Next Article in Journal
Filling the Polar Data Gap in Sea Ice Concentration Fields Using Partial Differential Equations
Previous Article in Journal
Impact of Satellite Remote Sensing Data on Simulations of Coastal Circulation and Hypoxia on the Louisiana Continental Shelf
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(5), 434; doi:10.3390/rs8050434

Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series

1
Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, Oswald-Kuelpe-Weg 86, D-97074 Wuerzburg, Germany
2
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, 100101 Beijing, China
3
German Remote Sensing Data Center (DFD), Earth Observation Center (EOC), German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Anton Vrieling, Yoshio Inoue and Prasad S. Thenkabail
Received: 4 March 2016 / Revised: 3 May 2016 / Accepted: 9 May 2016 / Published: 23 May 2016
View Full-Text   |   Download PDF [4536 KB, uploaded 23 May 2016]   |  

Abstract

Rice is an important food crop and a large producer of green-house relevant methane. Accurate and timely maps of paddy fields are most important in the context of food security and greenhouse gas emission modelling. During their life-cycle, rice plants undergo a phenological development that influences their interaction with waves in the visible light and infrared spectrum. Rice growth has a distinctive signature in time series of remotely-sensed data. We used time series of MODIS (Moderate Resolution Imaging Spectroradiometer) products MOD13Q1 and MYD13Q1 and a one-class support vector machine to detect these signatures and classify paddy rice areas in continental China. Based on these classifications, we present a novel product for continental China that shows rice areas for the years 2002, 2005, 2010 and 2014 at 250-m resolution. Our classification has an overall accuracy of 0.90 and a kappa coefficient of 0.77 compared to our own reference dataset for 2014 and correlates highly with rice area statistics from China’s Statistical Yearbooks (R2 of 0.92 for 2010, 0.92 for 2005 and 0.90 for 2002). Moderate resolution time series analysis allows accurate and timely mapping of rice paddies over large areas with diverse cropping schemes. View Full-Text
Keywords: rice; China; MODIS; time series; SVM; OCSVM; agriculture; change detection rice; China; MODIS; time series; SVM; OCSVM; agriculture; change detection
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Clauss, K.; Yan, H.; Kuenzer, C. Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series. Remote Sens. 2016, 8, 434.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top