Rice yield prediction using radar vegetation indices from Sentinel-1 data and multiscale one-dimensional convolutional long- and short-term memory network model

Abstract

Reliable rice yield information is critical for global food security. Optical vegetation indices (OVIs) are important parameters for rice yield estimation using remote sensing. Studies have shown that radar vegetation indices (RVIs) are correlated with OVIs. However, research on the implementation of RVIs in rice yield prediction is still in its early stages. In addition, existing deep learning yield prediction models ignore the contribution of temporal features at each time step to the predicted yield and lack the extraction of higher-level features. To address the above issues, this study proposed a rice yield prediction workflow using RVIs and a multiscale one-dimensional convolutional long- and short-term memory network (MultiscaleConv1d-LSTM, MC-LSTM). Sentinel-1 vertical emission and horizontal reception of polarization vertical emission and vertical reception of polarization data and county-level rice yield statistics covering Guangdong Province, China, from 2017 to 2021 were used. The experimental results show that the performance of the RVIs is comparable to that of the OVIs. The proposed MC-LSTM model can effectively improve the accuracy of rice yield prediction. For early rice yield prediction based on RVIs, the optimal accuracy of MC-LSTM [coefficient of determination R2 of 0.67, unbiased root mean square error (ubRMSE) of 217.77 kg/ha] was significantly better than that of the LSTM model (R2 of 0.61, ubRMSE of 229.52 kg/ha). For late rice yield prediction based on RVIs, the optimal accuracy of MC-LSTM (R2 of 0.61 and ubRMSE of 456.54 kg/ha) was significantly better than that of the LSTM model (R2 of 0.55 and ubRMSE of 486.76 kg/ha). The above results show that the proposed method has excellent application prospects in crop yield prediction. This work can provide a new feasible scheme for synthetic-aperture radar data to serve agricultural monitoring and improve the efficiency of rice yield monitoring in a large area.