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    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Remote Sensing on 02/11/2017, available online: http://www.tandfonline.com/10.1080/01431161.2017.1395969

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Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>2018
<mark>Journal</mark>International Journal of Remote Sensing
Issue number4
Volume39
Number of pages26
Pages (from-to)1042-1067
Publication StatusPublished
Early online date2/11/17
<mark>Original language</mark>English

Abstract

Sentinel-1A synthetic aperture radar (SAR) data present an opportunity for acquiring crop information without restrictions caused by weather and illumination conditions, at a spatial resolution appropriate for individual rice fields and a temporal resolution sufficient to capture the growth profiles of different crop species. This study investigated the use of multi-temporal Sentinel-1A SAR data and Landsat-derived normalized difference vegetation index (NDVI) data to map the spatial distribution of paddy rice fields across parts of the Sanjiang plain, in northeast China. The satellite sensor data were acquired throughout the rice crop-growing season (May–October). A co-registered set of 10 dual polarization (VH/VV) SAR and NDVI images depicting crop phenological development were used as inputs to Support Vector Machine (SVM) and Random Forest (RF) machine learning classification algorithms in order to map paddy rice fields. The results showed a significant increase in overall classification when the NDVI time-series data were integrated with the various combinations of multi-temporal polarization channels (i.e. VH, VV, and VH/VV). The highest classification accuracies overall (95.2%) and for paddy rice (96.7%) were generated using the RF algorithm applied to combined multi-temporal VH polarization and NDVI data. The SVM classifier was most effective when applied to the dual polarization (i.e. VH and VV) SAR data alone and this generated overall and paddy rice classification accuracies of 91.6% and 82.5%, respectively. The results demonstrate the practicality of implementing RF or SVM machine learning algorithms to produce 10 m spatial resolution maps of paddy rice fields with limited ground data using a combination of multi-temporal SAR and NDVI data, where available, or SAR data alone. The methodological framework developed in this study is apposite for large-scale implementation across China and other major rice-growing regions of the world.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Remote Sensing on 02/11/2017, available online: http://www.tandfonline.com/10.1080/01431161.2017.1395969