Improving Satellite Leaf Area Index Estimation Based On Various Integration Methods

Title: IMPROVING SATELLITE LEAF AREA INDEX ESTIMATION BASED ON VARIOUS INTEGRATION METHODS Dongdong Wang, Doctor of Philosophy, 2009 Directed by: Dr. Shunlin Liang, Professor Department of Geography Leaf Area Index (LAI) is an important land surface biophysical variable that is used to characterize vegetation amount and activity. Cur rent satellite LAI products, however, do not satisfy the requirements of the mod eling community due to their large uncertainties and frequent missing values. Ea ch LAI product is currently generated from only one satellite sensor data. Ther e is an urgent need for advanced methods to integrate multiple LAI products to impro ve the product’s accuracy and integrality for various applications. To meet this need, this study proposes four methods, including the Optimal Interpolation (OI), Bayesian Maximum Entropy (BME), Multi-Resolution Tree (MRT) and Empirical Or thogonal Function (EOF), to integrate multiple LAI products. Three LAI products have been considered in this study: Moderate Resolution Imaging Spectroradiomete r (MODIS), Multi-angle Imaging SpectroRadiometer (MISR) and Carbon cYcle a nd Change in Land Observational Products from an Ensemble of Satellit es (CYCLOPES) LAI. As the basis of data integration, this dissertation f rst validates and intercompares MODIS and CYCLOPES LAI products and also evaluates th ir geometric accuracies. The CYCLOPES LAI product has smoother temporal prof iles and fewer spatial variations, but tends to produce spurious large err ors in winter. The Locally Adjusted Cubic-spline Capping algorithm is revised to smooth multiple years' average and variance. Although OI, BME and MRT based methods have been us d in other fields, this is the first research to employ them in integrating multip le LAI products. This dissertation also presents a new integration method based on EOF to solve the problem of large data volume and inconsistent temporal resolution of dif erent datasets. High resolution LAI reference maps generated with ground measurements are used to validate these algorithms. Validation results show that all of these four methods can fill data gaps and reduce the errors of the existin g LAI products. The data gaps are filled with information from adjacent pixels and ba ckground. These algorithms remove the spurious large temporal and spatial vari ation of the original LAI products. The combination of multiple satellite products sign ificantly reduces bias. OI and BME can reduce the RMSE from 1.0 (MODIS) to 0.7 and re uce the bias from +0.3 (MODIS) and -0.2 (CYCLOPES) to -0.1. MRT can produc e similar results with OI but with significantly improved efficiency. EOF als o generates the results with the RMSE of 0.7 but zero bias. Limited ground measurement data hardly prove which methods outperform the others. OI and BME theoretically produce statistically opti mal results. BME relaxes OI's linear and Gaussian assumption and explicitly consi der data error, but bears a much higher computational burden. MRT has improved effic ien y but needs strict assumptions on the scale transfer function. EOF req ui s simpler model identification, while it is more "empirical" than "statistical". The original contributions of this study mainly inc lude: 1) a new application of several different integration methods to incorporat e multiple satellite LAI products to reduce uncertainties and improve integrality, 2) an enhancement of the Locally Adjusted Cubic-spline Capping by revising the end c ondition, 3) a novel comprehensive comparison of MODIS C5 LAI product wi h other satellite products, 4) the development of a new LAI normalization schem e by assuming the linear relationship between measurement error and LAI natu ral variance to account for the inconsistency between products, and finally, 5) the creation of a new data integration method based on EOF. IMPROVING SATELLITE LEAF AREA INDEX ESTIMATION BASED ON VARIOUS INTEGRATION METHODS