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Satellite-derived vegetation indices and their resulting surface parameters, such as the leaf area index (LAI), are inevitably affected by the atmosphere. Errors in the atmospheric corrections can often be easily identified in a seasonal trajectory of a surface parameter because the atmospheric effect generally causes erratic reductions in vegetation indices. A locally adjusted cubic-spline capping (LACC) method is developed here to screen affected data points in a pixel and to replace them through temporal interpolation. In LACC, a variable local smoothing parameter, which controls the local smoothness of the fitted curve, is automatically determined according to the local curvature of the original seasonal variation pattern. An iteration procedure is designed to produce a seasonal capping curve by progressively replacing abnormally low values with fitted values. This method has two advantages over existing methods based on harmonics, namely: 1) cubic splines are flexible for simulating a wide range of seasonal variation patterns and 2) a variable local smoothing parameter allows the fitted capping curve to mimic either rapid or slow variation patterns in various seasons. The capping curve is also mathematically differentiable for further applications. The effectiveness of this method is demonstrated through case studies for several cover types in China and processing a series of Moderate Resolution Imaging Spectroradiometer LAI images of China in 2001. Index Terms-Cubic spline, leaf area index (LAI), residual cloud screening, seasonal trajectory. and an AdjunctProfessor at York University, Toronto. His recent research interests are in the remote sensing of biophysical parameters, plant canopy radiation modeling, terrestrial water and carbon cycle modeling, and atmospheric inverse modeling for global and regional carbon budget estimation. He has published over 120 papers in refereed journals. Dr. Chen served as an Associate Editor of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING from 1996 to 2002.

Abstract-Leaf area index (LAI) is one of the most importantEarth surface parameters in modeling ecosystems and their interaction with climate. Based on a geometrical optical model (FourScale) and LAI algorithms previously derived for Canada-wide applications, this paper presents a new algorithm for the global retrieval of LAI where the bidirectional reflectance distribution function (BRDF) is considered explicitly in the algorithm and hence removing the need of doing BRDF corrections and normalizations to the input images. The core problem of integrating BRDF into the LAI algorithm is that nonlinear BRDF kernels that are used to relate spectral reflectances to LAI are also LAI dependent, and no analytical solution is found to derive directly LAI from reflectance data. This problem is solved through developing a simple iteration procedure. The relationships between LAI and reflectances of various spectral bands (red, near infrared, and shortwave infrared) are simulated with Four-Scale with a multiple scattering scheme. Based on the model simulations, the key coefficients in the BRDF kernels are fitted with Chebyshev polynomials of the second kind. Spectral indices-the simple ratio and the reduced simple ratio-are used to effectively combine the spectral bands for LAI retrieval. Example regional and global LAI maps are produced. Accuracy assessment on a Canada-wide LAI map is made in comparison with a previously validated 1998 LAI map and ground measurements made in seven Landsat scenes.Index Terms-Bidirectional reflectance distribution function (BRDF), Chebyshev polynomials, geometrical optical (GO) model, leaf area index (LAI), lookup table (LUT).