Examining the relationship between SPAD, LAI and NDVI values in a maize long-term experiment

Ragán, Péter
Harsányi, Endre
Nagy, János
Törő, Ágnes
Vántus, András
Csatári, Nándor
Rátonyi, Tamás
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Kötet címe (évfolyam száma)
In Hungary, the preconditions for the use of precision crop production have undergone enormous development over the last five years. RTK coverage is complete in crop production areas. Consultants are increasingly using the vegetation index maps from Landsat and Sentinel satellite data, but measurements with on-site proximal plant sensors are also needed to exclude the influence of the atmosphere. The aim of our studies was to compare the values measured by proximal plant sensors in the polyfactorial split-split-plot long-term maize field experiment at the trial site of the University of Debrecen (Hajdúság loess plato, N 47.554164, E 21.448111, 112 m elevation) in 2017. The soil type of the experimental site is a lowland calcareous chernozem. During these studies, we used Minolta SPAD502 chlorophyll meter, LI-COR LAI-2000 leaf area index meter and Trimble GreenSeeker NDVI measuring instrument. The measurements were performed in V4; V8 and R1 phenological stages. The statistical analyses were carried out in Rstudio. In V4 phenological phase the correlation between the SPAD values and the NDVI values has been found weak (r = 0.18). The correlation between the SPAD and NDVI values was not significant during the V8 phenological phase. During the phenological phase R1, the correlation between the SPAD and NDVI values was moderate (r = 0.32). The SPAD and LAI values measured in phenophase R1 significantly (P <0.001) influenced the measured NDVI values, the relationship between these values was moderate (r = 0.47), SPAD and LAI values had 22.34% influence on the NDVI values. When in the phenophase of maize R1 the variation coefficients of the NDVI measurements were taken into account, they significantly (P <0.001) affected the measured NDVI values by 43.1%
Maize, SPAD, NDVI, Leaf Area Index, multilinear regression