Tianjie Zhao

Estimate of Phase Transition Water Content in Freeze–Thaw Process Using Microwave Radiometer

Ground surface freeze-thaw cycles caused by changes in solar radiation have a great impact on soil-air water heat exchanges due to the phase transition of pore water. This influence should not be ignored in the land surface process and global environment change studies because of its large extent and the rapid changes in daily and seasonal frozen ground. The key index for evaluating the influence intensity is the content of water-ice phase transition in soil pores at the ground surface. In this paper, a data set was generated by observing field experiments and physical model simulations based on the configuration of the Advanced Microwave Scanning Radiometer-EOS (AMSR-E). The results showed that microwave radiation from freezing/thawing soil has an obvious correlation to the phase transition process of soil water. A large change in soil surface emissivity was shown after the freezing of soil. The magnitude of the difference in emissivity change is strongly related to the amount of water-ice phase transition. It can be shown that the higher the phase transition water content (PTWC), the greater the emissivity difference, and the higher the frequency, the smaller the emissivity difference. Based on an analysis of a large amount of random simulation data, an interesting characteristic was found, in that the emissivity difference in vertical polarization at each frequency is nearly proportional to the phase transition water content. Thus, a ratio index called Quasi-emissivity (Qe) was developed to eliminate temperature effects during retrieval. Using these clear rules, a physical statistical algorithm was put forth to estimate the phase transition water content. Finally, the inferred results by ground-based radiometer observation were compared with the ground truth. A satisfying agreement was achieved with a root mean square error of 0.0265 (v/v). This indicated that the microwave radiometer has a great potential in the measurement of PTWC.

Estimating Snow Water Equivalent with Backscattering at X and Ku Band Based on Absorption Loss

Snow water equivalent (SWE) is a key parameter in the Earth’s energy budget and water cycle. It has been demonstrated that SWE can be retrieved using active microwave remote sensing from space. This necessitates the development of forward models that are capable of simulating the interactions of microwaves and the snow medium. Several proposed models have described snow as a collection of sphere- or ellipsoid-shaped ice particles embedded in air, while the microstructure of snow is, in reality, more complex. Natural snow usually forms a sintered structure following mechanical and thermal metamorphism processes. In this research, the bi-continuous vector radiative transfer (bi-continuous-VRT) model, which firstly constructs snow microstructure more similar to real snow and then simulates the snow backscattering signal, is used as the forward model for SWE estimation. Based on this forward model, a parameterization scheme of snow volume backscattering is proposed. A relationship between snow optical thickness and single scattering albedo at X and Ku bands is established by analyzing the database generated from the bi-continuous-VRT model. A cost function with constraints is used to solve effective albedo and optical thickness, while the absorption part of optical thickness is obtained from these two parameters. SWE is estimated after a correction for physical temperature. The estimated SWE is correlated with the measured SWE with an acceptable accuracy. Validation against two-year measurements, using the SnowScat instrument from the Nordic Snow Radar Experiment (NoSREx), shows that the estimated SWE using the presented algorithm has a root mean square error (RMSE) of 16.59 mm for the winter of 2009–2010 and 19.70 mm for the winter of 2010–2011.

A combined microwave emission model for cold land

As the global warming intensifies, the environment changes in cold land receive more attention. In this paper, a combined microwave emission model is established for cold land researches. Through field observation experiment, the b-factor of winter wheat during winter is obtained to simulate radiation accurately from this typical ground object in China. Furthermore, the impacts of snow and vegetation cover on frozen soil radiation are investigated by sensitivity analysis.

Comparison of Dry Snow Emission Model and the Primary Study on Satellite Data Simulation

The parameterized emission model of dry snow developed by Jiang et al. could be used to simulate the microwave emission signal for one layer snow. On the basis of sensitivity analysis, this simple snow parameterized model is firstly compared with the HUT model using the PSR observation with the corresponding snow pits data over North Park area in Feb., 2003. At lower frequencies, both of the two models underestimated the measurements, while the parameterized model was closer to the PSR at 36.5 GHz, since the parameterized model considered the multiscattering in the snow layer. Finally, with this parameterized model, we performed the brightness temperature simulation of the Polarimetric Scanning Radiometer data similar as AMSR-E, with the outputs from the Snow Model. The difference between the simulated TBs and the measurements of PSR was as large as 20 K, even more at 10.7 GHz. Through analysis, the discrimination was possibly either linked with the emission model or due to snow surface simulations. This comparison case made us to better understand how accurate the simulations could be in reality.

Simulation of emission properties and snow-soil system status of a melting thin snow pack

Simulation of brightness temperature and related snow parameters is essential to understand the microwave emission property and its evolution with change of the snow-soil system status. In this paper, a typical thin snow pack on North China Plain is measured on Nov 13–16th, 2009 at Luancheng test site HUT (Helsinki University of Technology) wet snow emission model is used to predict the brightness temperatures at 10.65, 18.7 and 36.5 GHz. A physically-based snow process model, SNTHERM (SNow THERmal Model), is applied to simulate the snow melting process. The measured snow density and grain size is compared with SNTHERM prediction and HUT inputs. Results show that the application of snow emission model and process model can explain the variation trend of wet snow emission properties well.

Comparison of vegetation optical depth estimation methods using AMSR-E data

The vegetation optical depth (τ, tau), which describes microwave attenuation properties of vegetation, is a key parameter for vegetation biomass and soil moisture estimation. In this paper, we have implemented five semi-empirical/empirical methods for vegetation optical depth estimation. The Advanced Microwave Scanning Radiometer- Earth Observing System (AMSR-E) data and field experimental data provided by Climate Change Initiative (CCI) project were used to evaluate the estimated vegetation optical depth. Its dynamic ranges and time series trends were analyzed at one site located in USA. Correlations between optical depth estimated using the five methods and MVIs_B, MPDT, MDPI, and NDVI were calculated in order to explore the relationship between vegetation indices and optical depths. With exception of the Radiative Model (RT) method, the vegetation optical depths from other four methods exhibit a similar trend with time. The dynamic ranges and the correlation coefficients are significantly different from each other. This study would further help us to study the uncertainty of a variety of current soil moisture products.

A downscaling algorithm for combining radar and radiometer observations for SMAP soil moisture retrieval

In this study, a downscaling algorithm to disaggregate the radiometer Brightness Temperature (TB) using the radar backscatter observations for SMAP (Soil Moisture Active and Passive) was developed. The algorithm is based on the spectral downscaling which combines both phase and amplitude information in Fourier domain. Using the information from radar measurements at finer resolution, a new way to estimate the Fourier phase was proposed. The algorithm has been successfully applied to the PALS datasets from SMEX02 producing better results than radiometer-only inversions. The RMSE (Root-Mean-Square-Error) of the downscaling Brightness Temperature are 3.26K and 6.12K for V and H polarization, respectively. Then medium resolution soil moisture was retrieved from disaggregated/downscaled TB. The accuracy (RMSE) of the downscaling soil moisture retrievals is 0.0459m3/m3, which is very close to SMAP science requirement of 0.04. The results indicate that the downscaling algorithm presented in this study is a promising approach to achieve finer resolution and more accurate soil moisture retrievals for the future SMAP mission.

Simulation and measurement of relief effects on passive microwave radiation

To investigate relief effects on microwave radiation, it is essential to experiment, based on track-mounted microwave radiometer. There are four relief factors affecting microwave radiation features in this study we have researched, which are hill slopes, hill elevation, hill aspects, and hill shadows [4]. To compare with relief effect simulation, we built relief landscape in the field experiment to validate microwave radiation of hill-scale topography bias resulted from some of relief factors. In the final analysis, through the relief experiment observed results we consider hill-scale topography dose have influence on microwave radiation, and it can not be ignored in the retrieval of surface parameters.

Sensitivity analysis of snow parameters inversion procedure to the passive microwave mixed-pixel patterns

The snow coverage and physical parameters play a special role in the global water and energy budget study. The passive microwave brightness temperatures of snowpack depend not only on the snow depth or snow water equivalent, but also the snow fraction and possible vegetation canopy. In this paper, we established a mixed model for simulating the dry snow radiation, based on the advancements of recent years. Through simulation analysis, it is found that the underestimation of snow fraction will cause the snow depth or snow water equivalent to be overestimated. And the error increases with the increase of snow depth.