Improving Time Series Reconstruction by Fixing Invalid Values and its Fidelity Evaluation

Xinhui Li,Renxi Chen,Runping Shen

doi:10.1109/access.2019.2962757

Xinhui Li, Renxi Chen + Show 1 more

Open Access

https://doi.org/10.1109/access.2019.2962757

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Abstract

MODIS time series data have been widely used in the research of regional and global ecosystems and climate change. For vegetation monitoring, vegetation indices such as NDVI (normalized difference vegetation index), EVI (enhanced vegetation index) and NBR (normalized burn ratio), are usually derived from MODIS reflectance data. However, noise usually makes it difficult to generate reliable time series of vegetation indices. Although some methods have been developed for reconstructing NDVI time series data, they still suffer from some limitations. First, there is no reliable approach for detecting and dealing with low-quality data, resulting in poor outcomes. Second, no effective evaluation of the fidelity of the corrected data to the original data has been discussed. For these reasons, we developed a new time series reconstruction approach, named Fixing Invalid Value (FIV) method. The proposed method assumes that the noise in surface reflectance data stems from invalid data, such as clouds, ice, and missing values. The FIV method first uses the spatially and temporally neighboring pixels to estimate the invalid values and then applies morphology operations to remove the residual noise. Finally, the Savitzky-Golay (S-G) filter is employed to generate the final results. The FIV method is tested on 8-day composite MODIS surface reflectance time series data from 2001 to 2012 in Jiangxi and Fujian provinces, China. The results show that the FIV method outperforms the conventional S-G filter and the HANTS method both in terms of visual inspection and quantitative evaluation. Furthermore, the fidelity evaluation reveals that the proposed FIV method produces high-quality time series data under all weather conditions.

Highlights

In the recent three decades, Earth observation satellites have played a central role in monitoring land cover dynamics and ecologic environment
To generate high-quality time series data, this paper proposes a compound filtering method based on fixing invalid values (FIV) and S-G filtering
Instead of the MOD13 products, we used the MOD09A1 data because of the following reasons: 1) MOD13 provides only NDVI and EVI time series products, while with MOD09A1 we can calculate additional vegetation indices (e.g. NBR). 2) In the study, we found that some cloud covered pixels were not identified by the state flag files, we adopted another algorithm based on reflectance data to detect clouds

Summary

Introduction

In the recent three decades, Earth observation satellites have played a central role in monitoring land cover dynamics and ecologic environment. An example is the monitoring of forest by means of time series analysis with vegetation indices (VIs) [4]–[6]. These VIs, including NDVI, EVI, NBR, and other self-defined indices, represent the absorptive and. MODIS reflectance data unavoidably contain large amounts of noise caused by cloud contamination, atmospheric variability, and aerosol effects. For this reason, the sensors are not always able to obtain the actual information about the land surface, introducing low-quality or missing data. Noise causes inaccurate or severely biased VI breaks, resulting in the distortion of time series and significantly affects the accuracy of terrestrial monitoring

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