Near Real-time Fine-resolution Land Surface Phenological Prediction Using Convolutional Neural Network and Data Fusion

Abstract

Near real-time fine-resolution land surface phenology (LSP) prediction is essential for understanding surface attributes and ecosystem functions, and solving important ecological processes related to phenology at the landscape scale. In this paper, we applied the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to fuse image pairs of Landsat 8 and Moderate-resolution Imaging Spectroradiometer (MODIS) as train data, and then applied the first derivative method to retrieve phenophase transition dates from fused time series of satellite data as label data. The convolutional neural network (CNN) model was trained using fusion images as inputs and the label data as targets. The trained model was further used to predict LSP dates from individual Landsat images. As evaluated using the reference data, the predict land surface phenological dates and could match the reference well with the coefficient of determination of 0.77 and root mean squared errors of 3.535, and our study provides an alternative method to predict land surface phenological dates using individual Landsat images.