nerc.ac.uk

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

Onojeghuo, Alex O.; Blackburn, George A.; Wang, Qunming; Atkinson, Peter M.; Kindred, Daniel; Miao, Yuxin. 2018 Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series. GIScience & Remote Sensing, 55 (5). 659-677. https://doi.org/10.1080/15481603.2018.1423725

Full text not available from this repository.

Abstract/Summary

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.

Item Type: Publication - Article
Digital Object Identifier (DOI): https://doi.org/10.1080/15481603.2018.1423725
CEH Sections/Science Areas: Unaffiliated
ISSN: 1548-1603
Additional Keywords: NDVI, MODIS, Landsat, downscaling, spatiotemporal fusion
NORA Subject Terms: Electronics, Engineering and Technology
Agriculture and Soil Science
Date made live: 09 Aug 2018 11:29 +0 (UTC)
URI: http://nora.nerc.ac.uk/id/eprint/520681

Actions (login required)

View Item View Item

Document Downloads

Downloads for past 30 days

Downloads per month over past year

More statistics for this item...