Fang-Cheng Zhou

Bare surface soil moisture retrieval from the synergistic use of optical and thermal infrared data

Land surface soil moisture (SSM) is crucial to research and applications in hydrology, ecology, and meteorology. To develop a SSM retrieval model for bare soil, an elliptical relationship between diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR) is described and further verified using data that were simulated with the Common Land Model (CoLM) simulation. In addition, with a stepwise linear regression, a multi-linear model is developed to retrieve daily average SSM in terms of the ellipse parameters x0 (horizontal coordinate of the ellipse centre), y0 (vertical coordinate of the ellipse centre), a (semi-major axis), and θ (rotation angle), which were acquired from the elliptical relationship. The retrieval model for daily average SSM proved to be independent of soil type for a given atmospheric condition. Compared with the simulated daily average SSM, the proposed model was found to be of higher accuracy. For eight cloud-free days, the root mean square error (RMSE) ranged from 0.003 to 0.031 m3 m−3, while the coefficient of determination (R2) ranged from 0.852 to 0.999. Finally, comparison and validation were conducted using simulated and measured data, respectively. The results indicated that the proposed model showed better accuracy than a recently reported model using simulated data. A simple calibration decreased RMSE from 0.088 m3 m−3 to 0.051 m3 m−3 at Bondville Companion site, and from 0.126 m3 m−3 to 0.071 m3 m−3 at the Bondville site. Coefficients of determination R2 = 0.548 and 0.445 were achieved between the estimated daily average SSM and the measured values at the two sites, respectively. This paper suggests a promising avenue for retrieving regional SSM using LST and NSSR derived from geostationary satellites in future developments.

An Effective Emission Depth Model for Passive Microwave Remote Sensing

Knowing the exact penetration depth for passive microwave will enable us to gain a better understanding of the characteristics of the targets. But now, there is only a penetration depth model that can calculate the specific depth while it is suitable for active microwaves. However, due to the completely different working mechanisms, the penetration depth model is most likely limited when applying to passive microwaves. Therefore, an effective emission depth model for passive microwaves was first proposed based on the radiative transfer theory, assuming that a homogenous double-layer (soil and atmosphere) medium exists between the passive microwave radiometer and a certain soil depth. With simulated data from the advanced integral equation model (AIEM) and subsequent analysis, the effective emission depth model was further simplified to make it more practical with a root-mean-squared error (RMSE) of 0.46 cm and a bias of 0.032 cm. For the simplified model, only the soil moisture content and the noise-equivalent differential temperature and center frequency of the radiometer are required. By comparing the penetration depth model and simplified effective emission depth model, a certain soil moisture content that makes both the models reveal the same results is found.

An Algorithm for Retrieving Precipitable Water Vapor over Land Based on Passive Microwave Satellite Data

Precipitable water vapor (PWV) is one of the most variable components of the atmosphere in both space and time. In this study, a passive microwave-based retrieval algorithm for PWV over land without land surface temperature (LST) data was developed. To build the algorithm, two assumptions exist: (1) land surface emissivities (LSE) at two adjacent frequencies are equal and (2) there are simple parameterizations that relate transmittance, atmospheric effective radiating temperature, and PWV. Error analyses were performed using radiosonde sounding observations from Zhangye, China, and CE318 measurements of Dalanzadgad (43°34′37′′N, 104°25′8′′E) and Singapore (1°17′52′′N, 103°46′48′′E) sites from Aerosol Robotic Network (AERONET), respectively. In Zhangye, the algorithm had a Root Mean Square Error (RMSE) of 4.39 mm and a bias of 0.36 mm on cloud-free days, while on cloudy days there was an RMSE of 4.84 mm and a bias of 0.52 mm because of the effect of liquid water in clouds. The validations in Dalanzadgad and Singapore sites showed that the retrieval algorithm had an RMSE of 4.73 mm and a bias of 0.84 mm and the bigger errors appeared when the water vapor was very dry or very moist.

First Results of Estimating Surface Soil Moisture in the Vegetated Areas Using ASAR and Hyperion Data: The Chinese Heihe River Basin Case Study

This study introduces a new approach to estimate surface soil moisture in vegetated areas using Synthetic Aperture Radar (SAR) and hyperspectral data. To achieve this, the Michigan Microwave Canopy Scattering (MIMICS) model was initially used to simulate backscatter from vegetated surfaces containing various canopy water contents, across three frequency bands (i.e., L, S, and C). Using this simulated dataset, the influence of the canopy water content on the backscattered signals was further analyzed. In addition, we developed a modified Water-Cloud model which adds in the crown-ground interaction term. Finally, a soil moisture retrieval model for an agricultural region was developed. Alternating polarization data with ASAR and Hyperion hyperspectral data were used to retrieve soil moisture and validate the feasibility of the retrieval model. The field measured data from the Heihe river basin was used to confirm the proposed model. Results revealed an average absolute deviation (AAD) and average absolute relative deviation (AARD) of 0.051 cm3∙cm−3 and 19.7%, respectively, between the estimated soil moisture and the field measurements.

Estimation of surface soil moisture from ASAR dual-polarized data in the middle stream of the Heihe River Basin

In this paper, a new approach was introduced to estimate surface soil moisture using alternating polarization (AP) data of advanced synthetic aperture radar (ASAR). First, synthetic aperture radar (SAR) backscattering characteristic of bare surface at C band was simulated using advanced integrated equation model (AIEM), and four bare surface backscattering models with different polarization were established. In addition, with simultaneous equations of the former four formulas, the surface roughness was eliminated, and models used to estimate soil moisture on bare surface were derived from simulated multipolarization and multiangle ASAR-AP data. Based on these, the best combination of polarization and incident angle was determined. Finally, soil moisture in the middle stream of the Heihe River Basin was estimated. The field measured data demonstrated that the proposed method was capable of retrieving surface soil moisture for both sparse grassland and homogeneous farmland area.

A remote sensing method for retrieving land surface emissivity and temperature in cloudy areas: a case study over South China

ABSTRACT Land surface temperature (LST) is an important parameter at the surface–atmosphere interface, and measurements of the LST at continuous temporal and spatial scales are necessary in many research fields. Passive microwave radiation can penetrate clouds and detect land surface information under clouds; consequently, passive microwave remote sensing has the potential to obtain LST under almost all weather conditions. In this study, the relationships between the brightness temperature polarization ratio (PR) and horizontally polarized emissivities, and between the horizontally and vertically polarized emissivities are combined to develop a three-stage LST retrieval algorithm from 19 GHz microwave brightness temperature observations in cloudy areas. During the first two stages, the horizontally and vertically polarized emissivities are solely obtained from the brightness temperature data. In the last stage, the LST is calculated with the known emissivity data by ignoring the atmospheric effect. In the validation of South China, the root-mean-square errors (RMSEs) of the estimated horizontally and vertically polarized emissivities at 19 GHz are 0.0078 and 0.0023, respectively. The retrieved LST is compared to single-point measurements of the meteorological stations, and the RMSE is 4.1200 K. The LST errors are mainly due to the propagation of estimated land surface emissivity (LSE) errors, and the poor representativeness of the validated data in mixed pixels. The superiority of this LST retrieval algorithm lies in simultaneously obtaining the LST and both polarized LSE under almost all weather conditions with little input data.

An algorithm for retrieving land surface temperature from AMSR-E data over the desert regions

Land surface temperature is an important driving force in the exchange of water, heat, and even CO2 at the surface-atmosphere interface in the desert regions. The rapid and continuous measurements of land surface temperature are meaningful to the ecological and environmental researches. A physically based single-frequency and double-polarization algorithm for retrieving land surface temperature is developed in this study. The 18.7 GHz vertically polarized emissivities are firstly estimated from the Polarization Ratio (PR, defined as the ratio of the horizontal to vertical brightness temperature at the same frequency) at 18.7 GHz. And then the estimated emissivities can be directly used to retrieve land surface temperature without considering the atmospheric effect. A preliminary validation is done in the Taklimakan desert. The retrieved land surface temperatures are compared to the infrared land surface temperature products for all the year of 2007 with a Root Mean Square Error (RMSE) of 3.05 K.

An algorithm for retrieving instantaneous microwave land surface emissivity from passive microwave brightness temperature and precipitable water vapor data

An algorithm has been developed for retrieving instantaneous microwave land surface emissivity using brightness temperature and precipitable water vapor data. Unlike previous algorithms, the new technique does not need infrared land surface temperature as the input data, and overcomes the limitation of previous algorithms under cloudy conditions. Compared with the values from physical retrieval algorithm, the result demonstrates that this new algorithm has a Root Mean Square Error of 0.038 and a bias of 0.012. Although the accuracy is worse than 1%, this new algorithm presents the potential to obtain the instantaneous microwave land surface emissivity under both cloud-free and cloudy conditions, which can be applied in some weather prediction models.

A Novel Approach to Extract Water Body from ASAR Dual-Polarized Data

SAR (Synthetic Aperture Radar) has become a useful and efficient method for monitoring flood extent due to its capability of 24-hour and all weather observation. In this paper, a novel approach is proposed to extract water bodies from ASAR dual-polarized images. Firstly, a new SAR image was created from ASAR Dual-Polarized data using a discrete wavelet transformation (DWT) fusion method. Then, a modified Otsu threshold method was used to extract water bodies of Poyang Lake with the new fused image. Next, this image was compared with the one extracted from ETM+ data. The result showed that the fused image was feasible and more accurate. Besides, it could reduce the influences of shadow and noise. Moreover, the approach could be conducted automatically, which is very important under urgent condition for flood monitoring.