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Remote Sens. 2016, 8(3), 207; doi:10.3390/rs8030207

Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data

1
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Shanxi Climate Center, Taiyuan 030002, China
4
Department of Plant and Soil Sciences, College of Agriculture, Food and Environment, University of Kentucky, KY 40506, USA
5
Institute of Dry Farming Engineering, Shanxi Agricultural University, Shanxi Taigu 030801, China
6
National Meteorological Center, Beijing 100081, China
7
School of Life Sciences, University of Technology Sydney, P.O. Box 123, Broadway NSW 2007, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Clement Atzberger and Prasad S. Thenkabail
Received: 27 September 2015 / Revised: 27 January 2016 / Accepted: 29 January 2016 / Published: 3 March 2016
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Abstract

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. View Full-Text
Keywords: irrigated and rainfed areas; growth characteristics; phenology; support vector machines irrigated and rainfed areas; growth characteristics; phenology; support vector machines
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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. Remote Sens. 2016, 8, 207.

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