Haiyun Bi

An Improved Particle Filter Algorithm Based on Ensemble Kalman Filter and Markov Chain Monte Carlo Method

Data assimilation (DA) has developed into an important method in Earth science research due to its capability of combining model dynamics and observations. Among various DA methods, the particle filter (PF) is free from the constraints of linear models and Gaussian error distributions. Thus, it is now receiving increasing attention in DA. However, the particle degeneracy still remains a major problem in practical application of PF. In this paper, an improved PF is proposed based on ensemble Kalman filter (EnKF) and the Markov Chain Monte Carlo (MCMC) method. It uses an EnKF analysis to define the proposal density of PF instead of the prior density, thus reducing the risk of particle degeneracy. Furthermore, when particle degeneracy happens, resampling is performed follow by an MCMC move step to increase the diversity of particles, thus reducing the potential of particle impoverishment and improving the accuracy of the filter. Finally, the improved PF is tested by assimilating brightness temperatures from the Advanced Microwave Scanning Radiometer (AMSR-E) into the variance infiltration capacity (VIC) model to estimate soil moisture in the NaQu network region at the Tibetan Plateau. The experiment results show that the improved PF can provide more accurate assimilation results and also need fewer particles to get reliable estimations than the EnKF and the standard PF, thus demonstrating the effectiveness and practicality of the improved PF.

Method for Soil Moisture and Surface Temperature Estimation in the Tibetan Plateau Using Spaceborne Radiometer Observations

A method for soil moisture and surface temperature estimation in the Tibetan Plateau (TP) using spaceborne radiometer observations was presented. Based on the physical basis that the 36.5-GHz (Ka-band) vertical brightness temperature is highly sensitive to the topsoil temperature, a new surface temperature model was developed using all ground measurements available from three networks named CAMP/Tibet, Maqu, and Naqu, established in the TP, which can significantly improve the accuracy of surface temperature derived from the land parameter retrieval model (LPRM). Then, the new surface temperature model, which was calibrated with in situ data, was integrated into the soil moisture retrieval algorithm proposed in this letter using Advanced Microwave Scanning Radiometer (AMSR-E) observations. The algorithm combines the vegetation optical depth and roughness into an integrated factor to avoid making unreliable assumptions and using auxiliary data to get these two parameters. Finally, the algorithm was validated by ground measurements from the dense Naqu network and was compared with NASA AMSR-E and Soil Moisture and Ocean Salinity (SMOS) official algorithms. The results show that the proposed algorithm can provide much more accurate soil moisture retrievals than the other two satellite algorithms in the Naqu network region. The algorithm can be applied to the areas with spare vegetation but may not be very suitable for densely vegetated surfaces.

A Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product Over United States and Europe Using Ground-Based Measurements

The Soil Moisture Active Passive (SMAP) mission, which is the newest L-band satellite that is specifically designed for soil moisture monitoring, was launched on January 31, 2015. A beta quality version of the SMAP radiometer soil moisture product was recently released to the public. It is crucial to evaluate the reliability of this product before it can be routinely used in hydrometeorological studies at a global scale. In this paper, we carried out a preliminary evaluation of the SMAP radiometer soil moisture product against in situ measurements collected from three networks that cover different climatic and land surface conditions, including two dense networks established in the U.S. and Finland, and one sparse network set up in Romania. Results show that the SMAP soil moisture product is in good agreement with the in situ measurements, although it exhibits dry or wet bias at different network regions. It well reproduces the temporal evolution and anomalies of the observed soil moisture with a favorable correlation greater than 0.7. The overall ubRMSE (unbiased root mean square error) of SMAP product is 0.036 m3 · m-3, well within the mission requirement of 0.04 m3 · m-3. The error sources of SMAP soil moisture product may be associated with the parameterization of vegetation and surface roughness but still needs to be tested and confirmed in more extent. Considering that the algorithms are still under refinement, it can be reasonably expected that hydrometeorological applications will benefit from the SMAP radiometer soil moisture product.

A simplified physically-based algorithm for surface soil moisture retrieval using AMSR-E data

A simplified physically-based algorithm for surface soil moisture inversion from satellite microwave radiometer data is presented. The algorithm is based on a radiative transfer model, and the assumption that the optical depth of the vegetation is polarization independent. The algorithm combines the effects of vegetation and roughness into a single parameter. Then the microwave polarization difference index (MPDI) is used to eliminate the effects of surface temperature, and to obtain soil moisture, through a nonlinear iterative procedure. To verify the present algorithm, the 6.9 GHz dual-polarized brightness temperature data from the Advanced Microwave Scanning Radiometer (AMSR-E) were used. Then the soil moisture values retrieved by the present algorithm were validated by in-situ data from 20 sites in the Tibetan Plateau, and compared with both the NASA AMSR-E soil moisture products, and Soil Moisture and Ocean Salinity (SMOS) soil moisture products. The results show that the soil moisture retrieved by the present algorithm agrees better with ground measurements than the two satellite products. The advantage of the algorithm is that it doesn’t require field observations of soil moisture, surface roughness, or canopy biophysical data as calibration parameters, and needs only single-frequency brightness temperature observations during the whole retrieval process.

Using an unmanned aerial vehicle for topography mapping of the fault zone based on structure from motion photogrammetry

ABSTRACT High-precision and high-resolution topography is the basis of the quantitative study of active faults. Light detection and ranging (lidar) is currently the most popular method for obtaining such data, but its relatively high cost greatly limits its use in many geoscience applications. Recently, with the rapid development of computer vision science and the growing application of small unmanned aerial vehicles (UAVs), Structure from Motion (SfM) photogrammetry shows great potential for providing topographic information of comparable resolution and precision to lidar surveys, but at significantly lower cost. In this study, we examined the applicability of SfM photogrammetry in modelling the topography of the fault zone using images acquired with a low-cost digital camera mounted on a UAV over the Haiyuan fault. The resolution and accuracy of the SfM-derived topography were evaluated in detail using existing airborne lidar data as a benchmark. The results show that the density of the point cloud generated by SfM photogrammetry is nearly 70 times higher than that from the airborne lidar. Furthermore, considering the errors in the lidar data itself, the precision of the SfM point cloud is comparable to that of the lidar point cloud. Overall, our results demonstrate that the UAV-based SfM photogrammetry method can provide an inexpensive, effective, and flexible alternative to airborne lidar for the topography mapping of the fault zone.

A Comprehensive Analysis of Rough Soil Surface Scattering and Emission Predicted by AIEM With Comparison to Numerical Simulations and Experimental Measurements

Theoretical modeling plays a significant role as forward and inverse problem in active and passive microwave remote sensing. Understanding the validity and limitations of the models is essential for model refinements and, perhaps more importantly, model applications. Motivated by these, this paper presents a comprehensive analysis of the scattering, both backscattering and bistatic scattering, and emission of rough soil surface predicted by the advanced integral equation model (AIEM), a well-established theoretical model. Numerically simulated data, covering a wide range of surface parameters, and in situ measurement data set of well-characterized bare soil surfaces were used to evaluate the performance of AIEM in predicting the scattering coefficient and microwave emissivity over a wide range of geometric parameters and ground surface conditions. The results show that the AIEM predictions are generally in good consistency with both numerical simulations and experiment measurements in terms of angular, frequency, and polarization dependences, except for some deviations in a few cases (e.g., at large incident angles and dry soil conditions). Extensive comparison confirms the effectiveness and practicability of AIEM for both scattering and emission of rough soil surface. Possible explanations for the discrepancy between the model prediction and data are given, together with suggestions for model usage and refinements.

Radar Response of Off-Specular Bistatic Scattering to Soil Moisture and Surface Roughness at L-Band

This letter investigates the bistatic radar response of soil moisture and surface roughness of bare soil surfaces at L-band using the advanced integral equation model (AIEM). It focuses on the use of bistatic geometries away from the specular region. To better explore the potential of bistatic scattering for soil moisture sensing, both polarized and angular scattering coefficients, and their combinations, are evaluated using a defined sensitivity index. The results show that sensitivity is enhanced in a bistatic mode compared with the monostatic case. Using a combination of dual polarized and angular data suppresses an undesired impact of the surface correlation function. Moreover, the forward region is preferred to soil moisture sensing regardless of the surface correlation function. Among the combinations investigated, the dual angular observation reduces the influence of surface roughness, preserves a good sensitivity to soil moisture response, and thus seems to be a good candidate for soil moisture sensing in bistatic configuration.

The measurement and model construction of complex permittivity of corn leaves at the main frequency points of L/S/C/X-band

The complex permittivity of target has a crucial influence on its microwave radiation characteristics. In the quantitative research of microwave remote sensing, the study of the dielectric properties of vegetation to establish the relationship between its specific physical parameters and complex permittivity is the basic work in this field. In this study, corn leaves samples of different types and heights were collected at the city of Zhangye which is the key study area of the Heihe watershed allied telemetry experimental research and also the largest breeding base of hybrid corn seeds in China. Then the vector network analyzer E8362B was used to measure the complex permittivity of these samples from 0.2 to 20 GHz by coaxial probe technique. Based on these measurements, an empirical model of corn leaves which describes the relationship between the gravimetric moisture and both the real part and imaginary part of complex permittivity at the main frequency points of L/S/C/X-band was established. Finally, the empirical model and the classical Debye-Cole model were compared and validated by the measured data collected from the Huailai county in Hebei province. The results show that the empirical model has higher accuracy and is more practical than the traditional Debye-Cole model.

A preliminary assessment of the SMAP radiometer soil moisture product using three in-situ networks

The SMAP (soil moisture active passive) which is one of the satellites that specifically designed for soil moisture monitoring, was launched on 31 January 2015. Recently, the SMAP radiometer soil moisture product has been released to the public. It is very urgent to evaluate the reliability of this product before it can be widely used in hydrometeorological studies. In the study, we carried out an initial evaluation of SMAP radiometer soil moisture product against in-situ measurements from three networks. The three networks cover different land surface conditions, including two dense networks established in United States and Finland, and one sparse network set up in Romania. The results show that the SMAP soil moisture product agrees very well with the in-situ measurements although it sometimes exhibits dry or wet bias at different network regions. The overall ubRMSE of SMAP product is 0.036 m3 m-3, well within the mission requirement of 0.04 m3 m-3. Considering the algorithms are still under refinement, it can be reasonably expected that applications such as climate modeling and flood forecasting will benefit from the SMAP passive soil moisture product.

Validation of SMAP Soil Moisture analysis product using in-situ measurements over the Little Washita Watershed

Soil moisture is a key state variable which plays a significant role in many hydrological processes. The Soil Moisture Active Passive (SMAP) mission was launched on 31 January 2015 which can provide global information of soil moisture. Among the released SMAP data sets, the Level 4 Surface and Root Zone Soil Moisture Analysis Product (L4_SM) can not only provide information on surface soil moisture (top 5 cm of the soil column), but also provide estimates of root zone soil moisture (top 1 m of the soil column) which is very important for several key applications targeted by SMAP. However, since this product has been released only for a short time, its accuracy and reliability has not been validated so far. In this study, we evaluated the L4_SM soil moisture analysis product against in-situ soil moisture measurements collected from the Little Washita Watershed network located in southwest Oklahoma in the Great Plains region of the United States. The results show that both the surface and root zone soil moisture estimates in the L4_SM product are in good agreement with the in-situ measurements, and the RMSE is 0.027 m3/m3 and 0.032 m3/m3 for the surface and root zone soil moisture respectively which both have exceeded the RMSE requirement of 0.04 m3/m3 for this product.