An advanced tool for real-time crop monitoring in China

Presents a series of methods that monitor crops in China by remote sensing. First the paper describes the methodology used to integrate high resolution land cover data and 1 km²-resolution SPOT-Vegetation 10-day images for the extraction of NDVI profiles. That can increase the information content of the time series of NDVI. It is proved that information extracted this way is closer to crop behavior than a simple mixed vegetation profile. Next for cloud masking, we use the HANTS (harmonic analysis of time series) algorithm. This is because time series of NDVI data have a periodic characteristic that describes the yearly cycle of the vegetation dynamics. HANTS can analyze the time signal for each individual pixel and use the series of harmonic sine and cosine waves to reconstruct wave fits into the total period. Results showed that cloud affected data are recognized and replaced successfully by reconstruction of seasonal NDVI profiles. The application and the results fit the objective of the crop monitoring system in China, which is used during the cropping season to monitor crop growth and identify possible food shortages in real time.