Predicting poverty with vegetation index - Tang, Binh; Liu, Yanyan; Matteson, David S. (Wiley, 2022)
Accurate and timely predictions of the poverty status of communities in developing countries are critical to policymakers. Previous work has applied convolutional neural networks (CNNs) to high‐resolution satellite imagery to perform community‐level poverty prediction. Although promising, such imagery has limitations in predicting poverty among poor communities. We provide the first evidence that a publicly available, moderate‐resolution vegetation index (the normalized difference vegetation index [NDVI]), can be used with CNNs to produce accurate poverty predictions contemporaneously among poor communities heavily dependent on agriculture. We also show that the NDVI can effectively detect consumption variation over time. To our knowledge, this is the first attempt to use remote sensing data to predict future‐period consumption expenditure at the community level.