A modified version of the SMAR model for estimating root-zone soil moisture from time-series of surface soil moisture

Root-zone soil moisture at the regional scale has always been a missing element of the hydrological cycle. Knowing its value could be a great help in estimating evapotranspiration, erosion, runoff, permeability, irrigation needs, etc. The recently developed Soil Moisture Analytical Relationship (SMAR) can relate the surface soil moisture to the moisture content of deeper layers using a physically-based formulation. Previous studies have proved the effectiveness of SMAR in estimating root-zone soil moisture, yet there is still room for improvement in its application. For example, the soil water loss function (i.e. deep percolation and evapotranspiration), assumed to be a linear function in the SMAR model, may produce approximations in the estimation of water losses in the second soil layer. This problem becomes more critical in soils with finer textures. In this regard, the soil moisture profile data from two research sites (AMMA and SCAN) were investigated. The results showed that after a rainfall event, soil water losses decrease following a power pattern until they reach a minimum steady state. This knowledge was used to modify SMAR. In particular, SMAR was modified (MSMAR) by introducing a non-linear soil water loss function that allowed for improved estimates of root zone soil moisture.Keywords: surface soil moisture, root-zone soil moisture, SMAR, soil water loss function, MSMA

Scatterometer-Derived Soil Moisture Calibrated for Soil Texture With a One-Dimensional Water-Flow Model

Current global satellite scatterometer-based soil moisture retrieval algorithms do not take soil characteristics into account. In this paper, the characteristic time length of the soil water index has been calibrated for ten sampling frequencies and for different soil conductivity associated with 12 soil texture classes. The calibration experiment was independently performed from satellite observations. The reference soil moisture data set was created with a I-D water-flow model and by making use of precipitation measurements. The soil water index was simulated by applying the algorithm to the modeled soil moisture of the upper few centimeters. The resulting optimized characteristic time lengths T increase with longer sampling periods. For instance, a T of 7 days was found for sandy soil when a sampling period of I day was applied, whereas an optimized T-value of 18 days was found for a sampling period of 10 days. A maximum rmse improvement of 0.5% vol. can be expected when using the calibrated T-values instead of T = 20. The soil water index and the differentiated T-values were applied to European Remote Sensing (ERS) satellite scatterometer data and were validated against in situ soil moisture measurements. The results obtained using calibrated T-values and T = 20 did not differ (r = 0.39, rmse = 5.4% vol.) and can be explained by the averaged sampling period of 4-5 days. The soil water index obtained with current operational microwave sensors [Advanced Wind Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer-Earth Observation System] and future sensors (Soil Moisture and Ocean Salinity and Soil Moisture Active Passive) should benefit from soil texture differentiation, as they can record on a daily basis either individually or synergistically using several sensors. The proposed differentiated characteristic time length enables the continuation of the soil water index of sensors with varying sampling periods (e.g., ERS-ASCAT)

Scatterometer-Derived Soil Moisture Calibrated for Soil Texture With a One-Dimensional Water-Flow Model

Current global satellite scatterometer-based soil moisture retrieval algorithms do not take soil characteristics into account. In this paper, the characteristic time length of the soil water index has been calibrated for ten sampling frequencies and for different soil conductivity associated with 12 soil texture classes. The calibration experiment was independently performed from satellite observations. The reference soil moisture data set was created with a I-D water-flow model and by making use of precipitation measurements. The soil water index was simulated by applying the algorithm to the modeled soil moisture of the upper few centimeters. The resulting optimized characteristic time lengths T increase with longer sampling periods. For instance, a T of 7 days was found for sandy soil when a sampling period of I day was applied, whereas an optimized T-value of 18 days was found for a sampling period of 10 days. A maximum rmse improvement of 0.5% vol. can be expected when using the calibrated T-values instead of T = 20. The soil water index and the differentiated T-values were applied to European Remote Sensing (ERS) satellite scatterometer data and were validated against in situ soil moisture measurements. The results obtained using calibrated T-values and T = 20 did not differ (r = 0.39, rmse = 5.4% vol.) and can be explained by the averaged sampling period of 4-5 days. The soil water index obtained with current operational microwave sensors [Advanced Wind Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer-Earth Observation System] and future sensors (Soil Moisture and Ocean Salinity and Soil Moisture Active Passive) should benefit from soil texture differentiation, as they can record on a daily basis either individually or synergistically using several sensors. The proposed differentiated characteristic time length enables the continuation of the soil water index of sensors with varying sampling periods (e.g., ERS-ASCAT)

Field-scale root-zone soil moisture : spatio-temporal variability and mean estimation

This thesis is focused around improving soil moisture estimates of spatial variability and mean at the field scale, which are useful for many different applications. The objectives were: (1) examine soil moisture spatial patterns and variability within field scale, and (2) compare field-scale soil moisture determination methods. An observational study was conducted, in which soil moisture was monitored over a 500 m by 500 m area during two and a half growing seasons at a prairie pasture in central Saskatchewan. Analysis of the spatial patterns of root-zone soil moisture revealed two distinct spatial patterns representing wet (spring) and dry (fall) periods. The relationship between spatial variability and mean soil moisture was found to follow an unusual concave trend, where variability was smallest at mid-range moisture contents. These spatial variability characteristics are a result of differences in participation level. Some locations were non-participating having only small moisture changes over the growing season, while others were dynamic having large changes. At the pasture site, these participation differences are a result of high soil heterogeneity, which may be characteristic of Solonetzic soils. In the second part of this thesis, methods to determine mean field-scale root-zone soil moisture were evaluated. The cosmic-ray neutron probe has the most potential for providing field-scale measurements. However, these measurements are only representative of moisture in the top 20 cm of soil, and need to be scaled up in order to represent the entire root-zone (0–110 cm). The three scaling methods applied to obtain lower root-zone soil moisture were: (1) a single time stable location, (2) representative landscape unit, where a single monitoring profile for each vegetation type was used, and (3) modeling by exponential filter. The representative landscape unit approach estimated soil moisture changes well, but not volumetric water content. The time stability method performed the best, followed by the exponential filter. However, the exponential filter has more potential, as the time stability method is difficult to apply to other field sites; particularly those without existing soil moisture instrumentation, due to its calibration requirements. The findings of this thesis make a contribution to the large body of existing literature on soil moisture variability and scaling. Suggestions for future research are provided