Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series |
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| Authors: | Alex O Onojeghuo George A Blackburn Qunming Wang Peter M Atkinson Daniel Kindred Yuxin Miao |
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| Institution: | 1. Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK;2. Centre for Ecology &3. Hydrology, Lancaster LA1 4YQ, UK;4. Faculty of Science and Technology, Engineering Building, Lancaster University, Bailrigg, Lancaster LA1 4YR, UK;5. Geography and Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK;6. School of Geography, Archaeology, and Palaeoecology, Queen’s University Belfast, BT7 1NN, Northern Ireland, UK;7. ADAS UK Limited, Boxworth, Cambridge CB23 4NN, UK;8. Department of Plant Nutrition, College of Resource and Environmental Sciences, China Agricultural University, Beijing 100094, China |
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| 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. |
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| Keywords: | NDVI MODIS Landsat downscaling spatiotemporal fusion |
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