Nomination-favoured opinion pool for optical-SAR-synergistic rice mapping in face of weakened flooding signals

Yiqing Guo, Xiuping Jia, D. Paull, Jón Atli Benediktsson

Research output: Contribution to journalArticlepeer-review

Abstract

Using satellite image data it is possible to distinguish paddy rice from other crops by detecting its unique signals during the initial flooding period for crop mapping and growth monitoring. In recent years Australian rice growers have applied direct drilling sowing method to reduce water usage. This effort, however, has challenged existing algorithms for paddy rice planting area mapping, as the flooding signals have become weaker, especially for the newly introduced water-saving variety YRM 70. In order to alleviate this problem, an optical-SAR-synergistic rice mapping approach is proposed in the time domain in this study. Time-series images from both sensor types were collected in the rice growing seasons. Optical indices and SAR (Synthetic-Aperture Radar) features were generated and analysed. Pixel dependent multiple features are developed to reveal the strongest remote sensing signatures. As the information provided by optical and SAR data are complementary, a novel nomination-favoured opinion pool, NF-OP, is constructed where a pixel is identified as rice if either the optical or SAR data (or both) provide a relatively positive such opinion. In the research, mapping experiments were conducted for the Riverina region of Coleambally, New South Wales, Australia, during the 2016–2017 summer season. The experimental results suggest the following: (1) rice crops sown with the direct drilling method show weakened flooding signals due to the practice of flush floodings and the adoption of water-saving management strategies, especially for directly drilled YRM 70 rice; (2) conventional rice mapping algorithms using only optical or SAR data tend to generate high mis-detection rates (i.e., low true positive rates) for direct drilling rice as a result of weakened flooding signals; (3) by fusing the complementary optical and SAR data with the proposed NF-OP, the mis-detection problem is effectively alleviated, with the true positive rate for directly drilled YRM 70 rice being improved to 82.5% as compared to 54.5% or 44.0% obtained with optical or SAR data only; (4) an overall rice mapping accuracy of 94.7% is achieved with the proposed optical-SAR-synergistic approach, which is 9.5% and 11.5% higher than the respective optical-only and SAR-only algorithms.

Original languageEnglish
Pages (from-to)187-205
Number of pages19
JournalISPRS Journal of Photogrammetry and Remote Sensing
Volume155
DOIs
Publication statusPublished - Sept 2019

Bibliographical note

Funding Information: The authors would like to thank Emeritus Professor John Richards of The Australian National University for his important and valuable comments and suggestions to this study, and Ms. Emma Madigan of The University of New South Wales Canberra for her assistance in data acquisition and preparation. Our sincere thanks also go to Mr. Chris Quirk and Mr. Andrew Law at the Sunrice Milling Company and Mr. Graham Parton and Mr. Bernard Star at the Coleambally Irrigation Co-operative Ltd for providing the ground reference rice data. The authors are grateful to the anonymous reviewers for their insightful comments and suggestions for improving this manuscript. Publisher Copyright: © 2019 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)

Other keywords

  • Consensus theory
  • Data fusion
  • Opinion pool
  • Optical
  • Rice mapping
  • Rice variety
  • Sowing method
  • Synthetic-Aperture Radar (SAR)

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