Characterizing spatial patterns of phenology in China’s cropland based on remotely sensed data

Abstract

This study used time-series of NDVI datasets at a spatial resolution of 8 km and 15-day interval to identify the spatial patterns of cropland phenology in China. To do so, a smoothing algorithm based on an asymmetric Gaussian function was first performed on NDVI dataset to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination. Subsequent data processing for detecting cropping systems and phonological parameters was based on the smoothed NVDI time-series datasets. The results show that the cropping system in Chinapsilas cropland becomes complex as moving toward to the south from the north China. Under this cropping system, the starting date (SGS) and ending date (EGS) for the first growing season vary over space, and those regions with multiple cropping systems present a significant advanced SGS and EGS than the regions with a single cropping. On the contrary, the phenology of the second growing season including both the SGS and EGS show little difference between regions. This study concludes that spatial patterns of cropping system and phenology in Chinapsilas cropland are highly related to the geophysical environmental factors in China. In addition, several anthropogenic factors, such as crop variety, cultivation levels, irrigation and fertilizers, can profoundly influence crop phenological status. How to discriminate the impacts of biophysical forces and anthropogenic drivers on phenological events of cultivation remains a great challenge for further studies.