Por favor, use este identificador para citar o enlazar a este item:
http://hdl.handle.net/10261/344243
COMPARTIR / EXPORTAR:
SHARE CORE BASE | |
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE | |
Título: | Improved nitrogen retrievals with airborne-derived fluorescence and plant traits quantified from VNIR-SWIR hyperspectral imagery in the context of precision agriculture |
Autor: | Camino, Carlos CSIC ORCID; González-Dugo, Victoria CSIC ORCID; Hernández Molina, Pilar CSIC ORCID ; Sillero, Josefina C.; Zarco-Tejada, Pablo J. CSIC ORCID | Palabras clave: | Nitrogen concentration Airborne Chlorophyll content Chlorophyll fluorescence Hyperspectral NIR indices |
Fecha de publicación: | ago-2018 | Editor: | Elsevier | Citación: | International Journal of Applied Earth Observation and Geoinformation 70: 105-117 (2018) | Resumen: | In semi-arid conditions, nitrogen (N) is the main limiting factor of crop yield after water, and its accurate quantification remains essential. Recent studies have demonstrated that solar-induced chlorophyll fluorescence (SIF) quantified from hyperspectral imagery is a reliable indicator of photosynthetic activity in the context of precision agriculture and for early stress detection purposes. The role of fluorescence might be critical to our understanding of N levels due to its link with photosynthesis and the maximum rate of carboxylation (Vcmax) under stress. The research presented here aimed to assess the contribution played by airborne-retrieved solar-induced chlorophyll fluorescence (SIF) to the retrieval of N under irrigated and rainfed Mediterranean conditions. The study was carried out at three field sites used for wheat phenotyping purposes in Southern Spain during the 2015 and 2016 growing seasons. Airborne campaigns acquired imagery with two hyperspectral cameras covering the 400–850 nm (20 cm resolution) and 950–1750 nm (50 cm resolution) spectral regions. The performance of multiple regression models built for N quantification with and without including the airborne-retrieved SIF was compared with the performance of models built with plant traits estimated by model inversion, and also with standard approaches based on single spectral indices. Results showed that the accuracy of the models for N retrieval increased when chlorophyll fluorescence was included (r2LOOCV ≥ 0.92; p < 0.0005) as compared to models only built with chlorophyll a + b (Cab), dry matter (Cm) and equivalent water thickness (Cw) plant traits (r2LOOCV ranged from 0.68 to 0.77; p < 0.005). Moreover, nitrogen indices (NIs) centered at 1510 nm yielded more reliable agreements with N concentration (r2 = 0.69) than traditional chlorophyll indices (TCARI/OSAVI r2 = 0.45) and structural indices (NDVI r2 = 0.57) calculated in the VNIR region. This work demonstrates that under irrigated and non-irrigated conditions, indicators directly linked with photosynthesis such as chlorophyll fluorescence improves predictions of N concentration. | Versión del editor: | https://doi.org/10.1016/j.jag.2018.04.013 | URI: | http://hdl.handle.net/10261/344243 | DOI: | 10.1016/j.jag.2018.04.013 | ISSN: | 1569-8432 |
Aparece en las colecciones: | (IAS) Artículos |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
JAG_2018.pdf | 2,51 MB | Adobe PDF | Visualizar/Abrir |
CORE Recommender
SCOPUSTM
Citations
84
checked on 28-abr-2024
WEB OF SCIENCETM
Citations
76
checked on 29-feb-2024
Page view(s)
13
checked on 02-may-2024
Download(s)
14
checked on 02-may-2024
Google ScholarTM
Check
Altmetric
Altmetric
Este item está licenciado bajo una Licencia Creative Commons