Abstract
The objective of the study is to identify the rice heading date and analyse its spatial characteristics on a regional scale using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) normalized differential vegetation index (NDVI) data and a new approach: quadratic polynomial fitting. The cloud-contaminated NDVI value was identified by reliability data and linearly interpolated with values before and after the cloudy one. The discrete Fourier transformation (DFT) and quadratic polynomial fitting were implemented to generate new time series curves. Rice heading date was retrieved by calculating the day for maximum NDVI. Comparing with DFT, the proposed quadratic polynomial fitting significantly improves the computation efficiency, while providing approximate precision of estimation. In regional analysis, the rice heading date retrieved from polynomial fitting is more consistent than that from DFT. The study also suggests that multi-temporal MODIS NDVI data combined with different methods can retrieve crop phenology information on a large scale.
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