Estimating wheat crop coefficient using remote sensing data and data reduction approach

Subject Areas : Farm water management with the aim of improving irrigation management indicators

Laleh  Parviz ¹ (Faculty of Agriculture, Azarbaijan Shahid Madani University, Tabriz, Iran)

Received: 2017-12-10 Accepted : 2018-07-05 Published : 2018-11-22

Keywords: Improved Indices, preprocessing, Data Reduction, Rotation,

Abstract :

Optimal use of irrigation water requires to the precise irrigation planning and the accurate crop coefficient estimation is the prerequisite of that particularly in the global scale. The aim of research was the comparison of preprocessing approaches of artificial neural network: regression and data reduction (principle component analysis and rotation) for crop coefficient estimation using NDVI, RI, TVI, MSAVI, SAVI, mTVI, EVI, MNDVI and TVX for wheat crop coefficient of East Azarbaijan Province. The performance of regression and data reduction approaches indicated the error criteria decreasing of data reduction approach, for example RMSE increasing from rotation to regression and from principle component analysis to regression was 11.8 and 22.7%, respectively. The used approaches of crop coefficient estimation has overestimation as the average increase of crop coefficient in the validation period showed 7.7, 6.13 and 4.6% increasing of crop coefficient from FAO to the regression, principle component and rotation approaches, respectively. Therefore, using the rotation in the data reduction analysis increased the accuracy of estimation. Decreasing of correlation coefficient-39.13% - from MSAVI to NDVI indicated that the improved indices basis on the study area condition increased the performance of crop coefficient estimation using satellite images.