Retrieval Of Soil Moisture Profile Research Articles

On the use of dual-polarized multi-angular observations of P-band brightness temperature for soil moisture profile retrieval in thawed mineral soil

ABSTRACT This article investigated the possibility of remotely sensing the soil moisture profile in thawed soil from multi-angular dual-polarized brightness temperature (TB) observations at P-band frequencies of 750 MHz and 409 MHz using a modified Burke model. Moreover, it was found that an excellent agreement (coefficient of determination R2 = 0.999 and root-mean-square error (RMSE) no more than RMSE = 0.6 K) could be achieved between the Njoku coherent brightness temperature model and the modified Burke model by introducing a reflectivity from the air-soil interface that takes into account the phases of the multiple re-reflected waves in the underlying layers. Based on the modified Burke model, the depths from which apparent moisture and temperature could be retrieved in a dielectrically-inhomogeneous, non-isothermal soil were investigated, being approximately ten times less than the depth for which apparent soil temperature could be retrieved. In general, the thickness of the emitting layer depends on the TB look angle and polarization, along with the moisture and temperature profiles of the soil. It was also shown that due to the effect of the Brewster angle, the H-polarization of TB was twice as sensitive (4 K/1%) to changes in volumetric soil moisture than V-polarization (1.9 K/1%). Based on multi-angular (10°-50°) observations of TB at H- and V-polarizations, a method of moisture profile retrieval in the top 5–15 cm soil (depending on surface moisture) was proposed using an exponential fitting function, the parameters of which are found in the course of solving the inverse problem. A decrease in the sensing frequency from 750 MHz to 409 MHz makes it possible to increase the accuracy of soil moisture profiles retrieval by a factor of two, being from RMSE = 1.6% (R2 = 0.946) to RMSE = 0.85% (R2 = 0.982) in the top 15 cm layer of soil. The conducted investigation shows the promise of using P-band observations of TB for soil moisture profile retrieval.

Sequential data-worth analysis coupled with ensemble Kalman filter for soil water flow: A real-world case study

Given the high cost of data acquisition in soil water problems, it is becoming increasingly essential to collect the measurements as cost-efficient as possible. By introducing the data-worth analysis framework coupled with Ensemble Kalman Filter (EnKF), this real-world case study attempts to assess the worth of potential soil moisture observations before data collection. A field experiment was implemented to demonstrate the feasibility of quantifying the effect of future data on uncertainty reduction under real circumstances in a sequential way. The data worth of future observations is defined regarding soil hydraulic parameter estimation or soil moisture profile retrieval. Four information measures, including the trace (Tr), Shannon entropy difference (SD), relative entropy (RE) and degrees of freedom for signal (DFS), are introduced to quantify the information content. The sequential data worth analysis framework is examined by a number of cases, including under different irrigation intensities, with different prior data (existing observations that have already been collected), and with data of various depths and different measurement errors. We demonstrated the ability, and the challenge as well, of quantifying the data worth sequentially. Our results showed that data worth assessment regarding soil moisture profile retrieval is more difficult than that regarding parameter identification. Variance-type and covariance-type metrics have relatively loose accuracy requirement on potential observations (future possible observations to be collected), while mean-covariance-type metrics require higher accuracy. The vertical covariance of soil moisture is susceptible to the effect of atmospheric boundary condition, which eventually imposes a challenge on the quantification of data worth with covariance involved indices. The match between the expected and reference data worth can be improved by assimilating more prior data. However, more prior data cannot compensate for the damage from possible model structural error due to the changed scenarios between the prior stage and the posterior or preposterior stage. Shallow soil moisture data generally has larger data worth than deep observations in our study, but evaluating data worth with shallow data is subject to considerable uncertainty if covariance-type or mean-covariance-type index is employed. Smaller measurement error does not always lead to improved data worth estimation.

Assessing the Performance of the Ensemble Kalman Filter for Soil Moisture Profile Retrieval

The ensemble Kalman filter is an easy to use,flexible,and efficient data assimilation algorithm widely used in Land Surface Data Assimilation System.It bases on the normality approximation of model error and observational error as well as the linearity assumption of the model.However,the soil moisture equation is highly nonlinear and also soil moisture can be highly skewed toward the wet or dry ends.To evaluate the effects of these approximations and the performance of the ensemble Kalman filter (EnKF) in estimating soil moisture profile based on the near-surface soil moisture measurements,the results from the EnKF are compared with those obtained from a Sequential Importance Resampling (SIR) particle filter that is one of nonlinear filters.The comparative results show:The EnKF can quickly and accurately obtain the exact soil moisture profile regardless of a small ensemble size or a large ensemble size;however,the SIR needs very large ensemble members.The near-surface soil moisture marginal forecast probability densities,the skewness and kurtosis obtained from the EnKF are completely different from those from the SIR filter;the densities from the EnKF is only one peak mode during the total assimilation time window while those from the SIR experience processes from one peak mode to two peak modes and again to one peak mode.

Three‐dimensional soil moisture profile retrieval by assimilation of near‐surface measurements: Simplified Kalman filter covariance forecasting and field application

The Kalman filter data assimilation technique is applied to a distributed three‐dimensional soil moisture model for retrieval of the soil moisture profile in a 6 ha catchment using near‐surface soil moisture measurements. A simplified Kalman filter covariance forecasting methodology is developed based on forecasting of the state correlations and imposed state variances. This covariance forecasting technique, termed the modified Kalman filter, was then used in a 1 month three‐dimensional field application. Two updating scenarios were tested: (1) updating every 2 to 3 days and (2) a single update. The data used were from the Nerrigundah field site, near Newcastle, Australia. This study demonstrates the feasibility of data assimilation in a quasi three‐dimensional distributed soil moisture model, provided simplified covariance forecasting techniques are used. It also identifies that (1) the soil moisture profile cannot be retrieved from near‐surface soil moisture measurements when the near‐surface and deep soil layers become decoupled, such as during extreme drying events; (2) if simulation of the soil moisture profile is already good, the assimilation can result in a slight degradation, but if the simulation is poor, assimilation can yield a significant improvement; (3) soil moisture profile retrieval results are independent of initial conditions; and (4) the required update frequency is a function of the errors in model physics and forcing data.

Estimation of root zone soil moisture and surface fluxes partitioning using near surface soil moisture measurements

We examine the feasibility of retrieving root zone soil moisture and partitioning of surface fluxes through a model inversion technique using surface measurements. Using a four-layer land surface model and observed datasets from Field Experiment (FIFE) 1987, we show that the sensitivities of surface soil moisture to deeper layer soil moisture are different to those of surface fluxes. Consequently, if one chooses the initial soil moisture profile that optimizes surface soil moisture, in a root mean square error sense, it may not lead to optimal estimation of surface fluxes. We also show that the accuracy of soil moisture profile retrieval from surface measurements depends strongly on the initial surface soil moisture conditions. For wetter surface conditions, an initialization based on remotely sensed surface soil moisture appears to be adequate for the retrieval of the soil moisture profile. For drier surface conditions, however, a decoupling of surface and deeper layer soil moisture might be triggered and an initialization based on surface soil moisture would lead to larger error.

One-Dimensional Soil Moisture Profile Retrieval by Assimilation of Near-Surface Measurements: A Simplified Soil Moisture Model and Field Application

The Kalman filter assimilation technique is applied to a simplified soil moisture model for retrieval of the soil moisture profile from near-surface soil moisture measurements. First, the simplified soil moisture model is developed, based on an approximation to the Buckingham‐Darcy equation. This model is then used in a 12month one-dimensional field application, with updating at 1-, 5-, 10-, and 20-day intervals. The data used are for the Nerrigundah field site, New South Wales, Australia. This study has identified (i) the importance of knowing the depth over which the near-surface soil moisture measurements are representative (i.e., observation depth), (ii) soil porosity and residual soil moisture content as the most important soil parameters for correct retrieval of the soil moisture profile, (iii) the importance of a soil moisture model that represents the dominant soil physical processes correctly, and (iv) an appropriate forecasting model as far more important than the temporal resolution of near-surface soil moisture measurements. Although the soil moisture model developed here is a good approximation to the Richards equation, it requires a root water uptake term or calibration to an extreme drying event to model extremely dry periods at the field site correctly.

One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: a comparison of retrieval algorithms

This paper investigates the ability to retrieve the true soil moisture and temperature profiles by assimilating near-surface soil moisture and surface temperature data into a soil moisture and heat transfer model. The direct insertion and Kalman filter assimilation schemes have been used most frequently in assimilation studies, but no comparisons of these schemes have been made. This study investigates which of these approaches is able to retrieve the soil moisture and temperature profiles the fastest, over what depth soil moisture observations are required, and the effect of update interval on profile retrieval. These questions are addressed by a desktop study using synthetic data. The study shows that the Kalman filter assimilation scheme is superior to the direct insertion assimilation scheme, with retrieval of the soil moisture profile being achieved in 12 h as compared to 8 days or more, depending on observation depth, for hourly observations. It was also found that profile retrieval could not be realised for direct insertion of the surface node alone, and that observation depth does not have a significant effect on profile retrieval time for the Kalman filter. The observation interval was found to be unimportant for profile retrieval with the Kalman filter when the forcing data is accurate, whilst for direct insertion the continuous Dirichlet boundary condition was required for an increasingly longer period of time. It was also found that the Kalman filter assimilation scheme was less susceptible to unstable updates if volumetric soil moisture was modelled as the dependent state rather than matric head, because the volumetric soil moisture state is more linear in the forecasting model.