Estimating Soil Moisture and Energy Fluxes Using Assimilation of Remotely Sensed Land Surface State Variables
Chintalapati, Srinivas
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https://hdl.handle.net/2142/83289
Description
Title
Estimating Soil Moisture and Energy Fluxes Using Assimilation of Remotely Sensed Land Surface State Variables
Author(s)
Chintalapati, Srinivas
Issue Date
2006
Doctoral Committee Chair(s)
Kumar, Praveen
Department of Study
Civil Engineering
Discipline
Civil Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Agriculture, Soil Science
Language
eng
Abstract
Soil moisture plays a critical role in the land-atmosphere interactions. Given the approximate model physics representation in the land surface models predicting these fluxes, better predictions can be obtained by assimilating hydrologically relevant remotely sensed data into the predictive models. We consider two approaches. In the first approach, we update the soil moisture profile and thus the associated energy fluxes, using remotely sensed near-surface soil moisture. We propose a scheme based on unscented Kalman filter (UKF) for assimilation, which achieves at least a second order accuracy for any nonlinearity and at the same computational cost as the extended Kalman filter (EKF). UKF predictions show signatures in deeper layers when compared to EKF, while also predicting more spatial variability of soil moisture and energy fluxes. Another major issue to address while using remotely sensed near surface soil moisture data for assimilation, is related to the scale discrepancy between the model and observations. We use a multiscale Kalman filter to estimate soil moisture at a range of spatial scales (1 km to 32 km) using remotely sensed data at 1 km scale. These estimates are used as observations for assimilation into a land surface model using the UKF algorithm, to provide predictions of soil moisture profile and energy fluxes at several scales. Assessing the spatial statistics of moisture and energy fluxes across the scales, we find that the coefficient of variation of soil moisture suggests a higher spatial variability for finer scale and reduces as scale increases. In the second approach, we have developed a novel method to estimate the soil moisture using the energy fluxes estimated from the land surface state variables, obtained from MODIS (MODerate-resolution Imaging Spectrometer) and atmospheric boundary layer properties. The energy fluxes are assimilated into a land surface model using the UKF scheme, to update the soil moisture profile and the associated fluxes. Results show that the predictions of latent heat flux and root zone soil moisture from the assimilation simulations compare well with the in situ measurements.
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