Soil Spectral Imaging: Moving From Proximal Sensing to Spatial Quantitative Domain

Abstract

Abstract. Imaging spectroscopy (IS) is a relatively new technique that has attracted the attention of workers in many fields. In the soil sciences, this technology is not well developed and additional research is required – despite the fact that a large number of soil properties in the soil environment have already been studied from a reflectance perspective with much success (e.g. organic matter, cation exchange capacity, carbonate content and specific surface area). Going from proximal sensing to image spectrometry is not only a journey from the micro to macro scales; it is a lengthy one that is fraught with problems, such as dealing with data having a low signal-to-noise level, contamination of the atmosphere, large data sets, the Bidirectional Reflectance Distribution Function (BRDF) effect and more. In this paper, provide a brief history of both near infrared spectroscopy (NIRS) and IS approaches and attempt to understand why, despite its promise, IS has not yet been well developed for the soil sciences. We assume that research, education, exposure of the technology to end-users and governmental involvement are the major factors that require attention in this venue. Also provide some personal thoughts on the future of IS in soil and conclude that in 5 to 10 years, this application will have matured into one that is ready to use and well-known among soil scientists, end-users and decision-makers.

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

“…To establish the prediction model (or spectral transfer function) of soil properties, it needs to collect information on soil physical and hydraulic properties, terrain and environmental attributes (topographical, geological, pedological, and land-use/land-cover information together with hydro-meteorological datasets and soil physical and hydraulic properties), and topsoil spectral data [81].…”

“…Such a protocol will minimize soil disturbance, which will enhance the characterization of the soil surface hydraulic properties based on spectral data, because sampling soil to the laboratory may disturb the soil surface and hence its hydraulic properties may be compromised. In addition, the protocol to calibrate the field and airborne data and to measure soil temperature and emissivity should be developed as well [81]. Figure 6 shows the appearance of the disturbed and undisturbed soil surfaces at Afeka site, Israel.…”

A Comparative Study of Land Cover Classification by Using Multispectral and Texture Data

“…It gave the satisfactory results for updating in GIS database for the cropland and grassland region [ 11 ]. A new idea of image spectroscopy (IS) and near-infrared spectroscopy (NIRS) was presented by [ 12 ] and it predicted that in the future it will be potentially used in many disciplines like geology, environmental sciences, precision agriculture, urban development, water and soil sciences, and so forth. The objective of this study is to design a simple, concise, and robust framework to classify the five types of land cover data in an absolute natural environment by using spectral and texture features.…”

A Robust Fuzzy Neural Network Model for Soil Lead Estimation from Spectral Features

“…Regarding access to high resolution and accurate spectral data (which are obtained via field spectrometry or space-born hyper spectral imaging), spectroscopy is an appropriate alternative to the afore-mentioned traditional chemical analysis [7][8][9][10].…”