Observation and Study of Land Surface Parameters over Gobi in Typical Arid Region

According to the need of popular land surface process models, characteristics and rules of some key land surface process and soil parameters over Gobi in typical arid region of Northwest China are analyzed by using the data observed during the intensive observation period of the Dunhuang Land–Surface Process Field Experiment (DLSPFE) (May–June 2000). Using the relative reflection as weighting factor, the weighted mean of the surface albedo over Dunhuang Gobi in typical arid region is calculated and its values are 0.255 ± 0.021. After canceling the interference of the buildings, the mean values of the roughness length averaged with logarithm is 0.0019 ± 0.00071 m. After removing the influence of the oasis, the soil wetness factor computed with data under condition of no precipitation is 0.0045. After removing the influence of the precipitation , the mean values of the soil heat capacity over Dunhuang Gobi in typical arid region is 1.12 × 106 J m−3K−1, a bit smaller than the values observed in HEIFE. But the soil heat diffusivity and conductivity are about one of those observed in HEIFE. The soil water content over Dunhuang Gobi in typical synoptic condition is very little and does not exceed 1% basically.

Elucidating Dominant Factors Affecting Land Surface Hydrological Simulations of the Community Land Model over China

In order to compare the impacts of the choice of land surface model(LSM)parameterization schemes,meteorological forcing,and land surface parameters on land surface hydrological simulations,and explore to what extent the quality can be improved,a series of experiments with different LSMs,forcing datasets,and parameter datasets concerning soil texture and land cover were conducted.Six simulations are run for the Chinese mainland on 0.1°×0.1°grids from 1979 to 2008,and the simulated monthly soil moisture(SM),evapotranspiration(ET),and snow depth(SD)are then compared and assessed against observations.The results show that the meteorological forcing is the most important factor governing output.Beyond that,SM seems to be also very sensitive to soil texture information;SD is also very sensitive to snow parameterization scheme in the LSM.The Community Land Model version 4.5(CLM4.5),driven by newly developed observation-based regional meteorological forcing and land surface parameters(referred to as CMFD_CLM4.5_NEW),significantly improved the simulations in most cases over the Chinese mainland and its eight basins.It increased the correlation coefficient values from 0.46 to 0.54 for the SM modeling and from 0.54 to 0.67 for the SD simulations,and it decreased the root-mean-square error(RMSE)from 0.093 to 0.085 for the SM simulation and reduced the normalized RMSE from 1.277 to 0.201 for the SD simulations.This study indicates that the offline LSM simulation using a refined LSM driven by newly developed observation-based regional meteorological forcing and land surface parameters can better model reginal land surface hydrological processes.

Estimations of Land Surface Characteristic Parameters and Turbulent Heat Fluxes over the Tibetan Plateau Based on FY-4A/AGRI Data

Accurate estimates of land surface characteristic parameters and turbulent heat fluxes play an important role in the understanding of land-atmosphere interaction. In this study, Fengyun-4A (FY-4A) Advanced Geostationary Radiation Imager (AGRI) satellite data and the China Land Data Assimilation System (CLDAS) meteorological forcing dataset CLDAS-V2.0 were applied for the retrieval of broadband albedo, land surface temperature (LST), radiation flux components, and turbulent heat fluxes over the Tibetan Plateau (TP). The FY-4A/AGRI and CLDAS-V2.0 data from 12 March 2018 to 30 April 2018 were first used to estimate the hourly turbulent heat fluxes over the TP. The time series data of in-situ measurements from the Tibetan Observation and Research Platform were divided into two halves-one for developing retrieval algorithms for broadband albedo and LST based on FY-4A, and the other for the cross validation. Results show the root-mean-square errors (RMSEs) of the FY-4A retrieved broadband albedo and LST were 0.0309 and 3.85 K, respectively, which verifies the applicability of the retrieval method. The RMSEs of the downwelling/upwelling shortwave radiation flux and downwelling/upwelling longwave radiation flux were 138.87/32.78 W m^(−2) and 51.55/17.92 W m^(−2), respectively, and the RMSEs of net radiation flux, sensible heat flux, and latent heat flux were 58.88 W m^(−2), 82.56 W m^(−2) and 72.46 W m^(−2), respectively. The spatial distributions and diurnal variations of LST and turbulent heat fluxes were further analyzed in detail.

Localization and validation of an urbanized high-resolution land data assimilation system(u-HRLDAS)

This paper uses an urbanized high-resolution land data assimilation system(u-HRLDAS) to parameterize the urban land surface characteristics.The u-HRLDAS model is localized and developed in order to satisfy the need of the weather forecast in Beijing,China.The remote sensing data used to localize and drive u-HRLDAS include the soil type data and MODIS retrieved leaf area index(LAI) data.The evaporation and water depth for impervious surface in urban area are developed to improve the simulation of u-HRLDAS.The result of the urban weather forecast is used for the comparison based on the rapid update cycle system at Beijing Meteorological Bureau(BJ-RUC) without coupled with u-HRLDAS.The land surface temperature,land surface fluxes,and first layer soil moisture in several single sites and urban Beijing region by BJ-RUC are compared with u-HRLDAS after localization and development.The off-line simulation results indicate that compared with BJ-RUC,after the localization and development,u-HRLDAS can improve the simulation of land surface parameters and fluxes definitely.

Fusion of VNIR and Thermal Infrared Remote Sensing Data Based on GA-SOFM Neural Network

The multi-source data fusion methods are rarely involved in VNIR and thermal infrared remote sensing at present. Therefore, the potential advantages of the two kinds of data have not yet been adequately tapped, which results in low calculation precision of parameters related with land surface temperature. A new fusion method is put forward where the characteristics of the high spatial resolution of VNIR （visible and near infrared） data and the high temporal resolution of thermal infrared data are fully explored in this paper. Non-linear fusion is implemented to obtain the land surface temperature in high spatial resolution and the high temporal resolution between the land surface parameters estimated from VNIR data and the thermal infrared data by means of GA-SOFM （genetic algorithms ＆ self-organizing feature maps）-ANN （artificial neural net-work）. Finally, the method is verified by ASTER satellite data. The result shows that the method is simple and convenient and can rapidly capture land surface temperature distribution of higher resolution with high precision.