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



Rok publikování 2023
Druh Článek v odborném periodiku
Časopis / Zdroj Agriculture
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Klíčová slova waterlogged areas; remote sensing; hyperspectral survey; MAXENT; modelling
Popis 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.

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