Abstract
Leaf area index (LAI) is an important parameter for monitoring the physical and biological processes of vegetation canopy. Due to the constraints of cloud contamination, snowfall, and instrument conditions, most of the current satellite remote sensing LAI products have lower resolution that cannot satisfy the needs of vegetation remote sensing application in areas of high heterogeneity. We proposed a new model to generate high resolution LAI, by combining linear pixel unmixing and the Flexible Spatiotemporal Data Fusion (FSDAF) method. This method derived the input data of FSDAF by downscaling the MODIS (Moderate Resolution Imaging Spectroradiometer) data with a linear spectral mixture model. Through the improved input parameters of the algorithm, the fusion of MODIS LAI and LAI at Landsat spatial resolution estimated by Support Vector Regression model was realized. The fusion accuracy of generated LAI data was validated based on Sentinel-2 LAI products. The results showed that strong correlation between predicted LAI and Sentinel-2 LAI in sample sites was observed with higher correlation coefficients and lower Root Mean Square Error. Compared to the simulation results of FSDAF, the modified FSDAF model showed higher accuracy and reflected more spatial details in the boundary areas of different land cover types.
Highlights
Leaf Area Index (LAI) is defined as half of the total area of vegetation leaves per unit area [1,2]
LAI data with fine spatial resolution can be obtained from the inversion of high resolution surface reflectance data (e.g., Landsat)
Based on the radiative transfer model, PROSAIL, Li et al took the vegetation index derived from Landsat data as an input parameter and used the look-up table (LUT) algorithm for LAI inversion [8]
Summary
Leaf Area Index (LAI) is defined as half of the total area of vegetation leaves per unit area [1,2]. Satellite remote sensing provides an important method to obtain large scale vegetation canopy LAI. With their diversity and practicability, the current LAI products, such as VEGETATION [3] and Moderate Resolution Imaging Spectroradiometer (MODIS) [4] are helpful to vegetation monitoring [5]. LAI data with fine spatial resolution can be obtained from the inversion of high resolution surface reflectance data (e.g., Landsat). Based on the radiative transfer model, PROSAIL, Li et al took the vegetation index derived from Landsat data as an input parameter and used the look-up table (LUT) algorithm for LAI inversion [8]. Due to cloud contamination, the Landsat 16-day revisit cycle may be extended, which can be a major obstacle for monitoring continuous changes in vegetation LAI [9]
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