Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels

By M. Schlemmer, Anatoly A. Gitelson, James S. Schepers, Richard B. Ferguson, Yi Peng, John F. Shanahan, Donald Rundquist. International Journal of Applied Earth Observation and Geoinformation, 2013

420 citations recorded by the source corpus.

Subfields: Agronomy and Crop Science, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/b0a552d0a33d9f9797daebcf027c08bbde9d60df

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Citations in the corpus

Sentinel-2 Data for Land Cover/Use Mapping: A Review (2020, background): “The specific applications of Sentinel-2 in agriculture, include crop production monitoring [77,135,182], crop type mapping [183,184], irrigation agriculture monitoring [137], nitrogen content assessment [185] and assessment of crop health [186].”
Eyes in the Sky, Boots on the Ground: Assessing Satellite‐ and Ground‐Based Approaches to Crop Yield Measurement and Analysis (2018, positive): “The simulated values of total canopy nitrogen on the dates with available images were then translated into MTCI using published relationships (Schlemmer et al. 2013),”
Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems (2018, background): “, 2016) and (ii) indirect methods first relating the surface reflectance to the Cab and then the Cab to the N content (Cartelat et al., 2005; Baret et al., 2007a; Houlès et al., 2007; Schlemmer et al., 2013; Feng et al., 2015; Zhou et al., 2016; Zhao et al., 2016).”
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, methodology): “(Clevers and Gitelson 2013; Clevers and Kooistra 2012; Schlemmer et al. 2013).”
Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production (2014, background): “Chlorophylls are vital pigments for photosynthesis; strong correlations have been reported between leaf chlorophyll (Chl) ghts reserved. content and N content (Clevers & Kooistra, 2012; Houlès et al., 2006; Sage, Pearcy, & Seemann, 1987; Schlemmer et al., 2013).”
A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy (2016, neutral): “…well suited for ground-based spectroradiometric measurements, as well as for air-and satellite-borne hyperspectral measurements, all of them being usually characterized by a spatial resolution coarser than one meter (Colombo et al., 2008; Schlemmer et al., 2013; Zarco-Tejada, Rueda, & Ustin, 2003).”
Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest (2020, background): “The RE emphasizes the lower absorption of chlorophyll, which reduces the saturation effect; however, the reflectance value continues to be sensitive to chlorophyll absorption in moderate to high values [8,20].”
Variation in leaf chlorophyll concentration from tropical to cold-temperate forests: Association with gross primary productivity (2018, background): “However, most studies of Chl have been limited to specific species in a given region (Fleischer, 1935; Bojovic and Stojanovic, 2005; Luo et al., 2011; Schlemmer et al., 2013; Burzyński, 2014).”
Development of a critical nitrogen dilution curve based on leaf dry matter for summer maize (2017, methodology): “…methods such as chlorophyll meter measurements (Ata-Ul-Karim et al., 2016b), dynamic monitoring of N in plant roots (Xu et al., 2012), remote sensing (Schlemmer et al., 2013), and direct measurement of plant N concentration (Plénet and Lemaire, 1999) have been used to determine the plant N status.”
Using wavelet analysis of hyperspectral remote-sensing data to estimate canopy chlorophyll content of winter wheat under stripe rust stress (2018, methodology): “Schlemmer et al. (2013) found that chlorophyll content of maize was estimated using the red edge chlorophyll index (Clred-edge) with coefficient of determination (R 2) of 0.94.”
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.”