Next Article in Journal
Downscaling 250-m MODIS Growing Season NDVI Based on Multiple-Date Landsat Images and Data Mining Approaches
Next Article in Special Issue
A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data
Previous Article in Journal
Airborne Lidar for Woodland Habitat Quality Monitoring: Exploring the Significance of Lidar Data Characteristics when Modelling Organism-Habitat Relationships
Previous Article in Special Issue
MODIS-Based Fractional Crop Mapping in the U.S. Midwest with Spatially Constrained Phenological Mixture Analysis
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(4), 3467-3488; doi:10.3390/rs70403467

Rice Fields Mapping in Fragmented Area Using Multi-Temporal HJ-1A/B CCD Images

1
Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou 310058, China
2
Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou 311121, China
*
Author to whom correspondence should be addressed.
Academic Editors: Tao Cheng, Zhengwei Yang, Yoshio Inoue, Yan Zhu, Weixing Cao, Clement Atzberger and Prasad S. Thenkabail
Received: 30 December 2014 / Accepted: 17 March 2015 / Published: 24 March 2015
(This article belongs to the Special Issue Recent Advances in Remote Sensing for Crop Growth Monitoring)
View Full-Text   |   Download PDF [25824 KB, uploaded 24 March 2015]   |  

Abstract

Rice is one of the most important crops in the world; meanwhile, the rice field is also an important contributor to greenhouse gas methane emission. Therefore, it is important to get an accurate estimation of rice acreage for both food production and climate change related studies. The eastern plain region is one of the major single-cropped rice (SCR) growing areas in China. Subjected to the topography and intensified human activities, the rice fields are generally fragmented and irregular. How remote sensing can meet this challenge to accurately estimate the acreage of the rice in this region using medium-resolution imagery is the topic of this study. In this study, the applicability of the Chinese HJ-1A/B satellites and a two-band enhanced vegetation index (EVI2) was investigated. Field campaigns were carried out during the rice growing season and ground-truth data were collected for classification accuracy assessments in 2012. A stepwise classification strategy utilizing the EVI2 signatures during key phenology stages, i.e., the transplanting and the vegetative to reproductive transition phases, of the SCR was proposed, and the overall classification accuracy was 91.7%. The influence of the mixed pixel and boundary effects to classification accuracy was also investigated. This work demonstrates that the Chinese HJ-1A/B data are suitable data source to estimating SCR cropping area under complex land cover composition. View Full-Text
Keywords: rice fields mapping; HJ-1A/B CCD images; classification; phenology rice fields mapping; HJ-1A/B CCD images; classification; phenology
Figures

Figure 1

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

Wang, J.; Huang, J.; Zhang, K.; Li, X.; She, B.; Wei, C.; Gao, J.; Song, X. Rice Fields Mapping in Fragmented Area Using Multi-Temporal HJ-1A/B CCD Images. Remote Sens. 2015, 7, 3467-3488.

Show more citation formats Show less citations formats

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