Crop Phenology Stage Forecasting and Detection Using NDVI Time-Series and LSTM
Sushma Katari, Tapan K Bhowmik, Shabarinath S Nair, Aravind S, Akasha R Nayak, Praveen Pankajakshan · IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium · 2022
Forecasting crop phenology helps in crop production estimation, irrigation scheduling and crop identification. In this article’ we propose a methodology that uses the Normalized Difference Vegetation Index (NDVI), derived from the bands of an optical multispectral satellite (Sentinel-2) data for forecasting crop phenology stages using a Long Short-Term Memory (LSTM) network. Firstly, the LSTM network is built for forecasting the NDVI values of the potato crop, until the end of the season. Then the crop phenology estimation technique is applied to this forecast data to obtain the phenology points. The potato plots considered for the study were grown around the Gujarat state in India. The performance of the LSTM network on the test dataset is given by MAE≈ 0.19, MSE≈ 0.06 and RMSE≈ 0.23, and the phenology points are detected with a mean deviation of ±10 days.