Comparison of the Spatial Characteristics of Four Remotely Sensed Leaf Area Index Products over China: Direct Validation and Relative Uncertainties

Xinlu Li,Hui Lu,Le Yu,Kun Yang

doi:10.3390/rs10010148

Abstract

Leaf area index (LAI) is a key input for many land surface models, ecological models, and yield prediction models. In order to make the model simulation and/or prediction more reliable and applicable, it is crucial to know the characteristics and uncertainties of remotely sensed LAI products before they are input into models. In this study, we conducted a comparison of four global remotely sensed LAI products—Global Land Surface Satellite (GLASS), Global LAI Product of Beijing Normal University (GLOBALBNU), Global LAI Map of Chinese Academy of Sciences (GLOBMAP), and Moderate-resolution Imaging Spectrometer (MODIS) LAI, while the former three products are newly developed by three Chinese research groups on the basis of the MODIS land reflectance product over China between 2001 and 2011. Direct validation by comparing the four products to ground LAI observations both globally and over China demonstrates that GLASS LAI shows the best performance, with R2 = 0.70 and RMSE = 0.96 globally and R2 = 0.94 and RMSE = 0.61 over China; MODIS performs worst (R2 = 0.55, RMSE = 1.23 globally and R2 = 0.03, RMSE = 2.12 over China), and GLOBALBNU and GLOBMAP performs moderately. Comparison of the four products shows that they are generally consistent with each other, giving the smallest spatial correlation coefficient of 0.7 and the relative standard deviation around the order of 0.3. Compared with MODIS LAI, GLOBALBNU LAI is the most similar, followed by GLASS LAI and GLOBMAP. Large differences mainly occur in southern regions of China. LAI difference analysis indicates that evergreen needleleaf forest (ENF), woody savannas (SAV) biome types and temperate dry hot summer, temperate warm summer dry winter and temperate hot summer no dry season climate types correspond to high standard deviation, while ENF and grassland (GRA) biome types and temperate warm summer dry winter and cold dry winter warm summer climate types are responsible for the large relative standard deviation of the four products. Our results indicate that although the three newly developed products have improved the accuracy of LAI estimates, much work remains to improve the LAI products especially in ENF, SAV, and GRA regions and temperate climate zones. Findings from our study can provide guidance to communities regarding the performance of different LAI products over mainland China.

Highlights

Leaf area index (LAI), defined as one-half of the total green leaf area per unit of ground horizontal surface area [1], is a key biophysical parameter in land surface processes and Earth system models [2,3]
Considering the On Line Interactive Validation Exercise (OLIVE) sites are located in almost homogeneous land and the six China field measurements have been upscaled from high-resolution LAI images [30], we extracted the pixel value to directly match the field measurements
For direct validation over China, the lowest uncertainty was achieved by Global Land Surface Satellite (GLASS) LAI (R2 = 0.94, RMSE = 0.61), while the highest uncertainty was obtained by Moderate-resolution Imaging Spectrometer (MODIS) LAI (R2 = 0.03, RMSE = 2.12)

Summary

Introduction

Leaf area index (LAI), defined as one-half of the total green leaf area per unit of ground horizontal surface area [1], is a key biophysical parameter in land surface processes and Earth system models [2,3]. Global LAI products have been derived from satellites, which have the advantage of large spatial coverage and serve as inputs for many numerical models. LAI is used in the European Centre for Medium-Range Weather Forecasts land surface model and has obvious impacts on simulation of carbon and water fluxes [4]. LAI is the input of one-dimensional hydrology (1 dH) model for radiation flux estimation, for estimation of transmissivity of shortwave radiation for canopy [6]. In order to effectively use LAI derived from remote sensing in various disciplines, it is critical to understand the characteristics and uncertainties of these products [9], because the quality, accuracy, and spatial–temporal coverage of these products still requires significant improvements [10]

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Conclusion