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.
Laleh Parviz
Abstract
Introduction: The globally growing demand for water has shown the need for its efficient and judicial utilization, and particularly in agriculture being single largest consumer of water. Crop evapotranspiration represents crop water demand and governed by weather and crop conditions and most of the current ...
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Introduction: The globally growing demand for water has shown the need for its efficient and judicial utilization, and particularly in agriculture being single largest consumer of water. Crop evapotranspiration represents crop water demand and governed by weather and crop conditions and most of the current water demand models are non-spatial models, they use point data. Global scale satellite images can solve these problems. According to the high performance of satellite indices, it is necessary to estimate crop coefficient using combination of reflectance and thermal bands. The aim of this research was to estimate the effective crop coefficient of potato using vegetation indices and principle component analysis.
Materials and Methods: Principle component analysis (PCA) was used for effective crop coefficient estimation. Modeling of associations between vegetation indices and crop coefficient were conducted using artificial neural network. In the present study, NDVI, RI, EVI, SAVI, MSAVI, NVSWI, TVX, TVI, mNDVI and mTVI were the used as vegetation indices. PCA is designed to transform the original variables into new and uncorrelated variables (axes), namely the principal components, which are linear combinations of the original variables. The new axes lie along the directions of maximum variance. PCA provides an objective procedure of finding indices and information on the most meaningful parameters, which describes a whole data set affording data reduction with minimum loss of original information. Artificial neural networks are a computational model which is based on a large collection of simple neural units, loosely analogous to the observed behavior of a biological brain's axons. RMSE, MAE and MARE were the statistics used for investigating the performance of crop coefficient of vegetation indices with FAO crop coefficient.
Results and Discussion: Eleven MODIS vegetation indices are derived in the period of 2013 to 2016 for potato over the limited area between Tabriz and Bostanabad. The last year was considered as the validation period. According to the FAO-56 paper, the lengths of initial stage, crop development stage, mid-season stage, late season stage were considered to be 25, 30, 45, 30 days, respectively. The vegetation indices were derived using MODIS sensor with 2×2 pixels. The PCA showed that with increasing the number of components, the eigenvalues decreased. The analysis indicated that the three first components accounted for the 85.45 % of the total variance of data and the eigenvalues of them were greater than 1, the three first components were thus selected. NDVI, RI, TVI, MSAVI and NVSWI in the first component, mNDVI in the second component and LST in the third component had the highest coefficients. NDVI in the first component with high coefficient indicted the importance of index in the crop coefficient determination. The coefficients of SAVI and MSAVI were higher than NDVI. From the three investigations on the kind of principle component, the first investigation led to a 55.75 % decrease in RMSE relative to the second and third investigations. The first and second components together had less error rather than third component. The average of MAE for first, second and third investigations was, respectively, 0.17, 0.22 and 0.2. Therefore, component with exact values of particular vectors resulted in a reduced error. The sensitivity of artificial neural network led to an increase in the simulation accuracy (for example the RMSE decreased from epoch 100 to 50 was 48.27%). Crop coefficient estimation using vegetation indices of principle component analysis was underestimated about 1% in the validation period. Overestimation and underestimation were found in the initial and crop development stages, respectively.
Conclusions: The quantities of statistics showed the improvement of artificial neural network performance with combination of vegetation indices using principle component analysis. The vegetation indices with reflectance bands performed well. The combination of thermal and reflectance bands enhanced the vegetation indices efficiency. In addition to NDVI index for crop coefficient estimation, improvement of indices according to the study area condition increased the indices performance. The kind of mathematical equations of indices can increase the indices performance which using the same bands with different equations have different results. The selected component of principle component analysis has important role in increasing the simulation accuracy. The error reduction of simulated crop coefficients can increase the precision of irrigation consumption and agricultural planning which the principle component analysis has more important role.
