Annual and in-season mapping of cropland at field scale with sparse labels (Papers Track)
Gabriel Tseng (NASA Harvest); Hannah R Kerner (University of Maryland); Catherine L Nakalembe (University of Maryland); Inbal Becker-Reshef (University of Maryland)
Abstract
Spatial information about where crops are being grown, known as cropland maps, are critical inputs for analyses and decision-making related to food security and climate change. Despite a widespread need for readily-updated annual and in-season cropland maps at the management (field) scale, these maps are unavailable for most regions at risk of food insecurity. This is largely due to lack of in-situ labels for training and validating machine learning classifiers. Previously, we developed a method for binary classification of cropland that learns from sparse local labels and abundant global labels using a multi-headed LSTM and time-series multispectral satellite inputs over one year. In this work, we present a new method that uses an autoregressive LSTM to classify cropland during the growing season (i.e., partially-observed time series). We used these methods to produce publicly-available 10m-resolution cropland maps in Kenya for the 2019-2020 and 2020-2021 growing seasons. These are the highest-resolution and most recent cropland maps publicly available for Kenya. These methods and associated maps are critical for scientific studies and decision-making at the intersection of food security and climate change.