Hyperspectral and Thermal Sensors to Improve the Prediction of Agronomic Variables in Different Winter Wheat Genotypes-Reference-Cited by-同舟云学术

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Hyperspectral and Thermal Sensors to Improve the Prediction of Agronomic Variables in Different Winter Wheat Genotypes

Published:2024-07-07 Issue: Volume: Page:2815-2818
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Raya-Sereno M.D.¹,Camino C.²,Pancorbo J.L.³,Alonso-Ayuso M.⁴,Gabriel J.L.⁵,Beck P.S.A.²,Quemada M.⁶

Affiliation:

1. CSIC,Environmental Remote Sensing and Spectroscopy Laboratory (SpecLab),Madrid,Spain,28037

2. European Commission (EC),Joint Research Centre (JRC),Ispra,Italy,21027

3. Research Council—Institute of BioEconomy (CNR-IBE),Sesto Fiorentino,Italy,50019

4. Instituto Tecnológico Agrario de Castilla y León,Valladolid,Spain,47071

5. National Institute for Agriculture and Food Research and Technology (INIA),CSIC,Department of Environment and Agronomy,Madrid,Spain,28040

6. Universidad Politécnica de Madrid,CEIGRAM,Department of Agricultural Production,Madrid,Spain,28040

Funder

European Research Council

Ministerio de Ciencia e Innovación

Comunidad de Madrid

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10640931.pdf?arnumber=10640931

Reference10 articles.

1. Closing yield gaps through nutrient and water management

2. Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models

3. Simultaneous assessment of nitrogen and water status in winter wheat using hyperspectral and thermal sensors

4. Improved nitrogen retrievals with airborne-derived fluorescence and plant traits quantified from VNIR-SWIR hyperspectral imagery in the context of precision agriculture

5. Fluorescence, PRI and canopy temperature for water stress detection in cereal crops

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