Ms. Yunqing Li Profile

Yunqing Li

at Institute of Remote Sensing and Digital Earth

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Author

Publications (2)

SPIE Journal Paper | 6 October 2015

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

Yunqing Li, Jiancheng Shi, Tianjie Zhao

JARS, Vol. 9, Issue 01, 096003, (October 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.096003

KEYWORDS: Vegetation, Microwave radiation, Soil science, Scattering, Polarization, Satellites, Spatial resolution, Data modeling, Bohrium, Signal attenuation

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SPIE Journal Paper | 3 September 2014

Soil temperature independent algorithm for estimating bare surface soil moisture

Tao Zhang, Lingmei Jiang, Tianjie Zhao, Yunqing Li, Zhongjun Zhang

JARS, Vol. 8, Issue 01, 083558, (September 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.083558

KEYWORDS: Soil science, Algorithm development, Surface roughness, Microwave radiation, Dielectric polarization, Data modeling, Temperature metrology, Databases, Polarization, Terbium

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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 cm3/cm3 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 cm3/cm3 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.

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