Introduction
Soil survey, due to the complexity of collected information is a time loosing activity with high costs. Besides the survey, soil sample analysis are also costly. This high survey and analytical costs may be reduced by the adoption of new instruments as spectroradiometers. Materials & methods
Survey of Mugan Valley, Azerbaijan was carried out in the last years by Institute of Soil Science and Agro-chemistry of The National Academy of Science of Azerbaijan, Genesis, geography and soils mapping.
A related database was set up adopting the one freely available at the internet site of National Center for Soil Mapping, Italy (CREA-ABP). Soil observations, analyzed and geo-referenced have been digitalized. Sampling and analytical procedures were performed in accordance with national and international standards. The field observation method was conducted to ISIS (L’Abate et al., 2013) and with the relative methodology (Costantini et al., 2007). Every genetic horizon were sampled and air dried.
In total 194 soil samples, out of 46 mini-pits have been collected and analyzed to determine pH, carbonates, texture, and Electric Conductibility with standard analytical procedures. Both wet and dry bare soil samples, and dry sieved samples were scanned with an ASD FieldSpec 3 spectroradiometer (350 to 2500 nm with a step of 1 nm) to set up a soil signatures digital library with a total of 1164 signatures.
The library was than used to:
1) perform a munsell color conversion model on colorimetry data
2) orientate the selection of significant samples to be analyze
The colorimetry model
The adopted scanning procedure was saving a 20 measure scan and repeating scanning up to 3 times. The best result was choose for splice correction using the ASD software VieSpecPro. Colorimetry data have been than exported to text files (CIE, 1964 illuminate D65: X, Y, Z, x, y,u', v'). The color conversion was performed adopting the Virtual Colour Atlas software (v. 2.1.0730) on CIE X, Y, Z data of the raw dry samples. To attribute the nearest Munsell soil colour the Atlas Sample CMC function was selected. Results have been compared with the manual attribution with Munsell soil colour charts.
The samples selection model
To perform the best reduced dataset to be further investigated on Organic Carbon and Nitrogen content, the signatures of the sieved dry samples were selected. K-mean Spectral analyses was performed both on different genetic horizons (A, B, C) and on the whole dataset. The two analyses allowed us to determine two different datasets with, respectively 60 and 45 samples with a total of 103 selected samples. Out of the unselected samples were randomly selected 45 to use in the validation procedure of prediction. Four different calibrations were performed both on the two datasets, on their sum of 103 signatures, and on the whole datasets that included 149 samples. Prediction models were produced for Carbonates content and Electric Conductibility.
The Learned lesson
The colorimetry performed model highlighted several aspects of adopting colorimetry data: on one side the Munsell Hue determination seam to be consistent while the Munsell Value appears overestimated for the class 2 and the Chroma generally underestimated. The automatic attribution of Munsell color reduces the survey time and consequently positively impacts on the relatives costs but, on the other side, must be considered the higher uncertainty of modeled data.
The samples selection model highlighted that it is possible to reduce the calibration dataset maintaining relatively good results on prediction models.
References
Costantini, E.A.C.; Fantappiè, M.; L’Abate, G. (2007) Linee guida dei metodi di rilevamento e informatizzazione dei dati pedologici
L’Abate, G.; Allegri, G.; Barbera, R.; Bruno, R.; Fargetta, M.; Costantini, E.A.C. (2013) The ISIS webgis application for online Italian soil data consultation. EFITA conference, Turin, 24th-26th June, 2013 8 pp.
Virtual Colour Systems. VirtualAtlas Virtual Colour Atlas. Online:
http://www.vcsconsulting.co.uk/VirtualAtlas.htm