Retrieval of Harmonized LAI Product of Agricultural Crops from Landsat OLI and Sentinel-2 MSI Time Series

Jiří Tomíček (), Jan Mišurec, Petr Lukeš and Markéta Potůčková
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Jiří Tomíček: Gisat Ltd., Milady Horákové 57, 170 00 Prague, Czech Republic
Jan Mišurec: Gisat Ltd., Milady Horákové 57, 170 00 Prague, Czech Republic
Petr Lukeš: Global Change Research Institute, Czech Academy of Sciences, Bělidla 986/4a, 603 00 Brno, Czech Republic
Markéta Potůčková: Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University, Albertov 6, 128 43 Prague, Czech Republic

Agriculture, 2022, vol. 12, issue 12, 1-15

Abstract: In this study, an approach for the harmonized calculation of the Leaf Area Indices (LAIs) for agronomic crops from Sentinel-2 MSI and Landsat OLI multispectral satellite data is proposed in order to obtain a dense seasonal trajectory. It was developed and tested on dominant crops grown in the Czech Republic, including winter wheat, spring barley, winter rapeseed, alfalfa, sugar beet, and corn. The two-step procedure harmonizing Sentinel-2 MSI and Landsat OLI spectral data began with deriving NDVI, MSAVI, and NDWI_1610 vegetation indices (VIs) as proxy indicators of green biomass and foliage water content, the parameters contributing most to a stand’s spectral response. Second, a simple linear transformation was applied to the resulting VI values. The regression model itself was built on an artificial neural network, then trained on PROSAIL simulations data. The LAI estimates were validated using an extensive dataset of in situ measurements collected during 2017 and 2018 in the lowlands of the Central Bohemia Region. Very strong agreement was observed between LAI estimates from both Sentinel-2 MSI and Landsat OLI data and independent ground-based measurements ( r between 0.7 and 0.98). Very good results were also achieved in the mutual comparison of Sentinel-2 and Landsat-based LAI datasets ( rRMSE < 20%, r between 0.75 and 0.99). Using data from all currently available Sentinel-2 (A/B) and Landsat (8/9) satellites, a dense harmonized LAI time series can be created with high potential for use in precision agriculture.

Keywords: Sentinel-2; Landsat; leaf area index; harmonization; vegetation index; PROSAIL; radiative transfer; artificial neural network; time series (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: 2022
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