Uploaded October 2020 | Updated September 2026, 5 hours ago
Climate change is expected to increase the prevalence of malnutrition in the 21st century through higher temperatures, increasing rainfall volatility, and weather extremes that could have devastating effects on crops and freshwater availability. Using geo-referenced crop type data in the U.S. and moderate resolution (30-m) satellite imagery, we created a dataset of time windowed features using seven different spectral bands and used these features to train a multi-class crop classification model to monitor crop and water availability. We scored the model over Zambia, which has been greatly impacted by climate change and was ranked as 5th worst in the 2018 Global Hunger Index. Through this study, we were able to detect changes in natural resource accessibility as well as track crop availability and rotation over time thereby getting a sense of food insecurity in the region.
Climate change is expected to increase the prevalence of malnutrition in the 21st century through higher temperatures, increasing rainfall volatility, and weather extremes that could have devastating effects on crops and freshwater availability. Using geo-referenced crop type data in the U.S. and moderate resolution (30-m) satellite imagery, we created a dataset of time windowed features using seven different spectral bands and used these features to train a multi-class crop classification model to monitor crop and water availability. We scored the model over Zambia, which has been greatly impacted by climate change and was ranked as 5th worst in the 2018 Global Hunger Index. Through this study, we were able to detect changes in natural resource accessibility as well as track crop availability and rotation over time thereby getting a sense of food insecurity in the region.
