The Evaluation of Evapotranspiration Product of MODIS with Penman-Montieth FAO 56 and Priestley-Taylor Evapotranspiration at the Different Climate Types of Iran

Abstract
Background and Purpose
Evapotranspiration is one of the most critical components in the land branch of the hydrological cycle, which, as the link between water and energy cycles, plays an essential role in the interaction of the atmosphere and surface. Getting access to remote sensing images has made it possible to study evapotranspiration spatially and temporally, including actual evapotranspiration (AET) and potential evapotranspiration (PET). Evapotranspiration of the MOD16A2 MODIS sensor can be very useful among remote sensing images due to its very appropriate spatial (500 m) and temporal (8 daily) resolutions in regional studies in areas without data.
Materials and Methods
This study evaluated the MODIS global terrestrial potential evapotranspiration product (MOD16A2) using two reference evapotranspiration methods of Penman-Monteith FAO 56 and Priestley-Taylor in meteorological stations from 2001 to 2018. The study area is located in the southwestern provinces of Iran (Khuzestan and Bushehr), west of Iran (Hamedan and Kermanshah provinces), and north of Iran (Guilan and Mazandaran provinces), which is classified from arid to perhumid according to the UNESCO method. Then, Penman-Monteith FAO 56 and Priestley-Taylor reference evapotranspiration was prepared using meteorological data with the Evapotranspiration package R software, and the potential evapotranspiration data of the MOD16A2 product was provided using the Google Earth Engine system. Then, these data were compared based on evaluation metrics in different climates.
Findings
Compared to both the Penman-Monteith FAO 56 and Priestley-Taylor methods, the MOD16A2 product overestimates evapotranspiration in all climate types and has greater variance in data. The statistical properties of the MOD16A2 include: the first and third quarters in arid and semi-arid climates with Penman-Monteith FAO 56 evapotranspiration is less different than the Priestley-Taylor method. In contrast, the first and third quarters of the MOD16A2 are more similar to the Priestley-Taylor evapotranspiration in semi-humid, humid, and perhumid climates. MOD16A2 also estimates the seasonal evapotranspiration cycles well, but the date of the MOD16A2 peaks in all climate types occur mostly with one-week precedence. The evapotranspiration of the MOD16A2 is successful in estimating the Penman-Monteith (Priestley-Taylor) evapotranspiration in arid and semi-arid climates (semi-humid to perhumid climates), particularly semi-arid with cold winters and hot summers climate (perhumid climate), due to the small errors of the model, including PBIAS and RMSE respectively in the range of 40.3-46.5% and 14.19-6.6mm/8d (the range of 72.5-97% and 6-24.5 mm/8d), the high coefficient of the modified agreement index in the range of 0.5-0.61 (0.37-0.5), weighted determination in the range of 0.55-0.63 (0.44-0.51). Moreover, there is a strong positive linear relationship among MOD16A2, Priestley-Taylor, and Penman-Monteith in most climate types, because of their high correlation coefficients (more than 0.85).
Conclusion
The results of this study indicate less uncertainty in evapotranspiration of the MOD16A2 product with the Penman-Monteith FAO 56 method in the semi-arid and arid climates, especially semi-arid climates. In contrast, in the semi-humid to hyperhumid climates, MOD16A2 product has less uncertainty with the Priestley-Taylor method. Also, the MOD16A2 product has the least uncertainty in the semi-arid climates due to the least errors. Therefore, considering the recent climate change in terms of increasing temperature and consequently increasing evapotranspiration, particularly in arid and semi-arid regions around the world, and proposing the Penman-Monteith FAO 56 as the standard method of estimating evapotranspiration by FAO, the MOD16A2 evapotranspiration can play a crucial role in irrigation planning, water resources management, and drought monitoring in the arid and semi-arid climates without any observed dataset, especially semi-arid climates.