Should laser diffraction become the new standard for soil particle size analysis ?

 This is the title of one of the presentations of the different methods used to determine  the clay content in soils. You can download the presentation here.

The recording is also available with the other presentations:

Improving the data downloaded from the ASD spectra in USGS

 I continue downloading ASD spectra from thre USGS library,  in order to have a data set of several clay minerals and as soon as you look to the spectra I get more ideas to improve the data set. 

The header in the spectrum gives details such as the sample number, formula, type and spectral purity, so iI will use these data as variables that will improve the work.

I show you this spectra of the sample Kaolinite CM5 as an example:

USGS Reference Materials (Illite)

We can get spectra from the USGS library and with some work we can import them to R or any other chemometric software to work with them. In this case I search for "illite" ( a type of clay mineral) and I found these 12 samples to work in coming posts with them.

       

We can see the spectra samples overplotted:

The USGS Library give a lot of details about each sample, such as the origin, purity, particle size, ......, and even the picture of the sample:

Illite GDS4.2 Marblehead ASDNGb AREF

How much kaolinite could contain my soil sample?

Now that we have our reference kaolinite near infrared spectrum stored  and studied, we can acquire a we spectrum (unknown) and compare it with the reference material trying to have a clear idea od how much kaolinite could contain. Soils high or very high in kaolinite have disadvantages for agriculture, so looking to the comparison we can take some actions.

We are going to use one spectrum (the first one) from the soilspec package, where we have a data set called:  "rutherglenNIR" which is a subset of soil spectra samples from the Rutherglen Area.

First, we compare the kaolinite reference material spetrum with the spectrum of the unknown sample:

And we have a look to the kaolinite featured spectra region, so we can trimm the spectrum and start to make some comparissons after the normalization processes:

Normalization Step 1 :

Normalization Step 2:

Now we can compare the areas, and  get the conclussion that the unknown soil sample has a relative low content of kaolinite compared to the reference spactrum.

Of course we can confirm this with numbers such as Area, Relative Depth,....

Reference Materials (Kaolinite)

 In the spectra of reference materials there are two kaolinite spectra, one is called kaolinite_114 and the other kaolinite_113. We can take them apart and overplot them in the same scale to see the differences:

One of the characteristics of the kaolinite spectra is the double is the doublet spectra feature in the 2200 nm region, so we can trim this region, take it apart and keep it as a diagnostic spectral feature to compare with our soil samples to determine if our soil samples contain kaolinite and in what proportion.

We can trim the zone of interest for the kaolinite and make calculations for the Area, Depth, Slope, etc. After we calculate the same values for our soil sample and compare the results to estimate  how much of the reference material (in this case kaolinite) could contain our soil sample.

In the next figures we see how the spectrum is trimmed, normalized, and the area is calculated:

Reference Materials for Soil in soilspec package.

 

How "spectra2colour" function from "soilspec" get the colour predicted values

 In the soil spectrum the colour regions are defined, and the mean reflectance value obtained for each. With the three values (RGB) we obtain a colour prediction when proyected on the RGB cube space.