Comparative analysis of normalised difference spectral indices derived from MODIS for detecting surface water in flooded rice cropping systems

PLoS One. 2014 Feb 20;9(2):e88741. doi: 10.1371/journal.pone.0088741. eCollection 2014.


Identifying managed flooding in paddy fields is commonly used in remote sensing to detect rice. Such flooding, followed by rapid vegetation growth, is a reliable indicator to discriminate rice. Spectral indices (SIs) are often used to perform this task. However, little work has been done on determining which spectral combination in the form of Normalised Difference Spectral Indices (NDSIs) is most appropriate for surface water detection or which thresholds are most robust to separate water from other surfaces in operational contexts. To address this, we conducted analyses on satellite and field spectral data from an agronomic experiment as well as on real farming situations with different soil and plant conditions. Firstly, we review and select NDSIs proposed in the literature, including a new combination of visible and shortwave infrared bands. Secondly, we analyse spectroradiometric field data and satellite data to evaluate mixed pixel effects. Thirdly, we analyse MODIS data and Landsat data at four sites in Europe and Asia to assess NDSI performance in real-world conditions. Finally, we test the performance of the NDSIs on MODIS temporal profiles in the four sites. We also compared the NDSIs against a combined index previously used for agronomic flood detection. Analyses suggest that NDSIs using MODIS bands 4 and 7, 1 and 7, 4 and 6 or 1 and 6 perform best. A common threshold for each NDSI across all sites was more appropriate than locally adaptive thresholds. In general, NDSIs that use band 7 have a negligible increase in Commission Error over those that use band 6 but are more sensitive to water presence in mixed land cover conditions typical of moderate spatial resolution analyses. The best performing NDSI is comparable to the combined index but with less variability in performance across sites, suggesting a more succinct and robust flood detection method.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Agricultural Irrigation / methods*
  • Cambodia
  • Environmental Monitoring / methods
  • India
  • Italy
  • Oryza / growth & development*
  • Satellite Imagery / methods*
  • Spectrum Analysis / methods*
  • Vietnam
  • Water / chemistry*


  • Water

Grants and funding

Funding for this research came from RIICE (Remote Sensing based Information and Insurance for crops in emerging Economies) funded by the Swiss Agency for Development and Cooperation ( and from GRiSP (Global Rice Science Partnership) the CGIAR funded Research Program on Rice ( The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.