Winter Wheat Yield Estimation from Multitemporal Remote Sensing Images Based on Convolutional Neural Networks

Abstract

The development of deep learning and big data technology has introduced information and intelligent techniques to agricultural remote sensing estimation. The deep learning methods represented by Convolutional Neural Network (CNN) have abilities to extract the depth-dependent features of crop growth. In the field of crop yield estimation, the core challenge is to utilize CNN to extract the related information from remote sensing images. In this paper, we apply histogram dimensionality reduction and time series fusion to generate the input layer of CNN. In view of the data characteristics, the CNN network structure was designed to extract the features of winter wheat growth from multitemporal MODIS images for yield estimation in North China. The results showed that the estimated yield of winter wheat based on time-series remote sensing images is highly correlated with statistical data, with Pearson’s r of 0.82, RMSE of 724.72 kg.hm-2. In the case of sufficient statistical data, the provincial model performs better. CNN is able to mine more relevant information and has higher robustness. It also provides a technical reference for estimating large-scale crop yield.