET. These dynamic traits of daily ET included the day-to-day fluctuations related to changes in meteorological forcing (i.e., solar radiation, air temperature, and humidity) and sporadic peaks due to the increase in soil evaporation fluxes after rainfall or irrigation events (i.e., such as the peak values on DOY 132, 158, 176, 184 188, 208, 235, see Figure 6b). Moreover, the modeling of ET at such detailed temporal resolution allows the ability to capture long-term (weekly/monthly) trends, primarily dictated by plant growth (see Figure 11). The mean daily ET for different land-cover types showed similar seasonality with an increasing trend since the beginning of the growing season (late April) and a decreasing trend since September. Peak values of daily ET for most land-cover types appeared in July, while the winter wheat reached its highest ET value (7.2 mm d\({}^{-1}\)) in mid-June. The ET of wheat fields was obviously lower than that of the other vegetation types when the winter wheat was in its maturity period in summer. Among the vegetation classes, the ET values of maize fields were found to be always higher than those of the other land-cover types in June and July, when maize was in the jointing stage.