*4.2. Irrigated Daily ET Comparison*

The relationship between measured and Landsat ET for the irrigated lysimeter provided overall better agreement compared to the dryland field [38,41,51,52]. The Landsat ET estimates were closely matched most of the year, except the middle of the growing seasons, during the peak crop water requirements. The satellite-based approach underestimated ET toward the middle of the growing season for cotton and soybeans, and overestimated the ET early and late during the growing season.

A detailed statistical analysis was performed for the daily and monthly ET (Table 6). The irrigated daily ET estimates were considered poor with an NSE of 0.37, RMSE of 2.1 mm d−1, and %RMSE of 86.4%. However, there was a statistical improvement with the monthly ET values with an NSE of 0.57, RMSE of 1.5 mm d−1, and % RMSE of 56.7%. Similar to the dryland lysimeter, the RMSE during the growing season was greater compared to the non-growing season (Table 7), with values almost double for the growing season compared to the non-growing season due to low ET measured values during the non-growing season. Hence, there was less variation between the measured and satellite-based ET values. However, the %RMSE error was higher during the non-growing season than the growing season, and these results agree with Allen et al. [46]. Allen et al. [6] illustrated that the use of reference ET considers the advective effects on a METRIC model performance, which can make the METRIC ET overestimate the ET from irrigated fields, exceeding daily net radiation in arid and semi-arid conditions. Allen et al. [46] reported that daily ET had the largest differences due to ET fluctuating the most during the growing season, and the monthly and season ET lumped most of the daily variations [38,46], and this is in agreement with the current study results.

Similarly, for the dryland lysimeter, the deviation between Landsat and measured ET was related to higher LAI estimation [32,36,38,47], advective condition effects under irrigated conditions [6, 36], and extended gap periods [38]. In addition, most of the studies conducted evaluated the ET on the current scene (image) days with minimal EB closure errors [36,49,53], and no studies

evaluated the extrapolated daily ET assessment for dryland conditions with clumped crops [36]. However, the irrigated field difference magnitude was far less than for the dryland field.

Landsat LAI estimates were better for the irrigated lysimeter, as the METRIC model performance was affected with the wet and cold pixel determination, and the METRIC model performed better with full canopy (full irrigated), compared to dryland (partial canopy) [32,36,38,49]. As irrigated fields produced more vegetation vigor, higher NDVI values were obtained [36,38], and consequently higher estimates of LAI were obtained and resulted in better estimates of Landsat ET for areas managed under irrigated conditions (Figure 8) [32,36,38,41].

The overall Landsat LAI estimation somewhat matched the measured LAI for most cultivated crops from 2001 to 2010 (2004 and 2007 omitted due to large gaps in clear Landsat data during the growing season). Three indicators that the satellite imagery was able to differentiate between irrigated (full canopy) and dryland fields (partial canopy) as well as identify the growing season were as follows:


The reason behind this is likely that LAI is better estimated for the irrigated field than the dryland field [36,38] due to more vegetation coverage, resulting in higher NDVI values and consequently ET values. In 2007 and 2009, when forage sorghum and sunflowers were cultivated, respectively, the LAI estimated using Landsat was slightly lower than the measured LAI for the irrigated lysimeter.

### **5. Conclusions**

Remote sensing-based ET estimation is considered a promising tool for irrigation water management. However, uncertainties associated with satellite-based ET estimation still exist, especially with various remotely sensed platforms due to variations in spatial and temporal resolution. In this study, satellite-based ET was evaluated using Landsat under semi-arid conditions in Texas under irrigated and dryland conditions.

Ten years of lysimeter measured ET data were used in this study. The Landsat-based ET overestimated the measured ET early and late in the growing season and underestimated ET during the peak of the growing season. The daily and monthly ET for the dryland lysimeter was unacceptable with negative NSE (−1.38 and −0.19), indicating there was no correlation between the estimates and measured ET; however, the daily and monthly ET for the irrigated lysimeter values showed better statistics with an NSE of 0.37 and 0.57, respectively. Seasonal ET showed more variations during the growing season compared to the non-growing season, because higher ET values were estimated during the growing season.

Under dryland conditions, there was significant LAI underestimation compared to the measured LAI values due to water stress during the growing season. LAI plays a significant role in evapotranspiration; where greater values of NDVI were obtained, consequently greater LAI was obtained under irrigated conditions, resulting in more ET for irrigated conditions. There are several reasons behind uncertainties of LAI and ET estimation, including the following: (1) METRIC model uncertainties with partial canopy estimates, (2) dryland plants' rapid modification of LAI based on available soil water (partial canopy), and (3) uncertainties with aerodynamic resistance surface roughness length as well as surface temperature deviations between irrigated and dryland conditions.

Extended gap periods are another significant challenge, and the selection of the filling method can account for ET estimation errors. In this study, gap periods reached up to 184 days in 2004, and the minimum was in 2008 with 40 days. The linear interpolation method was utilized to extrapolate the daily ET estimates between every two consecutive images in this study.

More satellite-based ET assessment under arid and semi-arid conditions is required, where the magnitude and frequency of precipitation are erratic, and irrigation is the only source under arid conditions to replenish crop water needs. With advances in remote sensing, more frequent satellite imagery will be available, with high spatial resolutions. Other extrapolation methods should be considered to generate daily time-series ET datasets. This would likely improve overall ET estimation accuracy by improving the overall spatial and temporal resolution.

Future research opportunities that include the assessment of ET relationship with crop physiology, yield, and yield components (number of flowers, grain quality, etc.) would provide potential information on crop response under dryland and irrigated conditions. Economic analysis of commodity market prices would be another research project due to groundwater decline in the Ogallala aquifer.

**Author Contributions:** Writing—original draft, A.A.H.; methodology, A.A.H. and P.H.G.; software, J.E.M., G.W.M., and P.H.G.; resources, B.A.E. and P.H.G.; review and editing, B.A.E., G.W.M., V.F.B., S.A.R., and J.E.M.; supervision, B.A.E. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Egyptian Government General Mission Scholarship Program administrated by the Egyptian Cultural and Education Bureau, Washington, DC; the Purdue Research Foundation, and the Agricultural and Biological Engineering Department, Purdue University.

**Acknowledgments:** The authors express their sincere thanks to (1) the Egyptian government general mission scholarship administrated by the Egyptian Cultural and Education Bureau, Washington, DC, for partially supporting this research; (2) the Purdue Research Foundation and the Agricultural and Biological Engineering Department for funding support during this research; and (3) the USDA-ARS at Bushland, Texas, USA for sharing the lysimeter data and data analysis.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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