Information Technologies in Remote Sensing of the Earth

Usually, yield prediction requires a significant number of parameters, which are currently impossible to obtain using just remote sensing data. Using the SAFY model, we developed a method of above-ground phytomass estimation and yield forecast on the basis of meteorological information, Sentinel 2 MSI imagery and literature data. We tested the method for an experimental maize field in the Lipetsk region of Russia. As a result of our study, we estimated the total above-ground phytomass and yield one and a half months before harvest for the field. The maximum error in determining the yield using the SAFY crop development model, can be tentatively estimated at ± 10 %.

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Information Technologies in Remote Sensing of the Earth - RORSE 2018

Proceedings of the 16th Conference (November 12-16, 2018, Moscow, Russia)

Application of SAFY Crop Growth Model for Maize Yield Forecast

Section 1. Methods of modeling various phenomena focused on assimilation of remote sensing data