Data assimilation methods for land surface variable estimation

Abstract

Estimating land surface variables from remote sensing data is an ill-posed problem. Integration of observations from multiple satellite sensors with different spectral, spatial, temporal and angular signatures is now an important research frontier. Data assimilation (DA), integrating not only remotely sensed data products, but also other measurements and land dynamic models, is an advanced set of techniques for innovative parameter estimation. After a brief introduction, we describe the basic principles of DA, and then provide in-depth discussions of some relevant issues while using DA. The latest applications of DA for estimation of soil moisture, energy balance, carbon cycle and agricultural productivity are summarized. © Springer Science + Business Media B.V., 2008.