Mapping of Rice Varieties with Sentinel-2 Data via Deep CNN Learning in Spectral and Time Domains

Abstract

Generating rice variety distribution maps with remote sensing image time series provides meaningful information for intelligent management of rice farms and precise budgeting of irrigation water. However, as different rice varieties share highly similar spectral/temporal patterns, distinguishing one variety from another is highly challenging. In this study, a deep convolutional neural network (deep CNN) is constructed in both spectral and time domains. The purpose is to learn the fine features of each rice variety in terms of its spectral reflectance characteristics and growing phenology, which is a new attempt aiming for agriculture intelligence. An experiment was conducted at a major rice planting area in southwest New South Wales, Australia, during the 2016–17 rice growing season. Based on a ground reference map of rice variety distribution, more than one million labelled samples were collected. Five rice varieties currently grown in the study area are investigated and they are Reiziq, Sherpa, Topaz, YRM 70, and Langi. A time series of multitemporal remote sensing images recorded by the Multispectral Instrument (MSI) on-board the Sentinel-2A satellite was used as inputs. These images covered the entire rice growing season from November 2016 to May 2017. Experimental results showed that a good overall accuracy of 92.87% was achieved with the proposed approach, outperforming a standard support vector machine classifier that produced an accuracy of 57.49%. The Sherpa variety showed the highest producer's accuracy (98.46%), while the highest user's accuracy was observed for the Reiziq variety (97.93%). The results obtained with the proposed deep CNN learning provide the prospect of applying remote sensing image time series for rice variety mapping in an operational context in future.