The Use of Useful Statistics of Soil Texture According to Standardized Depth Between Two Digital Soil Maps
2018
Sonn, Y., National Institute of Agricultural Science, RDA, Wanju, Republic of Korea | Go, W., National Institute of Agricultural Science, RDA, Wanju, Republic of Korea | Jeon, S., National Institute of Agricultural Science, RDA, Wanju, Republic of Korea | Hyun, B., National Institute of Agricultural Science, RDA, Wanju, Republic of Korea | Im, M., National Institute of Agricultural Science, RDA, Wanju, Republic of Korea | Yun, S., National Institute of Agricultural Science, RDA, Wanju, Republic of Korea
In Korea, a soil survey was conducted at a scale of 1: 5,000, and classification was performed based on USDA's Soil Taxonomy. Now the computerized soil map is in use. DSM work has been performed based on the soil map. Even so far, we have been building DBs in various ways and trying DSM continuously. Since 2002, data can be found on the Internet through the Korean soil information system (heugtoram) and it is also available on mobile system. Based on the 1: 5,000 scale, various detailed thematic maps are produced. The useful information has been provided to farmers, policy makers, and researchers. The basic data of the soil was used around 1970. In 2014, we tried to compare it with the newly upgraded data. R program was used to generalize the texture data (sand, silt & clay content) of soil depth. Conversion of vector data (polygon, shp file) of soil map into raster data (30 m) was performed. Soil texture data for depth were generalized to 0-5, 5-15, 15-30, 30-60, 60-100, 100-200 cm for sand, silt and clay contents and tried to compare the difference of the results. The number of polygons was the same in both sand, silt and clay contents. The total number of polygons was 1,381,126 and the number of polygons contained in the soil series was 1,308,700. As the depth increased, soils with rocks or shallow soils were not shown. Mean, standard deviation and mean deviation were analyzed for comparisons of DSM. The standard deviations were very variable and difficult to compare. The results were different from those obtained when the mean deviation was used. To compare the differences between the two maps, by converting the state in which a negative number exists to an absolute value, the average deviation to be performed is more uniform.
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