Evapotranspiration estimation based on the SEBAL model in the Nansi Lake Wetland of China

Sun, Zhongchang; Wei, Bin; Su, Wei; Shen, Wei; Wang, Changzuo; You, Daian; Liu, Zheng

doi:10.1016/j.mcm.2010.11.039

Cited by 93 publications

(34 citation statements)

References 19 publications

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“…MODIS has been used in different studies to estimate ET from surface energy balance [25][26][27]. The Surface Energy Balance Algorithm for Land (SEBAL; [28,29]) is a validated and widely-used, remote-sensing-based model for estimating ET [13,24,[30][31][32][33]. This model can be implemented by using the thermal band of satellite sensors and various weather parameters such as solar radiation, air temperature, wind speed, and humidity [33].…”

Section: Introductionmentioning

confidence: 99%

“…Various studies focused on ET validation from the SEBAL model in different parts of the world like in India, Spain, Italy, Sri Lanka, China, Pakistan, and Iran [13,29,35]. Satisfactory results are observed in the works of Ahmad et al [36] in the Krishna river basin of India, Sun et al [30] and Du et al [31] in northeast China, and Bashir et al [34] in Sudan. In India, the performance of SEBAL-based ET was tested against the lysimeter ET on an agricultural farm located in a humid region and shows good accuracy of the model-generated ET [37].…”

Section: Introductionmentioning

confidence: 99%

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Projecting Climate and Land Use Change Impacts on Actual Evapotranspiration for the Narmada River Basin in Central India in the Future

et al. 2018

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Assessment of actual evapotranspiration (ET) is essential as it controls the exchange of water and heat energy between the atmosphere and land surface. ET also influences the available water resources and assists in the crop water assessment in agricultural areas. This study involves the assessment of spatial distribution of seasonal and annual ET using Surface Energy Balance Algorithm for Land (SEBAL) and provides an estimation of future changes in ET due to land use and climate change for a portion of the Narmada river basin in Central India. Climate change effects on future ET are assessed using the ACCESS1-0 model of CMIP5. A Markov Chain model estimated future land use based on the probability of changes in the past. The ET analysis is carried out for the years 2009-2011. The results indicate variation in the seasonal ET with the changed land use. High ET is observed over forest areas and crop lands, but ET decreases over crop lands after harvest. The overall annual ET is high over water bodies and forest areas. ET is high in the premonsoon season over the water bodies and decreases in the winter. Future ET in the 2020s, 2030s, 2040s, and 2050s is shown with respect to land use and climate changes that project a gradual decrease due to the constant removal of the forest areas. The lowest ET is projected in 2050. Individual impact of land use change projects decreases in ET from 1990 to 2050, while climate change effect projects increases in ET in the future due to rises in temperature. However, the combined impacts of land use and climate changes indicate a decrease in ET in the future.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Projecting Climate and Land Use Change Impacts on Actual Evapotranspiration for the Narmada River Basin in Central India in the Future

et al. 2018

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“…Evapotranspiration by using SEBAL algorithm and water balance model was calculated and results were compared, correlation by 70 % was reported (Mutiga et al, 2010). Useful results were reported about using Landsat images and SEBAL algorithm for actual ET estimation in multiple land uses of Nansi Lake basin, China (Sun et al, 2011). Surface temperature, net radiation, soil heat flux and hourly ET amounts were calculated by using SEBAL algorithm and 16 TM images were used in Texas plain.…”

Section: Review Of Literaturementioning

confidence: 99%

Estimation of Evapotranspiration Based on Surface Energy Balance Algorithm for Land (Sebal) Using Landsat 8 and Modis Images

Nouri¹

2017

Appl. Ecol. Env. Res.

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Nouri et al.: Estimation of evapotranspiration based on Surface Energy Balance Algorithm for Land (SEBAL) Abstract. The aim of this study was to determine the accuracy estimation of actual evapotranspiration by using Surface Energy Balance Algorithm for Land (SEBAL) in comparison with FAO-Penman-Monteith method in Landsat and MODIS images in Malayer County of Iran. In this study, Landsat 8 and MODIS images of 2013 were used. Result showed that there are not many differences between SEBAL and FAOPenman-Monteith methods in estimation of evapotranspiration. According to the aim of the study, result showed that MODIS sensor with high accuracy have calculated evapotranspiration more accurately than Landsat 8 by RMSE = 1.004 and MBE = 0.0033 respectively in surface temperature estimation which has great effect on estimation of the net radiation flux, soil heat flux and the sensible heat flux. On the other hand, according to low spatial resolution of MODIS sensor, we can conclude that this sensor has less value for zoning of evapotranspiration than Landsat 8 image according to the area topography and heterogeneous land uses.

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“…Among remote sensing-based ET algorithms, SEBAL [26] has been widely used worldwide and validated across multiple biomes and climate conditions [37][38][39][40], including cropland areas [31,[41][42][43]). Its application is recommended for water resources management due to its ability to provide high resolution estimates of ET [27,43].…”

Section: Introductionmentioning

confidence: 99%

Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest–Savanna Transition in Brazil

et al. 2020

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Evapotranspiration ( E T ) provides a strong connection between surface energy and hydrological cycles. Advancements in remote sensing techniques have increased our understanding of energy and terrestrial water balances as well as the interaction between surface and atmosphere over large areas. In this study, we computed surface energy fluxes using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm and a simplified adaptation of the CIMEC (Calibration using Inverse Modeling at Extreme Conditions) process for automated endmember selection. Our main purpose was to assess and compare the accuracy of the automated calibration of the SEBAL algorithm using two different sources of meteorological input data (ground measurements from an eddy covariance flux tower and reanalysis data from Modern-Era Reanalysis for Research and Applications version 2 (MERRA-2)) to estimate the dry season partitioning of surface energy and water fluxes in a transitional area between tropical rainforest and savanna. The area is located in Brazil and is subject to deforestation and cropland expansion. The SEBAL estimates were validated using eddy covariance measurements (2004 to 2006) from the Large-Scale Biosphere-Atmosphere Experiment in the Amazon (LBA) at the Bananal Javaés (JAV) site. Results indicated a high accuracy for daily ET, using both ground measurements and MERRA-2 reanalysis, suggesting a low sensitivity to meteorological inputs. For daily ET estimates, we found a root mean square error (RMSE) of 0.35 mm day−1 for both observed and reanalysis meteorology using accurate quantiles for endmembers selection, yielding an error lower than 9% (RMSE compared to the average daily ET). Overall, the ET rates in forest areas were 4.2 mm day−1, while in grassland/pasture and agricultural areas we found average rates between 2.0 and 3.2 mm day−1, with significant changes in energy partitioning according to land cover. Thus, results are promising for the use of reanalysis data to estimate regional scale patterns of sensible heat (H) and latent heat (LE) fluxes, especially in areas subject to deforestation.

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Evapotranspiration estimation based on the SEBAL model in the Nansi Lake Wetland of China

Cited by 93 publications

References 19 publications

Projecting Climate and Land Use Change Impacts on Actual Evapotranspiration for the Narmada River Basin in Central India in the Future

Projecting Climate and Land Use Change Impacts on Actual Evapotranspiration for the Narmada River Basin in Central India in the Future

Estimation of Evapotranspiration Based on Surface Energy Balance Algorithm for Land (Sebal) Using Landsat 8 and Modis Images

Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest–Savanna Transition in Brazil

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