Abstract
Indirect, non-destructive methods to derive biophysical parameters, such as leaf area index (LAI), are of major importance for optimal grassland growth modelling and management. In this study, we compared different methods for the estimation of LAI in permanent grassland including (i) two direct methods, (ii) two indirect optical methods (AccuPAR and LAI-2200C), (iii) a proximal (field spectrometer) and a satellite remote sensing approach using Sentinel-2 (S-2) data, both based on radiative transfer modelling (RTM) of vegetation. To consider the seasonal variability of LAI sufficiently, we performed in situ measurements weekly during the entire growing season of 2018 and 2019. The RTM-based methods showed the lowest root-mean-square error (RMSE) when compared with direct green LAI measurements, which ranged from 1.68 to 7.85. The indirect optical methods resulted in higher RMSE values but in similar high correlation coefficients (r). The comparison between the indirect optical methods showed that the AccuPAR and the LAI-2200C highly correlate with a systematic underestimation of the AccuPAR in the upper range of LAI values. As expected, S-2 and the field spectrometer showed the highest correlation (RMSE: 0.40 and r: 0.95). Generally, we observed a significant influence of the seasonal changes of the canopy structure and morphology on the estimation accuracy.
Zusammenfassung
Vergleich von direkter und indirekter Bestimmung des Blattflächenindexes (LAI) im Dauergrünland. Indirekte, zerstörungsfreie Methoden zur Ableitung von biophysikalischen Parametern wie Blattflächenindex (LAI) sind für eine optimale Grünlandbewirtschaftung von zentraler Bedeutung. In dieser Studie wurden verschiedene Methoden zur Bestimmung des LAI im Dauergrünland verglichen. Die untersuchten Methoden umfassten (i) zwei direkte, (ii) zwei indirekte optische Verfahren (AccuPAR und LAI-2200C), (iii) ein proximales (Feldspektrometer) und ein fernerkundliches Verfahren (Sentinel-2), die beiden letzteren jeweils in Kombination mit einer strahlungstransfermodell-basierten Berechnungsmethode. Um die saisonale Variabilität des LAI ausreichend zu berücksichtigen, wurden wöchentlich Messungen über den gesamten Verlauf der Vegetationsperioden 2018 und 2019 durchgeführt. Die direkten LAI-Messungen an ausschließlich grünen Blättern (GLAI) ergaben Werte von 1,68 bis 7,85. Die strahlungstransfermodell-basierten Methoden zeigten beim Vergleich zu den direkten GLAI-Messungen den geringsten root-mean-square error (RMSE). Die indirekten optischen Methoden führten zu höheren RMSE-Werten, aber zu ähnlich hohen Korrelationskoeffizienten (r). Der Vergleich zwischen den indirekt-optischen Methoden zeigte, dass AccuPAR und LAI-2200C trotz eines systematischen Fehlers des AccuPAR im oberen Bereich (zu niedrige Werte), stark korrelieren. LAI-Werte von Sentinel-2 und Feldspektrometer zeigten wie erwartet die höchste Übereinstimmung im Hinblick auf RMSE (0.40) und r (0.95). Insgesamt üben die strukturellen und morphologischen Unterschiede der Vegetationsdecke zwischen den einzelnen Aufwüchsen einen signifikanten Einfluss auf die Schätzgenauigkeit der untersuchten Methoden aus.
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Klingler, A., Schaumberger, A., Vuolo, F. et al. Comparison of Direct and Indirect Determination of Leaf Area Index in Permanent Grassland. PFG 88, 369–378 (2020). https://doi.org/10.1007/s41064-020-00119-8
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DOI: https://doi.org/10.1007/s41064-020-00119-8