Acta Univ. Agric. Silvic. Mendelianae Brun. 2018, 66(1), 119-129 | DOI: 10.11118/actaun201866010119

Assessment of Soil Variability of South Moravian Region Based on the Satellite Imagery

Jaroslav Novák1, Vojtěch Lukas1, Fernando Rodriguez-Moreno2, Jan Křen1
1 Department of Agrosystems and Bioclimatology, Faculty of AgriSciences, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic
2 Department of Remote Sensing, Global Change Research Centre AS CR, Bělidla 4a, 603 00 Brno, Czech Republic

The aim of this study was to analyse the sets of atmospherically corrected Remote Sensing (RS) data in order to assess the variability of bare arable land between different soil blocks in the selected area and evaluate the suitability of this approach for locally targeted management. The study was carried out on the territory of parts of South Moravian, Vysocina and Zlín regions. The RS data was analyzed by two models developed in the Arc GIS 10.22 environment using the Normalized Differential Vegetation Index (NDVI) and the Principal component analysis (PCA) analyzing bare soil (BS) with more than 50 % and more than 95 % of the block representation. A layer of agricultural land from the LPIS system was used to delimit arable land areas. For correct determinig of BS value for NDVI were carried out the terrestrial measurements in the monitored area by using the GreenSeeker handheld crop sensor and The FieldSpec® HandHeld 2 spectrometer. Based on these measurements and image dates, 0.2 NDVI was selected as the limit value. As a more suitable source for identifying BS, RapidEye appears to be able to identify an average of 26 % of the observed area as bare ground compared to Sentinel 2 data (22.5 % of the observed area) in models with more than 50 % By representing the BS in a block (NDVI 50, PCA 50).
In these versions of the model was more variable soil (VS) indicated by RapidEye. With more than 95 % of the BS in the block (NDVI 95, PCA 95) was found more variable soils by Sentinel 2.
This method of indirectly identifying soil variability can assist in the application of fertilizer or soil treatment in the area of site-specific management.

Keywords: soil variability, remote sensing, RapidEye, SENTINEL 2, LPIS, PCA, NDVI, coefficient of variation
Grants and funding:

This paper was supported by the project IGA FFWT MENDELU No. 59/2013, entitled "Evaluation of soil variability of the selected area with remote sensing data " Data from the system iLPIS, in the form of spatial and descriptive representation of blocks of arable land as SHP format, was provided by the Ministry of Agriculture.

Prepublished online: February 28, 2018; Published: September 1, 2018  Show citation

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Novák, J., Lukas, V., Rodriguez-Moreno, F., & Křen, J. (2018). Assessment of Soil Variability of South Moravian Region Based on the Satellite Imagery. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 66(1), 119-129. doi: 10.11118/actaun201866010119
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