• 方法与技术 •

用经验正交函数提取太湖MODIS-EVI时空分布特征

1. (江苏省农业科学院农业资源与环境研究所， 南京 210014)
• 出版日期:2018-12-10 发布日期:2018-12-10

Extracting temporal and spatial distribution features of Lake Taihu from MODIS-EVI data by empirical orthogonal function analysis.

ZHANG Heng-gan*, GU Ke-jun, ZHANG Si-mei

1. (Institute of Agricultural Resources and Environment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China).
• Online:2018-12-10 Published:2018-12-10

Abstract: Lake Taihu is the second largest lake in China, providing much of the irrigation and domestic water in the plains of the middle and lower reaches of the Yangtze River. However, cyanobacteria blooms in this lake have occurred frequently and seriously in recent years, which make harmful to local residents. To solve the problem, researchers have taken efforts to understand its external performance and internal reasons, including the spatiotemporal distribution. Due to the lack of continuous, regular, and longterm observation data, the knowledge is rather scarce. Here, a spatiotemporal MODIS-EVI dataset from 2000-2016 was constructed with MOD13Q1 (one of MODIS products) as data source, followed by the empirical orthogonal function (EOF) analysis and corresponding time coefficients calculations. After North test, the first four EOFs were chosen for further time series analysis, the time coefficients of which were decomposed by classical seasonal decomposition method. The first four EOFs accounted for 46% of the total variance (21.3%, 4.9%, 4.7% and 2.7% for EOF1 to EOF4 respectively), and their spatial patterns matched well with results in previous literatures, but being more accurate, robust and simple. In the time dimension, the trend and seasonal components of time series of the four EOFs had different patterns, which could be used to discriminate the sources of variation of EOF. Our results indicate that EOF method is suitable for extracting the spatiotemporal distribution of EVI in Lake Taihu.