Surface Energy Balance Algorithm For Land Evapotranspiration Research Articles

Simulation and Assessment of Daily Evapotranspiration in the Heihe River Basin over a Long Time Series Based on TSEB-SM

The high spatial and temporal resolution of recently developed evapotranspiration (ET) products facilitates agricultural water-savings in irrigated areas as well as improved estimates of crop yield, especially in arid and semi-arid regions. However, cloud cover interferes with ET estimates, in particular when using thermal-infrared-based models in temperate and tropical regions. Previous studies have shown that the two-source energy balance (TSEB) model coupled with soil moisture (TSEB-SM) has great potential for estimating surface ET by overcoming this issue. In this study, the TSEB-SM model was first used to generate a spatiotemporally continuous 1 km daily ET dataset across the Heihe River Basin in China from 2000 to 2020, which was then evaluated against four spatially distributed sites (Arou, Huazhaizi, Daman, and Sidaoqiao) and further compared with the two most widely used daily ET datasets (PML-V2 (Penman–Monteith–Leuning) and SEBAL (surface energy balance algorithm for land)). The results showed that the newly developed ET dataset agrees well with ground-based observations and outperforms the PML-V2 and SEBAL products in precisely characterizing the seasonal fluctuations and spatial distribution as well as the spatiotemporal trends of ET. In particular, ET in the Heihe River Basin exhibits clear regional differences. The upstream and midstream grassland and irrigated oasis areas provide much higher annual ET than the downstream desert areas, with a difference of up to 600 mm/year. A three-cornered hat (TCH)-based pixel-by-pixel analysis further demonstrated that the TSEB-SM and PML-V2 products have substantially smaller relative uncertainties as compared to SEBAL ET. In general, the proposed ET datasets are expected to be more beneficial for irrigation scheduling and to provide more efficient water management across the Heihe River Basin.

Evapotranspiration Estimation Using Remote Sensing Technology Based on a SEBAL Model in the Upper Reaches of the Huaihe River Basin

Understanding the spatial and temporal variations of evapotranspiration (ET) is vital for water resources planning and management and drought monitoring. The development of a satellite remote sensing technique is described to provide insight into the estimation of ET at a regional scale. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) was used to calculate the actual ET on a daily scale from Landsat-8 data and daily ground-based meteorological data in the upper reaches of Huaihe River on 20 November 2013, 16 April 2015 and 23 March 2018. In order to evaluate the performance of the SEBAL model, the daily SEBAL ET (ETSEBAL) was compared against the daily reference ET (ET0) from four theoretical methods: the Penman-Monteith (P-M), Irmak-Allen (I-A), the Turc, and Jensen-Haise (J-H) method, the ETMOD16 product from the MODerate Resolution Imaging Spectrometer (MOD16) and the ETVIC from Variable Infiltration Capacity Model (VIC). A linear regression equation and statistical indices were used to model performance evaluation. The results showed that the daily ETSEBAL correlated very well with the ET0, ETMOD16, and ETVIC, and bias between the ETSEBAL with them was less than 1.5%. In general, the SEBAL model could provide good estimations in daily ET over the study region. In addition, the spatial-temporal distribution of ETSEBAL was explored. The variation of ETSEBAL was significant in seasons with high values during the growth period of vegetation in March and April and low values in November. Spatially, the daily ETSEBAL values in the mountain area were much higher than those in the plain areas over the study region. The variability of ETSEBAL in this study area was positively correlated with elevation and negatively correlated with surface reflectance, which implies that elevation and surface reflectance are the important factors for predicting ET in this study area.

Long time series of daily evapotranspiration in China based on the SEBAL model and multisource images and validation

Abstract. Satellite observations of evapotranspiration (ET) have been widely used for water resources management in China. An accurate ET product with a high spatiotemporal resolution is required for research on drought stress and water resources management. However, such a product is currently lacking. Moreover, the performances of different ET estimation algorithms for China have not been clearly studied, especially under different environmental conditions. Therefore, the aims of this study were as follows: (1) to use multisource images to generate a long-time-series (2001–2018) daily ET product with a spatial resolution of 1 km × 1 km based on the Surface Energy Balance Algorithm for Land (SEBAL); (2) to comprehensively evaluate the performance of the SEBAL ET in China using flux observational data and hydrological observational data; and (3) to compare the performance of the SEBAL ET with the MOD16 ET product at the point scale and basin scale under different environmental conditions in China. At the point scale, both the models performed best in the conditions of forest cover, subtropical zones, hilly terrain, or summer, respectively, and SEBAL performed better in most conditions. In general, the accuracy of the SEBAL ET (rRMSE = 44.91 %) was slightly higher than that of the MOD16 ET (rRMSE = 48.72 %). In the basin-scale validation, both the models performed better than in the point-scale validation, with SEBAL obtaining results superior (rRMSE = 13.57 %) to MOD16 (rRMSE = 32.84 %). Additionally, both the models showed a negative bias, with the bias of the MOD16 ET being higher than that of the SEBAL ET. In the daily-scale validation, the SEBAL ET product showed a root mean square error (RMSE) of 0.92 mm d−1 and an r value of 0.79. In general, the SEBAL ET product can be used for the qualitative analysis and most quantitative analyses of regional ET. The SEBAL ET product is freely available at https://doi.org/10.5281/zenodo.4243988 and https://doi.org/10.5281/zenodo.4896147 (Cheng, 2020a, b). The results of this study can provide a reference for the application of remotely sensed ET products and the improvement of satellite ET observation algorithms.

Monitoring water use efficiency of irrigated sugarcane production in Mpumalanga, South Africa, using SEBAL

The objective of this study was to assess the accuracy, spatial variation and potential value of remote sensing (RS) estimates of evapotranspiration (ET) and biomass production for irrigated sugarcane in Mpumalanga, South Africa. Weekly ET and biomass production were estimated from RS data from 2011 to 2013 using the Surface Energy Balance Algorithm for Land (SEBAL). Ground estimates of canopy interception of photosynthetically active radiation (FPAR) and aerial biomass were compared to RS estimates. ET was estimated with a surface renewal (SR) system in one field. Evaporation coefficient (Kc) values were calculated from ET and reference grass evaporation. Remote sensing FPAR and biomass estimates compared well with field measurements (R2 = 0.89 and 0.78). SEBAL ET estimates exceeded SR estimates by 5 mm/week, while full canopy Kc values for SEBAL compared better with literature values than with SR Kc values. SEBAL estimates of ET and biomass were regarded as reliable. Considerable spatial variation was observed in seasonal RS ET (1 034 ± 223 mm), biomass (45 ± 17 t/ha) and biomass water use efficiency (WUEBIO, defined as dry biomass produced per unit of ET) (4.1 ± 1.0 kg/m3). About 32% of sugarcane fields had values below economic thresholds, indicating an opportunity to increase productivity. Actual yields correlated well with WUEBIO values, suggesting that this may be used for monitoring crop performance and identifying areas that require remedial treatment.

Mapping Maize Evapotranspiration at Field Scale Using SEBAL: A Comparison with the FAO Method and Soil-Plant Model Simulations

The surface energy balance algorithm for land (SEBAL) has been successfully applied to estimate evapotranspiration (ET) and yield at different spatial scales. However, ET and yield patterns have never been investigated under highly heterogeneous conditions. We applied SEBAL in a salt-affected and water-stressed maize field located at the margin of the Venice Lagoon, Italy, using Landsat images. SEBAL results were compared with estimates of evapotranspiration by the Food and Agriculture Organization (FAO) method (ETc) and three-dimensional soil-plant simulations. The biomass production routine in SEBAL was then tested using spatially distributed crop yield measurements and the outcomes of a soil-plant numerical model. The results show good agreement between SEBAL evapotranspiration and ETc. Instantaneous ET simulated by SEBAL is also consistent with the soil-plant model results (R2 = 0.7047 for 2011 and R2 = 0.6689 for 2012). Conversely, yield predictions (6.4 t/ha in 2011 and 3.47 t/ha in 2012) are in good agreement with observations (8.64 t/ha and 3.86 t/ha, respectively) only in 2012 and the comparison with soil-plant simulations (8.69 t/ha and 5.49 t/ha) is poor. In general, SEBAL underestimates land productivity in contrast to the soil-plant model that overestimates yield in dry years. SEBAL provides accurate predictions under stress conditions due to the fact that it does not require knowledge of the soil/root characteristics.

Estimation and Validation of Actual Evapotranspiration for Wheat Crop Using SEBAL Model over Hisar District, Haryana, India

Evapotranspiration (ET) is one of the complex, but essential components of the hydrologic cycle. Advances in remote sensing (RS) and geographical information systems (GIS) have enabled us to estimate ET spatially. In the present study, both, RS and GIS tools have been utilized to estimate the actual crop ET by surface energy balance algorithm for land (SEBAL) model using high spatial resolution satellite image Landsat7 ETM+ for Hisar district, Haryana in north India. Previously calibrated and validated SEBAL model with lysimeter data within the same agroclimatic zone were used in the study. Derived actual ET from lysimeter data validated SEBAL method was again validated using Penman-Montieth (PM) method for the study area located in the same agro-climatic zone. Based on the primary and secondary data analysis, it can be inferred that SEBAL ET is the best spatial ET estimation model for Hisar district or regions having similar agro-climatic conditions. Validation of SEBAL ET with ground-observed lysimeter data showed high coefficient of correlation (R 2 = 0.91). Validation using the PM method also showed high coefficient of correlation (R 2 = 0.835). Other statistical parameters (RMSE = 0.583, NRMSE = 0.236) also showed good agreement between actual SEBAL ET c and PM ET c (crop evapotranspiration). It was also found that any prior knowledge about the crops, their types and cropping seasons is not required for the estimation of actual ET by SEBAL model.

Mapping daily and seasonal evapotranspiration from irrigated crops using global climate grids and satellite imagery: Automation and methods comparison

AbstractThe surface energy balance algorithm for land (SEBAL) estimates land surface evapotranspiration (ET) from radiometric surface temperature (TR), but requires manual selection of calibration pixels, which can be impractical for mapping seasonal ET. Here pixel selection is automated and SEBAL implemented using global climate grids and satellite imagery. SEBAL is compared with the MOD16 algorithm, which uses remotely sensed data on vegetation condition to constrain reference ET from the Penman‐Monteith equation. The difference between the evaporative fraction (Λ, range 0–1) from SEBAL and six eddy flux correlation towers was less than 0.10 for three of six towers, and less than 0.24 for all towers. SEBAL ET was moderately sensitive to surface roughness length and implementation over regions smaller than ∼10,000 km2 provided lower error than larger regions. Pixel selection based on TR had similar errors as those based on a vegetation index. For maize, MOD16 had lower error in mean seasonal evaporative fraction (−0.02) compared to SEBAL Λ (0.23), but MOD16 significantly underestimated the evaporative fraction from rice (−0.52) and cotton fields (−0.67) compared with SEBAL (−0.09 rice, −0.09 cotton). MOD16 had the largest error over short crops in the early growing season when vegetation cover was low but land surface was wet. Temperature‐based methods like SEBAL can be automated and likely outperform vegetation‐based methods in irrigated areas, especially under conditions of low vegetation cover and high soil evaporation.

SEBAL 모형과 Terra MODIS 영상을 이용한 혼효림, 논 지역에서의 공간증발산량 산정 연구

AbstractThis study is to estimate Surface Energy Balance Algorithm for Land (SEBAL) daily spatial evapotranspiration (ET) comparing with eddy covariance flux tower ET in Seolmacheon mixed forest (SMK) and Cheongmicheon rice paddy (CFK). The SEBAL input data of Albedo, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) from Terra MODIS products and the meteorological data of wind speed, and solar radiation were prepared for 2 years (2012-2013). For the annual average flux tower ET of 302.8 mm in SMK and 482.0 mm in CFK, the SEBAL ETs were 183.3 mm and 371.5 mm respectively. The determination coefficients (R 2 ) of SEBAL ET versus flux tower ET for total periods were 0.54 in SMK and 0.79 in CFK respectively. The main reason of SEBAL ET underestimation for both sites was from the determination of hot pixel and cold pixel of the day and affected to the overestimation of sensible heat flux.Keywords:Evapotranspiration, Terra MODIS, Flux tower, SEBAL SEBAL 모형과 Terra MODIS 영상을 이용한 혼효림, 논 지역에서의 공간증발산량 산정 연구

SEBAL-A: A Remote Sensing ET Algorithm that Accounts for Advection with Limited Data. Part I: Development and Validation

The Surface Energy Balance Algorithm for Land (SEBAL) is one of the remote sensing (RS) models that are increasingly being used to determine evapotranspiration (ET). SEBAL is a widely used model, mainly due to the fact that it requires minimum weather data, and also no prior knowledge of surface characteristics is needed. However, it has been observed that it underestimates ET under advective conditions due to its disregard of advection as another source of energy available for evaporation. A modified SEBAL model was therefore developed in this study. An advection component, which is absent in the original SEBAL, was introduced such that the energy available for evapotranspiration was a sum of net radiation and advected heat energy. The improved SEBAL model was termed SEBAL-Advection or SEBAL-A. An important aspect of the improved model is the estimation of advected energy using minimal weather data. While other RS models would require hourly weather data to be able to account for advection (e.g., METRIC), SEBAL-A only requires daily averages of limited weather data, making it appropriate even in areas where weather data at short time steps may not be available. In this study, firstly, the original SEBAL model was evaluated under advective and non-advective conditions near Rocky Ford in southeastern Colorado, a semi-arid area where afternoon advection is common occurrence. The SEBAL model was found to incur large errors when there was advection (which was indicated by higher wind speed and warm and dry air). SEBAL-A was then developed and validated in the same area under standard surface conditions, which were described as healthy alfalfa with height of 40–60 cm, without water-stress. ET values estimated using the original and modified SEBAL were compared to large weighing lysimeter-measured ET values. When the SEBAL ET was compared to SEBAL-A ET values, the latter showed improved performance, with the ET Mean Bias Error (MBE) reduced from −17.1% for original SEBAL to 2.2% for SEBAL-A and the Root Mean Square Error (RMSE) reduced from 25.1% to 10.9%, respectively. It was therefore concluded that the developed SEBAL-A model was capable of accounting for advection and therefore suitable for arid and semi-arid regions where advection is common.

Changes to the energy budget and evapotranspiration following conversion of tropical savannas to agricultural lands in São Paulo State, Brazil

AbstractIn Brazil, as much as 50% of the native tropical savannas and woodlands (known as the Cerrado) have been converted to farmland under intensive agriculture. Land cover change at this scale has important implications for water and energy budgets, but at present, these implications are poorly understood. A promising new technology for elucidating these relationships at large scales is remote sensing; but results obtained by this method need to be verified with ground‐based measurements. For this study, we used remote sensing to estimate changes in energy balance and evapotranspiration (ET) in São Paulo State following conversion of native Cerrado woodlands to cultivated land for sugar cane and eucalyptus. We applied the surface energy balance algorithm for land (SEBAL) to ten TM–Landsat 5 images taken during 2005 at two locations (sugar cane fields and Cerrado woodlands). The SEBAL ET results were compared with ET data obtained via eddy covariance. There was good agreement between the two (R2 = 0·87), with no systematic overestimation or underestimation by SEBAL. Our SEBAL analysis indicated that land cover change causes strong shifts in albedo, land‐surface temperature, and vegetation index – all of which affect energy balance and ET. Both the SEBAL and eddy covariance measurements indicate that ET for sugar cane is only about 70% of that for native woodlands. SEBAL estimates found that, for cultivated eucalyptus plantations, ET was about 88% of that for Cerrado forests. Our results indicate that the SEBAL methodology is a reliable tool for estimating energy budgets and ET in Cerrado landscapes and, further, that large‐scale conversion of Cerrado woodlands to agricultural land significantly alters energy budgets and ET. Copyright © 2014 John Wiley & Sons, Ltd.

Evapotranspiration data assimilation with genetic algorithms and SWAP model for on-demand irrigation

Evapotranspiration (ET) is one of the indicators of water use efficiency. Periodic information of ET based on remote sensing is useful for an on-demand irrigation (ODI) management. The main objective of this paper was to develop an ET data assimilation scheme to optimize the parameters of an agro-hydrology model for ODI scheduling. The soil, water, atmosphere, and plant (SWAP) simulation model has been utilized for this purpose. We computed remote sensing-based ET for a wheat field in the Sirsa Irrigation Circle, Haryana, in India using 18 cloud-free moderate resolution imaging spectroradiometer images taken between December 2001 and April 2002. The surface energy balance algorithm for land (SEBAL) was used for this purpose. Because ET estimates from SEBAL provide information on the surface soil moisture state, they were treated as observations to estimate unknown parameters of the SWAP model via a stochastic data assimilation (genetic algorithm) approach. The SWAP parameters were optimized by minimizing the residuals between SEBAL and SWAP model-based ET values. The optimized parameters were used as input to SWAP to estimate soil water balance for ODI scheduling. The results showed that the selected parameters (i.e. sowing, harvesting, and irrigation scheduling dates) were successfully estimated with the data assimilation methodology. The SWAP model produced reasonable states of water balance by assimilating ET observations. The root mean square of error was 0.755 and 2.132 cm3/cm3 for 0–15 and 15–30 cm soil depths the same layers, respectively. With optimized parameters for ODI, SWAP predicted higher yield and water use efficiency than traditional farmer’s irrigation criteria. The data assimilation methodology produced can be considered as an operational tool at the field scale to schedule irrigation or predict irrigation requirements from remote sensing-based ET.