Geostatistical approaches to refinement of digital elevation data

Data refinement refers to the processes by which a dataset’s resolution,in particular,the spatial one,is refined,and is thus synonymous to spatial downscaling.Spatial resolution indicates measurement scale and can be seen as an index for regular data support.As a type of change of scale,data refinement is useful for many scenarios where spatial scales of existing data,desired analyses,or specific applications need to be made commensurate and refined.As spatial data are related to certain data support,they can be conceived of as support-specific realizations of random fields,suggesting that multivariate geostatistics should be explored for refining datasets from their coarser-resolution versions to the finerresolution ones.In this paper,geostatistical methods for downscaling are described,and were implemented using GTOPO30 data and sampled Shuttle Radar Topography Mission data at a site in northwest China,with the latter’s majority grid cells used as surrogate reference data.It was found that proper structural modeling is important for achieving increased accuracy in data refinement;here,structural modeling can be done through proper decomposition of elevation fields into trends and residuals and thereafter.It was confirmed that effects of semantic differences on data refinement can be reduced through properly estimating and incorporating biases in local means.

A Remote Sensing Model to Estimate Sunshine Duration in the Ningxia Hui Autonomous Region,China

Sunshine duration（SD） is strongly correlated with solar radiation, and is most widely used to estimate the latter. This study builds a remote sensing model on a 100 m × 100 m spatial resolution to estimate SD for the Ningxia Hui Autonomous Region, China. Digital elevation model（DEM） data are employed to reflect topography, and moderate-resolution imaging spectroradiometer（MODIS） cloud products（Aqua MYD06-L2 and Terra MOD06-L2） are used to estimate sunshine percentage. Based on the terrain（e.g.,slope, aspect, and terrain shadowing degree） and the atmospheric conditions（e.g., air molecules, aerosols,moisture, cloud cover, and cloud types）, observation data from weather stations are also incorporated into the model. Verification results indicate that the model simulations match reasonably with the observations,with the average relative error of the total daily SD being 2.21%. Further data analysis reveals that the variation of the estimated SD is consistent with that of the maximum possible SD; its spatial variation is so substantial that the estimated SD differs significantly between the south-facing and north-facing slopes,and its seasonal variation is also large throughout the year.