Using in‐situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status

By J. Eitel, D. Long, Paul E. Gessler, A. Smith. 2007

195 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/937facefe72a85300d1a69ecfbc82e71b5d836f4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Elhadi Adam
2. Elfatih M. Abdel-Rahman
3. Onisimo Mutanga
4. John Odindi
5. Phan Thanh Noi
6. Martin Kappas

In Environmental Engineering

1. Elfatih M. Abdel-Rahman
2. Elhadi Adam
3. Onisimo Mutanga

Citations in the corpus

• A visible band index for remote sensing leaf chlorophyll content at the canopy scale (2013, background): “RE–NIR MCARI/MTVI2 Eitel et al. (2007, 2008) Triangular greenness index Vis TGI −0.5[( r − b)(Rr − Rg) − ( r − g)(Rr − Rb)] Hunt et al. (2011) a Indices are grouped based on the major wavelengths used: NIR (n, 760–900 nm), red edge of chlorophyll absorption (re, 700–730 nm), red (r, 630–690 nm), gre”
• Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest (2020, background): “One study unveiled TCARI/OSAVI as very sensitive to estimate the chlorophyll content and is the least sensitive to variations in the Leaf Area Index (LAI) [69].”
• Review of optical-based remote sensing for plant trait mapping (2013, unknown): “…% C / C Hyperion (S) PLSR of log(1/R) 0.64 n.a. 13.1 3 / 4 (Coops et al., 2003) N crops % L / C FieldSpec (F) LR – VI (MCARI/MTVI2) n.a. 0.48 n.a. - / 3 (Eitel et al., 2007) N crops % L / C FieldSpec (F) LR with red edge 0.75 n.a. 17.8 5a / - (Feng et al., 2008) N crops g m-2 L / C FieldSpec (F)…”
• Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers (2014, background): “…of RapidEye imagery, for instance, has been demonstrated in a number of diverse studies that include among others, land-use classification (Schuster, Förster, and Kleinschmit 2012), estimation of nitrogen in a savanna ecosystem (Ramoelo et al. 2012), and crop assessment (Eitel et al. 2007).”