Mapping daily and seasonally evapotranspiration using remote sensing techniques over the Nile delta

Abstract The rapid escalation in water demands for agriculture, domestic, and industry sectors requires skillful management of this limited resource. Globally, the agriculture sector is considered the main user of the water resource. Actual evapotranspiration (ETc) is an important tool in determining the water requirements of different crops. Therefore, precise estimation of the ETc is the major parameter in the water balance of arid and semi-arid agriculture regions such as Egypt. Recently, both Remote Sensing and Geographical Information Systems (GIS) become the main techniques that can be efficiently used for estimating the ETc on regional and global coverage. The main goal of this study was to estimate the daily and seasonally ETc over the Nile delta using remote sensing techniques. These techniques were Surface Energy Balance for Land (SEBAL) algorithm with 24 Landsat 8 images. Additionally, FAO-Penman-Monteith method was used to validate the derived ETc from SEBAL algorithm under the same conditions using several performance criteria to assess the performance of the SEBAL algorithm with Landsat 8 in estimating the ETc over the Nile delta. The results revealed that the SEBAL algorithm with Landsat 8 images appears to provide an acceptable estimation of the spatial and temporal distributions of ETc over the Nile delta with acceptable accuracy with R2 = 97.83%, RMSE about 0.469 mm/day and 15.9% NRMSE. The derived ETc from SEBAL algorithm was then used to estimate the water balance and the irrigation efficiency of the study area. Results of water balance estimates revealed that most of the seasonal ETc (93%) was originally met by surface water and groundwater supplies; however, the remaining portion (7%) was particularly met by precipitation. Furthermore, the estimated irrigation efficiency was about 48.6% in the central portion of the Nile delta. Overall, the performance of the derived ETc from SEBAL algorithm compared to available ground datasets demonstrates the potential of using the SEBAL algorithm with Landsat 8 images for water use and water balance estimates within the Nile delta.

[1]  G. Senay,et al.  Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin , 2015 .

[2]  Yanbo He,et al.  Assessing relative soil moisture with remote sensing data: theory, experimental validation, and application to drought monitoring over the North China Plain , 2003 .

[3]  L. S. Pereira,et al.  Evapotranspiration information reporting: I. Factors governing measurement accuracy , 2011 .

[4]  F. Ling,et al.  Analysis of Landsat-8 OLI imagery for land surface water mapping , 2014 .

[5]  Zhuguo Ma,et al.  Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China , 2014 .

[6]  M. El-Shirbeny,et al.  Crop Water Requirements in Egypt Using Remote Sensing Techniques , 2014 .

[7]  Simon Beecham,et al.  A review of ET measurement techniques for estimating the water requirements of urban landscape vegetation , 2013 .

[8]  Dorothy H. Tillman,et al.  Water Quality Modeling of Allatoona and West Point Reservoirs Using CE-QUAL-W2 , 2001 .

[9]  H. Nouri,et al.  Remote sensing techniques for predicting evapotranspiration from mixed vegetated surfaces , 2013 .

[10]  A. Ahmadi,et al.  Evapotranspiration Estimation Using Remote Sensing Technology Based on SEBAL Algorithm , 2017 .

[11]  Richard G. Allen,et al.  Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study. , 2000 .

[12]  H. F. Blaney,et al.  Determining Water Requirements in Irrigated Areas From Climatological and Irrigation Data , 2017 .

[13]  Timothy K. Gates,et al.  Performance Measures for Evaluation of Irrigation‐Water‐Delivery Systems , 1990 .

[14]  Massimo Menenti,et al.  S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance , 2000 .

[15]  Ramesh K. Singh,et al.  Application of SEBAL Model for Mapping Evapotranspiration and Estimating Surface Energy Fluxes in South-Central Nebraska , 2008 .

[16]  Bernardo Barbosa da Silva,et al.  Assessment of daily actual evapotranspiration with SEBAL and S-SEBI algorithms in cotton crop , 2010 .

[17]  C. Ottlé,et al.  Future directions for advanced evapotranspiration modeling: Assimilation of remote sensing data into crop simulation models and SVAT models , 2005 .

[18]  B. Choudhury,et al.  Parameterization of land surface evaporation by means of location dependent potential evaporation and surface temperature range , 2007 .

[19]  Giuseppe Mendicino,et al.  DISTRIBUTED ESTIMATION OF ACTUAL EVAPOTRANSPIRATION THROUGH REMOTE SENSING TECHNIQUES , 2007 .

[20]  M. S. Moran,et al.  Remote Sensing for Crop Management , 2003 .

[21]  Richard G. Allen,et al.  Estimating Reference Evapotranspiration Under Inaccurate Data Conditions , 2002 .

[22]  Z. Su A Surface Energy Balance System (SEBS) For Estimation of Turbulent Heat Fluxes From Point to Continental Scale , 2002 .

[23]  Zheng Liu,et al.  Evapotranspiration estimation based on the SEBAL model in the Nansi Lake Wetland of China , 2011, Math. Comput. Model..

[24]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[25]  Ronglin Tang,et al.  An intercomparison of three remote sensing-based energy balance models using Large Aperture Scintillometer measurements over a wheat–corn production region , 2011 .

[26]  Yuei-An Liou,et al.  Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review , 2014 .

[27]  Kenneth K. Tanji,et al.  Soil salinization in the Nile Delta and related policy issues in Egypt , 2000 .

[28]  Mosaad Khadr,et al.  Forecasting of meteorological drought using Hidden Markov Model (case study: The upper Blue Nile river basin, Ethiopia) , 2016 .

[29]  Ling Tong,et al.  Partitioning evapotranspiration into soil evaporation and transpiration using a modified dual crop coefficient model in irrigated maize field with ground-mulching , 2013 .

[30]  A. Tawfik,et al.  Using Remote Sensing Techniques for Estimating Water Stress Index for Central of Nile Delta , 2018 .

[31]  C. W. Thornthwaite An approach toward a rational classification of climate. , 1948 .

[32]  Zongming Wang,et al.  Evapotranspiration estimation based on MODIS products and surface energy balance algorithms for land (SEBAL) model in Sanjiang Plain, Northeast China , 2013, Chinese Geographical Science.

[33]  J. Liu,et al.  Mapping evapotranspiration based on remote sensing : An application to Canada ’ s landmass , 2003 .

[34]  Pamela L. Nagler,et al.  Comparing Three Approaches of Evapotranspiration Estimation in Mixed Urban Vegetation: Field-Based, Remote Sensing-Based and Observational-Based Methods , 2016, Remote. Sens..

[35]  D. Randall,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part I: Model Formulation , 1996 .

[36]  Kaicun Wang,et al.  Comparison of evapotranspiration estimates based on the surface water balance, modified Penman‐Monteith model, and reanalysis data sets for continental China , 2017 .

[37]  H. Dehghanisanij,et al.  Evapotranspiration Partitioning in Surface and Subsurface Drip Irrigation Systems , 2011 .

[38]  H. L. Penman Natural evaporation from open water, bare soil and grass , 1948, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[39]  Akula Venkatram,et al.  Estimating the Monin-Obukhov length in the stable boundary layer for dispersion calculations , 1980 .

[40]  Matthew Montanaro,et al.  Stray Light Artifacts in Imagery from the Landsat 8 Thermal Infrared Sensor , 2014, Remote. Sens..

[41]  Heiko Balzter,et al.  Estimating Daily Reference Evapotranspiration in a Semi-Arid Region Using Remote Sensing Data , 2017, Remote. Sens..

[42]  O. El-Kawy,et al.  Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data , 2011 .

[43]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

[44]  Holly M. Miller,et al.  Landsat and water: case studies of the uses and benefits of landsat imagery in water resources , 2014 .

[45]  Ioannis Manakos,et al.  Application of the Sebs Water Balance Model in Estimating Daily Evapotranspiration and Evaporative Fraction from Remote Sensing Data Over the Nile Delta , 2011 .

[46]  James L. Wright,et al.  Operational aspects of satellite-based energy balance models for irrigated crops in the semi-arid U.S. , 2005 .

[47]  M. Khadr,et al.  On-Farm Water Management in the Nile Delta , 2016 .

[48]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .