Comparative study of FEEMD decomposition of multisource time series remote sensing data at different time scales
WANG Zheng1,2， QIU Shike1,2， ZENG Qun3， LYU Yanli4， WANG Chao1,2， ZHANG Qiping4，LI Shuangquan1
(1.Institute of Geographical Science， Henan Academy of Science， Zhengzhou 450052， China；2.Key Laboratory of Remote Sensing and GIS of Henan Province， Zhengzhou 450052， China；3.College of Urban and Environmental Sciences Research Institute of Sustainable Development， Central China Normal University， Wuhan 430079， China； 4. Earth View Image Co.， Ltd.， Beijing 100083， China)
Abstract：The long time series chlorophyll a concentration and related environmental factors in the northeastern South China Sea are affected by multi-scale physical forcing， which have nonlinear and non-stationary characteristics. Therefore， it is difficult to decompose the data in this region. In this study， an adaptive， non-linear， non-stationary FEEMD method is utilized to decompose the 8-day-scale and monthly-scale datasets of chlorophyll a concentration and associated environmental factors. The results are shown as follows. 1) FEEMD can effectively overcome the high-frequency mode mixing problem of EMD and EEMD； 2) FEEMD is 10 times faster than EMD and EEMD； 3) The overall trends of the 21-year data decomposed based on 8-day and monthly scale data are consistent； 4) The 8-day scale datas can be decomposed into more physically significant high-frequency modes than monthly data. The calculation of these high-frequency modes reveals that the 8-day scale datas can be decomposed into periods as short as about 2 months， 4 months (seasons)， and 6 months (half year)； 5) The 8-day chlorophyll a concentration data can be decomposed into cycles of up to about 5 years， and other related environmental factors can be decomposed into very long cycles of 10-14 years， while the monthly scale data can only be decomposed into annual scale cycles. The analysis results of this paper demonstrated that the FEEMD method can effective decompose long time series data in the study area with complex environment， high dynamic factors. The optimal results achieved by FEEMD in data decomposition in complex regions can provide implications for subsequent studies of multifactor-driven relationships in this area.
王 正,邱士可,曾 群,吕言利,王 超,张起萍,李双权. 不同时间尺度多源时序数据的FEEMD分解比较研究[J]. 华中师范大学学报(自然科学版), 2023, 57(6): 821-836.
WANG Zheng,QIU Shike,ZENG Qun,LYU Yanli,WANG Chao,ZHANG Qiping,LI Shuangquan. Comparative study of FEEMD decomposition of multisource time series remote sensing data at different time scales. journal1, 2023, 57(6): 821-836.
2023, Vol. 57 
