Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Alex O. Onojeghuo, George A. Blackburn, Qunming Wang, Peter M. Atkinson, Daniel Kindred, Yuxin Miao

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.

Original languageEnglish (US)
Pages (from-to)659-677
Number of pages19
JournalGIScience and Remote Sensing
Volume55
Issue number5
DOIs
StatePublished - Sep 3 2018

Bibliographical note

Funding Information:
The publication was made possible through funding from the UK Science & Technology Facilities Council (STFC) Newton Agri-Tech Programme. The project is titled “Remote Sensing for Sustainable Intensification in China through Improved Farm Decision-Making.”

Publisher Copyright:
© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

Keywords

  • Landsat
  • MODIS
  • NDVI
  • downscaling
  • spatiotemporal fusion

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