Global Scale Crop Yield and Condition Forecasting System Using Multiple Earth Observation Datasets

Earth observations (EO) can play a key role in monitoring progress made towards the achievement of the sustainable development goal of ending hunger through achieving food security and promotion of sustainable agriculture. By enabling the timely dissemination and usage of EO data, the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) crop monitor has contributed to the efficient functioning of global markets by enabling the international community to collaboratively produce operational crop condition assessments. Based on user feedback, we have developed the Global Earth Observation based Crop Yield and Condition Forecasting (GEOCIIF) system to use EO data to produce in-season alerts to assess crop yields and conditions globally for maize, soybean, rice, spring wheat and winter wheat. We have designed GEOCIIF to flexibly accommodate any number of EO data sources, with the current EO inputs including NDVI, LAI, evaporative stress index, soil moisture, precipitation and temperature. This is also the first study to use the best available crop-specific maps and crop calendars produced in collaboration with GEOGLAM partner organizations globally. By applying a suite of machine learning algorithms on EO data, GEOCIIF produces in-season crop yield forecasts and uses them to derive crop conditions by considering the varying response of each crop to abiotic factors, geography and phenological growth stage. We apply the algorithm to > 80% of global maize and soybean producing areas, > 60% of rice producing regions, and > 65% of wheat producing regions and assess performance by comparing our yield forecasts to regional scale observed yields from 2000 - 2016. In back testing, we find that our global yield forecasts have errors ranging between 3 - 5%, with the quality of crop maps playing a key role in determining forecast quality. We compare the performance of various machine learning algorithms to forecast crop yields and provide practical tips on the merits of each. Our results indicate the utility of EO data in monitoring progress towards achieving global food security by producing timely and useful crop yield and condition forecasts.