Use of High Resolution Remote Sensing Data for Generating Site- Specific Soil Mangement Plan

This present study explores the use high-resolution multi-spectral remote sensing data for generating within-field soil variability map as an inputs required for site-specific management of agriculture. The study was conducted for an experimental plot in Central Potato Research Station of Jalandhar, India. Thirty-five soil samples were collected from the field at regular intervals. The samples were analyzed for soil organic carbon, available nitrogen, available phosphorus, available potassium and soil texture. Various soil-related indices were calculated from IKONOS multispectral data, which included Brightness Index (BNI), Hue Index (HI), Saturation Index (SI), Coloration Index (CI), Redness Index (RI) and three principal components (PC1, PC2 and PC3). Variability of soil and spectral parameters were analyzed by estimating coefficient of variation (CV). The correlation analysis was carried out to study the relationship between soil and spectral parameters. Multiple regression models were generated, using stepwise regression technique, to estimate soil properties from RS data. The results showed that, CV of soil parameters was highest for available P (29.9%), followed by silt percentage (20.8%). Among the spectral parameters the CV was highest for PC3 (161.9%), followed PC2 (101.4%) and PC1 (84.0%). The soil organic carbon, available N and silt content were significantly correlated with spectral indices. The multiple regression equation between OC and spectral indices was significant with R = 0.733 and F = 6.277. Available N, silt and sand also formed significant multiple regression equations with spectral parameters. These empirical equations were used to generate soil fertility variability plans.