Next Article in Journal
Addendum: Xu, X.-Y. et al. Evaluation of Coastal Sea Level Offshore Hong Kong from Jason-2 Altimetry. Remote Sens. 2018, 10, 282
Next Article in Special Issue
Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France
Previous Article in Journal
The Development of Near Real-Time Biomass and Cover Estimates for Adaptive Rangeland Management Using Landsat 7 and Landsat 8 Surface Reflectance Products
Previous Article in Special Issue
Scalable Parcel-Based Crop Identification Scheme Using Sentinel-2 Data Time-Series for the Monitoring of the Common Agricultural Policy
Open AccessArticle

Fusion of Moderate Resolution Earth Observations for Operational Crop Type Mapping

1
Applied Geosolutions, 15 Newmarket Rd, Durham, NH 03824, USA
2
United States Department of Agriculture, National Agricultural Statistics Service, 1400 Independence Ave., SW, Washington, DC 20250, USA
3
NASA Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771, USA
4
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; https://doi.org/10.3390/rs10071058
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
Show Figures

Graphical abstract

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop