J. Deines et al.: Sub-Field Yield Estimation with Satellites: How Good Is It and What Can We Learn?

Jillian Deines is a postdoctoral scholar at the Center on Food Security and the Environment in the Department of Earth System Science at the Stanford University working with David Lobell. Jill’s research links agriculture, hydrology, and advanced spatial tools to promote food security, water management, and sustainable land use systems. She specializes in applying statistical and modeling techniques to take satellite data from bits to dynamic maps to process-based understanding, with a goal to inform effective management.

Her work is part of the NASA Harvest multidisciplinary consortium to support food security efforts and agricultural decision-making in the US and around the globe. Jill holds a Ph.D. in Environmental Geosciences from Michigan State University, a M.S. in Biology from the University of Notre Dame, and a B.S. in Ecology and Evolutionary Biology from Saint Louis University.

Crop yield maps estimated from satellite data are increasingly used to understand drivers of yield trends and variability. However, satellite-derived regional maps are rarely validated with location-specific yields due to the difficulty of acquiring sub-field ground data at scale. Here, we leverage an extensive ground dataset spanning 11 years across the US Corn Belt to evaluate and improve the Scalable Crop Yield Mapper (SCYM), a yield mapping approach that uses crop model simulations to interpret vegetation indices from satellite time series.

We then demonstrate how this retrospective record of field-based yields can be used to assess and monitor agricultural trends and management. As a case study, we focus on the yield impacts of conservation tillage in the US Corn Belt, demonstrating that soil conservation practices can be used with minimal and typically positive yield impacts.

Authors: Jillian Deines & David Lobell