Linna Chai

Comparison of the classification accuracy of three soil freeze–thaw discrimination algorithms in China using SSMIS and AMSR-E passive microwave imagery

This study compared the classification accuracies of three soil freeze–thaw discrimination algorithms in China using Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) passive microwave imagery from 2008. The algorithms used were the dual-index algorithm (DIA), the decision tree algorithm (DTA), and the discriminant function algorithm (DFA). The comparison was conducted based on 0 cm land-surface temperature data from 756 meteorological stations across China by constructing error meta-matrices, and it is divided into two parts. The first part compared the overall classification accuracies from two aspects: temporal variation and spatial distribution. In the second part, the classification accuracies of frozen and thawed soils were evaluated. Results showed that both SSMIS and AMSR-E data can be applied to the DIA, DTA, and DFA algorithms, although they were originally developed from different satellite data sets. However, each of the three algorithms has its own advantages and disadvantages. Possible improvements in the three algorithms for future work are also discussed.

Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method

Land surface temperature (LST) plays a major role in the study of surface energy balances. Remote sensing techniques provide ways to monitor LST at large scales. However, due to atmospheric influences, significant missing data exist in LST products retrieved from satellite thermal infrared (TIR) remotely sensed data. Although passive microwaves (PMWs) are able to overcome these atmospheric influences while estimating LST, the data are constrained by low spatial resolution. In this study, to obtain complete and high-quality LST data, the Bayesian Maximum Entropy (BME) method was introduced to merge 0.01° and 0.25° LSTs inversed from MODIS and AMSR-E data, respectively. The result showed that the missing LSTs in cloudy pixels were filled completely, and the availability of merged LSTs reaches 100%. Because the depths of LST and soil temperature measurements are different, before validating the merged LST, the station measurements were calibrated with an empirical equation between MODIS LST and 0~5 cm soil temperatures. The results showed that the accuracy of merged LSTs increased with the increasing quantity of utilized data, and as the availability of utilized data increased from 25.2% to 91.4%, the RMSEs of the merged data decreased from 4.53 °C to 2.31 °C. In addition, compared with the filling gap method in which MODIS LST gaps were filled with AMSR-E LST directly, the merged LSTs from the BME method showed better spatial continuity. The different penetration depths of TIR and PMWs may influence fusion performance and still require further studies.

An empirical model to estimate the microwave penetration depth of frozen soil

The technique of microwave remote sensing has been used to monitor the soil frozen/thawed status for many years. The dielectric constant of frozen soil is relatively lower than that of unfrozen soil, so that microwave could penetrate deeper into frozen soil. However, we are still lack of the knowledge of the Microwave Penetration Depth (MPD) of frozen soil. In this study, a noncoherent microwave radiation was used to find the factors that influence the MPD of frozen soil and then validated by experiments. The results showed that the frequency of microwave, the temperature of frozen soil and the soil texture are the main factors that determine the MPD of frozen soil. An empirical model that estimates the MPD of frozen soil was proposed based on the simulation data.

A parameterized microwave model for short vegetation layer

Vegetation is the most important part of the terrestrial ecosystems which results in a large proportion of studies on vegetation parameters, such as coverage, biomass, water content and so on. Since the ultimate goal of remote sensing is to accurately and efficiently inverse land surface parameters, it is of great significance to find a good forward vegetation model with simple form and high accuracy for the inversion. Though the zeroth-order model is good for fast inversion with its simple form, it always underestimates the total emission at high frequency or for dense vegetation. The first-order model has higher accuracy due to the consideration of volume scattering contribution, but it is complex and computationally intensive. In this regard, we developed a parameterized model base on emissivity simulations from the first-order model for short vegetation covered ground in this paper. This parameterized model takes a similar form as that of the zeroth-order model. It is of great significance for accurate and efficient inversion.

Modeling of the Permittivity of Holly Leaves in Frozen Environments

The dielectric property of vegetation has a considerable effect on the characteristics of the microwave radiation of vegetation. In frozen environments, when the temperature is colder than normal, changes such as increased soluble sugar and decreased moisture content (MC) can occur in the vegetation. The dielectric property of vegetation, which is almost entirely controlled by its free and bound water content, will also change. To characterize the dielectric behavior of vegetation in frozen regions, a sensitive experiment was conducted on holly leaves with a high-performance coaxial probe over a frequency range from 0.5 to 40 GHz and a temperature range from 0°C to -20°C. Based on the measurements and the physical properties of the constituent substances of vegetation, a semiempirical dielectric model for holly leaves in low temperature environments was developed. In this model, a decrease in MC, which causes a reduction in the complex permittivity, was described as an increase in the ice content. The complex permittivity of bound water was measured using a saturated sucrose solution at -6.5°C. The research will provide a reference for the dielectric property study of the vegetation in frozen environments.

The influence of organic matter on soil dielectric constant at microwave frequencies (0.5–40 GHZ)

In this study, the dielectric constants of 12 types of soil with different organic matter content were measured using the coaxial probe method by network analyzer (0.5-40 GHz) at room temperature (approx. 23°C). The observed dielectric constant increases only slowly with soil volumetric water content up to a transition point. Beyond the transition point, it increases rapidly with volumetric water content. It was found that the value of the transition point was higher and the observed dielectric constant was lower at the same soil volumetric water content and frequency for soil with higher organic matter content. A simple semi-empirical model was proposed to describe the dielectric behavior of soil with organic matter. This model was developed based on the refractive mixing dielectric model (RMDM).

Calibration of the L-MEB Model for Croplands in HiWATER Using PLMR Observation

The Soil Moisture and Ocean Salinity (SMOS) mission was initiated in 2009 with the goal of acquiring global soil moisture data over land using multi-angular L-band radiometric measurements. Specifically, surface soil moisture was estimated using the L-band Microwave Emission of the Biosphere (L-MEB) radiative transfer model. This study evaluated the applicability of this model to the Heihe River Basin in Northern China for specific underlying surfaces by simulating brightness temperature (BT) with the L-MEB model. To analyze the influence of a ground sampling strategy on the simulations, two resampling methods based on ground observations were compared. In the first method, the simulated BT of each point observation was initially acquired. The simulations were then resampled at a 1 km resolution. The other method was based on gridded data with a resolution of 1 km averaged from point observations, such as soil moisture, soil temperature, and soil texture. The simulated BTs at a 1 km resolution were then obtained using the L-MEB model. Because of the large variability in soil moisture, the resampling method based on gridded data was used in the simulation. The simulated BTs based on the calibrated parameters were validated using airborne L-band data from the Polarimetric L-band Multibeam Radiometer (PLMR) acquired during the HiWATER project. The root mean square errors (RMSEs) between the simulated results and the PLMR data were 6 to 7 K for V-polarization and 3 to 5 K for H-polarization at different angles. These results demonstrate that the model effectively represents agricultural land surfaces, and this study will serve as a reference for applying the L-MEB model in arid regions and for selecting a ground sampling strategy.

Estimating Mixed-Pixel Component Soil Moisture Contents Using Biangular Observations From the HiWATER Airborne Passive Microwave Data

Determination of the component soil moisture content within one pixel using passive microwave remote sensing data is important for predicting soil moisture contents in ecohydrological research within the Heihe River Basin. The Heihe Watershed Allied Telemetry Experimental Research was conducted in 2012 to address this issue. An airborne polarimetric L-band microwave radiometer (PLMR) instrument was used to measure surface emissions over the middle stream of the Heihe River Basin. Extensive ground-based soil moisture content and temperature data were obtained during the PLMR flights. In this letter, an algorithm for estimating the component soil moisture content was developed using biangular PLMR observations. Based on a theoretical analysis, the linear relationship between the soil emissivities at two different incidence angles was obtained. Therefore, the component soil moisture could be derived based on the tau-omega model. In addition, the component soil moisture contents determined over the bare surface were lower than those over the vegetated surface. The root-mean-square errors between the calculated soil moisture contents and the measured soil moisture contents over the bare and vegetated surfaces were 0.050 and 0.051 cm3/cm3, respectively. Overall, the results indicate that the component soil moisture contents can be estimated using biangular observations from airborne radiometer data.

A new method to determine the freeze-thaw erosion

Freeze-thaw erosion is the third largest soil erosion type after water erosion and wind erosion, which is a serious threat to agricultural land and various buildings, especially for water projects. In this paper, a new method based on the passive microwave remote sensing technique was proposed to classify and assess the freeze-thaw erosion. The core of this new method is at two important indices: the freeze-thaw cycling days per year, and the phase transition water content per day. The first index can be used to determine the freeze-thaw erosion region and the second index can be used to evaluate the degree of freeze-thaw erosion. The application of this new method in China shows good result. It indicates that the freeze-thaw erosion regions in China are mainly distributed in Tibet, Mongolia and the province of Qinghai, Xinjiang, Gansu, Sichuan and Heilongjiang. Furthermore, the comparably serious freeze-thaw erosion region is located in Tibet Plateau.

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.