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

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

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.

0143-1161
1042-1067
Onojeghuo, Alex O.
412845ac-87b0-4b1d-b8fd-7c42d20971e0
Blackburn, George A.
33baa57f-97a5-4750-985e-67a5e6400cf9
Wang, Qunming
3ceb1e88-bd7f-4481-8a46-c1efcbb2e54b
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Kindred, Daniel
81346b52-65ab-421d-a108-e1134046877e
Miao, Yuxin
87983caf-fdd4-4764-8355-95674383ea83
Onojeghuo, Alex O.
412845ac-87b0-4b1d-b8fd-7c42d20971e0
Blackburn, George A.
33baa57f-97a5-4750-985e-67a5e6400cf9
Wang, Qunming
3ceb1e88-bd7f-4481-8a46-c1efcbb2e54b
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Kindred, Daniel
81346b52-65ab-421d-a108-e1134046877e
Miao, Yuxin
87983caf-fdd4-4764-8355-95674383ea83

Onojeghuo, Alex O., Blackburn, George A., Wang, Qunming, Atkinson, Peter M., Kindred, Daniel and Miao, Yuxin (2018) Mapping paddy rice fields by applying machine learning algorithms to multi-temporal sentinel-1A and landsat data. International Journal of Remote Sensing, 39 (4), 1042-1067. (doi:10.1080/01431161.2017.1395969).

Record type: Article

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.

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More information

Accepted/In Press date: 15 October 2017
e-pub ahead of print date: 2 November 2017
Published date: 16 February 2018

Identifiers

Local EPrints ID: 421770
URI: http://eprints.soton.ac.uk/id/eprint/421770
ISSN: 0143-1161
PURE UUID: 255c6d8a-3cd0-444f-8580-33cb35d11348
ORCID for Peter M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880

Catalogue record

Date deposited: 27 Jun 2018 16:30
Last modified: 07 Oct 2020 01:37

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Contributors

Author: Alex O. Onojeghuo
Author: George A. Blackburn
Author: Qunming Wang
Author: Peter M. Atkinson ORCID iD
Author: Daniel Kindred
Author: Yuxin Miao

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