Utility of METRIC model for estimating actual monthly evapotranspiration of Vanak Basin using MODIS sensor images
Subject Areas : Agriculture, rangeland, watershed and forestryMaryam Rezaei 1 , Hoda Ghasemieh 2 , Khodayar Abdollahi 3
1 - PhD. of Watershed Management Engineering and Science, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran
2 - Associate Professor, Department of Rangeland and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran
3 - Assistant Professor, Department of Natural Engineering, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, Iran
Keywords: remote sensing, Spatial-temporal variation, Actual Evapotranspiration, energy balance, Vanak basin,
Abstract :
Background and ObjectiveNowadays, in order to logical use of water for agricultural products, an accurate understanding of the evapotranspiration process is needed. Evapotranspiration is one of the most significant components of water balance hence it is a key variable for the optimal management of water resources. In this paper, we aim to the analysis of the spatial and temporal and distribution of actual evapotranspiration (AET) at monthly time scale using the METRIC approach, driven by MODIS satellite observations over the Vanak Basin and check the accuracy of the METRIC results with (SEBAL, Surface Energy Balance Algorithm for Land). Materials and Methods There are many methods for correct estimation of point evapotranspiration, such as weighing lysimeters, the Bowen ratio, and the eddy correlation methods. The weakness of the mentioned methods is that these techniques only provide evapotranspiration for a specific site and they can't estimate regional evaporation. The METRIC model was developed by Allen et al., (2007) based on the well-known SEBAL model (Bastiaanssen, 1998). METRIC model is a remote sensing-based method that estimates actual evapotranspiration as a residual of the surface energy balance. Herein, the spatial and temporal distribution of actual evapotranspiration of the Vanak Basin from April to November 2013–2014 was estimated using the METRIC model and using MODIS satellite data, the feasibility of using METRIC was investigated. Vanak Basin is located in the southeastern part of the Northern Karoon Basin. It is geographically placed between Chaharmahal va Bakhtiari and Isfahan provinces. 60 MODIS products of Leaf Area Index (MOD15A2), land surface temperature LST (MOD11A2) and surface reflectance (MOD09A1) in 8-day time step were extracted. The mentioned images were downloaded from the USGS website and the images were re-projected from the Sinusoidal projection to UTM projection. The scale factor for LAI, LST and Surface Reflectance were 0.1,0,02 and 0.0001, respectively. Estimation of ET with the METRIC model begins with energy balance. Data sets such as MODIS observations and weather data from the stations in and near the Vanak Basin are used to calculate instantaneous surface energy fluxes including net radiation flux (Rn), soil heat flux (G) and sensible heat flux (H) in the processing technique. ET at the instant of the satellite image is computed for each pixel by dividing LE values by latent heat of vaporization and density of water. Results and Discussion Throughout this research, the upper limit of the variation of AET showed a gradual increase from April to July in both 2013 and 2014. According to the results, the maximum amount of actual evapotranspiration in 2013 and 2014 for the July month was obtained 244 and 263 mm per month respectively. In general, the results of this paper will help us better understand the variations of regional AET. Comparison of the spatial distributions of AET, LAI and LST in the study area showed that the spatial distribution of AET was affected by two factors, LAI and LST, that Pearson correlation test was used to assess the relationship between two variables LAI and LST with actual evapotranspiration. Based on the results, the regions which had dense vegetation and low land surface temperatures had high AET rates, while in the regions with sparse vegetation and high land surface temperatures, the AET rate was low. The results showed that the trend of changes in the mean monthly temperature is in line with the monthly actual evapotranspiration; the same trend was observed in the case of albedo and net radiation flux. It should be noted that the absence of ground measurements for comparing them to the modelled AET amounts was a potential limitation of the current study. However, our approach of evaluating AET estimates derived from the METRIC model with the AET estimates derived from SEBAL model is a widely used (as standard approach) approach to tackle such limitations. In the second step of the analysis, this paper compares the estimated monthly AET using the equations of the METRIC versus the SEBAL, for the Vanak Basin in 2014. The outcome of the SEBAL model was used as a reference to compare the results obtained from the METRIC model. The statistical analysis was performed to determine the differences between monthly AET derived from METRIC vs. monthly AET derived from SEBAL. The Nash–Sutcliffe model efficiency coefficient (NSE), Coefficient of Determination (R2) and Mean absolute error (MAE) are used, that the results showed high R2 values and NS coefficients and low MAE values indicate that METRIC is closely related to SEBAL Model in the most of the months. The monthly AET values estimated by the METRIC model versus the monthly AET values estimated from the SEBAL model were evaluated and compared for the Vanak Basin from April to November 2014. Based on the overall results the scatter of estimations is in an acceptable range. In 2014, there was good agreement between METRIC and SEBAL models (R2=0.96–0.99, NSE = 0.93–0.99 and MAE = 1.3–7.53 mm month−1). In 2014, other results indicated that in both models, the upper limit of the variation of AET showed a gradual increase from April to July. Conclusion According to the results, the regions with high leaf area index (LAI) and low land surface temperature have more evapotranspiration than other regions with low leaf area index and high land surface temperature. The trend of the time series of LAI index and evapotranspiration in this study was consistent with the trend of changes in the parameters mentioned in the study, which was described by Reyes-González et al (2019) that use of the METRIC model in Dacota.
Allen RG, Tasumi M, Trezza R. 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-Model. Journal of irrigation and drainage engineering, 133(4): 380-394. doi:https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(380).
Ashraf Sadreddini A, Hamed Sabzchi Dehkharghani H, Nazemi A, Majnooni Heris A. 2020. Application of SEBAL algorithm in estimating maximum daily demand of rain-fed wheat from green water sources using MODIS images (Case study: Ahar county). Journal of RS and GIS for Natural Resources, 11(1): 1-28. (In Persian)
Bastiaanssen WGM, Menenti M, Feddes RA, Holtslag AAM. 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, 212-213: 198-212. doi:https://doi.org/10.1016/S0022-1694(98)00253-4.
Bayati S, Nasr EMA, Abdollahi K. 2018. Comparing the Response Characteristics and Volumetric Water Balance in Three Unit Hydrograph Methods (A case study: Vanak Basin). Iranian Journal of Eco Hydrology, 5(2): 373-385. (In Persian)
Carrillo-Rojas G, Silva B, Córdova M, Célleri R, Bendix J. 2016. Dynamic mapping of evapotranspiration using an energy balance-based model over an Andean páramo catchment of southern Ecuador. Remote Sensing, 8(2): 160. doi:https://doi.org/10.3390/rs8020160.
Esmaeili S, Khoshkhoo Y, Babaei KH, Y AO. 2018. Estimating Rice Actual Evapotranspiration Using METRIC Algorithm in a part of the North of Iran. Journal of Water and Soil Conservation, 24(6): 105-122. (In Persian)
Gebler S, Franssen HH, Pütz T, Post H, Schmidt M, Vereecken H. 2015. Actual evapotranspiration and precipitation measured by lysimeters: a comparison with eddy covariance and tipping bucket. Hydrology and Earth System Sciences, 19(5): 2145. doi:https://doi.org/10.5194/hess-19-2145-2015.
Glenn EP, Neale CM, Hunsaker DJ, Nagler PL. 2011. Vegetation index‐based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems. Hydrological Processes, 25(26): 4050-4062. doi:https://doi.org/10.1002/hyp.8392.
Häusler M, Conceição N, Tezza L, Sánchez JM, Campagnolo ML, Häusler AJ, Silva JMN, Warneke T, Heygster G, Ferreira MI. 2018. Estimation and partitioning of actual daily evapotranspiration at an intensive olive grove using the STSEB model based on remote sensing. Agricultural Water Management, 201: 188-198.
doi:https://doi.org/10.1016/j.agwat.2018.01.027.
He R, Jin Y, Kandelous MM, Zaccaria D, Sanden BL, Snyder RL, Jiang J, Hopmans JW. 2017. Evapotranspiration estimate over an almond orchard using landsat satellite observations. Remote Sensing, 9(5): 436. doi:https://doi.org/10.3390/rs9050436.
Huntington TG. 2006. Evidence for intensification of the global water cycle: Review and synthesis. Journal of Hydrology, 319(1): 83-95. doi:https://doi.org/10.1016/j.jhydrol.2005.07.003.
Johnson LF, Trout TJ. 2012. Satellite NDVI assisted monitoring of vegetable crop evapotranspiration in California’s San Joaquin Valley. Remote Sensing, 4(2): 439-455.
doi:https://doi.org/10.3390/rs4020439.
Lian J, Huang M. 2016. Comparison of three remote sensing based models to estimate evapotranspiration in an oasis-desert region. Agricultural Water Management, 165: 153-162. doi:https://doi.org/10.1016/j.agwat.2015.12.001.
Liaqat UW, Choi M. 2017. Accuracy comparison of remotely sensed evapotranspiration products and their associated water stress footprints under different land cover types in Korean peninsula. Journal of Cleaner Production, 155: 93-104. doi:https://doi.org/10.1016/j.jclepro.2016.09.022.
Mobasheri MR, Khavarian H, Zeaiean P, Kamaly G. 2007. Evapo-transpiration assessment using Terra/MODIS images in the Gorgan general district. The Journal of Spatial Planning, 11(1): 121-142. (In Persian)
Rawat KS, Bala A, Singh SK, Pal RK. 2017. Quantification of wheat crop evapotranspiration and mapping: A case study from Bhiwani District of Haryana, India. Agricultural Water Management, 187: 200-209.
doi:https://doi.org/10.1016/j.agwat.2017.03.015.
Reyes-González A, Kjaersgaard J, Trooien T, Reta-Sánchez DG, Sánchez-Duarte JI, Preciado-Rangel P, Fortis-Hernández M. 2019. Comparison of Leaf Area Index, Surface Temperature, and Actual Evapotranspiration Estimated Using the METRIC Model and In Situ Measurements. Sensors, 19(8): 1857. doi:https://doi.org/10.3390/s19081857.
Senay GB, Budde ME, Verdin JP. 2011. Enhancing the Simplified Surface Energy Balance (SSEB) approach for estimating landscape ET: Validation with the METRIC model. Agricultural Water Management, 98(4): 606-618. doi:https://doi.org/10.1016/j.agwat.2010.10.014.
Su Z. 2002. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrology and earth system sciences, 6(1): 85-99. doi:https://doi.org/10.5194/hess-6-85-2002.
Tasumi M. 2003. Progress in operational estimation of regional evapotranspiration using satellite imagery. Ph.D. dissertation, Univ. of Idaho, Moscow, Id. 216 p.
Trezza R, Allen RG, Tasumi M. 2013. Estimation of actual evapotranspiration along the Middle Rio Grande of New Mexico using MODIS and landsat imagery with the METRIC model. Remote Sensing, 5(10): 5397-5423. doi:https://doi.org/10.3390/rs5105397.
Vermote EF, Kotchenova S. 2008. Atmospheric correction for the monitoring of land surfaces. Journal of Geophysical Research: Atmospheres, 113(D23). doi:https://doi.org/10.1029/2007JD009662.
Wagle P, Bhattarai N, Gowda PH, Kakani VG. 2017. Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum. ISPRS Journal of Photogrammetry and Remote Sensing, 128: 192-203. doi:https://doi.org/10.1016/j.isprsjprs.2017.03.022.
Wan Z, Zhang Y, Zhang Q, Li Z-l. 2002. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment, 83(1): 163-180. doi:https://doi.org/10.1016/S0034-4257(02)00093-7.
Waters R, Allen R, Tasumi M, Trezza R, Bastiaanssen W. 2002. SEBAL (Surface Energy Balance Algorithms for Land): advanced training and users manual. Department of Water Resources, University of Idaho, Kimberly, 98p.
Yang Y, Chen R, Song Y, Han C, Liu J, Liu Z. 2019. Sensitivity of potential evapotranspiration to meteorological factors and their elevational gradients in the Qilian Mountains, northwestern China. Journal of Hydrology, 568: 147-159. doi:https://doi.org/10.1016/j.jhydrol.2018.10.069.
Zamani Losgedaragh S, Rahimzadegan M. 2018. Evaluation of SEBS, SEBAL, and METRIC models in estimation of the evaporation from the freshwater lakes (Case study: Amirkabir dam, Iran). Journal of Hydrology, 561: 523-531. doi:https://doi.org/10.1016/j.jhydrol.2018.04.025.
Zhang Y, Yang S, Ouyang W, Zeng H, Cai M. 2010. Applying Multi-source Remote Sensing Data on Estimating Ecological Water Requirement of Grassland in Ungauged Region. Procedia Environmental Sciences, 2: 953-963. doi:https://doi.org/10.1016/j.proenv.2010.10.107.
Zhang X-c, Wu J-w, Wu H-y, Li Y. 2011. Simplified SEBAL method for estimating vast areal evapotranspiration with MODIS data. Water Science and Engineering, 4(1): 24-35. doi:https://doi.org/10.3882/j.issn.1674-2370.2011.01.003.
Zhang B, Chen H, Xu D, Li F. 2017. Methods to estimate daily evapotranspiration from hourly evapotranspiration. Biosystems Engineering, 153: 129-139. doi:https://doi.org/10.1016/j.biosystemseng.2016.11.008.
_||_Allen RG, Tasumi M, Trezza R. 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-Model. Journal of irrigation and drainage engineering, 133(4): 380-394. doi:https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(380).
Ashraf Sadreddini A, Hamed Sabzchi Dehkharghani H, Nazemi A, Majnooni Heris A. 2020. Application of SEBAL algorithm in estimating maximum daily demand of rain-fed wheat from green water sources using MODIS images (Case study: Ahar county). Journal of RS and GIS for Natural Resources, 11(1): 1-28. (In Persian)
Bastiaanssen WGM, Menenti M, Feddes RA, Holtslag AAM. 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, 212-213: 198-212. doi:https://doi.org/10.1016/S0022-1694(98)00253-4.
Bayati S, Nasr EMA, Abdollahi K. 2018. Comparing the Response Characteristics and Volumetric Water Balance in Three Unit Hydrograph Methods (A case study: Vanak Basin). Iranian Journal of Eco Hydrology, 5(2): 373-385. (In Persian)
Carrillo-Rojas G, Silva B, Córdova M, Célleri R, Bendix J. 2016. Dynamic mapping of evapotranspiration using an energy balance-based model over an Andean páramo catchment of southern Ecuador. Remote Sensing, 8(2): 160. doi:https://doi.org/10.3390/rs8020160.
Esmaeili S, Khoshkhoo Y, Babaei KH, Y AO. 2018. Estimating Rice Actual Evapotranspiration Using METRIC Algorithm in a part of the North of Iran. Journal of Water and Soil Conservation, 24(6): 105-122. (In Persian)
Gebler S, Franssen HH, Pütz T, Post H, Schmidt M, Vereecken H. 2015. Actual evapotranspiration and precipitation measured by lysimeters: a comparison with eddy covariance and tipping bucket. Hydrology and Earth System Sciences, 19(5): 2145. doi:https://doi.org/10.5194/hess-19-2145-2015.
Glenn EP, Neale CM, Hunsaker DJ, Nagler PL. 2011. Vegetation index‐based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems. Hydrological Processes, 25(26): 4050-4062. doi:https://doi.org/10.1002/hyp.8392.
Häusler M, Conceição N, Tezza L, Sánchez JM, Campagnolo ML, Häusler AJ, Silva JMN, Warneke T, Heygster G, Ferreira MI. 2018. Estimation and partitioning of actual daily evapotranspiration at an intensive olive grove using the STSEB model based on remote sensing. Agricultural Water Management, 201: 188-198.
doi:https://doi.org/10.1016/j.agwat.2018.01.027.
He R, Jin Y, Kandelous MM, Zaccaria D, Sanden BL, Snyder RL, Jiang J, Hopmans JW. 2017. Evapotranspiration estimate over an almond orchard using landsat satellite observations. Remote Sensing, 9(5): 436. doi:https://doi.org/10.3390/rs9050436.
Huntington TG. 2006. Evidence for intensification of the global water cycle: Review and synthesis. Journal of Hydrology, 319(1): 83-95. doi:https://doi.org/10.1016/j.jhydrol.2005.07.003.
Johnson LF, Trout TJ. 2012. Satellite NDVI assisted monitoring of vegetable crop evapotranspiration in California’s San Joaquin Valley. Remote Sensing, 4(2): 439-455.
doi:https://doi.org/10.3390/rs4020439.
Lian J, Huang M. 2016. Comparison of three remote sensing based models to estimate evapotranspiration in an oasis-desert region. Agricultural Water Management, 165: 153-162. doi:https://doi.org/10.1016/j.agwat.2015.12.001.
Liaqat UW, Choi M. 2017. Accuracy comparison of remotely sensed evapotranspiration products and their associated water stress footprints under different land cover types in Korean peninsula. Journal of Cleaner Production, 155: 93-104. doi:https://doi.org/10.1016/j.jclepro.2016.09.022.
Mobasheri MR, Khavarian H, Zeaiean P, Kamaly G. 2007. Evapo-transpiration assessment using Terra/MODIS images in the Gorgan general district. The Journal of Spatial Planning, 11(1): 121-142. (In Persian)
Rawat KS, Bala A, Singh SK, Pal RK. 2017. Quantification of wheat crop evapotranspiration and mapping: A case study from Bhiwani District of Haryana, India. Agricultural Water Management, 187: 200-209.
doi:https://doi.org/10.1016/j.agwat.2017.03.015.
Reyes-González A, Kjaersgaard J, Trooien T, Reta-Sánchez DG, Sánchez-Duarte JI, Preciado-Rangel P, Fortis-Hernández M. 2019. Comparison of Leaf Area Index, Surface Temperature, and Actual Evapotranspiration Estimated Using the METRIC Model and In Situ Measurements. Sensors, 19(8): 1857. doi:https://doi.org/10.3390/s19081857.
Senay GB, Budde ME, Verdin JP. 2011. Enhancing the Simplified Surface Energy Balance (SSEB) approach for estimating landscape ET: Validation with the METRIC model. Agricultural Water Management, 98(4): 606-618. doi:https://doi.org/10.1016/j.agwat.2010.10.014.
Su Z. 2002. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrology and earth system sciences, 6(1): 85-99. doi:https://doi.org/10.5194/hess-6-85-2002.
Tasumi M. 2003. Progress in operational estimation of regional evapotranspiration using satellite imagery. Ph.D. dissertation, Univ. of Idaho, Moscow, Id. 216 p.
Trezza R, Allen RG, Tasumi M. 2013. Estimation of actual evapotranspiration along the Middle Rio Grande of New Mexico using MODIS and landsat imagery with the METRIC model. Remote Sensing, 5(10): 5397-5423. doi:https://doi.org/10.3390/rs5105397.
Vermote EF, Kotchenova S. 2008. Atmospheric correction for the monitoring of land surfaces. Journal of Geophysical Research: Atmospheres, 113(D23). doi:https://doi.org/10.1029/2007JD009662.
Wagle P, Bhattarai N, Gowda PH, Kakani VG. 2017. Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum. ISPRS Journal of Photogrammetry and Remote Sensing, 128: 192-203. doi:https://doi.org/10.1016/j.isprsjprs.2017.03.022.
Wan Z, Zhang Y, Zhang Q, Li Z-l. 2002. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment, 83(1): 163-180. doi:https://doi.org/10.1016/S0034-4257(02)00093-7.
Waters R, Allen R, Tasumi M, Trezza R, Bastiaanssen W. 2002. SEBAL (Surface Energy Balance Algorithms for Land): advanced training and users manual. Department of Water Resources, University of Idaho, Kimberly, 98p.
Yang Y, Chen R, Song Y, Han C, Liu J, Liu Z. 2019. Sensitivity of potential evapotranspiration to meteorological factors and their elevational gradients in the Qilian Mountains, northwestern China. Journal of Hydrology, 568: 147-159. doi:https://doi.org/10.1016/j.jhydrol.2018.10.069.
Zamani Losgedaragh S, Rahimzadegan M. 2018. Evaluation of SEBS, SEBAL, and METRIC models in estimation of the evaporation from the freshwater lakes (Case study: Amirkabir dam, Iran). Journal of Hydrology, 561: 523-531. doi:https://doi.org/10.1016/j.jhydrol.2018.04.025.
Zhang Y, Yang S, Ouyang W, Zeng H, Cai M. 2010. Applying Multi-source Remote Sensing Data on Estimating Ecological Water Requirement of Grassland in Ungauged Region. Procedia Environmental Sciences, 2: 953-963. doi:https://doi.org/10.1016/j.proenv.2010.10.107.
Zhang X-c, Wu J-w, Wu H-y, Li Y. 2011. Simplified SEBAL method for estimating vast areal evapotranspiration with MODIS data. Water Science and Engineering, 4(1): 24-35. doi:https://doi.org/10.3882/j.issn.1674-2370.2011.01.003.
Zhang B, Chen H, Xu D, Li F. 2017. Methods to estimate daily evapotranspiration from hourly evapotranspiration. Biosystems Engineering, 153: 129-139. doi:https://doi.org/10.1016/j.biosystemseng.2016.11.008.