Remote Estimation of Crop Chlorophyll Content Using Spectral Indices Derived From Hyperspectral Data

By D. Haboudane, N. Tremblay, John R. Miller, P. Vigneault. IEEE Transactions on Geoscience and Remote Sensing, 2008

314 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Plant Science

View Paper: https://www.semanticscholar.org/paper/6ba70aaa28ae759103a421c6d495ccdbf9911661

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Citations in the corpus

Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture (2020, background): “[166] examined 11 hyperspectral vegetation indices for estimating crop chlorophyll content.”
Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression (2014, background): “As plant N concentration is linked to the amount of chlorophyll, many studies have focused on estimating crop leaf chlorophyll concentration, which give an indirect assessment of canopy- or leaf-based N status of crops (Haboudane et al., 2008).”
Using wavelet analysis of hyperspectral remote-sensing data to estimate canopy chlorophyll content of winter wheat under stripe rust stress (2018, methodology): “The traditional method of measuring chlorophyll content of vegetation is destructive, time-consuming, and laborious (Riccardi et al. 2014; Haboudane et al. 2008), so it is”
Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM (2014, background): “This type of imagery can be very useful for agriculture [6], [7] or forestry [8], [9], since it allows to discriminate types of vegetation, by fully exploiting subtle differences in their spectral reflectance [10]– [12].”
Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study (2018, background): “Another work found similar trends between field-collected spectral data and PROSAIL simulated reflectance of maize and wheat canopies [78], concluding that both, the model as well as field-collected spectral measurements have the unique potential to determine crop biochemical variables, such as leaf”
Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content (2011, background): “Apart from the NDVI, numerous alternative indices have been proposed showing sensitivity towards LAI [38,41,43-46].”
Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages (2010, background): “As leaf N concentration is linked to the amount of chlorophyll, many studies have focused on estimating crop leaf chlorophyll concentration, which is easier to estimate, to give an indirect assessment of crop N status (Haboudane et al. 2008).”