A Modified SEBAL Modeling Approach for Estimating Crop Evapotranspiration in Semi-arid Conditions

Remote sensing methods are becoming attractive to estimate crop evapotranspiration, as they cover large areas and can provide accurate and reliable estimations; intensive field monitoring is also not required, although some ground-truth measurements can be helpful in interpreting satellite images. For the purposes of this paper, modeling and remote sensing techniques were integrated for estimating actual evapotranspiration of groundnuts (Arachishypogaea, L.) that is cultivated near Mandria Village in Paphos District of Cyprus. The Surface Energy Balance Algorithm for Land (SEBAL) was adopted for the first time in Cyprus, employing the essential adaptations for local soil and meteorological conditions. Landsat-5 TM and 7 ETM+ images were used to retrieve the needed spectral data. The SEBAL model is enhanced with empirical equations determined as part of the present study, regarding crop canopy factors, in order to increase its accuracy. Maps of ETa were created using the SEBAL modified model (CYSEBAL) for the area of interest. The results have been compared to the measurements from an evaporation pan (which was used as a reference) and those of the original SEBAL model. The statistical comparison has shown that the modified SEBAL yields results that are comparable to those of the evaporation pan. T-test application has revealed that the statistical difference between SEBAL and CYSEBAL is significant and quite crucial, especially in a place with limited surface and underground water resources.

[1]  Giorgos Papadavid,et al.  Norm input-output data for the main crop and livestock enterprises of Cyprus , 2007 .

[2]  Wim G.M. Bastiaanssen,et al.  Relating Crop Water Consumption to Irrigation Water Supply by Remote Sensing , 1997 .

[3]  P. Hellegers,et al.  Remote Sensing and Economic Indicators for Supporting Water Resources Management Decisions , 2010 .

[4]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[5]  Jiaguo Qi,et al.  External factor consideration in vegetation index development , 1994 .

[6]  A. Huete,et al.  A review of vegetation indices , 1995 .

[7]  W. Bastiaanssen,et al.  A remote sensing surface energy balance algorithm for land (SEBAL). , 1998 .

[8]  Diofantos G. Hadjimitsis,et al.  Spectral signature measurements during the whole life cycle of annual crops and sustainable irrigation management over Cyprus using remote sensing and spectro-radiometric data: the cases of spring potatoes and peas , 2009, Remote Sensing.

[9]  W. Cohen,et al.  Land cover mapping in an agricultural setting using multiseasonal Thematic Mapper data , 2001 .

[10]  A. Lang,et al.  Validity of surface area indices of Pinus radiata estimated from transmittance of the sun's beam , 1991 .

[11]  K.M.P.S. Bandara,et al.  Assessing irrigation performance by using remote sensing , 2006 .

[12]  Luis S. Pereira,et al.  Evapotranspiration: Concepts and Future Trends , 1999 .

[13]  E. Noordman,et al.  SEBAL model with remotely sensed data to improve water-resources management under actual field conditions , 2005 .

[14]  J. S. Rogers,et al.  Evapotranspiration from a Humid-Region Developing Citrus Grove with Grass Cover , 1983 .

[15]  Richard G. Allen,et al.  Aerodynamic Parameterization of the Satellite-Based Energy Balance (METRIC) Model for ET Estimation in Rainfed Olive Orchards of Andalusia, Spain , 2012, Water Resources Management.

[16]  S. Running,et al.  A general model of forest ecosystem processes for regional applications I. Hydrologic balance, canopy gas exchange and primary production processes , 1988 .

[17]  Guido D'Urso,et al.  Mapping crop coefficients in irrigated areas from Landsat TM images , 1995, Remote Sensing.

[18]  J. Norman,et al.  Instrument for Indirect Measurement of Canopy Architecture , 1991 .

[19]  Diofantos G. Hadjimitsis,et al.  Estimating irrigation demand using satellite remote sensing: a case study of Paphos District area in Cyprus , 2008, Remote Sensing.

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

[21]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[22]  W. Bastiaanssen.  SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey , 2000 .

[23]  S. G. Nelson,et al.  Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape , 2008, Sensors.

[24]  J. Clevers.  Application of a weighted infrared-red vegetation index for estimating leaf Area Index by Correcting for Soil Moisture , 1989 .

[25]  Yann Kerr,et al.  Leaf area index estimates using remotely sensed data and BRDF models in a semiarid region. , 2000 .

[26]  C. Wolfe,et al.  Wetland evaporation and energy partitioning: Indiana Dunes National Lakeshore , 1996 .

[27]  Peter Droogers,et al.  Comparing evapotranspiration estimates from satellites, hydrological models and field data , 2000 .

[28]  Aaldrik Tiktak,et al.  Review of sixteen forest-soil-atmosphere models , 1995 .

[29]  O. Hagolle,et al.  LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm , 2007 .

[30]  Adrianos Retalis,et al.  Environmental Monitoring of Spatio-temporal Changes Using Remote Sensing and GIS in a Mediterranean Wetland of Northern Greece , 2008 .

[31]  D. Watson.  Comparative Physiological Studies on the Growth of Field Crops: I. Variation in Net Assimilation Rate and Leaf Area between Species and Varieties, and within and between Years , 1947 .