Improving LAI Mapping by Integrating MODIS and CYCLOPES LAI Products Using Optimal Interpolation

Abstract

Leaf area index (LAI) is an important land surface biophysical variable used to characterize vegetation amount and activity. Current satellite LAI products, however, do not satisfy the requirements of the modeling community due to their large uncertainties and frequent missing values. There is an urgent need for advanced methods to integrate multiple LAI products to improve the product's accuracy and integrity for various applications. To meet this need, this study proposes a method based on Optimal Interpolation (OI) to integrate MODIS true LAI and CYCLOPES effective LAI retrievals. Multiple years' LAI means and variances (LAI climatology) are pre-calculated and used as the baseline for data integration. The locally adjusted cubic-spline capping algorithm is used to smooth the climatology data. An LAI normalization scheme, based on a linear measurement error model, is developed to account for the systematic difference between the two products and to generate the LAI predictions. This integration process removes the unrealistically large temporal variations of the original LAI products. The data gaps are filled with information from adjacent pixels and the prior knowledge acquired from multiyear climatology. A spatially and temporally continuous true LAI data set is generated. High resolution reference maps of true LAI are collected to validate the two true LAI data sets: MODIS LAI product and the integrated LAI values. The validation results suggest that the integrated results agree better with the LAI reference maps than the MODIS LAI product, showing higher R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , smaller bias and root mean squared error.