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

Alex O. Onojeghuo, George A. Blackburn, Qunming Wang, Peter M. Atkinson, Daniel Kindred, Yuxin Miao

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

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 multitemporal 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.

Original languageEnglish (US)
Pages (from-to)1042-1067
Number of pages26
JournalInternational Journal of Remote Sensing
Volume39
Issue number4
DOIs
StatePublished - Feb 16 2018

Bibliographical note

Funding Information:
The authors thank the graduate students from China Agricultural University for their contribution to conducting field surveys at the study site. The UK Science and Technology Facilities Council (STFC) Newton Agri-Tech programme financially supported this research, under project “Remote Sensing for Sustainable Intensification in China”.

Funding Information:
The UK Science and Technology Facilities Council (STFC) Newton Agri-Tech programme financially supported this research, under project ‘Remote Sensing for Sustainable Intensification in China.’

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