Field Scale Crop Type Mapping and Yield Estimation
Maize yield estimates in the US corn belt from 2001-2017 based on methods from Jin et al. 2017. Our approach (SCYM: Scalable Crop Yield Mapper) combines satellite observations with crop model simulations to estimate crop yields at sub-field resolution even where reliable ground-based estimates are unavailable.
What We Do
We develop globally robust approaches for mapping crop locations and yields to inform agricultural management and food security efforts.
Location
US Corn Belt, Kenya, Tanzania, Nepal
How Satellites Make This Work
This project is focused on fine resolution (i.e. field scale) mapping of crop area and yield. The goal is to develop and implement globally robust approaches to achieving these maps. Yield maps are then combined with other data sources to evaluate management such as no-till/conservation agriculture practices. Satellite instruments used include Landsat 5, 7 and 8, Sentinel 1, Sentinel 2, MODIS, Planet and Digital Globe.
Lead
David Lobell, Stanford University
Team Members
Jillian Deines, Stanford University
Learn More
Data Science for Food Security, Center on Food Security and the Environment, Stanford website
Azzari, G., Grassini, P., Edreira, J.I.R., Conley, S., Mourtzinis, S. and Lobell, D.B. 2019. Satellite mapping of tillage practices in the North Central US region from 2005 to 2016. Remote sensing of environment, 221, pp.417-429.
Seifert, C. A., Azzari, G., & Lobell, D. B. 2018. Satellite detection of cover crops and their effects on crop yield in the Midwestern United States. Environmental Research Letters, 13(6), 064033.
Lobell, D. B., Azzari, G., Marshall, B., Gourlay, S., Jin, Z., Kilic, T., Murray, S. 2018. Eyes in the sky, boots on the ground : assessing satellite- and ground-based approaches to crop yield measurement and analysis in Uganda (English). Policy Research working paper; no. WPS 8374. Washington, D.C. World Bank Group.
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