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GLAD - Evaluating Landsat and Rapid Eye data for winter wheat mapping and area estimation, Soy yield field to national

Global Land Analysis & Discovery group
What We Do

Our project advances global crop type area estimation and mapping, integrating yield estimation as feasible, focusing on commodity crops, including wheat, soybeans and corn.

Location

Southern Hemisphere, Argentina, Brazil, Pakistan

How Satellites Make This Work

Our research has been implemented at scale in a research to operations mode. For example, we have estimated Southern Hemisphere soybean and Punjab winter wheat for the 2018/19 growing seasons with area estimates made in season with low uncertainty. Our approach has been shared with in country agencies, universities and private industry. Maps of soybean are in development from 2000-forward and will serve as a key input to entities working on commodity flows in the context of Brazil’s soy moratorium, which seeks to limit sourcing of soybeans from newly deforested lands. In Pakistan, we have added field cuts to our probability-based area estimation samples, with the hopes of developing a production estimate for the province at the time of harvest.

Harvest support has enabled the advancement of our generic method applied to winter wheat monitoring in Punjab province, Pakistan. Harvest support enables the extension of our methods to different geographies in prototyping activities. With support from other projects, for example the Gordon and Betty Moore Foundation’s funding of South America soybean assessment and past NASA support for USA soybean assessment, we have been able to development and repeatedly implement our method.

Lead
Matthew C. Hansen, University of Maryland
Team Members
Xiaopeng Song, University of Maryland
Viviana Zalles, University of Maryland

Peter Popatov, University of Maryland

Ahmad Khan, University of Maryland

Bernard Adusei, University of Maryland

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