Advances in the Study of Biochemical, Morphological and Physiological Traits of Wheat and Sorghum Crops in Australia Using Hyperspectral Data and Machine Learning-Reference-Cited by-同舟云学术

Advances in the Study of Biochemical, Morphological and Physiological Traits of Wheat and Sorghum Crops in Australia Using Hyperspectral Data and Machine Learning

Published:2023-07-16 Issue: Volume: Page:
ISSN:
Container-title:IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
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Author:

B Potgieter A.¹,C Camino²,T Poblete³,X Zhi⁴,S Reynolds-Massey-Reed⁴,Y Zhao¹,A Belwalkar⁵,J Ruizhu¹,B George-Jaeggli⁴,S Chapman⁶,D Jordan⁴,A Wu¹,L Hammer G.¹,J Zarco-Tejada P.³

Affiliation:

1. The University of Queensland, Queensland Alliance for Agriculture and Food Innovation,St Lucia,Queensland,Australia,4072

2. Joint Research Centre,European Commission,Ispra,VA,Italy,I-21027

3. The University of Melbourne,School of Agriculture, Food and Ecosystem Sciences (SAFES),Faculty of Science,Melbourne,Victoria,Australia

4. The University of Queensland, Queensland Alliance for Agriculture and Food Innovation,Hermitage Research Facility,Warwick,Queensland,Australia,4370

5. The University of Melbourne,Faculty of Engineering and Information Technology (FEIT),Melbourne,Victoria,Australia

6. The University of Queensland,School of Agriculture and Food Sciences,St Lucia,Queensland,Australia,4072

Funder

Australian Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10281394/10281399/10282230.pdf?arnumber=10282230

Reference18 articles.

1. An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance

2. Unified Optical-Thermal Four-Stream Radiative Transfer Theory for Homogeneous Vegetation Canopies

3. Setting of the Learning Problem

4. SCOPE 2.0: a model to simulate vegetated land surface fluxes and satellite signals

5. PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle

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