Spatiotemporal Comparison and Validation of Three Global-Scale Fractional Vegetation Cover Products

Duanyang Liu,Yunjun Yao,Kun Jia,Xiaotong Zhang,Xiwang Zhang,Xiangqin Wei,Bing Wang,Mu Xia

doi:10.3390/rs11212524

Abstract

Fractional vegetation cover (FVC) is an important parameter for many environmental and ecological models. Large-scale and long-term FVC products are critical for various applications. Currently, several global-scale FVC products have been generated with remote sensing data, such as VGT bioGEOphysical product Version 2 (GEOV2), PROBA-V bioGEOphysical product Version 3 (GEOV3) and Global LAnd Surface Satellite (GLASS) FVC products. However, studies comparing and validating these global-scale FVC products are rare. Therefore, in this study, the performances of three global-scale time series FVC products, including the GEOV2, GEOV3, and GLASS FVC products, are investigated to assess their spatial and temporal consistencies. Furthermore, reference FVC data generated from high-spatial-resolution data are used to directly evaluate the accuracy of these FVC products. The results show that these three FVC products achieve general agreements in terms of spatiotemporal consistencies over most regions. In addition, the GLASS and GEOV2 FVC products have reliable spatial and temporal completeness, whereas the GEOV3 FVC product contains much missing data over high-latitude regions, especially during wintertime. Furthermore, the GEOV3 FVC product presents higher FVC values than GEOV2 and GLASS FVC products over the equator. The main differences between the GEOV2 and GLASS FVC products occur over deciduous forests, for which the GLASS product presents slightly higher FVC values than the GEOV2 product during wintertime. Finally, temporal profiles of the GEOV2 and GLASS FVC products show better consistency than the GEOV3 FVC product, and the GLASS FVC product presents more reliable accuracy (R2 = 0.7878, RMSE = 0.1212) compared with the GEOV2 (R2 = 0.5798, RMSE = 0.1921) and GEOV3 (R2 = 0.7744, RMSE = 0.2224) FVC products over these reference FVC data.

Highlights

Fractional vegetation cover (FVC) is defined as the fraction of green vegetation seen from nadir, which can characterize the growth conditions and horizontal density of land surface live vegetation [1,2,3,4,5]
In January, high FVC values are mainly concentrated around the equator areas, such as north South America, Central Africa and Indonesia, where tropical and subtropical moist broadleaf forests are the dominant vegetation types [54]
The following conclusions can be drawn: (1) The Global LAnd Surface Satellite (GLASS) FVC product is much more continuous and complete based on the comparison

Summary

Introduction

Fractional vegetation cover (FVC) is defined as the fraction of green vegetation seen from nadir, which can characterize the growth conditions and horizontal density of land surface live vegetation [1,2,3,4,5]. Many FVC estimation algorithms have been developed based on remote sensing data, which can be divided into three major types: empirical methods, pixel unmixing models and machine learning methods [10,11]. Empirical methods become invalid over large-scale regions, in which the various vegetation types and land conditions increase uncertainties in the established relationships [10,12]. Pixel unmixing models assume that each pixel is composed of several components, and the fraction of vegetation composition is the corresponding FVC value of the pixel [6,12,13]. Machine learning methods estimate the FVC through training on a representative sample database containing pre-processed reflectance and corresponding simulated land surface parameters data [7]. Several algorithms of machine learning methods are proposed for FVC product generation over regional and global scales with satisfying results [1,10,12]

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Results

Discussion

Conclusion