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Retrieval of maize leaf area index using hyperspectral and multispectral data
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Orientador(es)
Resumo(s)
Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra
were used to investigate the applicability of hyperspectral and multispectral data in retrieving the
maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and
low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising
two and three band indices, and nine machine learning regression algorithms (MLRA) were tested
for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion
approach. The three band vegetation indices were selected, specifically the modified difference index
(mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for
the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for
the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of
field spectra (multispectral and hyperspectral) while the support vector machine was selected for
the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation
and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices
outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At
the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the
study area
Descrição
Palavras-chave
field-spectroradiometer Sentinel-2 hyperspectral multispectral leaf area index vegetation indices machine learning regression algorithms PROSAIL LUT inversion
Contexto Educativo
Citação
Remote Sens. 2018, 10, 1942
Editora
MDPI