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Soil erosion by water is the main cause of soil degradation in large areas of the Mediterranean belt. Soil erosion determines loss of surface horizon, which is rich in organic matter. The content of soil organic matter(SOM)is a key property for evaluating soil erosion. Conventional methods to estimate qualitatively SOM loosed by soil erosion based on the use of advantage of soil spectral reflectance, which has the advantage to be rapid, non destructive and cost effective. Visible near infrared diffuse reflectance(vis-NIR) spectroscopy is a fast, non-destructive technique well suited for analyses of some of the essential constituents of the soil and effect that cause soil erosion. These constituents, mainly clay minerals, organic matter and soil water strongly affect conditions for plant growth and influence plant nutrition. Here we describe the process by which vis-NIR spectroscopy can be used to collect soil spectra in the laboratory. Because it is an indirect technique, the succeeding model calibrations and validations that are necessary to obtain reliable predictions about the soil properties and cause of soil erosion, are also described in the chapter.

The aim of this paper is to provide a review on current state and future of vis-NIR spectroscopy to estimate soil properties. Soil Erosion is main problem of soil and agriculture. Therefore it is necessary to study and solve this problem by scientific ways. spectroscopy is one of the method to study this problem for solution.

Many spectroscopy journal and chemical journal i.e. Fystro, 2002, Ben-Dor and Banin, 1995 & dunn et.al., 2002 has been studied for this research.     .

To generate soil spectrum, radiation soil have to absorb light radiation which cause individual wants to vibrate either by bending or stretching, they will absorb light between to energy levels with difference in energy quantum when NIR radiation interacts with a soil sample fundamental vibration combined that are detected in NIR region which is characterized by broad, superimpose, and weak vibrational modes, giving soil NIR spectra with two visible reason because of main processes as the energy of radiation is high and reason contain useful information on organic and inorganic materials in soil. Absorption in the NIR region result from the overtones of OH, SO4, and CO3 groups and combination of fundamental features of SO2 and CO2.  soil minerals can absorbs vis-NIR region due to metal-OH bands. Carbonates also have weak absorption peaks in the near infrared. Water has a strong influence on vis-NIR spectra of soils so giving dominant absorption bands which is characteristics of soil spectra and weaker bands in other parts of vis-NIR range.

Spectroscopic Multivariate Calibrations

Due to overlapping of soil constituents the vis-NIR very non specific. This characteristic lack of specificity is compounded by scatter effect caused by soil structure. All these factors result in complex absorption patterns that extracted from the spectra and with correlated with soil properties.

Soil Organic Matter (SOM)

SOM often approximated to 1.72 times soil organic carbon (SOC), is the property most frequently estimated by vis-NIR calibrations. Stretching and bending of NH, CH, and Co groups due to overtones and combination bands in vis-NIR.

Due to nitrogen content very low in soil the nitrogen specific absorptions in the vis-NIR is very weak.

Potassium availability for plant uptake is dependent on its release from the weathering of primary soil minerals. Soil minerals absorb light in UV, visible, vis-NIR. Iron oxide absorb strongly in the UV and absorb weakly in the vis-NIR region. This review concentrate on diagnostic absorption for the most commonly iron oxides and clay minerals.

Clay minerals absorptions are mostly due to OH2, H2O, and CO3 overtones and combination vibrations of fundamentals.

Organic Matter Quality

The importance of soil biological processes for agriculture and forestry, as well as for environmental management, is unquestionable. As discussed earlier, organic matter absorbs in the vis-NIR region. Even more intresting with regard to biological processes is the potential ability to correlate vis-NIR to the quality of the organic matter.

Heavy metals in the soil do not absorb in the vis-NIR region. However, they can be detected because of co-variation with spectrally active components and used vis-NIR for predictions of As, Cd, Cu, Fe, Hg, Pb, S, Sb, and Zn in soils polluted by mining accident and reported.

Vis-NIR used directly for the characterisation of soil quality or soil fertility. So the spectra contains information on soil quality and soil fertility so the spectra contain the information of soil organic and mineral composition- the fundamentals building blocks of soil. Therefore the spectra themselves should be useful for characterizing changes in quality and or fertility therefore spectra themselves be useful for characterizing changes in quality and fertility so near infrared  spectroscopy as a tool for diagnosing soil condition for agriculture and environmental management.

Materials

Instruments

A variety of vis-NIR spectrophotometers from several manufactures exists today, and these provide a number of different solution for light dispersion, detectors and sample presentation configurations. What instrument to choose is largely dependent on the application and basically there is a trade-off between price and performance.

Resolution and Noise

For scientific purpose, an instrument with high resolution, 10 nm or better, is favourable; however there is a direct trade-off between resolution and noise.

Spectral Range

Similarly, a wavelength range covering both visible (400-780 nm) and entire NIR region (780-2500 nm) is recommended for scientific purposes, to make sure that as much of the important wavelength bands as possible is included. If, however the instrument is to be used for a very specific purpose, the need for full vis-NIR spectra may not be necessary.

Methods

Soil Sample Preparations

Use air or oven dried soil

Grind soil to <2 mm particle size

Measurement

Each instrument often has its own sample presentation setup and compatible sample containers. That is, the general recommendation is to follow the instructions for the specific instrument. However, presented below are some general aspects to consider.

Sample presentation and handling

Soils are heterogeneous- therefore, it is very important to measure a representative part of the soil sample and a configuration that allows for a large part of the sample to be scanned is favourable. If the sampled area is very small the use of replicate spectral sampling is recommended.

Make sure that sample is thoroughly mixed in the sample container. Do not shake the sample to get an even surface because this will stratify the sample, with the smaller particles moving down towards the bottom of the container. If an even surface is required, instead use a tool to carefully flatten the sample surface.

Pack the containers the same way for all samples. Try to use the same volume of soil and to use the same amount of pressure.

If the same containers to be used for several samples it is important to clean it between samples, however, avoid using water or alcohol/organic solvents.

If the measurement window has direct contact with the soil, it is important to also clean this between samples. Again, avoid using water or alcohol/organic solvents but wipe it clean using a dry dust free tissue.

White and Dark Reference

Depending on instrument, this may be done automatically, however for some instruments it needs to be done manually. This is a crucial step, and to ensure good quality spectra this needs to be done thoroughly and regularly.

The white and dark reference should be taken every 10 minutes. In many instruments the dark reference is taken automatically when a white one is taken.

If a configuration where an external white and dark reference is used it is important that the configuration is the same as that used for the sample measurements.

If it possible to monitor the spectrum of white reference, this should represent 100% reflection at all wavelengths across the 400-25000 nm range.

If the measurements are done in such a way that other light source than that related to the measurement might influence the result, minimize or standardize all other light sources during measurements- e.g. fluorescent light, ambient light from windows, etc.

Calibration and Validation

There are many different algorithms that can be used to calibrate soil vis-NIR spectra to predict soil properties and which cause soil erosion. They include multiple linear regression (MLR), principal component regression (PCR) and partial least squares regression (PLS) as well as data mining techniques like artificial neural networks (ANN), multivariate adaptive regression splines (MARS) and boosted regression trees (Viscarra Rossel and Behrens, 2010). They all have merits and disadvantages and we will not make specific recommendations on which technique to choose, but the linear ones are more straight forward and most commonly used. At the same time, the use of data mining is increasing, especially for large diverse data sets where data mining is indicated to perform slightly better than linear analyses (Viscarra Rossel and Behrens, 2010) Rather, we will give some general recommendations on what to think about when choosing calibration samples and how to validate your model.

The two steps in the soil sample pre-treatment are similar to common standard pre-treatments for many chemical and physical analyses, which has practical advantages.

It is possible to make good calibrations also on moist soils. Actually there are examples where calibrations on field moist samples have been beneficial (Fystro, 2002) etc. and standardised remoistening has led to substantial improved performance of clay and SOC calibrations (Stenberg, 2010). However, typically calibrations on dry soil in the lab perform better compared to those on field moist soil. This is mainly due to the higher degree of standardisation and the fact that broad water bands near 1400 and 1900 nm tend to override adjacent absorption bands. The crushing and sieving of soils removes stones and larger plant residues and also forms a basis for representative sub-sampling. Further grinding and sieving will lead to a more constant particles size, which will have an effect on spectra (Ben-Dor and Banin, 1995; dunn etal., 2002). Grinding of soil particles increases the overall reflectance and the effect is especially large for clay as aggregates are crushed. However, this effect can be more or less eliminated by a pre-treatment step of the spectra discussed in section 2.4 pre-treatment of the spectra. This is recommended because soil spectra also are affected by structural properties of the sample, causing non linear light scattering. This means that some of the light that is not measured as reflectance is not directly related to absorbance but is scattered.

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