Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data

management of water resources, and estimation of greenhouse gas (methane) emissions. Paddy rice agriculture
has expanded rapidly in northeastern China in the last decade, but there are no updated maps of
paddy rice fields in the region. Existing algorithms for identifying paddy rice fields are based on the
unique physical features of paddy rice during the flooding and transplanting phases and use vegetation
indices that are sensitive to the dynamics of the canopy and surface water content. However, the flooding
phenomena in high latitude area could also be from spring snowmelt flooding. We used land surface temperature
(LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to determine
the temporal window of flooding and rice transplantation over a year to improve the existing
phenology-based approach. Other land cover types (e.g., evergreen vegetation, permanent water bodies,
and sparse vegetation) with potential influences on paddy rice identification were removed (masked out)
due to their different temporal profiles. The accuracy assessment using high-resolution images showed
that the resultant MODIS-derived paddy rice map of northeastern China in 2010 had a high accuracy (producer
and user accuracies of 92% and 96%, respectively). The MODIS-based map also had a comparable
accuracy to the 2010 Landsat-based National Land Cover Dataset (NLCD) of China in terms of both area
and spatial pattern. This study demonstrated that our improved algorithm by using both thermal and
optical MODIS data, provides a robust, simple and automated approach to identify and map paddy rice
fields in temperate and cold temperate zones, the northern frontier of rice planting.
 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier