B. Bahmanabadi; A. Kaviani
Abstract
Introduction: The exact estimation of evapotranspiration has significant importance in the programming of irrigation development and other distribution systems and water usage. Since the main user of water in the country is the agriculture sector, therefore, the exact estimation of plants’ water demand ...
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Introduction: The exact estimation of evapotranspiration has significant importance in the programming of irrigation development and other distribution systems and water usage. Since the main user of water in the country is the agriculture sector, therefore, the exact estimation of plants’ water demand has been adverted extensively. The assessment methods of reference evapotranspiration are classified in two types of direct and indirect. The calculation of reference evapotranspiration in scientific and in vitro form and with high accuracy is possible by using lysimeter but in comparison to the indirect methods that are based on the climatic data of weather stations, the use of lysimeter is unfortunately inefficient. This is not just for the time consuming and high cost of lysimeter but it is for the limitation of weather stations and spottiness of the estimated values; in this way it is not possible to expand the obtained results to the large scale. Remote sensing is an authentic technique for the assessment of evapotranspiration in large scale which do not consume much time and money. The existence of different satellites by having different spatial and temporal resolution, redouble the importance and usability of this technique
Material and Methods: Actual evapotranspiration assessment in the region were done based on SEBAL, SSEB and TSEB algorithms on 46 imageries of MODIS, seven imageries of Landsat7 (ETM+) and seven imageries of Landsat5 (TM) in years of 2001-2003. Multiplicity of imageries of MODIS show the proper time resolution of this sensor and is a reason for less errors in the assessment of reference evapotranspiration. In the evaluation of the three algorithms of SEBAL, SSEB and TSEB in the three satellites.
Result and Discussion: In the evaluation of the three algorithms of SEBAL, SSEB and TSEB in the three satellites, MODIS shows the least errors (respectively, RMSE=0.856, 1.385 and 2.7 mm/day), then Landsat7 is placed in the second class by having higher spatial resolution (respectively, RMSE=1.042, 1.56 and 2.76 mm/day) and Landsat5 has the highest errors (respectively, RMSE = 1.14, 1.97 and 3.06 mm/day). NDVI was found at the lowest amount in the beginning of cultivation period because of germination and sparseness of vegetation, and increase respectively by increasing temperature and crop canopy. L factor has a significant importance in the assessment of SAVI which is related to the area crop coverage percentage. Amount of L has been estimated as L=0.6 that has the least errors in comparison to the others.
Conclusion: In this study, the proper amount for L factor in estimation of the SAVI amount was about 0.6 which was based on the investigations on soil correction factor, the results of statistical indexes and the type and dispersal of vegetation in the region. The accuracy estimation of evapotranspiration of two single-source algorithms of SEBAL and SSEB and one two-source algorithm of TSEB in Bushehr province were evaluated. SEBAL algorithm presented more exact results based on statistical indexes among two single-source algorithms and the obtained results in 95% level of this algorithm showed significant differences with lysimetric measurements. This algorithm was chosen as the premier algorithm in the region. Two-source algorithm of TSEB showed the highest amount of errors. Satellite imageries by having higher spatial resolution estimated evapotranspiration with higher accuracy, the reason of which is proper choosing of cold and hot pixels. Although, because of having proper time resolution and variation of image numbers and also presenting of more time series in comparison to ETM+ and TM, MODIS was more adverted. ETM+ which is located on Landsat satellite was lied in the second place because of its resolution and having higher spatial resolution.
seyed vahidodin rezvani
Abstract
Today, remote sensing is used in various sciences, such as: geography, biology, meteorology, agriculture, water resources management and etc. Easy and inexpensive data access and data precision, digital and extensive and comprehensive of images that having the frequency spectrum, are some advantages ...
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Today, remote sensing is used in various sciences, such as: geography, biology, meteorology, agriculture, water resources management and etc. Easy and inexpensive data access and data precision, digital and extensive and comprehensive of images that having the frequency spectrum, are some advantages of remote sensing, than other methods of providing information. Therefore, by using algorithms in remote sensing that having the evaporation and transpiration, you can have a big step in the management of water resources. Among of these algorithms, SEBAL is a remote sensing algorithm that calculating surface energy balance for each pixel of a satellite image at each moment. In this study, surface albedo, surface temperature and vegetation status index were calculated by using this algorithm and multi-spectral satellite data and meteorological information such as degree of temperature, hours of sunshine, wind, saturated vapor pressure, soil humidity and etc. Finally evapotranspiration of Miandarband plain (west of Iran) was determined and the evapotranspiration maps were prepared. Also, the actual evapotranspiration computed for wheat using FAO conventional method and was compared with SEBAL method. The results showed that there was a high correlation (0.84) between these two methods.
B. Hassanpour; F. Mirzaei; S. Arshad; H. Kossari
Abstract
In the present study, two methods of predicting evapotranspiration by the use of satellite images were compared. Field data in a corn site was measured at agricultural engineering research institute private farm in 6 days. Consequently MODIS images were used for predicting evapotranspiration by SEBAL ...
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In the present study, two methods of predicting evapotranspiration by the use of satellite images were compared. Field data in a corn site was measured at agricultural engineering research institute private farm in 6 days. Consequently MODIS images were used for predicting evapotranspiration by SEBAL and S-SEBI algorithms. These algorithms are different in predicting sensible heat flux. The results show that RSME value for the net radiation and soil heat flux was respectively 46 and 43 (w/m2). SEBAL algorithm is capable to estimate sensible heat flux more accurate than S-SEBI so it is able to estimate latent heat flux more accurate. The RSME amount in sensible heat flux and latent heat flux for SEBAL algorithm are 58 and 31 (w/m2) respectively. These amounts in S-SEBI algorithm are 111 and 74 (w/m2). The differences between two algorithms could be because of the use of meteorological data in predicting sensible heat flux and aerodynamic resistance in SEBAL algorithm. Also the results show that SEBAL algorithm estimates hourly evapotranspiration by the difference of 0.05 mm/hour which is about 1% of hourly evapotranspiration Whereas S-SEBI predicted it by the difference of 0.11 and 11%. The difference between measured daily evapotranspiration and SEBAL -based daily evapotranspiration was 0.4 mm that is about 1% less than measured. Whereas these differences by S_SEBI are 1mm and 12%.
