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Deep spatial-temporal graph modeling for efficient NDVI forecasting
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DOI：10.1016/j.atech.2023.100172.png)
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Spatio-temporal graph modelling is a new, prominent predictive tool to use on datasets with complex spatial and temporal relationships. Normalized Difference Vegetation Index (NDVI) is a remote measure offering these complex relationships, used by agricultural producers and researchers due to its strong correlation with crop growth. Accurate periodic field-level NDVI forecasting helps project crop yield, crucial for planning agricultural production. This NDVI forecasting problem was previously studied, with best results obtained by Convolutional Long Short-Term Memory (ConvLSTM) architecture. We modify the ConvLSTM architecture, improving over the original paper. Additionally, we propose a new architecture based on Graph WaveNet (GWNN). GWNN captures spatial relationships in the non-tabular data with an adaptive dependency matrix and long-range temporal relationships with stacked spatial-temporal layers. We test each model (original ConvLSTM, new ConvLSTM, and GWNN) over the same geographical points. Under Root Mean Square Error metric, GWNN outperforms original ConvLSTM by 31% and our new one by 15%. Moreover, the GWNN is more than 170 times faster at training. We compare these models on other NDVI datasets, up to 50 times larger than the original set. The consistent results show the GWNN is most efficient in both quality and runtime for the NDVI forecasting problem.
Keyword:
Deep learning
Graph neural networks
Remote sensing
Normalized difference vegetation index
Graph WaveNet networks
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