Alfalfa yield estimation using Sentinel-2 satellite images- a case study in Magsal Agricultural and Production Company (Qazvin)

Document Type : علمی - پژوهشی

Authors

1 Remote sensing expert, Iranian Space Research Center

2 Assistant Professor in Remote Sensing, Remote Sensing and GIS Research Center

3 PhD student in Remote Sensing, Remote Sensing and GIS department, School of Geography, University of Tehran

4 PhD student in Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology

Abstract

Over the past several decades, many vegetation indices have been developed for crop yield estimation, each being sensitive to different levels of crop density and leaf area index, based on the bands and the algebraic formulas used in its design. However, the study of some perennial crops such as alfalfa, which are harvested several times annually, is very complicated and has received less attention. Therefore, in this paper, the most important vegetation indices developed to estimate alfalfa yield are using Sentinel-2 time series images. In this research, 144 alfalfa samples were collected periodically in a destructive way from alfalfa farms of Magsal Agricultural and Production Company (Qazvin) near the time of satellite pass, and then the efficiency of 10 of the most famous vegetation indices to estimate alfalfa yield was evaluated based on Sentinel-2 images. The results of this research showed that the estimated alfalfa yield using the  index had the highest correlation () and the lowest root-mean-square-error (RMSE = 0.316 ) compared to the field data collected in the middle of August. In addition, the results showed that the red edge indices did not solve the saturation problem of vegetation indices and that the green vegetation indices were more capable of estimating alfalfa yield than the red edge indices.

Keywords

References

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