Abstract

Repeated monitoring of paddy rice is essential for government agencies and policy makers to maintain the balance of supply and demand for rice. Recent studies have mostly concentrated on the mapping of paddy rice with temporal satellite imagery during growing seasons. Given the phenological variation within paddy rice fields and spectral confusion between paddies and other vegetation classes, our ability to identify paddy rice fields with temporal imagery remains limited. The objective of this study is to develop new phenology and textural-based strategies to detect paddies with HJ-1A, MODIS and PALSAR FNF imagery. Two phenology-based strategies that track the seasonal trajectory of crops and one textural-based strategy that contains image surface characteristics are presented. With the proposed strategies, temporal, spectral and textural features were investigated for paddy rice detection. The results indicate that the phenology-based strategies could reveal the phenological variation within paddy rice and significantly improved the detection accuracy. Seasonal amplitude, grey level co-occurrence matrix entropy and spectral features of the heading stage were proven to be important in identifying paddy rice. It was concluded that the combination of HJ-1A, MODIS and PALSAR FNF imagery are promising in facilitating the rapid mapping of paddy rice at a regional scale.

Details

Title
Incorporating crop phenological trajectory and texture for paddy rice detection with time series MODIS, HJ-1A and ALOS PALSAR imagery
Author
Singha, Mrinal 1 ; Sarmah, Sangeeta 2 

 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China 
 Institute of Atmospheric Physics, University of Chinese Academy of Sciences, Beijing, China 
Pages
73-87
Publication year
2019
Publication date
Nov 2019
Publisher
Taylor & Francis Ltd.
e-ISSN
22797254
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2351066972
Copyright
© 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.