Comparison of three NDVI time-series fitting methods in crop phenology detection in Northeast China

35th International Symposium on Remote Sensing of Environment

地球科学;生态环境科学

Wang, Meng^1,2 ; Tao, Fulu^1

Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing, 100101, China^1

University of Chinese Academy of Sciences, Beijing, 10049, China^2

关键词: Agricultural productions;    Comparative analysis;    NDVI time series;    Phenological changes;    Smoothing methods;    Spatial patterns;    Standard deviation;    Vegetation index;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/17/1/012007/pdf
DOI  :  10.1088/1755-1315/17/1/012007

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Phenological changes of cropland are the pivotal basis for farm management, agricultural production, and climate change research. Over the past decades, a range of methods have been used to extract phenological events based on satellite derived continuous vegetation index time series, however, large uncertainties still exist. In this study, three smoothing methods were compared to reduce the potential uncertainty and to quantify crop green-up dates over Northeast China. The results indicated that the crop spring onset dates estimated by three methods show variance in the dates, but with similar spatial pattern. In 60% of the study area, the standard deviation (SD) of the estimated starting date from different method is less than 10 days, while 39.5% of total pixels have SDs between 10days and 30 days. Through comparative analysis against the observation phenological data, we concluded that Asymmetric Gaussians produced the most approximative results of all, followed by Double Logistic algorithm, and Savizky-Glolay algorithm performed worst. The starting dates of crops occur mostly between May and June in this region. The Savitzky-Golay has the earliest estimates, while the Asymmetric Gaussians and Double logistic fitting method show similar and later estimates, which are more consistent with the observed data.