DeepCropMapping: A multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping

Abstract

Abstract Accurate crop mapping provides important and timely information for decision support on the estimation of crop production at large scale. Most existing crop-specific cover products based on remote sensing data and machine learning algorithms cannot serve large agriculture production areas as a result of poor model transfer capabilities. Developing a generalizable crop classification model for spatial transfer across regions is greatly needed. A deep learning approach, named DeepCropMapping (DCM), has been developed based on long short-term memory structure with attention mechanisms through integrating multi-temporal and multi-spectral remote sensing data for large-scale dynamic corn and soybean mapping. Full cross validation of classification experiments were conducted in six sites each covering 2,890,000 pixels at 30 m resolution in the U.S. corn belt from Year 2015 to 2018. Landsat Analysis Ready Data (ARD) and Cropland Data Layer (CDL) were adopted as the input satellite observations and ground reference, respectively. Transformer, Random Forest (RF), and Multilayer Perceptron (MLP) models were built for comparison. The DCM model produced a mean kappa score of 85.8% in base sites and a mean average kappa score of 82.0% in transfer sites at the end of the growing season. It yielded a comparable performance to Transformer and better than RF and MLP at the local test. The DCM model significantly outperformed other three models with a 95% confidence interval in the spatial transfer analysis. The results demonstrated the capability of learning generalizable features by the DCM model from ARD time series. The computational complexity analysis suggested that the DCM model required a shorter training time than Transformer but longer than MLP and RF. The results of the in-season classification experiment indicated the DCM model captured critical information from key growth phases and achieved higher accuracy than other models after the beginning of July. By monitoring the classification confidence in each time step, the results showed that the increased length of seasonal remote sensing time series would reduce the classification uncertainty in all sites. This study provided a viable option toward large-scale dynamic crop mapping through the integration of deep learning and remote sensing time series.