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Abstract

A remote-sensing yield estimation method is described that is applicable to a crop whole growth period and belongs to the field of data processing methods and photogrammetry technologies with prediction purpose. In the present disclosure, in combination with agricultural knowledge of crop yields and multi-source remote-sensing data, with the biomass estimation model of whole growth period, the biomass contribution rate curve and the crop harvest index as basis, a crop yield estimation model of whole growth period with stable spatiotemporal extensibility is constructed. The yield estimation model uses the relative accumulated temperature and the remote-sensing vegetation index as variable inputs. Further, based on genetic algorithm, the parameters of the model are optimized in a case of failing to directly construct the model with data fitting due to less sample points, so as to help solve the problems of poor spatiotemporal mobility of the yield estimation model.

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