The Use of Spectral Indices to Recognize Waterlogged Agricultural Land in South Moravia, Czech Republic

Marek Bednář, Bořivoj Šarapatka (), Patrik Netopil, Miroslav Zeidler, Tomáš Hanousek and Lucie Homolová
Additional contact information
Marek Bednář: Department of Ecology and Environmental Sciences, Faculty of Science, Palacký University Olomouc, 779 00 Olomouc, Czech Republic
Bořivoj Šarapatka: Department of Ecology and Environmental Sciences, Faculty of Science, Palacký University Olomouc, 779 00 Olomouc, Czech Republic
Patrik Netopil: Department of Ecology and Environmental Sciences, Faculty of Science, Palacký University Olomouc, 779 00 Olomouc, Czech Republic
Miroslav Zeidler: Department of Ecology and Environmental Sciences, Faculty of Science, Palacký University Olomouc, 779 00 Olomouc, Czech Republic
Tomáš Hanousek: Global Change Research Institute CAS, 60300 Brno, Czech Republic
Lucie Homolová: Global Change Research Institute CAS, 60300 Brno, Czech Republic

Agriculture, 2023, vol. 13, issue 2, 1-18

Abstract: The agricultural landscape of the Czech Republic is facing climate change, and drought is among the most severe stress factors. Thousands of small ponds and naturally wet areas have been drained and transformed into agricultural parcels. Their restoration could increase the landscape’s resilience to climate change. Therefore, we describe the possibility of using hyperspectral aerial surveying for the identification of waterlogged areas in the agricultural landscape based on the example of one of the warmest and driest regions of the Czech Republic—the South Moravian region, an area where water retention in the landscape is highly relevant. Within our study, a total of 33 spectral indices related to the waterlogging of soil selected from previous studies were evaluated. The maximum entropy model (MAXENT) was used in the analysis of these indices. The analysis, which was carried out in several locations during different periods of the year (spring and autumn), shows the varying applicability of individual groups of indices. Regardless of the season, chlorophyll-based indices (MCARI—31.8, CARI—26.3, TCARI2—24.3 average percentage contribution) made the most significant contribution to the creation of probability maps of the occurrence of waterlogged areas. However, more accurate results could be achieved in the spring period by using the NVI index (40.5 average percentage contribution). The results show that remote sensing could be used for the identification of waterlogged sites, especially for initial identification, which should then be confirmed by field survey. Furthermore, the research points out the role of the LAI and chlorophyll content. According to the NVI, low LAI contributes the most to the probability of occurrence in the spring season, while chlorophyll-based indices prove to be the best, contributing high values, which is rather contradictory but could be resolved only by subsequent field research.

Keywords: waterlogged areas; remote sensing; hyperspectral survey; MAXENT; modelling (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/2/287/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/2/287/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:13:y:2023:i:2:p:287-:d:1046662

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().