Sentence examples for use of ancillary data from inspiring English sources

Digital soil mapping involves the use of ancillary data (e.g. proximal and remotely sensed) as surrogates for soil information to produce maps of soil type or classes.

Such opportunities include the use of ancillary data, scale-specific methods, improved development of spectral libraries and better integration of RS technologies into empirical and simulation SOC models.

The use of ancillary data generally increased classification accuracy and showed a similar potential for increasing classification accuracy than the selection of the classifier.

Two of the most popularly used sources of ancillary data are the proximally sensing electromagnetic (EM) induction instruments and remotely sensed digital elevation model data (DEM).

More complex disaggregation techniques use a wider range of ancillary data, for example crop-type maps to produce global N and P fertilizer application maps [ 8], or census data and land cover maps to produce rainfed and irrigated agriculture maps [ 9,42,43], which can then be used to map the cropping intensity of agriculture [ 44].

Underestimation of tree and impervious cover may also be partly due to masking during map development, including the varied selection of ancillary data used for masking (Homer and others 2007; Homer and others 2004; Huang and others 2001; MRLC 2009; Yang and others 2003).

We also outline selection of appropriate proxy outcome measures for detecting response to treatment and collection of ancillary data, such as diet and substance use, that are critical for interpretation of results.

Spatial patterns in the difference maps were then related to spatial patterns of ancillary data.

See Supporting Information for a summary list of ancillary data and observations.

Many factors such as tap water use and bathing habits could be considered when determining exposure to volatile chemicals in domestic water supplies, but our analysis suggests that the use of such ancillary data does not always result in an improvement in exposure accuracy if the ancillary data are inaccurate or if they have little effect on an individual's exposure level.

This study offers a solution strategy to this problem by the use of multi-angle radar images, and thus provides estimates of roughness and soil moisture without the use of ancillary field data.