عنوان مقاله [English]
Evapotranspiration is one of the crucial parts of the water cycle balance. In Iran, the total annual rainfall is estimated at 413 billion cubic meters. According to an analysis, 296 billion cubic meters, or 72% of this amount, became out of reach due to evapotranspiration. Accurate estimation of evapotranspiration plays a crucial role in studies on the issues such as global climate changes, environmental evolution, and control of water resources. Due to the limited number of meteorological stations and the high costs and time of collecting ground data, using remote sensing techniques and satellite images to have accurate and appropriate outputs can be a suitable tool to determine the actual evapotranspiration rate. One remote sensing algorithm for estimating evapotranspiration is the Surface Energy Balance (SEBAL).
The SEBAL is a model based on image processing that includes twenty-five models for calculating the evapotranspiration (ET) rate as the remainder of the Earth's surface energy balance. This model was introduced by Bastiansen in the Netherlands and also developed for the Idaho Highlands based on measured evapotranspiration at ground level. The SEBAL model uses digital image information captured by the Landsat satellite or other remote sensing sensors capable of recording thermal infrared and visible and near-infrared radiations. The ET value per pixel (e.g., in 30 by 30 square meters of TM and ETM Landsat images) is calculated for the specific moment at which the photo is taken.
The ET value will equal the net radiation minus the heat entering the soil minus the heat entering the air. Further details of this model have been provided by Bastiansen et al., but the general equation used by the SEBAL is as follows:
LE = Rn – H – G
Where LE is the latent heat flux (Wm-2), which can be easily converted to ET; Rn is the net solar radiation (Wm-2); H is the sensible heat flux (Wm-2), and G is the ground or soil heat flux (Wm-2). From this formula, the formula can be inferred that the radiation that reaches the Earth's surface from the atmosphere is separated into three parts: a part of the Earth or soil is heated, another part of it near the surface of the Earth is heated, and the rest of the remaining energy is evaporated. The SEBAL aims to calculate the latent heat flux (ET), considering the actual ET. It should be noted that the essential accuracy of the results is for the LE or ET. It is affected by the accuracy of the shortwave band as well as the thermal band of the satellite. In the following equation, the net radiation from the surface energy equilibrium equation is calculated as:
Rn= (1-α) Rs + (Lin-Lout)
Where a is the surface albedo; Rs is the solar radiation (Wm-2); e is the reflection of the Earth's surface (emission), and Lin-Lout is the radiations entering and leaving the Earth in the form of long waves. A value is obtained by mixing spectral reflections from six shortwave bands on the Landsat satellite. Lin-Lout is also considered a function of the surface temperature, which can be extracted from the satellite image. The value of e is obtained by plant indices created from two short-wavelength bands. The potential importance of Rs per pixel with a definite slope can be determined using the precise sky theory curves. The soil heat flux or G can be obtained empirically using Bastiansen's et al. (1998).
Discussion and Conclusion
According to the results obtained from analyzing the data and output maps captured by the Landsat satellite, and considering the LST map, in which green color indicates a deficiency and red color represents very high, there was an oscillating trend from 2000 to 2008. Still, according to LST maps, since 2010, there has been a sharp incremental trend, the peak of which has been in 2020, and the LST has reached its highest level. Such a trend has also been seen concerning the soil temperature map of Mazandaran province. A remarkable point about the increase in soil temperature is that it was significant and instantaneous in the fifth month of 2010, and the soil surface temperature has increased, just as in the LST, since 2010. Regarding the NDVI map of Mazandaran province, significant and impressive changes have occurred since 2012, and this trend has risen from this year until 2020. According to the maps obtained from the soil moisture in the province, the data show that oscillating changes occurred until 2012, and since the fourth month of 2012, the region's soil moisture has also increased. All the factors mentioned have a direct relationship with evapotranspiration. According to the results obtained and the increasing trends, especially from 2010 to 2020, there is expected to be an increasing trend for evapotranspiration using the SEBAL algorithm. The primary outcome of this research, which studies the changes in the evapotranspiration rates in Mazandaran province, is that: as expected, due to the increase in all the factors affecting the evapotranspiration increase, the results show that since 2010 the evapotranspiration trend has dramatically increased; Of course, due to the geographical location and proximity to the Caspian Sea, the evapotranspiration has always been relatively high, but there have been significant changes and a sharp increase from 2010 to 2020.