Principal Investigator: Matthew Hansen Sponsor: NASA Harvest Time Period: 2017 to 2023 Researchers: Peter PotapovViviana ZallesAhmad KhanBernard AduseiXiao-Peng Song Project Description 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. Related Publications: Li, H., Song, X. P., Hansen, M. C., Becker-Reshef, I., Adusei, B., Pickering, J., Wang, L., Wang, L., Lin, Z., Zalles, V., Potapov, P., Stehman, S.V., & Justice, C. (2023). Development of a 10-m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation. Remote Sensing of Environment, 294, 113623. Khan, A., Hansen, M.C., Potapov, P.V., Adusei, B., Stehman, S.V., Steininger, M.K. (2021) An operational automated mapping algorithm for in-season estimation of wheat area for Punjab, Pakistan. International Journal of Remote Sensing, 42(10), 3833-3849. Pickering, J., Tyukavina, A., Khan, A., Potapov, P., Adusei, B., Hansen, M.C., Lima, A. (2021) Using multi-resolution satellite data to quantify land dynamics: applications of PlanetScope imagery for cropland and tree-cover loss area estimation. Remote Sensing, 13(11), 2191. Related Projects: Advancing Methods for Global Crop Area Estimation Agricultural Information Systems Project Building Provincial Capacity for Crop Estimation and Forecasting in Pakistan Agricultural Monitoring in Tanazania and Uganda for Food Security The Global Agriculture Monitoring (GLAM) project