Integrating Landsat 7, 8 and Sentinel 2 Data in Improving Crop Type Identification and Area E stimation
Project Start Date
07/01/2015
Project End Date
12/31/2018
Grant Number
ROSES-2014 NNH14ZDA001N-LCLUC
Project Call Name
Science Theme Name
Region
Solicitation
Team Members:
Person Name | Person role on project | Affiliation |
---|---|---|
Matthew Hansen | Principal Investigator | University of Maryland, College Park, College Park, United States |
Peter Potapov | Co-Investigator | University of Maryland, College Park, USA |
Xiao-Peng Song | Collaborator | University of Maryland, College Park, , United States |
Pierre Defourny | Collaborator | Universite catholique de Louvain, Louvain-la-Neuve , Belgium |
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 an d 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 represen t 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.
Project Research Area