Vegetation optical depth (VOD) and effective vegetation optical depth (EVOD) are key factors for estimating soil moisture and vegetation parameters. Microwave vegetation indices (MVIs, including A and B parameters) have been recently developed for short-vegetation covered surfaces. The MVIs parameter B (MVIs_B) is mainly related to vegetation conditions, which makes it provide a potential way of EVOD retrieval. A theoretical expression deriving EVOD was deduced using MVIs_B from WindSat data. Global patterns of EVOD were analyzed subsequently. It has been shown that EVOD retrieved from MVIs performed a consistent global pattern and seasonal variation with normalized difference vegetation index. Time-series data from the Central Tibetan Plateau Soil Moisture/Temperature Monitoring Network, which is grassland dominated, was selected for temporal analysis. It was found that the temporal EVOD from WindSat MVIs can capture the growth trend of vegetation. Comparisons between EVOD estimations from MVIs and a radiative transfer model were also performed over this network. It was found that EVOD from the two methods exhibited comparable values and similar trends. MVIs_B-derived EVOD can be obtained without any other auxiliary data and has great potential in land-surface parameter retrieval over short-vegetation covered areas.
Besides uncertainties introduced by atmospheric forcing and initial states, land surface simulation results are mainly
determined by model structure and related model parameters. Traditional data assimilation approaches, as they only
focus on mathematically updating the simulated states when observations become available, have little intrinsic
improvement in the model performance. Model parameter optimization will lead to reduced biases in simulation results
and then a better forecasting skill can be expected. Therefore, calibrating model parameters and updating states
simultaneously in the framework of sequential model-data fusion would be valuable for uncertainty quantification. A
dual state-parameter estimation land data assimilation system is implemented in this paper by coupling the Variable
Infiltration Capacity(VIC) land surface model, the Tau-Omega Radiative Transfer Model(RTM) and Sampling
Importance Resampling Particle Filter(SIR-PF) algorithm. Passive microwave brightness temperature observations from
Passive/Active L and S band (PALS) sensor in SMEX02 are assimilated and the results demonstrate that both soil
moisture states and model lumped parameters can be estimated simultaneously.
In this study, a bare surface soil moisture retrieval algorithm independent of the soil temperature is developed for use with advanced microwave scanning radiometer-Earth observing system measurements. The quasiemissivity is parameterized as the ratio of the brightness temperature in the other channels to that in the 36.5 GHz vertical (V-) polarization in order to correct the soil temperature effects in the estimation of soil moisture. To analyze the surface roughness effect on quasiemissivity, a simulation database covering a large range of soil properties is generated. The advanced integral equation model (AIEM) is used to simulate the soil emissivities at different frequencies. The parameters describing the soil roughness effect on quasiemissivity at two polarizations are found to be expressed by a linear function. Using this relationship and the quasiemissivity at two polarizations, the surface roughness effect is minimized in the estimation of the soil moisture. Thus, soil moisture can be estimated using the brightness temperatures at a given frequency in the V- and horizontal (H-) polarizations and at 36.5 GHz of V-polarization. Compared with the data simulated using AIEM, the algorithm has a root-mean-square error (RMSE) of approximately 0.009 cm3/cm3 for the volumetric soil moisture. For validation, a controlled field experiment is conducted using a truck-mounted multifrequency microwave radiometer. Moreover, the experimental data acquired from the Institute National de Recherches Agronomiques (INRA) field experiment are also used to evaluate the accuracy of the algorithm. The RMSE is approximately 0.04 cm3/cm3 for these two experimental data. In order to analyze the performance or capability of this algorithm using satellite data, the soil moisture derived from WindSat data using this algorithm is compared to the Murrumbidgee soil moisture monitoring network dataset. These results indicate that the newly developed inversion technique has an acceptable accuracy and is expected to be useful for application for bare surface soil moisture estimation.
The Chinese HY-2, a satellite designed for ocean dynamic environment monitoring, was launched on August 16, 2011. The onboard scanning microwave radiometer (RM) is primarily designed for sea surface temperature and wind speed mapping. However, our objective of this investigation is to exploit the large amount of land observations of RM and to extend the mission scope to the retrieval of surface soil moisture, which is also an essential boundary condition for coupling with atmospheric dynamics. The single-channel algorithm (SCA) was implemented using only the RM observed brightness temperature to estimate the surface soil moisture. Ancillary data of a normalized difference vegetation index were processed and used as inputs for the SCA to calculate the vegetation water content, which is a required parameter for estimating the vegetation optical depth. The retrieved soil moisture results agree with the global climate pattern of wet and dry regions. Initial assessments were performed using soil moisture measurements by in situ underground sensors over two selected networks: REMEDHUS in Spain and CTP-SMTMN network over the Tibetan Plateau. Results showed a good performance of soil moisture estimation for these land surface conditions for the year 2012, with the lowest root mean square error of 0.047 m3/m3. This product will contribute to continuous soil moisture information on a global scale for global change studies.
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