Akademik Veri Yönetim Sistemi
Journal of Food, Agriculture and Environment, cilt.11, sa.3-4, ss.2141-2144, 2013 (Scopus)
Correct prediction of soil characteristics by remote sensing studies based on near-infrared depends on sampling, time, environment and tools used. Particle size, structure, organic matter, iron and moisture content of the samples are very effective in measurements. The effects of organic matter, moisture and iron content on prediction are already eliminated by specific applications. However, particle size and structure properties are not taken into account. The purpose of this study is to assess the impact of grain size and structural differences on reflection values of soils and to reveal the most appropriate physical state of the sample to determine any content of soil. In this study, 60 soil samples were collected from the fields with serial classification in Isparta (Atabey) region and crushed samples were separated into five different mesh sizes (4.76-2.00, 2.00-1.00, 1.00-0.50, 0.5-0.25 and <0.25 mm). Measurements were taken with one-nanometre intervals between 350 nm and 2500 nm wavelength in samples with different particle size. Cluster analysis was applied to compare absorbance means as statistical procedure. While it was limited to determine the soil type in entisols and inceptisols soil orders in relation to young soil formation presenting more heterogenous structure, it was distinctive in vertisols and mollisols soil orders in spectral regions except visible spectral region (VIS). According to discriminant analysis, the successes of accurate prediction of the existing 5 particle sizes were found to be 50.00%. When each grain size was examined separately the size of the soil sample with <0.25 mm sieve size was predicted with 83.3% accuracy.