HJ-1A hyperspectral data were used to distinguish topsoil salt components and estimate soil salinity, and the relationship between soil salt chemical components and sensitive bands of soil reflectance spectra was analyzed. The correlation between the soil salt content and the soil spectra obtained from the hyperspectral data was analyzed, proving that topsoil salinity has a very significant correlation with soil reflectance spectra. The relationship between soil reflectance spectra and salt chemical ions was investigated. The soil spectral reflectance at wavelength 510.975 nm and a difference vegetation index were selected to estimate soil salinity and the dominant salt chemical ion concentrations at a depth of 0 to 10 cm using a partial least squares regression model. It was found that the bands sensitive to various levels of chemical components of soil salt were shown to differ, controlled by the dominant component of the soil salt. The sensitive bands in the soil salinity estimation will change with differences in salt components. Estimating the dominant salt in the soil using soil reflectance spectra will lead to greater prediction accuracy. This study provided a possible method for the estimation of salinity and chemical component levels in topsoil, using the hyperspectral data to estimate topsoil salt components.
For the large-area snow depth (SD) data sets with high spatial resolution in the Altay region of Northern Xinjiang, China, we present a deterministic ensemble Kalman filter (DEnKF)-albedo assimilation scheme that considers the common land model (CoLM) subgrid heterogeneity. In the albedo assimilation of DEnKF-albedo, the assimilated albedos over each subgrid tile are estimated with the MCD43C1 bidirectional reflectance distribution function (BRDF) parameters product and CoLM calculated solar zenith angle. The BRDF parameters are hypothesized to be consistent over all subgrid tiles within a specified grid. In the SCF assimilation of DEnKF-albedo, a DEnKF combining a snow density-based observation operator considers the effects of the CoLM subgrid heterogeneity and is employed to assimilate MODIS SCF to update SD states over all subgrid tiles. The MODIS SCF over a grid is compared with the area-weighted sum of model predicted SCF over all the subgrid tiles within the grid. The results are validated with in situ SD measurements and AMSR-E product. Compared with the simulations, the DEnKF-albedo scheme can reduce errors of SD simulations and accurately simulate the seasonal variability of SD. Furthermore, it can improve simulations of SD spatiotemporal distribution in the Altay region, which is more accurate and shows more detail than the AMSR-E product.
Kriging interpolation provides the best linear unbiased estimation for unobserved locations, but its heavy computation limits the manageable problem size in practice. To address this issue, an efficient interpolation procedure incorporating the fast Fourier transform (FFT) was developed. Extending this efficient approach, we propose an FFT-based parallel algorithm to accelerate regression Kriging interpolation on an NVIDIA® compute unified device architecture (CUDA)-enabled graphic processing unit (GPU). A high-performance cuFFT library in the CUDA toolkit was introduced to execute computation-intensive FFTs on the GPU, and three time-consuming processes were redesigned as kernel functions and executed on the CUDA cores. A MODIS land surface temperature 8-day image tile at a resolution of 1 km was resampled to create experimental datasets at eight different output resolutions. These datasets were used as the interpolation grids with different sizes in a comparative experiment. Experimental results show that speedup of the FFT-based regression Kriging interpolation accelerated by GPU can exceed 1000 when processing datasets with large grid sizes, as compared to the traditional Kriging interpolation running on the CPU. These results demonstrate that the combination of FFT methods and GPU-based parallel computing techniques greatly improves the computational performance without loss of precision.
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