The random function theory forms the basis of geostatistics and allows modelling of the uncertainty associated with spatial estimation and simulation. Remote sensing involves gathering information about an object by measuring signals composed of radiation, particles and fields emanating from an object with a sensor that is not in direct contact with it. This article reviews the present state of the art of how geostatistics is used in remote-sensing studies by reviewing the 2000–2010 literature in this field. This article first addresses the issue of stationarity in the context of image analysis and reviews whether this holds as it is a basic assumption of most geostatistical techniques. Ways to relax the assumption are discussed. Following is the use of variograms to quantify image structure and texture, and the use of variograms to address issues of optimal scale of observation. Next, various kriging-based estimation techniques (parametric and non-parametric) and how these can be used to enhance image information, fill missing pixel information and downscale information through super-resolution techniques (e.g. downscaling with preservation of spatial structure at the finer resolution) are discussed. Cokriging techniques are discussed to enable the combination of various variables at different support sizes and to link field and image data. To address issues of uncertainty and to characterize landscape heterogeneity, stochastic simulation techniques are discussed. A bibliometric analysis is presented which places the field of ‘remote sensing and geostatistics’ in a broader geosciences context and explores who are the key scientists and research groups that have contributed to the development of this field. It is concluded that the field of remote sensing and geostatistics has further developed the use of various pre-existing numerical techniques, which has led to new application areas. However, few new geostatistical techniques have been developed for use in remote sensing. In particular, aspects of scale and spatial sampling have been extensively addressed, but issues related to monitoring and space–time analysis have been largely neglected.

Reflectance spectra of carbonate minerals in the shortwave infrared (SWIR) and thermal infrared (TIR) wavelength regions contain a number of diagnostic absorption features. The shape of these features depends on various physical and chemical parameters. To accurately identify carbonate minerals or rocks in pure and mixed form, it is necessary to analyze the effects of the parameters on spectral characteristics. In this study, we analyzed spectral absorption feature characteristics of calcite and dolomite in the SWIR (features at 2.3 and 2.5 μm) and TIR (features at 11.5 and 14 μm) wavelength regions, as a function of grain size and carbonate mineral mixtures. Results showed that varying grain sizes and mineral contents in the sample, influence reflectance values and absorption feature characteristics. Absorption band positions of pure and mixed calcite and dolomite in the SWIR and TIR regions for both features were displaced slightly as observed in previous studies. The band positions of calcite and dolomite varied relative to grain size only in the TIR region. These positions shifted to longer wavelengths for the feature at 11.5 μm and to shorter wavelengths for the feature at 14 μm from fine to coarse grain size. The band positions of calcite-dolomite mixtures in the SWIR and TIR regions were determined by the quantity of calcite and dolomite in the sample. These results can be applied for the identification of pure and mixed calcite and dolomite, as well as estimating the relative abundance of both minerals with different grain size and mineral mixtures in a synthetic sample or rock. They can also be used as a preliminary proxy for assessing dolomitization patterns in carbonate rocks.

In this article we describe a new instrumental setup at the University of Twente Faculty ITC with an optimized processing chain to measure absolute directional-hemispherical reflectance values of typical earth science samples in the 2.5 to 16 µm range. A Bruker Vertex 70 FTIR spectrometer was chosen as the base instrument. It was modified with an external integrating sphere with a 30 mm sampling port to allow measuring large, inhomogeneous samples and quantitatively compare the laboratory results to airborne and spaceborne remote sensing data. During the processing to directional-hemispherical reflectance values, a background radiation subtraction is performed, removing the effect of radiance not reflected from the sample itself on the detector. This provides more accurate reflectance values for low-reflecting samples. Repeat measurements taken over a 20 month period on a quartz sand standard show that the repeatability of the system is very high, with a standard deviation ranging between 0.001 and 0.006 reflectance units depending on wavelength. This high level of repeatability is achieved even after replacing optical components, re-aligning mirrors and placement of sample port reducers. Absolute reflectance values of measurements taken by the instrument here presented compare very favorably to measurements of other leading laboratories taken on identical sample standards.

Swelling soils are a major engineering problem and their detection is one of the most important site investigation undertakings in the construction industry. Methods of their detection have been developed over the years, most of which utilize the soil physical properties as the indices of their identification. Some of these have proved quite expensive and time-consuming. In this research an alternative, relatively faster and cheaper method is explored. The method involves the use of Methylene Blue Absorption (MBA) as the engineering index to estimate the Cation Exchange Capacity (CEC) and spectral absorption feature characteristics as the mineral identification method. The relationship between the MBA and the characteristics of the absorption features is used to explore the possibility of estimating CEC from spectral data.

Displacement of soil particles caused by erosion influences soil condition and fertility. To date, the cesium 137 isotope (¹³⁷Cs) technique is most commonly used for soil particle tracing. However when large areas are considered, the expensive soil sampling and analysis present an obstacle. Infrared spectral measurements would provide a solution, however the small concentrations of the isotope do not influence the spectral signal sufficiently. Potassium (K) has similar electrical, chemical and physical properties as Cs. Our hypothesis is that it can be used as possible replacement in soil particle tracing. Soils differing in texture were sampled for the study. Laboratory soil chemical analyses and spectral sensitivity analyses were carried out to identify the wavelength range related to K concentration. Different concentrations of K fertilizer were added to soils with varying texture properties in order to establish spectral characteristics of the absorption feature associated with the element. Changes in position of absorption feature center were observed at wavelengths between 2,450 and 2,470 nm, depending on the amount of fertilizer applied. Other absorption feature parameters (absorption band depth, width and area) were also found to change with K concentration with coefficient of determination between 0.85 and 0.99. Tracing soil particles using K fertilizer and infrared spectral response is considered suitable for soils with sandy and sandy silt texture. It is a new approach that can potentially grow to a technique for rapid monitoring of soil particle movement over large areas.