Detección de estrés híbrido y nutricional mediante fluorescencia clorofílica y modelos de transferencia radiativa a partir de imágenes hiperespectrales y térmicas

• Autores: Carlos Camino
• Directores de la Tesis: Maria Victoria González Dugo (dir. tes.), Pablo J. Zarco Tejada (dir. tes.)
• Lectura: En la Universidad de Córdoba (ESP) ( España ) en 2019
• Idioma: español
• Tribunal Calificador de la Tesis: Alfonso Garacía Ferrer (presid.), Miguel Quemada (secret.), José Luis Araus Ortega (voc.)
• Programa de doctorado: Programa de Doctorado en Ingeniería Agraria, Alimentaria, Forestal y del Desarrollo Rural Sostenible por la Universidad de Córdoba y la Universidad de Sevilla
• Materias:
  • Ciencias de la tierra y del espacio
    • Geología
      • Teledetección (geología)
  • Ciencias agrarias
    • Agronomía
      • Producción de cultivos
• Enlaces
  • Tesis en acceso abierto en: Helvia
• Resumen

  • El nitrógeno (N) y el agua son los factores limitantes más importantes en la producción y crecimiento de un cultivo. Conocer el estado fisiológico de un cultivo durante sus etapas de crecimiento es crítico para la optimización de la aplicación de insumos agrícolas, la predicción del rendimiento y la vigilancia de enfermedades. Desde un punto nutricional, el N es un elemento esencial en la producción de clorofila, fundamental para el proceso de fotosíntesis, y otros componentes celulares de la planta (proteínas, ácidos nucleicos, aminoácidos). Por su parte, el déficit hídrico afecta los procesos de crecimiento, rasgos filogenéticos tales como estructura de la hoja y la forma, la eficiencia fotosintética, por lo que su detección temprana es sumamente importante.

    En la última década, la estimación de parámetros fisiológicos a partir del uso de sensores hiperespectrales y térmicos se ha desarrollado ampliamente. A diferencia de los sensores multiespectrales de banda ancha, los sensores hiperespectrales se caracterizan por un elevado número de bandas estrechas y contiguas a lo largo del espectro electromagnético que permiten una mejor descripción de porciones del espectro y, por tanto, una mejor cuantificación de rasgos bioquímicos y biofísicos a través de modelos físicos de transferencia radiativa. El uso de sensores de imagen de tipo hiperespectral y térmico permite cubrir grandes áreas y cuantificar la variabilidad espacial de parámetros relacionados con el estado fisiológico del cultivo, siendo una alternativa real a los métodos destructivos tradicionales de muestreo en campo con medidas foliares.

    La presente tesis doctoral tiene como principal objetivo explorar la contribución que tiene la fluorescencia clorofílica (solar-induced fluorescence, SIF) cuantificada mediante sensores hiperespectrales a bordo de plataformas aéreas en la cuantificación de N y en la estimación de la tasa máxima de carboxilación (Vcmax), como proxy de la actividad fotosintética. Para ello, se han utilizado sensores hiperespectrales de imagen y modelos de transferencia radiativa en ensayos de fenotipado de selección de variedades de trigo en condiciones de secano y regadío. En el estudio se evaluaron las relaciones fisiológicas obtenidas entre las medidas realizadas en campo con los rasgos bioquímicos, biofísicos y fotosintéticos obtenidos mediante inversión de modelos de transferencia radiativa (PROSPECT-SAILH y SCOPE), índices espectrales de vegetación obtenidos con bandas situadas entre la región del visible y el infrarrojo de onda corta (400-1750 nm), la fluorescencia clorofílica cuantificada mediante el método de la profundidad de las líneas de Fraunhofer, e indicadores obtenidos con cámaras térmicas sensibles al rango espectral de 8-14 µm.

    Dada la importancia de los efectos estructurales en la estimación de parámetros biofísicos y bioquímicos mediante sensores remotos de alta resolución, esta tesis doctoral ha estudiado los efectos de la heterogeneidad estructural dentro de las copas de los árboles. Para ello, se han desarrollado métodos automáticos de segmentación de las imágenes obtenidas con sensores aerotransportados hiperespectrales y térmicos de alta resolución. El objetivo de este primer trabajo, fue analizar la variabilidad estructural dentro del árbol, y su efecto en las relaciones obtenidas entre las medidas fisiológicas de fluorescencia clorofílica y los indicadores térmicos.

    En la tesis doctoral se destaca el potencial que tienen las herramientas de detección remota para cuantificar la concentración de nitrógeno, detectar el estrés hídrico y estimar los rasgos de la fotosíntesis de la planta mediante el uso de imágenes hiperespectrales y térmicas combinadas con modelos de transferencia radiativa. Los resultados demuestran que la fluorescencia clorofílica natural mejora la estimación de la concentración de N y el parámetro Vcmax debido a la estrecha relación que tiene con la actividad fotosintética y la detección del estrés hídrico. Los resultados también resaltan la capacidad para estimar la tasa máxima de carboxilación utilizando inversiones con el modelo SCOPE y SIF cuantificado a partir de imágenes hiperespectrales de alta resolución en aplicaciones de fenotipado de alto rendimiento y agricultura de precisión.

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