Mapping Miscanthus Using Multi-Temporal Convolutional Neural Network and Google Earth Engine

Grasslands play an essential role in ecology and agriculture. Accurately mapping the grasslands at a large scale is essential for productivity monitoring, policymaking, and environmental assessment. The advancements in remote sensing and machine learning technologies have enabled the generation of high accuracy national level crop layers. Although the national crop layer for the US includes grasslands, it does not differentiate them well at the species level. To fill the gap of mapping grassland at the national scale with high accuracy, we used a Convolutional Long Short-Term Memory (Convolutional-LSTM) neural network model for grass identification using multi-temporal Sentinel-2 images. Miscanthus (Miscanthus x giganteus) is used as a case study for this short paper. The classification of Miscanthus using the Convolutional-LSTM model yielded a 98.8% accuracy, which is significantly higher than the 92% accuracy produced by a benchmark model 3-layer fully connected neural network. Additionally, we demonstrated the efficiency and effectiveness of cloud computing practices by implementing the entire analytical process in a cloud-based environment.