Yunqing Li

Progresses on microwave remote sensing of land surface parameters

Highly accurate observations at various scales on the land surface are urgently needed for the studies of many areas, such as hydrology, meteorology, and agriculture. With the rapid development of remote sensing techniques, remote sensing has had the capacity of monitoring many factors of the Earth’s land surface. Especially, the space-borne microwave remote sensing systems have been widely used in the quantitative monitoring of global snow, soil moisture, and vegetation parameters with their all-weather, all-time observation capabilities and their sensitivities to the characteristics of land surface factors. Based on the electromagnetic theories and microwave radiative transfer equations, researchers have achieved great successes in the microwave remote sensing studies for different sensors in recent years. This article has systematically reviewed the progresses on five research areas including microwave theoretical modeling, microwave inversion on soil moisture, snow, vegetation and land surface temperatures. Through the further enrichment of remote sensing datasets and the development of remote sensing theories and inversion techniques, remote sensing including microwave remote sensing will play a more important role in the studies and applications of the Earth systems.

Refinement of SMOS Multiangular Brightness Temperature Toward Soil Moisture Retrieval and Its Analysis Over Reference Targets

Soil moisture ocean salinity (SMOS) mission has been providing L-band multiangular brightness temperature observations at a global scale since its launch in November 2009 and has performed well in the retrieval of soil moisture. The multiple incidence angle observations also allow for the retrieval of additional parameters beyond soil moisture, but these are not obtained at fixed values and the resolution and accuracy change with the grid locations over SMOS snapshot images. Radio-frequency interference (RFI) issues and aliasing at lower look angles increase the uncertainty of observations and thereby affect the soil moisture retrieval that utilizes observations at specific angles. In this study, we proposed a two-step regression approach that uses a mixed objective function based on SMOS L1c data products to refine characteristics of multiangular observations. The approach was found to be robust by validation using simulations from a radiative transfer model, and valuable in improving soil moisture estimates from SMOS. In addition, refined brightness temperatures were analyzed over three external targets: Antarctic ice sheet, Amazon rainforest, and Sahara desert, by comparing with WindSat observations. These results provide insights for selecting and utilizing external targets as part of the upcoming soil moisture active passive (SMAP) mission.

The development of Microwave Vegetation indices according to WindSat data

As an important vegetation indicator, vegetation indices have become a widely used tool in vegetation parameters retrieval and condition monitoring. A newly developed MVI was deduced and evaluated using WindSat data. During the deduction, the w-τ model was utilized as the theory foundation. The emission from ground can be rearranged into a two component model including the vegetation emission component and the vegetation transmission component. In order to characterize the frequency dependence of surface emission signals on the objective of minimizing the effects of the ground surface emission signals, we have built a simulation database for the configurations of WindSat using the Advanced Integral Equation Model (AIEM) at 6.8, 10.7, and 18.7 GHz, dual-polarization (v and h) and the corresponding incidence angles. Unlike previous MVIs, this simulation contains both Gaussian and Exponential correlation surfaces. On the basis of simulation data analysis, we found that bare soil emissivity at two adjacent WindSat frequencies has a linear relationship, which makes it possible to minimize the surface emission signal and maximize the vegetation signal. As a result, brightness temperature at a higher frequency can be a function of the adjacent lower frequency at the same polarization, whose slope and intercept are the newly developed Microwave Vegetation Index (MVI) from WindSat data. The new MVI shared the same vegetation distribution pattern as AMSR-E based MVIs and was also negative to NDVI.

Effects of spatial distribution of soil parameters on soil moisture retrieval from passive microwave remote sensing

In this paper, we studied the effect of spatial distribution of soil parameters on passive soil moisture retrieval at pixel scale. First, we evaluated the forward microwave emission model and soil moisture retrieval algorithm accuracy through the observation of field experiments. Then, we used soil parameters in different spatial distribution patterns, including random, normal, and uniform distribution, to determine the different levels of heterogeneity on soil moisture retrieval, in order to seek the relationship between heterogeneity and soil moisture retrieval error. Finally, we conducted a controlled heterogeneity effect experiment measurements using a Truck-mounted Multi-frequency Radiometer (TMMR) to validate our simulation results. This work has proved that the soil moisture retrieval algorithm had a high accuracy (RMSE=0.049 cm3 cm−3) and can satisfy the need of this research. The simulation brightness temperatures match well with observations, with RMSE=9.89 K. At passive microwave remote sensing pixel scale, soil parameters with different spatial distribution patterns could have different levels of error on soil moisture estimation. Overall, we found that soil moisture with a random distribution in a satellite pixel scale can cause the largest error, with a normal distribution being the second, and a uniform distribution the least due to the smallest heterogeneity.

Effective vegetation optical depth retrieval using microwave vegetation indices from WindSat data for short vegetation

Abstract. 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.

Soil temperature independent algorithm for estimating bare surface soil moisture

Abstract 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     cm 3 / cm 3 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     cm 3 / cm 3 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.

Estimating vegetation water content during a growing season of cotton

Vegetation water content (VWC) is a useful parameter in agriculture, forestry and hydrology studies. It is particularly valuable in accounting for vegetation effects in retrieving soil moisture using microwave radiometers. Microwave vegetation indices (MVIs) reflect information of the whole vegetation canopy. They may provide a mean for estimating VWC. In this study, a methodology for retrieving VWC using MVIs is presented. Coefficients of the relationship were found to be dependent only on a vegetation structure parameter. The methodology was tested with brightness temperature observations at C and X bands collected over a growing season of cotton. It was found that results compared well with field observations of VWC measured during the early growing season. The methodology should be useful for vegetation monitoring and soil moisture retrieval over low vegetated areas.

Study of microwave emissivity characteristics of city

The spectrums of different land types are very important in the application of remote sensing, which can be used in surface classification, change detection, and so on. The microwave emissivity of these land types are the foundation of land parameters retrieval using passive microwave remote sensing. As one of the most important land types, citys contribution in a passive microwave pixel cannot be ignored. In this paper, some “pure” city microwave pixel was selected and RFI effect was evaluated using several indicators. Eliminating days of RFI contaminated, city microwave emissivity in lower frequencies was extracted from AMSR-E brightness temperature data in 2008. Then the characteristic of city emissivity was analyzed. Based on precipitation data, we are trying to find the factors to affect city emissivity. The results have shown that the RFI effect was little except some channels. City emissivity was different for different frequencies and polarizations. It increased with the frequency becoming higher. The emissivity fluctuated along with seasons. According to comparison with meteorological data, there was an obvious correlation between the city emissivity and rainfall in higher precipitation.

Retrieve optical depth using microwave vegetation indices from WindSat data

Obtaining reliable vegetation optical depth (τ, tau) is essential for vegetation parameter estimation and soil moisture retrieval. In this paper, a lookup table method was developed to retrieve optical depth using MVIs, which can derive the optical depth and single scattering albedo simultaneously without any auxiliary data. The lookup table method is based on the relationship of the single scattering albedo and optical depth at two adjacent frequencies, respectively. The relationships are explored by using a large simulation database generated using a physical model. Then, a lookup table will be established according to the relationships. Following this, we will validate this method and analyze the source of errors.

Refinement of SMOS multi-angular brightness temperature and its analysis over reference targets

The Soil Moisture Ocean Salinity (SMOS) mission has been providing L-band multi-angular brightness temperature observations at a global scale since its launch in November 2009 and has performed well in the retrieval of soil moisture. The multiple incidence angle observations are not obtained at fixed values and the resolution and accuracy change with the grid locations over SMOS snapshot images. Radio frequency interference issues and aliasing at lower look angles increases the uncertainty of observations and thereby affects the soil moisture retrieval that utilizes observations at specific angles. In this study, we propose a processing chain that uses a mixed objective function based on SMOS L1c data products to refine the characteristics of multi-angular observations. The approach was validated using simulations from a radiative transfer model and analyzed over three external targets: Amazon rainforest, Sahara desert, and Antarctic ice. These results could provide insights for selecting and utilizing external targets as part of the upcoming Soil Moisture Active Passive (SMAP) mission.