Recent Advances in Profile Soil Moisture Retrieval Jeffrey P. Walker , Garry R. Willgoose and Jetse D. Kalma Department of Civil, Surveying and Environmental Engineering The University of Newcastle, Callaghan, 2308, AUSTRALIA Ph: 02 49 215331; Email:cejpw@cc.newcastle.edu.au Summary Remote sensing provides a capability to make frequent and spatially distributed measurements of surface soil moisture, whilst recent advances in affordable Time Domain Reflectometry probes allow continuous monitoring of profile soil moisture at specific points. We believe that reliable estimation of the spatial and temporal variation of profile soil moisture on a routine basis will require a combination of calibration and evaluation of an unsaturated soil moisture model using point measurements, and model updating using remote sensing observations to account for spatial inhomogeneities. This study investigates which of two commonly used assimilation techniques is most efficient for the retrieval of soil moisture and temperature profiles, over what depth soil moisture observations are required, and the effect of update interval on profile retrieval. These questions are addressed through a desktop study using synthetic data. Introduction Soil moisture in the root zone is a key parameter in numerous environmental studies, including meteorology, hydrology and agriculture. The significance of soil moisture is its role in the partitioning of energy at the ground surface into sensible and latent (evapotranspiration) heat exchange with the atmosphere, and the partitioning of precipitation into infiltration and runoff [1, 2]. Soil moisture can be estimated from: (i) point measurements; (ii) soil moisture models and (iii) remote sensing (see Figure 1). Traditional techniques for soil moisture estimation yield data on a point basis [3, 4], which does not always represent the spatial distribution [5]. The alternative has been to estimate the spatial distribution of soil moisture using a distributed hydrologic model [6, 7]. However, these estimates are generally poor, due to the fact that soil moisture exhibits large spatial and temporal variation [8], as a result of inhomogeneity in soil properties, vegetation and precipitation [4]. Remote sensing can be used to collect spatial data over large areas on a routine basis, providing a capability to make frequent and spatially comprehensive measurements of the near surface soil moisture. However, problems with this data include the current satellite repeat time (typically 25 days) and the depth over which soil moisture estimates are valid, consisting of the top few centimetres at most [2, 8, 9]. These upper few centimetres of the soil are the most exposed to the atmosphere, and their soil moisture varies rapidly in response to rainfall and evaporation [10]. Thus to be useful for hydrologic, climatic and agricultural studies, such observations of surface soil moisture must be related to the complete soil moisture profile in the unsaturated zone [11-13]. The problem of relating soil moisture content at the surface to that of the profile as a whole has been studied for the past two decades. Four approaches have been adopted: (i) regression, (ii) knowledge-based, (iii) inversion, and (iv) combinations of remotely sensed data with soil water balance models [14]. As remote sensing observations have a poor resolution in time, it is necessary to apply the water balance approach in order to obtain soil moisture estimates during the inter-observation period. Remote Sensing Satelite Surface Soil Moisture Soil Moisture Sensors Logger Soil Moisture Model [q , D ( ), ( ) θ = f θ θ ψ ψ s (z) Figure 1: Illustration of the soil moisture estimation problem.