Impact of sampling density on efficiency of soil salinity mas (A case study: Karkaj research station, university of Tabriz)

Soil surveying and mapping emphasize in various aims at different soil science aspects for applicants. Paying attention for scale and reconciliation of estimated with real data should be considered in all studies related to remote sensing and soil spatial variability. Although estimating of spatial variability of various soil parameters and soil surveying were studied many times, unfortunately the map accuracy and efficiency were not assessed in many cases. Salinization is one of the most important problems in arid and semi-arid regions. Therefore, it is essential to be studied the spatial variability of soil salinity in east Azerbaijan province because of its climatic condition. As well as easy readily of salinity measuring, it was used to assess the map efficiency.
This study was carried out in the part of Karkaj Agriculture Research Station belongs to the university of Tabriz in an area of about 4.2 ha. Sampling density is one of the parameters which influences not only on observation accuracy but also on map efficiency. Accordingly, two kinds of high and low sampling density including grids of 25 m and 50 m were designated, respectively. The numbers of 106 samples were taken at surface soil which its salinity was then measured in the laboratory. Statistical analysis, mean comparison, F-test and t-test performed by using MSTATC software.GS software was also used for Geostatistical analysis. Two methods of Jacknife and direct evaluating were applied for validity of models in order to testing maps accuracy. Soil salinity maps created with integrating the kriging geostatistics procedure and GIS. The map efficiency is being evaluated using four aspects: (1) map scale and texture, (2) map legend, (3) base map quality, and (4) ground truth, as well as may be interpreted by average size delineation, density of delineation, index of maximum reduction, effective scale number, maximum location accuracy and minimum legible delineation. Therefore, above indices were determined for two created maps according to sampling density.
The results revealed that the provided map at low sampling density is closed to the optimum condition compared with high sampling density. In despite of various number of polygons in both soil salinity maps, statistical analysis showed that there is no significant differences (p
It can be finally reported that reducing the number of soil samples may not decrease the optimum density of delineations nor has distinct impact on soil salinity map. Thus, to exploit more number of samples is not economic in all cases. On the other hand, using low density maps can be recommended to save money and time.