Mapping irrigated and rainfed wheat areas using multi-temporal satellite data

Jin, N; Tao, B; Ren, W; Feng, M; Sun, R; He, L; Zhuang, W; Yu, Q

Mapping irrigated and rainfed wheat areas using multi-temporal satellite data

Jin, N Tao, B Ren, W Feng, M Sun, R He, L Zhuang, W Yu, Q

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Publication Type:: Journal Article
Citation:: Remote Sensing, 2016, 8 (3)
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Jin, N	en_US
dc.contributor.author	Tao, B	en_US
dc.contributor.author	Ren, W	en_US
dc.contributor.author	Feng, M	en_US
dc.contributor.author	Sun, R	en_US
dc.contributor.author	He, L	en_US
dc.contributor.author	Zhuang, W	en_US
dc.contributor.author	Yu, Q https://orcid.org/0000-0001-6950-1821	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Remote Sensing, 2016, 8 (3)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122742
dc.description.abstract	© 2016 by the authors. Irrigation is crucial to agriculture in arid and semi-arid areas and significantly contributes to crop development, food diversity and the sustainability of agro-ecosystems. For a specific crop, the separation of its irrigated and rainfed areas is difficult, because their phenology is similar and therefore less distinguishable, especially when there are phenology shifts due to various factors, such as elevation and latitude. In this study, we present a simple, but robust method to map irrigated and rainfed wheat areas in a semi-arid region of China. We used the Normalized Difference Vegetation Index (NDVI) at a 30 × 30 m spatial resolution derived from the Chinese HJ-1A/B (HuanJing(HJ) means environment in Chinese) satellite to create a time series spanning the whole growth period of wheat from September 2010 to July 2011. The maximum NDVI and time-integrated NDVI (TIN) that usually exhibit significant differences between irrigated and rainfed wheat were selected to establish a classification model using a support vector machine (SVM) algorithm. The overall accuracy of the Google-Earth testing samples was 96.0%, indicating that the classification results are accurate. The estimated irrigated-to-rainfed ratio was 4.4:5.6, close to the estimates provided by the agricultural sector in Shanxi Province. Our results illustrate that the SVM classification model can effectively avoid empirical thresholds in supervised classification and realistically capture the magnitude and spatial patterns of rainfed and irrigated wheat areas. The approach in this study can be applied to map irrigated/rainfed areas in other regions when field observational data are available.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP130101566
dc.relation.ispartof	Remote Sensing	en_US
dc.relation.isbasedon	10.3390/rs8030207	en_US
dc.title	Mapping irrigated and rainfed wheat areas using multi-temporal satellite data	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	8	en_US
utslib.for	0909 Geomatic Engineering	en_US
utslib.for	0203 Classical Physics	en_US
utslib.for	0406 Physical Geography and Environmental Geoscience	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

© 2016 by the authors. Irrigation is crucial to agriculture in arid and semi-arid areas and significantly contributes to crop development, food diversity and the sustainability of agro-ecosystems. For a specific crop, the separation of its irrigated and rainfed areas is difficult, because their phenology is similar and therefore less distinguishable, especially when there are phenology shifts due to various factors, such as elevation and latitude. In this study, we present a simple, but robust method to map irrigated and rainfed wheat areas in a semi-arid region of China. We used the Normalized Difference Vegetation Index (NDVI) at a 30 × 30 m spatial resolution derived from the Chinese HJ-1A/B (HuanJing(HJ) means environment in Chinese) satellite to create a time series spanning the whole growth period of wheat from September 2010 to July 2011. The maximum NDVI and time-integrated NDVI (TIN) that usually exhibit significant differences between irrigated and rainfed wheat were selected to establish a classification model using a support vector machine (SVM) algorithm. The overall accuracy of the Google-Earth testing samples was 96.0%, indicating that the classification results are accurate. The estimated irrigated-to-rainfed ratio was 4.4:5.6, close to the estimates provided by the agricultural sector in Shanxi Province. Our results illustrate that the SVM classification model can effectively avoid empirical thresholds in supervised classification and realistically capture the magnitude and spatial patterns of rainfed and irrigated wheat areas. The approach in this study can be applied to map irrigated/rainfed areas in other regions when field observational data are available.

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http://hdl.handle.net/10453/122742