Deep Neural Networks for Mapping Integrated Crop-Livestock Systems Using Planetscope Time Series

Abstract

Mapping highly dynamic cropping systems using satellite image time series is still challenging even when robust approaches are used. We assessed the potential of using high spatial and temporal resolution PlanetScope time series and deep neural networks (Convolutional Neural Networks (CNN) in one dimension - Conv1D, Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP)) for mapping integrated crop-livestock systems (ICLS) and different land covers in the western region of São Paulo State, Brazil. We used 10-day and 15-day composite EVI and NDVI time series (both individually and combined) as input data in the neural network classifiers. Conv1D using both EVI and NDVI 10 day-composite time series outperformed the other classifiers evaluated in this study (LSTM and MLP), allowing improved discrimination of land parcels with ICLS in our study area.