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Funded Research

Integrating Landsat 7, 8 and Sentinel 2 Data in Improving Crop Type Identification and Area Estimation

Hansen, Matthew (Matt): University of Maryland (Project Lead)

Project Funding: 2015 - 2017

NRA: 2014 NASA: Land Cover / Land Use Change   

Funded by NASA

Abstract:
Identification of crop type and areal extent is a challenge, made difficult by the variety of cropping systems, including crop types, management practices, and field sizes. The goal of this project is to evaluate the integrated use of Landsat and Sentinel 2 data in quantifying cultivated area by major commodity crop type. The first evaluation objective is correct identification of crop type. MODIS data, due to its high image cadence, are appropriate for and have been extensively used for mapping crop. Using MODIS as a high temporal reference, an assessment of combined Landsat and Sentinel 2 observations in identifying crop type will be performed. For any given crop type, its areal extent is required in estimating production. RapidEye data represent a high temporal, high spatial resolution imaging capability over limited areas. RapidEye data will be used to evaluate area estimation of selected crop types and fine-scale agricultural landscapes using combined Landsat and Sentinel 2 data. Results will inform users of the potential value of Landsat and Sentinel 2 data to identify and map the extent of key commodity crops for a variety of landscapes, including wheat, corn and soybean.

Publications:

Zalles, V., Hansen, M. C., Potapov, P. V., Stehman, S. V., Tyukavina, A., Pickens, A., Song, X., Adusei, B., Okpa, C., Aguilar, R., John, N., Chavez, S. 2018. Near doubling of Brazil's intensive row crop area since 2000. Proceedings of the National Academy of Sciences. 116(2), 428-435. DOI: 10.1073/pnas.1810301115