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Knowledge-guided fitting method for sparse time series remote sensing data

  • role： First author第一作者
  • Affiliation：

    College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

  • Email：fanjing@zjut.edu.cn
  • Introduction：范菁(1969—　)，女，教授，主要研究方向为森林虚拟仿真与可视化。E-mail：fanjing@zjut.edu.cn
FAN Jing,  
  • Affiliation：

    College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

YU Weize,  
  • Affiliation：

    College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

WU Wei,  
  • Affiliation：

    College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

SHEN Ying

Resümee

Time series remote sensing plays an important role in forest monitoring, and the performance of the fitting method of time series Normalized Difference Vegetation Index (NDVI) determines the precision of phenology parameter estimation. However, in cloudy and rainy areas, obtaining cloud free remote sensing data for time series application is greatly difficult, leading to the difficulty in precisely fitting the time series NDVI. We present a knowledge-guided fitting method for sparse time series remote sensing data, given that the NDVI is underestimated by noise. First, we use prior knowledge and time difference method to identify and remove the noise, and then the Gaussian second-order model is used to fit the original data. Third, the iterative fitting is carried out by updating the weights of original data according to the fitting residuals. Finally, this process is repeated until a stable result is obtained. We use NDVI derived from Landsat 8 OLI as the data source to fit the forest data in Hangzhou, Zhejiang province. MODIS data are used as the referenced data to test accuracy of fitting result. The correlation coefficient between the fitting result of sparse time series data and the MODIS data fitting results is 0.92, and the difference of the day of max NDVI is five days, which indicates the proposed can be used to estimate the growth intensity, growth period, and other biological parameters effectively, the result is closer to the MODIS data fitting result compared with the state of art method. Our research provides the knowledge-guided fitting method, which is more effective than the other conventional methods, especially for the sparse time series data. This method reduces the human subjective influence and the excessive disturbance caused by the noise in the original data, making the fitting result more close to the actual fitting curve. The advantages of this method include less input parameters, fast converge, and the stability of fitting result. However, the proposed method suppresses the noise in the fitting processing to obtain relatively smooth fitting curve, which also may lead to the loss of the curve information; further improvements can be performed in the future.

Schlüsselwort

sparse time series data;iterative weighted;data fitting;Landsat 8;Gaussian model

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