Abstract
Land Surface Phenology is an important characteristic of vegetation, which can be informative of its response to climate change. However, satellite-based identification of vegetation transition dates is hindered by inconsistencies in different observation platforms, including band settings, viewing angles, and scale effects. Therefore, time-series data with high consistency are necessary for monitoring vegetation phenology. This study proposes a data harmonization approach that involves band conversion and bidirectional reflectance distribution function (BRDF) correction to create normalized reflectance from Landsat-8, Sentinel-2A, and Gaofen-1 (GF-1) satellite data, characterized by the same spectral and illumination-viewing angles as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Nadir BRDF Adjusted Reflectance (NBAR). The harmonized data are then subjected to the spatial and temporal adaptive reflectance fusion model (STARFM) to produce time-series data with high spatio–temporal resolution. Finally, the transition date of typical vegetation was estimated using regular 30 m spatial resolution data. The results show that the data harmonization method proposed in this study assists in improving the consistency of different observations under different viewing angles. The fusion result of STARFM was improved after eliminating differences in the input data, and the accuracy of the remote-sensing-based vegetation transition date was improved by the fused time-series curve with the input of harmonized data. The root mean square error (RMSE) estimation of the vegetation transition date decreased by 9.58 days. We concluded that data harmonization eliminates the viewing-angle effect and is essential for time-series vegetation monitoring through improved data fusion.
Highlights
Land surface phenology (LSP) is an important indicator of climate change [1]
A data fusion method was performed on the harmonized reflectance data to generate time-series reflectance with high spatio–temporal resolutions
The proposed data harmonization method can eliminate the inconsistency between fine-resolution reflectance data acquired by different sensors under various illumination-viewing geometries, which further improved the consistency of vegetation index (VI) data
Summary
Land surface phenology (LSP) is an important indicator of climate change [1]. In the past few decades, affected by global climate change, the phenology of terrestrial vegetation has undergone significant changes. European deciduous forest leaf unfolding is 4.2 days earlier every ten years on average [2]. In China, the time of leaf unfolding in deciduous forests is an average of 5.5 days earlier every ten years, which is a greater rate of phenological change than in Europe [3]. Vegetation monitoring can clarify the dynamics of different vegetation types and reveal the spatial and temporal characteristics of climate change [2]. Accurate and detailed phenological information is of great value for regional and global climate change studies [4]
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