Hyperspectral remote sensing in laboratory, field and airborne levels as auxiliary tools in soil management

Abstract

Agricultural production has increased in the last years stimulated by the population growth and technological advances. This can cause significant environmental impacts including soil degradation if suitable agricultural planning and management are not applied in order to ensure a competitive and sustainable production. For this purpose, the soil variability assessment is needed and it is conventionally performed using soil sampling and analysis. However, these conventional methods of variability assessment have high costs and require considerable time and labor. When the amount of information needed increases, costs to describe soil spatial variability may become an obstacle if only conventional methodologies are applied. Therefore, alternative methods can help to depict soil properties variability on a scale suitable to agricultural management. So, Vis-NIR-SWIR reflectance spectroscopy, in which radiometric measurements are taken from 400 to 2500 nm, is proposed as a mean to predict soil properties. This is possible because spectral information has a direct relationship with soil constituents and characteristics. Therefore, in this study Vis-NIR-SWIR hyperspectral airborne imagery will be evaluated as an information source to be used in soil properties quantification and mapping, via the PLSR method and kriging. The performance of models derived from airborne imagery data will be compared with results obtained by models calculated from laboratory sensor data. Soil properties quantification using spectral information collected on-the-go in the field will be evaluated too in an experiment with different rates of lime. The experiment will be allocated in two fields with different soil textures (one with about 100 g kg-1 of clay and other with about 350 g kg-1 of clay). Soil properties predictions based on the on-the-go spectral measurements will be compared to predictions done by models derived from spectra collected in the laboratory using moist samples. PLSR method will be applied to calculate models and predict soil properties. Lime requirement maps will be interpolated by the inverse distance weighted technique. Maps derived from values obtained by conventional methods and from values predicted using spectra will be compared in order to evaluate the accuracy of predicted values interpolations. It is expected that spectral data collected in the field (on-the-go) and using hyperspectral airborne sensor can be used with considerable accuracy to predict and map soil properties. (AU)