Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content

By Jan G. P. W. Clevers, Lammert Kooistra. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012

321 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/0d8241775f2e1786f536d5f65c22cce01e45674b

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Citations in the corpus

Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison (2015, methodology): “Proposed methodologies using simulated S2 data range from vegetation indices (Delegido et al., 2011, 2013; Clevers and Kooistra, 2012; Hill, 2013; Schlemmera et al., 2013; Vincini et al., 2014) to machine learning regression algorithms (Verrelst et al.”
Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs (2013, background): “The papers [14,15,36] of this issue illustrate how remote sensing can be used in pest, water and nitrogen management (see also [40–42]).”
Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop (2017, methodology): “However, since errors in the estimates of LAI and LCC will accumulate in this way, it is preferred to use specific indices for estimating CCC, like the CIred-edge and the CIgreen [6,13].”
Review of optical-based remote sensing for plant trait mapping (2013, unknown): “…L / C GER 3700 (F) LR with red edge n.a. 0.82 n.a. 6e / - (Cho and Skidmore 2006) N grassland g m-2 C/C FieldSpec (F) LR – VI 0.77 n.a. 23.8 5,6 / v (Clevers and Kooistra, 2012) N crops g m-2 C/C CropScan (F) LR – VI 0.85 n.a. 42.3 8,9 / vi (Clevers and Kooistra, 2012) N broadleaf % C / C…”
Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping (2017, background): “Interestingly, the correlation between chlorophyll and nitrogen contents makes it possible to use chlorophyll VIs for quantifying the nitrogen status of crops (Clevers and Gitelson 2013; Clevers and Kooistra 2012; Schlemmer et al. 2013).”
Generation of Spectral–Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications (2015, background): “Hyperspectral indices are capable of targeting reflectance at specific wavelengths for estimating chlorophyll [3], canopy nitrogen content [4], and carotenoids [5].”
Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods (2018, unknown): “are typically used to derive canopy chlorophyll content, being the product of LAI and leaf chlorophyll content (Clevers and Kooistra, 2012; Li et al, 2017).”
Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels (2013, methodology): “Clevers and Kooistra (2012), using PROSAIL simulations, showed that the rededge chlorophyll index (CIred-edge, Table 1) with a wide red-edge band (700–730 nm) was linearly related to the canopy Chl content over the full range of potential Chl values.”
PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents (2020, background): “significant amount of literature reported a strong correlation between leaf N and chlorophyll content in crops (Baret et al., 2007; Clevers and Kooistra, 2012; Vos and Bom, 1993; Yoder and Pettigrew-Crosby, 1995), this relationship does not hold during their senescence and does not appear to be univ”