Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories

By H. Mcnairn, C. Champagne, J. Shang, D. Holmstrom, G. Reichert. 2009

361 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/21980d2924ead5240bd7996a29680d2129cfee32

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. Abdulhakim M. Abdi
2. Yuan Yuan
3. Lei Lin

In Environmental Engineering

1. M. Lavreniuk
2. Andrii Shelestov
3. Alexei Novikov
4. S. Skakun
5. Weimin Huang
6. Nataliia Kussul
7. K. Papathanassiou
8. Brian G. Brisco

Citations in the corpus

• Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping (2017, background): “Accuracy of 85% is usually considered as target accuracy for agriculture applications (McNairn et al., 2009).”
• Remote sensing for wetland classification: a comprehensive review (2018, background): “…wetland classification accuracy (Dechka et al. 2002; Ozesmi and Bauer 2002; Leahy et al. 2005; Li and Chen 2005; Racine, Bernier, and Ouarda 2005; Bourgeau-Chavez et al. 2009; McNairn et al. 2009; Gosselin, Touzi, and Cavayas 2014; Moser et al. 2016; Amani et al. 2017c; Mahdavi et al. 2017b).”
• Radar Remote Sensing of Agricultural Canopies: A Review (2017, background): “Many studies have confirmed that the 641 cross polarization (HV or VH) is the single most important polarization to identify the majority 642 of crops [63], [102], [186], [199]–[201].”