Shi Jiancheng

A physics-based statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data

AMSR-E and MODIS are two EOS (Earth Observing System) instruments on board the Aqua satellite. A regression analysis between the brightness of all AMSR-E bands and the MODIS land surface temperature product indicated that the 89 GHz vertical polarization is the best single band to retrieve land surface temperature. According to simulation analysis with AIEM, the difference of different frequencies can eliminate the influence of water in soil and atmosphere, and also the surface roughness partly. The analysis results indicate that the radiation mechanism of surface covered snow is different from others. In order to retrieve land surface temperature more accurately, the land surface should be at least classified into three types: water covered surface, snow covered surface, and non-water and non-snow covered land surface. In order to improve the practicality and accuracy of the algorithm, we built different equations for different ranges of temperature. The average land surface temperature error is about 2–3°C relative to the MODIS LST product.

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.

Fractional snow cover estimation in Tibetan Plateau using MODIS and ASTER

Seasonal snow cover plays a significant role in regional, large even global scale climate processes, hydrological cycle and surface thermal balance processes. MODIS snow cover methods provide an operational model for mapping each pixel into snow or no-snow by using a normalized difference snow Index (NDSI) and its threshold test. But they cannot provide the fractional snow cover information. This paper presents an automated snow-mapping technique at sub-pixel resolution in Tibetan Plateau based on MODIS normalized difference snow fraction and normalized difference Vegetation fraction. This paper presents an automated snow-mapping technique at sub-pixel resolution based on MODIS normalized difference snow Index (NDSI) and normalized Difference Vegetation Index(NDVI) in order to avoid overestimations due to vegetation cover. And it takes the ASTER data as ground true data to verified this method. Keywords-MODIS, ASTER, sub pixel snow mapping, Tibetan plateauSeasonal snow cover plays a significant role in regional, large even global scale climate processes, hydrological cycle and surface thermal balance processes. MODIS snow cover methods provide an operational model for mapping each pixel into snow or no-snow by using a normalized difference snow Index (NDSI) and its threshold test. But they cannot provide the fractional snow cover information. This paper presents an automated snow-mapping technique at sub-pixel resolution in Tibetan Plateau based on MODIS normalized difference snow fraction and normalized difference Vegetation fraction. This paper presents an automated snow-mapping technique at sub-pixel resolution based on MODIS normalized difference snow Index (NDSI) and normalized Difference Vegetation Index(NDVI) in order to avoid overestimations due to vegetation cover. And it takes the ASTER data as ground true data to verified this method.

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.

Retrieval algorithm for microwave surface emissivities based on multi-source, remote-sensing data: An assessment on the Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau plays a very important role in studying severe weather in China and around the globe because of its unique characteristics. Moreover, the surface emissivities of the Qinghai-Tibet Plateau are also important for retrieving surface and atmospheric parameters. In the current study, a retrieval algorithm was developed to retrieve the surface emissivities of the Qinghai-Tibet Plateau. The developed algorithm was derived from the radiative transfer model and was first validated using simulated data from a one-dimensional microwave simulator. The simulated results show good precision. Then, the surface emissivities of the Qinghai-Tibet Plateau were retrieved using brightness temperatures from the advanced microwave-scanning radiometer and atmospheric profile data from the moderate resolution imaging spectroradiometer. Finally, the features of the time and space distribution of the retrieved results were analyzed. In terms of spatial characteristics, a spatial distribution consistency was found between the retrieved results and surface coverage types of the Qinghai-Tibet Plateau. In terms of time characteristics, the changes in emissivity, which were within 0.01 for every day, were not evident within a one-month time scale. In addition, surface emissivities are sensitive to rainfall. The reasonability of the retrieved results indicates that the algorithm is feasible. A time-series surface emissivity database on the Qinghai-Tibet Plateau can be built using the developed algorithm, and then other surface or atmospheric parameters would have high retrieval precision to support related geological research on the Qinghai-Tibet Plateau.

Physical statistical algorithm for precipitable water vapor inversion on land surface based on multi-source remotely sensed data

Water vapor plays a crucial role in atmospheric processes that act over a wide range of temporal and spatial scales, from global climate to micrometeorology. The determination of water vapor distribution in the atmosphere and its changing pattern is very important. Although atmospheric scientists have developed a variety of means to measure precipitable water vapor (PWV) using remote sensing data that have been widely used, there are some limitations in using one kind satellite measurements for PWV retrieval over land. In this paper, a new algorithm is proposed for retrieving PWV over land by combining different kinds of remote sensing data and it would work well under the cloud weather conditions. The PWV retrieval algorithm based on near infrared data is more suitable to clear sky conditions with high precision. The 23.5 GHz microwave remote sensing data is sensitive to water vapor and powerful in cloud-covered areas because of its longer wavelengths that permit viewing into and through the atmosphere. Therefore, the PWV retrieval results from near infrared data and the indices combined by microwave bands remote sensing data which are sensitive to water vapor will be regressed to generate the equation for PWV retrieval under cloud covered areas. The algorithm developed in this paper has the potential to detect PWV under all weather conditions and makes an excellent complement to PWV retrieved by near infrared data. Different types of surface exert different depolarization effects on surface emissions, which would increase the complexity of the algorithm. In this paper, MODIS surface classification data was used to consider this influence. Compared with the GPS results, the root mean square error of our algorithm is 8 mm for cloud covered area. Regional consistency was found between the results from MODIS and our algorithm. Our algorithm can yield reasonable results on the surfaces covered by cloud where MODIS cannot be used to retrieve PWV.

Improving ground surface temperature and heat flux simulation with satellite derived emissivity in arid and semiarid regions

Land surface emissivity is a critical factor controlling the energy budget on earth surface. However, this important parameter is poorly represented utilizing the “constant-ε” assumption in the state-of-the-art land surface models as well as climate models due to lack of observations. Satellite sensors such as the Advanced Very High Resolution Radiometer(AVHRR) and Moderate-resolution Imaging Spectrometer(MODIS) can provide Narrow Band Emissivity (NBE) products. These NBE products need to be preprocessed to produce reliable Broad Band Emissivity (BBE) which can be then assimilated into land surface models. This paper presents a preliminary sensitivity study of land surface energy balance simulation utilizing the long-term Global Land Surface Satellite (GLASS) BBE product in the arid and semiarid regions of northwestern China. We find that the GLASS-based land surface emissivities in the study region show great spatial and temporal variabilities. Satellite derived emissivity for bare soil ranges from 0.90 to 0.985 and more than half of bare soil grids over our study region have emissivity values less than 0.94. Decreased emissivity would lead to increased surface temperature and sensible heat flux. In-situ simulation results indicate that the ground surface temperature and heat fluxes simulations can be improved when satellite derived emissivity is assimilated.

A physically based parameterized method to estimate cloud liquid water over land using satellite passive microwave sensor AMSR-E

This paper presents a new scheme to retrieve cloud liquid water (CLW) over land using AMSR-E brightness temperatures (TB) without the help of ancillary data. A surface emission model, Advanced Integral Equation Model (AIEM) and an one-dimensional atmosphere transfer model (1DRTM) were combined to generate a database. Through analysis of the simulated dataset, it is found that the ratio of the polarization difference obtained from 36.5 and 89 GHz (ΔTB(36.5)/ΔTB(89)), called PDR_CLW later) is sensitive to CLW. The algorithm was validated using AMSR-E observations over the Southern Great Plains. The CLW data retrieved by five ground based microwave radiometers (MWR) were used to validate the algorithm, with RMS error of 0.11 mm.