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

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

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.

downscaling, Landsat, MODIS, NDVI, spatiotemporal fusion
1548-1603
1-19
Onojeghuo, Alex O.
412845ac-87b0-4b1d-b8fd-7c42d20971e0
Blackburn, George A.
33baa57f-97a5-4750-985e-67a5e6400cf9
Wang, Qunming
3ceb1e88-bd7f-4481-8a46-c1efcbb2e54b
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Kindred, Daniel
81346b52-65ab-421d-a108-e1134046877e
Miao, Yuxin
87983caf-fdd4-4764-8355-95674383ea83
Onojeghuo, Alex O.
412845ac-87b0-4b1d-b8fd-7c42d20971e0
Blackburn, George A.
33baa57f-97a5-4750-985e-67a5e6400cf9
Wang, Qunming
3ceb1e88-bd7f-4481-8a46-c1efcbb2e54b
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Kindred, Daniel
81346b52-65ab-421d-a108-e1134046877e
Miao, Yuxin
87983caf-fdd4-4764-8355-95674383ea83

Onojeghuo, Alex O., Blackburn, George A., Wang, Qunming, Atkinson, Peter M., Kindred, Daniel and Miao, Yuxin (2018) Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series. GIScience and Remote Sensing, 1-19. (doi:10.1080/15481603.2018.1423725).

Record type: Article

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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More information

Accepted/In Press date: 31 December 2017
e-pub ahead of print date: 10 January 2018
Keywords: downscaling, Landsat, MODIS, NDVI, spatiotemporal fusion

Identifiers

Local EPrints ID: 419814
URI: http://eprints.soton.ac.uk/id/eprint/419814
ISSN: 1548-1603
PURE UUID: f89bbbcb-98b1-4951-b9e0-5e85af9b6e27
ORCID for Peter M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880

Catalogue record

Date deposited: 20 Apr 2018 16:30
Last modified: 07 Oct 2020 01:37

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Contributors

Author: Alex O. Onojeghuo
Author: George A. Blackburn
Author: Qunming Wang
Author: Peter M. Atkinson ORCID iD
Author: Daniel Kindred
Author: Yuxin Miao

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