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Article

Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

1
Applied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW 2351, Australia
2
Riverina Local Land Services, Hanwood, NSW 2680, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 96; https://doi.org/10.3390/rs12010096
Received: 18 November 2019 / Revised: 10 December 2019 / Accepted: 24 December 2019 / Published: 26 December 2019
Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently. View Full-Text
Keywords: land cover mapping; crop type classification; remote sensing; satellite image time series; Sentinel-1; Sentinel-2; machine learning land cover mapping; crop type classification; remote sensing; satellite image time series; Sentinel-1; Sentinel-2; machine learning
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MDPI and ACS Style

Brinkhoff, J.; Vardanega, J.; Robson, A.J. Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data. Remote Sens. 2020, 12, 96. https://doi.org/10.3390/rs12010096

AMA Style

Brinkhoff J, Vardanega J, Robson AJ. Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data. Remote Sensing. 2020; 12(1):96. https://doi.org/10.3390/rs12010096

Chicago/Turabian Style

Brinkhoff, James; Vardanega, Justin; Robson, Andrew J. 2020. "Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data" Remote Sens. 12, no. 1: 96. https://doi.org/10.3390/rs12010096

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