Jiang Lingmei

Improvement of snow depth retrieval for FY3B-MWRI in China

The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager (FY3B-MWRI) in China. Based on 7-year (2002–2009) observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and snow depth from Chinese meteorological stations, we develop a semi-empirical snow depth retrieval algorithm. When its land cover fraction is larger than 85%, we regard a pixel as pure at the satellite passive microwave remote-sensing scale. A 1-km resolution land use/land cover (LULC) map from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences, is used to determine fractions of four main land cover types (grass, farmland, bare soil, and forest). Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data. Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell. Through evaluation of this algorithm using station measurements from 2006, the root mean square error (RMSE) of snow depth retrieval is about 5.6 cm. In forest regions, snow depth is underestimated relative to ground observation, because stem volume and canopy closure are ignored in current algorithms. In addition, comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover products (MYD10C1) in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%. Finally, we compared snow water equivalence (SWE) derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere. The results show that AMSR-E overestimated SWE in China, which agrees with other validations.

Analysis of spatial distribution and multi-year trend of the remotely sensed soil moisture on the Tibetan Plateau

Long-term highly accurate surface soil moisture data of TP (Tibetan Plateau) are important to the research of Asian monsoon and global atmospheric circulation. However, due to the sparse in-situ networks, the lack of soil moisture observations has seriously hindered the progress of climate change researches of TP. Based on the Dual-Channel soil moisture retrieval algorithm and the satellite observation data of AMSR-E (Advanced Microwave Scanning Radiometer for EOS), we have produced the surface soil moisture data of TP from 2003 to 2010 and analyzed the seasonal characteristic of the soil moisture spatial distribution and its multi-year changing trend in area of TP. Compared to the in-situ observations, the accuracy of the soil moisture retrieved by the proposed algorithm is evaluated. The evaluation result shows that the new soil moisture product has a better accuracy in the TP region than the official product of AMSR-E. The spatial distribution of the annual mean values of soil moisture and the seasonal variations of the monthly-averaged soil moisture are analyzed. The results show that the soil moisture variations in space and time are consistent with the precipitation distribution and the water vapor transmission path in TP. Based on the new soil moisture product, we also analyzed the spatial distribution of the changing trend of multi-year soil moisture in TP. From the comparisons with the precipitation changing trend obtained from the meteorological observation sites in TP, we found that the spatial pattern of the changing trend of soil moisture coincides with the precipitation as a whole.

Review of snow water equivalent microwave remote sensing

Accurate quantitative global scale snow water equivalent information is crucial for meteorology, hydrology, water cycle and global change studies, and is of great importance for snow melt-runoff forecast, water resources management and flood control. With land surface process model and snow process model, the snow water equivalent can be simulated with certain accuracy, with the forcing data as input. However, the snow water equivalent simulated using the snow process models has large uncertainties spatially and temporally, and it may be far from the needs of practical applications. Thus, the large scale snow water equivalent information is mainly from remote sensing. Beginning with the launch of Nimbus-7 satellite, the research on microwave snow water equivalent remote sensing has developed for more than 30 years, researchers have made progress in many aspects, including the electromagnetic scattering and emission modeling, ground and airborne experiments, and inversion algorithms for future global high resolution snow water equivalent remote sensing program. In this paper, the research and progress in the aspects of electromagnetic scattering/emission modeling over snow covered terrain and snow water equivalent inversion algorithm will be summarized.

The coherent microwave emission of freezing soil: Experimental research and model simulation

Interference effect happens in layered medium. The brightness temperature oscillation has been observed during the freezing process of over-saturated soil, which could be explained by interference effect and a three layer coherent model. The modeled BT is qualitatively in consistent with the measurement. It is shown that the interference must be considered when measuring frozen soil with ground based microwave radiometer especially when using the frequency is low.

Validation of an improved TES algorithm based on corrected ALPHA difference spectra

On IGARSS’04 conference, we introduced a new TES algorithm we developed basing on corrected ALPHA difference spectra. Although that algorithm can separate temperature and emissivity successfully, it didn’t take downwelling sky irradiance into account.That means it can only be applied in very special circumstances where downwelling sky irradiance can be neglected. In fact, the surface thermal infrared radiance can be expressed as:Lj=εjBj(Ts)+(1-εj)Latj↓. According to that equation, we must take the influence of downwelling sky irradiance into account in most cases to separate temperature and emissivity correctly. In this paper, we developed an improved TES algorithm which takes downwelling sky irradiance into account on the basis of our old algorithm. The new algorithm is applicable in most cases. To validate our algorithm, we compare it with ASTER TES algorithm and found that they agree quite well, especially for the inverted temperatures.