Estimation of flood-damaged cropland area using a convolutional neural network

Abstract

Abstract Flood damage to croplands poses a significant threat to global food security. Effective disaster management to cope with future climate change, especially extreme precipitation, requires a robust framework to estimate such damage. For this study, we develop a model based on a convolutional neural network to estimate the area (in acres) of cropland damaged by flooding at the county level. Then we demonstrate the model’s performance for the period 2008–2019 over corn and soybean fields in the midwestern United States, which suffer frequent damage from recurrent flooding. We fed the network with remote sensing images and weather fields and divide the growing season into two windows, the early season (May–June) and the late season (July–November) for better performance. The results show mean relative error within ± 25% and relative root mean square error within 35%–75% in majority of the counties for most years. Finally, we show that the model forced with meteorological variables alone can provide acceptable accuracy, which indicates it can be applied to forecasting crop damage area in the upcoming season or the studying of future climate impact on crop productivity. In principle, the model can also be applied to food security assessment at the global scale using available records.