Application Of A Remote Sensing Technique In Estimating Evapotranspiration For Nyazvidzi Sub- Catchment., Zimbabwe

  • N. Ncube Chinhoyi University of Technology, Department of Environmental Engineering, Chinhoyi, Zimbabwe
  • D. Zhakata Chinhoyi University of Technology, Department of Agricultural Engineering and Technology, Chinhoyi, Zimbabwe
  • A. Muchandiona Chinhoyi University of Technology, Department of Agricultural Engineering and Technology, Chinhoyi, Zimbabwe

Abstract

The integration of Remote Sensing and ground data into hydrological and cropwater requirement models enables water resources managers to adequately quantify the availability of water for irrigation in space and time. The SEBS algorithm was used to derive actual evapotranspiration estimates using MODIS images to assess cropwater requirements in the Ruti irrigation scheme after validation with ground based evapotranspiration measurements. Results show that actual evapotranspiration computed using SEBS (EToS) were comparable to those obtained using Penman Monteith method (R2=0.96). The Kendall’s tau test showed that there is significant statistical association (α = 0.05) between Pan Coefficient (Kp) values determined using EToS and EToPM and Kp values from the Snyder equation. In conclusion, the study highlights the potential use of GIS and remote sensed data for catchment management, planning and irrigation scheduling at irrigation scheme level. Welch’s t test showed that there is no evidence to reject Ho: Kp determined from EToPM – Kp from EToS = 0. The above is crucial in the evaluation and comparison of performance of different irrigation systems in the country for food security and improvement of livelihoods in the light of integrated water resources management.

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Published
2016-07-29
How to Cite
Ncube, N., Zhakata, D., & Muchandiona, A. (2016). Application Of A Remote Sensing Technique In Estimating Evapotranspiration For Nyazvidzi Sub- Catchment., Zimbabwe. European Scientific Journal, ESJ, 12(21), 101. https://doi.org/10.19044/esj.2016.v12n21p101