Deep learning models to map an agricultural expansion area with MODIS and Sentinel-2 time series images

Abstract

Mapping changing land use and land cover (LULC) is important for land management and environment analysis. We tried to build deep learning models to classify LULC over time at an agricultural expansion area in Matopiba region, Brazil with MCD43A4 V006 moderate resolution imaging spectroradiometer (MODIS), and Sentinel-2 multispectral instrument (MSI) time series data. We collected time series MODIS data and Sentinel-2 A/B MSI data from 2015 to 2020 and prepared small patches with containing blue, green, red, near-infrared, and shortwave-infrared-1 bands as features. Then the both datasets were used to build and train the convolutional neural network (CNN) model, the CNN gate recurrent unit (CNN-GRU) model, and the CNN long short-term memory (CNN-LSTM) model, respectively. We evaluated these three trained models with ground truth data, and the CNN-LSTM model (overall accuracy: 91.29% from MODIS data and 89.47% from Sentinel-2 data) was better than the CNN-GRU model (overall accuracy: 89.19% from MODIS data and 88.61% from Sentinel-2 data) and the CNN model (overall accuracy: 89.17% from MODIS data and 86.02% from Sentinel-2 data). Our results also showed that the accuracy from cropland and savanna classes were higher than grassland and forest classes in all three models. These two classes generated from the CNN-LSTM model performed better than the other two deep learning models. The results from these two datasets indicated that the methods were reliable for both coarse and medium spatial resolution satellite images and time series remote sensing images worked better than single image for classification problems when considering LULC change over time. The results also provided an alternative way to prepare input data from satellite images for deep learning models. Furthermore, the classification results of the whole agricultural expansion area were reasonable and it can be used as an additional dataset for further environmental analysis at a regional scale.