A Framework of Spatio-Temporal Fusion Algorithm Selection for Landsat NDVI Time Series Construction

Yangnan Guo,Yibo Zhao,Shaogang Lei,Cangjiao Wang,Junzhe Yang

doi:10.3390/ijgi9110665

Abstract

Spatio-temporal fusion algorithms dramatically enhance the application of the Landsat time series. However, each spatio-temporal fusion algorithm has its pros and cons of heterogeneous land cover performance, the minimal number of input image pairs, and its efficiency. This study aimed to answer: (1) how to determine the adaptability of the spatio-temporal fusion algorithm for predicting images in prediction date and (2) whether the Landsat normalized difference vegetation index (NDVI) time series would benefit from the interpolation with images fused from multiple spatio-temporal fusion algorithms. Thus, we supposed a linear relationship existed between the fusion accuracy and spatial and temporal variance. Taking the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM) as basic algorithms, a framework was designed to screen a spatio-temporal fusion algorithm for the Landsat NDVI time series construction. The screening rule was designed by fitting the linear relationship between the spatial and temporal variance and fusion algorithm accuracy, and then the fitted relationship was combined with the graded accuracy selecting rule (R2) to select the fusion algorithm. The results indicated that the constructed Landsat NDVI time series by this paper proposed framework exhibited the highest overall accuracy (88.18%), and lowest omission (1.82%) and commission errors (10.00%) in land cover change detection compared with the moderate resolution imaging spectroradiometer (MODIS) NDVI time series and the NDVI time series constructed by a single STARFM or ESTARFM. Phenological stability analysis demonstrated that the Landsat NDVI time series established by multiple spatio-temporal algorithms could effectively avoid phenological fluctuations in the time series constructed by a single fusion algorithm. We believe that this framework can help improve the quality of the Landsat NDVI time series and fulfill the gap between near real-time environmental monitoring mandates and data-scarcity reality.

Highlights

Landsat images recording the Earth’s surface status since 1972 are irreplaceable in terrestrial ecosystem dynamics monitoring and biosphere processes modeling [1,2]
The screening rule was constructed by first fitting the linear relationship between the spatial and temporal variance and fusion algorithm accuracy, and the fitted relationship was combined with the graded accuracy selecting rule to select the fusion algorithm
Further steps for near real-time monitoring were carried out by comparing the target Landsat normalized difference vegetation index (NDVI) time series (TTS) generated from the framework that this paper proposed with the moderate resolution imaging spectroradiometer (MODIS) NDVI time series (MTS) and NDVI time series using single algorithm fusion (STS: the STRAFM based Landsat NDVI time series, and ETS: the ESTRAFM based Landsat NDVI time series)

Summary

Introduction

Landsat images recording the Earth’s surface status since 1972 are irreplaceable in terrestrial ecosystem dynamics monitoring and biosphere processes modeling [1,2]. Limited by the 16-day revisit time, frequent cloud contamination, and 22% data loss of the Enhanced Thematic Mapper Plus (ETM+) sensor since 2003, there is a dearth of dual high resolution (spatial and temporal resolution) Landsat NDVI time series [7,8,9,10]. These missing observation data have caused the Landsat time series to fail to catch land cover change events or to extract important phenological nodes [5,11]. Missing image reconstruction is expected to solve the mentioned problems [12,13,14]

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Discussion

Conclusion