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Scalable Parcel-Based Crop Identification Scheme Using Sentinel-2 Data Time-Series for the Monitoring of the Common Agricultural Policy

Fusion of Moderate Resolution Earth Observations for Operational Crop Type Mapping

Applied Geosolutions, 15 Newmarket Rd, Durham, NH 03824, USA
United States Department of Agriculture, National Agricultural Statistics Service, 1400 Independence Ave., SW, Washington, DC 20250, USA
NASA Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771, USA
United States Department of Agriculture, Agricultural Research Service, Delta Water Management Research Unit, 504 University Loop E, Jonesboro, AR 72401, USA
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(7), 1058;
Received: 1 June 2018 / Revised: 20 June 2018 / Accepted: 2 July 2018 / Published: 4 July 2018
(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)
Crop type inventory and within season estimates at moderate (<30 m) resolution have been elusive in many regions due to the lack of temporal frequency, clouds, and restrictive data policies. New opportunities exist from the operational fusion of Landsat-8 Operational Land Imager (OLI), Sentinel-2 (A & B), and Sentinel-1 (A & B) which provide more frequent open access observations now that these satellites are fully operating. The overarching goal of this research application was to compare Harmonized Landsat-8 Sentinel-2 (HLS), Sentinel-1 (S1), and combined radar and optical data in an operational, near-real-time (within 24 h) context. We evaluated the ability of these Earth observations (EO) across major crops in four case study regions in United States (US) production hot spots. Hindcast time series combinations of these EO were fed into random forest classifiers trained with crop cover type information from the Cropland Data Layer (CDL) and ancillary ground truth. The outcomes show HLS achieved high (>85%) accuracies and the ability to provide insight on crop location and extent within the crop season. HLS fused with S1 had, at times, a higher accuracy (5–10% relative overall accuracy and kappa increases) within season although the combination of fused data was minimal at times, crop dependent, and the accuracies tended to converge by harvest. In cloud prone regions and certain temporal periods, S1 performed well overall. The growth in the availability of time dense moderate resolution data streams and different sensitivities of optical and radar data provide a mechanism for within season crop mapping and area estimates that can help improve food security. View Full-Text
Keywords: crop type; HLS; Sentinel-1; fusion; classification; food security crop type; HLS; Sentinel-1; fusion; classification; food security
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MDPI and ACS Style

Torbick, N.; Huang, X.; Ziniti, B.; Johnson, D.; Masek, J.; Reba, M. Fusion of Moderate Resolution Earth Observations for Operational Crop Type Mapping. Remote Sens. 2018, 10, 1058.

AMA Style

Torbick N, Huang X, Ziniti B, Johnson D, Masek J, Reba M. Fusion of Moderate Resolution Earth Observations for Operational Crop Type Mapping. Remote Sensing. 2018; 10(7):1058.

Chicago/Turabian Style

Torbick, Nathan, Xiaodong Huang, Beth Ziniti, David Johnson, Jeff Masek, and Michele Reba. 2018. "Fusion of Moderate Resolution Earth Observations for Operational Crop Type Mapping" Remote Sensing 10, no. 7: 1058.

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