WO2010072876A1 - Method for characterising vegetation - Google Patents

Method for characterising vegetation Download PDF

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Publication number
WO2010072876A1
WO2010072876A1 PCT/ES2009/070602 ES2009070602W WO2010072876A1 WO 2010072876 A1 WO2010072876 A1 WO 2010072876A1 ES 2009070602 W ES2009070602 W ES 2009070602W WO 2010072876 A1 WO2010072876 A1 WO 2010072876A1
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vegetation
images
pri
index
spri
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PCT/ES2009/070602
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Spanish (es)
French (fr)
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José A. JIMÉNEZ BERNI
Elías FERERES CASTIEL
Mª Dolores SUAREZ BARRANCO
Pablo J. Zarco Tejada
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Consejo Superior De Investigaciones Científicas (Csic)
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Publication of WO2010072876A1 publication Critical patent/WO2010072876A1/en

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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/90Determination of colour characteristics
    • AHUMAN NECESSITIES
    • A01AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01GHORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G7/00Botany in general
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01JMEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28Investigating the spectrum
    • G01J3/2823Imaging spectrometer
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/47Scattering, i.e. diffuse reflection
    • G01N21/4738Diffuse reflection, e.g. also for testing fluids, fibrous materials
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84Systems specially adapted for particular applications
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29Geographical information databases
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00Scenes; Scene-specific elements
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00Scenes; Scene-specific elements
    • G06V20/10Terrestrial scenes
    • G06V20/13Satellite images
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00Scenes; Scene-specific elements
    • G06V20/10Terrestrial scenes
    • G06V20/188Vegetation
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01JMEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28Investigating the spectrum
    • G01J3/2823Imaging spectrometer
    • G01J2003/2826Multispectral imaging, e.g. filter imaging
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01JMEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J2005/0077Imaging
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N2021/1793Remote sensing
    • G01N2021/1797Remote sensing in landscape, e.g. crops
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10032Satellite or aerial image; Remote sensing
    • G06T2207/10036Multispectral image; Hyperspectral image
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30181Earth observation
    • G06T2207/30188Vegetation; Agriculture

Definitions

  • the main object of the present invention is a method of estimating the theoretical PRI (index of Photochemical Reflectance) corresponding to situations of absence of water stress in vegetation from a simulation with inversion of simulation models generated from data obtained by remote sensing , as well as its use combined with temperature data of the vegetation to identify water stress.
  • Precision agriculture was born in the late 80s and early 90s in the US. Its objective is to adjust the use of agricultural resources and cultivation methods to adapt them to the heterogeneity present in the soil or crop. In other words, precision agriculture allows for greater profitability, productivity, sustainability, product quality, environmental protection, food security, and finally, greater rural development. To achieve all these objectives, precision agriculture needs to make use of the so-called information and communication technologies: global positioning systems (GPS), geographic information systems (GIS), Remote Sensing, data entry application technologies with doses variable (VRT), etc.
  • GPS global positioning systems
  • GIS geographic information systems
  • VRT doses variable
  • the two main methods used for the estimation of biophysical variables by remote sensing are: vegetation indices and the inversion of simulation models.
  • vegetation indices is an effective tool for the determination of the properties of the vegetation cover, since these are capable of increasing the signal of the vegetation while minimizing the side effects (and undesirable in most cases) derived from lighting and floor conditions.
  • Vegetation indices are combinations of two or more bands that can be calculated from the sensor outputs: voltage, reflectance or numerical counts. All are correct but each will produce different values of the vegetation index for the same observation conditions. It is considered that vegetation indices should be calculated from the reflectance so that the indices can be comparable between images taken on different dates. This ease of calculation has made vegetation indices widely used today as a non-destructive tool for estimating biophysical variables
  • a good index should be sensitive to the variation of the variable studied, but be resistant (or be minimally affected) to other factors such as the atmosphere, the soil, the architecture of the vegetation cover and the topography. According to the effects that an index is capable of facing, it is classified as: intrinsic, resistant to the ground or resistant to the atmosphere.
  • the use of these indexes presents some drawbacks, given that none of them have achieved to date completely eliminate unwanted influences.
  • its use does not allow estimating more than one variable at the same time, which has to be specifically calibrated by means of an empirical equation whose mathematical form and coefficients are particular for each estimate.
  • vegetation indices are valid empirical relationships for each image (as they are associated with their acquisition conditions) and, therefore, their operational use to estimate biophysical variables is not evident.
  • the inversion of simulation models consists in adjusting the values of the biophysical variables used as input data of the radiative transfer model, so that the reflectance simulated with them is as close as possible to that measured by the sensor.
  • These models of simulation of radiative transfer simulate, therefore, the so-called bidirectional reflectance function (known as BDRF), which allows the calculation of the reflectance of a surface based on the viewing and lighting angles , as well as a description of the biophysical and radiative characteristics thereof.
  • BDRF bidirectional reflectance function
  • Another advantage of the physical inversion of the simulation model is the fact that it is possible to use all the radiometric information provided by the multispectral sensor; contrary to vegetation indices that essentially use only two bands (red and near infrared).
  • the information contained in the different bands of a sensor is never completely correlated and, therefore, the use of all spectral information provides additional information.
  • this method allows working with the directional information provided by most of the new sensors. This type of study presents several problems due to the diversity existing between the different crops or the determination of the parameters necessary for carrying out the study. You also have to take into account
  • the object of this invention is a method for determining the theoretical index of water stress in vegetation by estimating the temperature of the vegetation, as well as the simulation and by using simulation models of radiative transfer and its inversion.
  • sPRI photochemical reflectance index
  • a remote sensing or remote sensing is carried out with thermal cameras and narrow band multispectral cameras that will be responsible for the acquisition of spectral and thermal images that will be used to make the model.
  • the cameras used in this method are two types, on the one hand of 6-band multispectral type, while thermal images are captured by thermal cameras.
  • the multispectral camera comprises 6 image sensors with 10nm pass filters calibrated radiometrically in the laboratory.
  • the parameters of the multispectral camera are obtained by means of the Bouguet calibration method; through this method, the intrinsic parameters of the camera are recovered, such as: focal length, coordinates of the main points and the radial distortion of the lenses.
  • a simulation model is used based on the WoIf simulation model, by which you can estimate both
  • the camera responsible for the acquisition of images thermal this is calibrated in the laboratory using a black body and stabilizing it before capturing.
  • the camera incorporates an FPA sensor with a spectral range of 7.5 -13 ⁇ m and allows working in a range of 233-393K;
  • the sensor has two internal calibrations implemented: one referred to the internal temperature calibration and the other is a non-uniformity correction calibration (NUC).
  • NUC non-uniformity correction calibration
  • the theoretical PRI is determined in situations of absence of water stress for the crop or part of the crop studied. This theoretical PRI obtained determines the value considered as the baseline for the determination of water stress, being therefore possible to estimate the water stress situation of a plantation or crop by obtaining in-situ PRI of said crop and its comparison with the sPRI or theoretical PRI using this method.
  • a PROSPECT radiative transfer model connected to a FLIGHT radiative transfer model (3D Forest Light Interaction Model), which is based on the Montecarlo method of "ray tracing" (MCRT), This is a model that refers to the interaction between light and Ia vegetation.
  • MCRT Montecarlo method of "ray tracing"
  • a radiative transfer model is made for the structure of the upper layer of the vegetation.
  • the FLIGHT radiative transfer model is used together with the PROSPECT radiative transfer model.
  • the results sought are obtained through an inversion of the PROSPECT-FLIGHT model based on independent tables for each crop and image acquisition conditions.
  • the method of inversion of the simulation model is based on the inversion of the pair of simulation model "leaves-upper layer” for the values of Cab (chlorophyll content) and LAI (Leaf Area Index or leaf index).
  • the simulation model is reversed by keeping the structural parameter (N), the water content (Cw) and the amount of dry matter (Cm) fixed, all of them obtained from the specific literature published for each type of crop (in this case Kempeneers et al. for peach trees and Zarco-Tejada et al. for olive trees), while variations in the values related to Cab (chlorophyll content) and LAI (Leaf Area Index or leaf index) are allowed both at the level of sheet as in the upper layers or canopy.
  • the rest of the parameters remain fixed, being characteristic for each crop and based on the representative data of the plantation, obtaining as a result a LUT (results table called by its acronym Look Up Table in English) simulated for each crop.
  • sPRI a theoretical or simulated PRI is obtained, called sPRI; from which a baseline is established that determines the limit for the water stress situation of a given crop.
  • the images in digital format acquired by the multispectral and thermal sensors are obtained.
  • the captured images are taken to the laboratory where the process of calibration and correction of the images begins and the images are calibrated radiometrically applying calibration coefficients previously generated in the laboratory with calibration instruments.
  • the atmospheric correction of the images is carried out by means of an atmospheric simulation model and data measured in the optical thickness field at the time of capturing.
  • a Geometric correction and mosaics are generated by joining all the images taken by the cameras.
  • the simulation model based on the pair based on the reflectance index in absorption of transformed chlorophyll TCARI (Transformed Chlorophyll Absorption in Reflectance Index) / and the vegetation index OSAVI is applied (Optimized Soil Adjusted Vegetation Index in its acronym in English) for the estimation of chlorophyll content.
  • Simulation models based on NDVI reflectance index (standardized vegetation differential index, also known as NDVI - Normalized Difference Vegetation Index for its acronym in English) are used, an index used to estimate the quantity, quality and development of Ia vegetation) for the estimation of leaf area index;
  • thermal-based simulation models are applied to estimate the temperature of the vegetation.
  • the average spectrum is used as input to the simulation model of radiative transfer for its investment, using input data for all its parameters except for the N and chlorophyll a + b foliar, and LAI cover.
  • the simulation model is inverted from said average spectrum of the scene taken with the multispectral camera (from which PRI is calculated), and the theoretical spectrum for non-stress conditions (from which sPRI is calculated) is obtained by inversion.
  • the sPRI baseline will define the spectral region above which it is considered that there will be stress.
  • stress classes are generated, specifically low, medium and high stress, therefore mapping the state of stress of the vegetation from multispectral and thermal images.

Abstract

The invention relates to a method that can be used to determine situations of water stress in vegetation. The method is based on radiative transfer models formed from thermal and multi-spectrum images, which are subsequently inverted to obtain a theoretical photochemical reflectance index (PRI) which can be used to determine the situation of the vegetation by means of a PRI comparison.

Description

MÉTODO DE CARACTERIZACIÓN DE VEGETACIÓN. VEGETATION CHARACTERIZATION METHOD.
D E S C R I P C I Ó ND E S C R I P C I Ó N
OBJETO DE LA INVENCIÓNOBJECT OF THE INVENTION
El objeto principal de Ia presente invención es un método de estimación del PRI (índice de Reflectancia Fotoquímica) teórico correspondiente a situaciones de ausencia de estrés hídrico en vegetación a partir de una simulación con inversión de modelos de simulación generados a partir de datos obtenidos por teledetección, así como su uso combinado con datos de temperatura de Ia vegetación para identificar estrés hídrico.The main object of the present invention is a method of estimating the theoretical PRI (index of Photochemical Reflectance) corresponding to situations of absence of water stress in vegetation from a simulation with inversion of simulation models generated from data obtained by remote sensing , as well as its use combined with temperature data of the vegetation to identify water stress.
ANTECEDENTES DE LA INVENCIÓNBACKGROUND OF THE INVENTION
La agricultura de precisión nació a finales de los años 80 y principios de los 90 en los EE.UU. Su objetivo es ajustar el uso de recursos agrícolas y métodos de cultivo para adaptarlos a Ia heterogeneidad presente en el suelo o cultivo. En otras palabras, Ia agricultura de precisión permite conseguir una mayor rentabilidad, productividad, sostenibilidad, calidad del producto, protección medioambiental, seguridad alimentaría, y finalmente, un mayor desarrollo rural. Para conseguir todos estos objetivos, Ia agricultura de precisión necesita hacer uso de las llamadas tecnologías de Ia información y comunicación: sistemas de posicionamiento global (GPS), sistemas de información geográfica (SIG), Teledetección, tecnologías de aplicación de entradas de datos con dosis variable (VRT), etc.Precision agriculture was born in the late 80s and early 90s in the US. Its objective is to adjust the use of agricultural resources and cultivation methods to adapt them to the heterogeneity present in the soil or crop. In other words, precision agriculture allows for greater profitability, productivity, sustainability, product quality, environmental protection, food security, and finally, greater rural development. To achieve all these objectives, precision agriculture needs to make use of the so-called information and communication technologies: global positioning systems (GPS), geographic information systems (GIS), Remote Sensing, data entry application technologies with doses variable (VRT), etc.
En efecto, Ia teledetección se ha convertido en uno de los pilares más sólidos sobre los que se sustenta Ia agricultura de precisión. Así, desde el lanzamiento del primer satélite comercial para Ia observación de Ia tierra enIn fact, remote sensing has become one of the strongest pillars on which precision agriculture is based. Thus, since the launch of the first commercial satellite for the observation of the earth in
1972, LANDSAT-1 , esta ciencia se ha mostrado como una herramienta excelente para monitorizar todos los procesos biofísicos que tienen lugar en nuestro planeta, tanto a una escala global como local.1972, LANDSAT-1, this science has been shown as a tool excellent for monitoring all biophysical processes that take place on our planet, both globally and locally.
Los dos principales métodos utilizados para Ia estimación de variables biofísicas mediante teledetección son: los índices de vegetación y Ia inversión de modelos de simulación.The two main methods used for the estimation of biophysical variables by remote sensing are: vegetation indices and the inversion of simulation models.
El uso de los índices de vegetación es una herramienta eficaz para Ia determinación de las propiedades de las cubiertas vegetales, puesto que éstos son capaces de aumentar Ia señal de Ia vegetación mientras que minimizan los efectos colaterales (e indeseables en Ia mayoría de los casos) derivados de las condiciones de iluminación y del suelo.The use of vegetation indices is an effective tool for the determination of the properties of the vegetation cover, since these are capable of increasing the signal of the vegetation while minimizing the side effects (and undesirable in most cases) derived from lighting and floor conditions.
Los índices de vegetación son combinaciones de dos o más bandas que pueden ser calculadas a partir de las salidas del sensor: voltaje, reflectancia o conteos numéricos. Todos son correctos pero cada uno producirá diferentes valores del índice de vegetación para las mismas condiciones de observación. Se considera que los índices de vegetación deben calcularse a partir de Ia reflectancia con objeto de que los índices puedan ser comparables entre imágenes tomadas en distintas fechas. Esta facilidad de cálculo ha hecho que los índices de vegetación sean ampliamente usados en Ia actualidad como una herramienta no destructiva para Ia estimación de variables biofísicasVegetation indices are combinations of two or more bands that can be calculated from the sensor outputs: voltage, reflectance or numerical counts. All are correct but each will produce different values of the vegetation index for the same observation conditions. It is considered that vegetation indices should be calculated from the reflectance so that the indices can be comparable between images taken on different dates. This ease of calculation has made vegetation indices widely used today as a non-destructive tool for estimating biophysical variables
Un buen índice debe ser sensible a Ia variación de Ia variable estudiada, pero ser resistente (o verse mínimamente afectado) a otros factores como Ia atmósfera, el suelo, Ia arquitectura de Ia cubierta vegetal y Ia topografía. De acuerdo a los efectos que un índice es capaz de afrontar éste se clasifica en: intrínseco, resistente al suelo o resistente a Ia atmósfera. Sin embargo, el uso de estos índices presenta algunos inconvenientes, dado que hasta Ia fecha ninguno de ellos ha conseguido eliminar completamente las influencias no deseadas. Además, su uso no permite estimar más de una variable al mismo tiempo, Ia cual ha de ser específicamente calibrada mediante una ecuación empírica cuyos forma matemática y coeficientes son particulares para cada estimación.A good index should be sensitive to the variation of the variable studied, but be resistant (or be minimally affected) to other factors such as the atmosphere, the soil, the architecture of the vegetation cover and the topography. According to the effects that an index is capable of facing, it is classified as: intrinsic, resistant to the ground or resistant to the atmosphere. However, the use of these indexes presents some drawbacks, given that none of them have achieved to date completely eliminate unwanted influences. In addition, its use does not allow estimating more than one variable at the same time, which has to be specifically calibrated by means of an empirical equation whose mathematical form and coefficients are particular for each estimate.
En resumen, los índices de vegetación son relaciones empíricas validas para cada imagen (pues están asociados a sus condiciones de adquisición) y, por tanto, su uso operativo para estimar variables biofísicas no resulta evidente. La inversión de modelos de simulación consiste en ajustar los valores de las variables biofísicas usadas como datos de entrada de los modelo de transferencia radiativa, de tal manera que Ia reflectancia simulada con ellos se aproxime Io más posible a Ia medida por el sensor. Estos modelos de simulación de transferencia radiativa simulan, por tanto, Ia llamada función de reflectancia bidireccional (conocida como BDRF, por sus siglas en inglés), Ia cual permite el cálculo de Ia reflectancia de una superficie en función de los ángulos de observación e iluminación, así como de una descripción de las características biofísicas y radiativas de Ia misma.In summary, vegetation indices are valid empirical relationships for each image (as they are associated with their acquisition conditions) and, therefore, their operational use to estimate biophysical variables is not evident. The inversion of simulation models consists in adjusting the values of the biophysical variables used as input data of the radiative transfer model, so that the reflectance simulated with them is as close as possible to that measured by the sensor. These models of simulation of radiative transfer simulate, therefore, the so-called bidirectional reflectance function (known as BDRF), which allows the calculation of the reflectance of a surface based on the viewing and lighting angles , as well as a description of the biophysical and radiative characteristics thereof.
La determinación de Ia reflectancia a través de Ia BDRF se conoce como "problema directo", y ha sido tradicionalmente aplicado para validar los modelos de simulación de transferencia radiativa.The determination of the reflectance through the BDRF is known as "direct problem", and has been traditionally applied to validate the simulation models of radiative transfer.
Otra ventaja de Ia inversión física del modelo de simulación es el hecho de poder usar toda Ia información radiométrica aportada por el sensor multiespectral; contrariamente a los índices de vegetación que fundamentalmente usan solamente dos bandas (rojo e infrarrojo cercano). La información contenida en las diferentes bandas de un sensor nunca está completamente correlada y, por tanto, el uso de toda Ia información espectral aporta información adicional. Finalmente, pero no menos importante, este método permite trabajar con Ia información direccional proporcionada por Ia mayoría de los nuevos sensores. Este tipo de estudios presenta varios problemas debido a Ia diversidad existente entre los diferentes cultivos o Ia determinación de los parámetros necesarios para Ia realización del estudio. También hay que tener en cuentaAnother advantage of the physical inversion of the simulation model is the fact that it is possible to use all the radiometric information provided by the multispectral sensor; contrary to vegetation indices that essentially use only two bands (red and near infrared). The information contained in the different bands of a sensor is never completely correlated and, therefore, the use of all spectral information provides additional information. Finally, but not least, this method allows working with the directional information provided by most of the new sensors. This type of study presents several problems due to the diversity existing between the different crops or the determination of the parameters necessary for carrying out the study. You also have to take into account
Ia diferencia que se puede encontrar entre los índices tomados a nivel foliar o en las capas superiores (dosel o cubierta) de Ia vegetación. La resolución requerida para este tipo de estudios representa otro inconveniente, ya que se necesita de técnicas de adquisición de imágenes con una alta resolución espacial y espectral, y con Ia calidad necesaria para obtener los índices necesarios.The difference that can be found between the indexes taken at the foliar level or in the upper layers (canopy or cover) of the vegetation. The resolution required for this type of study represents another drawback, since image acquisition techniques with a high spatial and spectral resolution are needed, and with the quality necessary to obtain the necessary indexes.
DESCRIPCIÓN DE LA INVENCIÓNDESCRIPTION OF THE INVENTION
El objeto de esta invención es un método para Ia determinación del índice teórico del estrés hídrico en vegetación mediante Ia estimación de temperatura de Ia vegetación, así como de Ia simulación y mediante Ia utilización de modelos de simulación de transferencia radiativa y su inversión. Esto significa que para un determinado cultivo se realiza, mediante cámaras específicas tales como cámaras térmicas o multiespectrales, una captura de imágenes térmicas y espectrales que son unidas a modo de mosaico para generar una escena o imagen total a partir de Ia cual se extraen los índices de reflectancia medios que se usan como datos de entrada en modelos de transferencia radiativa; a partir de estos índices de reflectancia se obtienen parámetros biofísicos tales como el índice de contenido clorofílico Cab (contenido clorfofílico), índices de área foliar LAI (Leaf Área Index o índice foliar) y a partir de las imágenes térmicas captadas se obtiene Ia temperatura de Ia vegetación.The object of this invention is a method for determining the theoretical index of water stress in vegetation by estimating the temperature of the vegetation, as well as the simulation and by using simulation models of radiative transfer and its inversion. This means that for a given crop, by means of specific cameras such as thermal or multispectral cameras, a capture of thermal and spectral images that are joined as a mosaic to generate a scene or total image from which the indices are extracted is performed of reflectance means that are used as input data in radiative transfer models; From these reflectance indices biophysical parameters are obtained such as the Cab chlorophyll content index (chlorophyll content), LAI leaf area indexes (Leaf Area Index or leaf index) and from the thermal images captured the temperature of the Ia is obtained. vegetation.
A partir de estos índices que son utilizados como datos de entrada en modelos de transferencia radiativa y mediante inversión de estos modelos, se obtiene un PRI (índice de reflectancia fotoquímica en sus siglas en ingles Photochemical Resistance Index) teórico denominado sPRI a partir del cual se puede trazar una línea base que delimitaría las zonas de presencia o ausencia de estrés hídrico. Una vez obtenido esto, resulta fácil determinar Ia situación de estrés hídrico de una plantación o cultivo mediante Ia comparación del PRI que se obtenga de dicha vegetación con el sPRI determinado anteriormente mediante inversión de modelos de transferencia radiativa.From these indexes that are used as input data in radiative transfer models and through inversion of these models, a theoretical PRI (photochemical reflectance index) is obtained called sPRI from which You can draw a baseline that would define the areas of presence or absence of water stress. Once this is achieved, it is easy to determine the water stress situation of a plantation or crop by comparing the PRI that is obtained from said vegetation with the sPRI previously determined by inverting radiative transfer models.
Para ello se realiza una teleobservación o teledetección con cámaras térmicas y cámaras multiespectrales de banda estrecha que serán las encargadas de Ia adquisición de imágenes espectrales y térmicas que se utilizarán para confeccionar el modelo. Las cámaras utilizadas en este método son dos tipos, por una parte de tipo multiespectral de 6 bandas, mientras que las imágenes térmicas son capturadas mediante cámaras térmicas.For this, a remote sensing or remote sensing is carried out with thermal cameras and narrow band multispectral cameras that will be responsible for the acquisition of spectral and thermal images that will be used to make the model. The cameras used in this method are two types, on the one hand of 6-band multispectral type, while thermal images are captured by thermal cameras.
La cámara multiespectral comprende 6 sensores de imagen con filtros de paso de 10nm calibrados radiométricamente en laboratorio. Los parámetros de Ia cámara multiespectral se obtienen mediante el método de calibración de Bouguet; mediante este método se consigue recuperar los parámetros intrínsecos de Ia cámara, tales como: distancia focal, coordenadas de los puntos principales y Ia distorsión radial de las lentes. Para este último parámetro se utiliza un modelo de simulación basado en el modelo de simulación de WoIf, mediante el cual se puede estimar tantoThe multispectral camera comprises 6 image sensors with 10nm pass filters calibrated radiometrically in the laboratory. The parameters of the multispectral camera are obtained by means of the Bouguet calibration method; through this method, the intrinsic parameters of the camera are recovered, such as: focal length, coordinates of the main points and the radial distortion of the lenses. For this last parameter a simulation model is used based on the WoIf simulation model, by which you can estimate both
Ia distorsión radial como Ia tangencial, aunque en el caso de este método sólo se hace necesario tener en cuenta ésta última.The radial distortion as the tangential one, although in the case of this method it is only necessary to take into account the latter.
Para Ia utilización de estas imágenes adquiridas se hace necesario el uso de una georeferenciación, sin estos datos tenemos imágenes de las que no sabemos a qué lugar o posición geográfica corresponden, para ello se utiliza un sistema de triangulación aérea haciendo uso del sistema Leica LPS.For the use of these acquired images it is necessary to use a georeferencing, without these data we have images of which we do not know to which place or geographical position they correspond, for this an aerial triangulation system is used making use of the Leica LPS system.
En cuanto a Ia cámara encargada de Ia adquisición de imágenes térmicas, ésta se calibra en laboratorio utilizando un cuerpo negro y estabilizándola antes de realizar las capturas. La cámara lleva incorporado un sensor FPA con un rango espectral de 7.5 -13 μm y permite trabajar en un rango de 233-393K; el sensor lleva implementado dos calibraciones internas: una referida a Ia calibración de Ia temperatura interna y Ia otra es una calibración de corrección de no uniformidad (NUC).As for the camera responsible for the acquisition of images thermal, this is calibrated in the laboratory using a black body and stabilizing it before capturing. The camera incorporates an FPA sensor with a spectral range of 7.5 -13 μm and allows working in a range of 233-393K; The sensor has two internal calibrations implemented: one referred to the internal temperature calibration and the other is a non-uniformity correction calibration (NUC).
A partir de las imágenes térmicas obtenidas, y mediante diversos métodos basados en modelos de transferencia radiativa e inversión de los mismos se llega a Ia determinación del PRI teórico en situaciones de ausencia de estrés hídrico para el cultivo o parte del cultivo estudiado. Este PRI teórico obtenido determina el valor considerado como línea base para Ia determinación del estrés hídrico siendo por tanto posible Ia estimación de situación de estrés hídrico de una plantación o cultivo mediante Ia obtención in-situ del PRI de dicho cultivo y su comparación con el sPRI o PRI teórico mediante este método.From the thermal images obtained, and through various methods based on radiative transfer models and their investment, the theoretical PRI is determined in situations of absence of water stress for the crop or part of the crop studied. This theoretical PRI obtained determines the value considered as the baseline for the determination of water stress, being therefore possible to estimate the water stress situation of a plantation or crop by obtaining in-situ PRI of said crop and its comparison with the sPRI or theoretical PRI using this method.
Para este tipo de estudios se utilizan diferentes modelos de transferencia radiativa dependiendo de Ia estructura y características propias de Ia vegetación a estudiar. Es de uso común Ia utilización de modelos de transferencia radiativa tipo PROSPECT, que simula Ia reflectividad/transmisividad de una hoja, y tipo SAILH que simula Ia reflectividad del dosel o parte alta de Ia vegetación; estos modelos de simulación pueden ir vinculados a diferentes modelos de simulación SAIL, FLIGHT dependiendo del tipo de cultivo a estudiar y de los niveles de hoja o cubierta a estudiar.For this type of studies, different models of radiative transfer are used depending on the structure and characteristics of the vegetation to be studied. It is common use the use of PROSPECT type radiative transfer models, which simulates the reflectivity / transmissivity of a leaf, and SAILH type that simulates the reflectivity of the canopy or upper part of the vegetation; These simulation models can be linked to different SAIL, FLIGHT simulation models depending on the type of crop to study and the levels of leaf or cover to study.
Para Ia inversión de modelos de transferencia radiativa se hace uso de un modelo transferencia radiativa PROSPECT conectado a un modelo de transferencia radiativa FLIGHT (3D Forest Light Interaction Model), el cual está basado en el método Montecarlo de "ray tracing" (MCRT), éste es un modelo que hace referencia a Ia interacción entre Ia luz y Ia vegetación. Mediante esta herramienta se realiza un modelo de transferencia radiativa para Ia estructura de Ia capa superior de Ia vegetación. Para este estudio se utiliza el modelo de transferencia radiativa FLIGHT junto con el modelo de transferencia radiativa PROSPECT. Los resultados buscados se obtienen mediante una inversión del modelo PROSPECT-FLIGHT basada en tablas independientes para cada cultivo y condiciones de adquisición de imagen. El método de inversión del modelo de simulación se basa en Ia inversión de Ia pareja de modelo de simulación "hojas-capa superior" para los valores de Cab (contenido clorfofílico) y LAI (Leaf Área Index o índice foliar). La inversión del modelo de simulación se realiza manteniendo fijo el parámetro estructural (N), el contenido de agua (Cw) y el Ia cantidad de materia seca (Cm), obtenidos todos ellos de Ia literatura específica publicada para cada tipo de cultivo (en este caso Kempeneers et al. para árboles melocotoneros y Zarco-Tejada et al. para olivos), mientras que se permiten variaciones en los valores relacionados con el Cab (contenido clorfofílico) y LAI (Leaf Área Index o índice foliar) tanto a nivel de hoja como en las capas superiores o dosel. El resto de parámetros se mantienen fijos siendo característicos para cada cultivo y basados en los datos representativos de Ia plantación, obteniendo como resultado una LUT (tabla de resultados denominada por sus siglas Look Up Table en inglés) simulado para cada cultivo.For the inversion of radiative transfer models, use is made of a PROSPECT radiative transfer model connected to a FLIGHT radiative transfer model (3D Forest Light Interaction Model), which is based on the Montecarlo method of "ray tracing" (MCRT), This is a model that refers to the interaction between light and Ia vegetation. Through this tool, a radiative transfer model is made for the structure of the upper layer of the vegetation. For this study, the FLIGHT radiative transfer model is used together with the PROSPECT radiative transfer model. The results sought are obtained through an inversion of the PROSPECT-FLIGHT model based on independent tables for each crop and image acquisition conditions. The method of inversion of the simulation model is based on the inversion of the pair of simulation model "leaves-upper layer" for the values of Cab (chlorophyll content) and LAI (Leaf Area Index or leaf index). The simulation model is reversed by keeping the structural parameter (N), the water content (Cw) and the amount of dry matter (Cm) fixed, all of them obtained from the specific literature published for each type of crop (in this case Kempeneers et al. for peach trees and Zarco-Tejada et al. for olive trees), while variations in the values related to Cab (chlorophyll content) and LAI (Leaf Area Index or leaf index) are allowed both at the level of sheet as in the upper layers or canopy. The rest of the parameters remain fixed, being characteristic for each crop and based on the representative data of the plantation, obtaining as a result a LUT (results table called by its acronym Look Up Table in English) simulated for each crop.
Una vez finalizado el proceso anterior se obtiene un PRI teórico o simulado, denominado sPRI; a partir de cual se establece una línea base que determina el límite para Ia situación de estrés hídrico de un determinado cultivo. Mediante Ia comparación directa del PRI del cultivo en cuestión con el sPRI obtenido se determina si ese cultivo está sometido a estrés hídrico o no. Adicionalmente, Ia relación del PRI real con Ia temperatura de cada elemento de vegetación permite demostrar Ia conexión existente entre el estrés hídrico detectado por el PRI y el nivel de transpiración de Ia vegetación que afecta a su temperatura.Once the previous process is finished, a theoretical or simulated PRI is obtained, called sPRI; from which a baseline is established that determines the limit for the water stress situation of a given crop. By means of the direct comparison of the PRI of the crop in question with the sPRI obtained, it is determined whether that crop is subjected to water stress or not. Additionally, the relationship of the real PRI with the temperature of each vegetation element allows to demonstrate the connection between the water stress detected by the PRI and the level of transpiration of the vegetation that affects its temperature.
REALIZACIÓN PREFERENTE DE LA INVENCIÓN.PREFERRED EMBODIMENT OF THE INVENTION.
Se realiza un estudio sobre una plantación de melocotones (Prunus pérsica). Para Ia realización de este estudio se regaron distintas zonas del cultivo con distintos aportes hídricos, en concreto 6 líneas de 30 árboles melocotoneros cada una. Todas las zonas de estudio se regaron mediante sistema de riego por goteo, una de ellas recibiendo el 100% de Ia tasa de ET (evapotranspiración) calculada para este cultivo (zona de no estrés hídrico), mientras que otra zona se sometió a un Riego Deficitario Controlado (RDI), recibiendo el 80% de Ia ET. Esta fase de RDI tuvo lugar durante Ia fase de desarrollo del fruto, que más tarde se volvió a regar por encima de Ia ET hasta recuperar el nivel hídrico del árbol sin estrés hídrico. Durante este estudio se realiza una adquisición de imágenes a una resolución muy alta (15 cm de pixel), estas imágenes son de tipo térmico y multiespectral de banda estrecha. La captura se realiza mediante varias cámaras, con posterior calibración, corrección atmosférica, y calibración geométrica de las imágenes obtenidas para extraer Ia información radiométrica necesaria.A study is carried out on a peach plantation (Prunus pérsica). To carry out this study, different areas of the crop were irrigated with different water contributions, specifically 6 lines of 30 peach trees each. All the study areas were irrigated by drip irrigation system, one of them receiving 100% of the rate of ET (evapotranspiration) calculated for this crop (non-water stress zone), while another area was irrigated Controlled Deficit (RDI), receiving 80% of the ET. This RDI phase took place during the fruit development phase, which was later re-irrigated above the ET until the tree's water level was recovered without water stress. During this study an image acquisition is performed at a very high resolution (15 cm pixel), these images are thermal and multispectral narrow band. The capture is done through several cameras, with subsequent calibration, atmospheric correction, and geometric calibration of the images obtained to extract the necessary radiometric information.
Una vez terminada Ia captura se obtienen las imágenes en formato digital adquiridas por los sensores multiespectrales y térmicos.Once the capture is finished, the images in digital format acquired by the multispectral and thermal sensors are obtained.
Las imágenes captadas son llevadas al laboratorio donde comienza el proceso de calibración y corrección de las imágenes y se calibran las imágenes radiométricamente aplicando coeficientes de calibración generados previamente en laboratorio con instrumentos de calibración. Seguidamente se procede a Ia corrección atmosférica de las imágenes mediante modelo de simulación atmosférico y datos medidos en campo de espesor óptico en el momento de realización de las capturas. Posteriormente se realiza una corrección geométrica y se generan mosaicos uniendo todas las imágenes tomadas por las cámaras.The captured images are taken to the laboratory where the process of calibration and correction of the images begins and the images are calibrated radiometrically applying calibration coefficients previously generated in the laboratory with calibration instruments. Next, the atmospheric correction of the images is carried out by means of an atmospheric simulation model and data measured in the optical thickness field at the time of capturing. Subsequently a Geometric correction and mosaics are generated by joining all the images taken by the cameras.
Una vez realizado el pegado de las imágenes a modo de mosaico, se aplica el modelo de simulación basados en Ia pareja basada en el índice de reflectancia en absorción de clorofila transformada TCARI (Transformed Chlorophyll Absorption in Reflectance Index) / y el índice de vegetación OSAVI (Optimised Soil Adjusted Vegetation Index en sus siglas en inglés) para Ia estimación de contenido clorofílico. A su vez se utilizan modelos de simulación basados en índice de reflectancia NDVI (índice diferencial de vegetación normalizado, también conocido como NDVI - Normalized Difference Vegetation Index por sus siglas en inglés, es un índice usado para estimar Ia cantidad, calidad y desarrollo de Ia vegetación) para Ia estimación de índice de área foliar; a continuación se aplican modelos de simulación basados en térmico para estimación de temperatura de Ia vegetación.Once the images have been pasted as a mosaic, the simulation model based on the pair based on the reflectance index in absorption of transformed chlorophyll TCARI (Transformed Chlorophyll Absorption in Reflectance Index) / and the vegetation index OSAVI is applied (Optimized Soil Adjusted Vegetation Index in its acronym in English) for the estimation of chlorophyll content. Simulation models based on NDVI reflectance index (standardized vegetation differential index, also known as NDVI - Normalized Difference Vegetation Index for its acronym in English) are used, an index used to estimate the quantity, quality and development of Ia vegetation) for the estimation of leaf area index; Next, thermal-based simulation models are applied to estimate the temperature of the vegetation.
En el caso del índice PRI, el proceso es el siguiente:In the case of the PRI index, the process is as follows:
• Se identifican pixeles puros de vegetación. • Se calcula Ia media de Ia reflectancia para Ia escena completa.• Pure vegetation pixels are identified. • The average reflectance is calculated for the entire scene.
• Se utiliza el espectro medio como entrada al modelo de simulación de transferencia radiativa para su inversión, utilizando datos de entrada para todos sus parámetros excepto para los foliares N y clorofila a+b, y de cubierta LAI. « Se invierte el modelo de simulación a partir de dicho espectro medio de Ia escena tomado con Ia cámara multiespectral (del que se calcula PRI), y se obtiene mediante inversión el espectro teórico para condiciones de no estrés (del que se calcula sPRI).• The average spectrum is used as input to the simulation model of radiative transfer for its investment, using input data for all its parameters except for the N and chlorophyll a + b foliar, and LAI cover. «The simulation model is inverted from said average spectrum of the scene taken with the multispectral camera (from which PRI is calculated), and the theoretical spectrum for non-stress conditions (from which sPRI is calculated) is obtained by inversion.
• La línea base sPRI definirá Ia región espectral por encima de Ia cuál se considera que habrá estrés.• The sPRI baseline will define the spectral region above which it is considered that there will be stress.
• Se extrae Ia reflectancia de cada copa de Ia imagen tomada por Ia cámara multiespectral, y se calcula el PRI de dicho árbol. Se compara sistemáticamente sPRI (modelizado, no estrés) con el PRI de cada árbol extraído de Ia imagen. Si PRI > sPRI se considera que dicha vegetación (árbol) está estresada.• The reflectance of each cup of the image taken by Ia is extracted multispectral camera, and the PRI of said tree is calculated. SPRI (modeling, not stress) is systematically compared with the PRI of each tree extracted from the image. If PRI> sPRI it is considered that said vegetation (tree) is stressed.
Para PRI, y los productos de clorofila, LAI, y temperatura se generan "clases de estrés", en concreto bajo, medio y alto estrés, por Io tanto cartografiando el estado de estrés de Ia vegetación a partir de las imágenes multiespectrales y térmicas. For PRI, and the chlorophyll, LAI, and temperature products, "stress classes" are generated, specifically low, medium and high stress, therefore mapping the state of stress of the vegetation from multispectral and thermal images.

Claims

R E I V I N D I C A C I O N E S
1. Método de caracterización de vegetación que hace uso de:1. Vegetation characterization method that makes use of:
• una cámara multiespectral encargada de tomar imágenes multiespectrales, y• a multispectral camera responsible for taking multispectral images, and
• una cámara térmica encargada de tomar imágenes térmicas• a thermal camera responsible for taking thermal images
caracterizado porque comprende las siguientes fases:characterized in that it comprises the following phases:
• captar imágenes térmicas mediante Ia cámara térmica y captar imágenes de banda estrecha de Ia vegetación mediante Ia cámara multiespectral,• capture thermal images by means of the thermal camera and capture narrow-band images of the vegetation by means of the multispectral camera,
• calibrar y corregir las imágenes captadas, • generar un mosaico a partir de todas las imágenes captadas,• calibrate and correct the captured images, • generate a mosaic from all the captured images,
• obtener el índice clorofílico de Ia vegetación a partir de índice TCARI/OSAVI,• obtain the chlorophyll index of the vegetation from the TCARI / OSAVI index,
• obtener el índice foliar de Ia vegetación a partir de índice NDVI,• obtain the foliar index of the vegetation from the NDVI index,
• obtener Ia temperatura de Ia vegetación a partir de las imágenes térmicas, y• obtain the temperature of the vegetation from the thermal images, and
• obtener el índice PRI teórico de Ia vegetación.• obtain the theoretical PRI index of the vegetation.
2. Método de caracterización de vegetación según reivindicación 1 caracterizado porque Ia fase de calibración y corrección comprende:2. Vegetation characterization method according to claim 1 characterized in that the calibration and correction phase comprises:
• realizar una calibración radiométrica de las imágenes de banda estrecha captadas,• perform a radiometric calibration of the narrowband images captured,
• realizar una corrección atmosférica de las imágenes térmicas mediante un modelo de simulación atmosférico y datos medidos en campo de espesor óptico, y• make an atmospheric correction of thermal images using an atmospheric simulation model and data measured in optical thickness field, and
• realizar una corrección geométrica. • perform a geometric correction.
3. Método de caracterización de vegetación según reivindicación 1 caracterizado porque Ia fase de obtención del índice PRI teórico de no estrés hídrico comprende las siguientes fases:3. Vegetation characterization method according to claim 1, characterized in that the phase of obtaining the theoretical PRI index of non-water stress comprises the following phases:
• identificar los pixeles puros de vegetación,• identify pure vegetation pixels,
• calcular Ia media de Ia reflectancia para el mosaico completo generado a partir de las imágenes captadas por Ia cámara multiespectral,• calculate the mean reflectance for the complete mosaic generated from the images captured by the multispectral camera,
• introducción de los datos en un modelo de transferencia radiativa,• introduction of data in a radiative transfer model,
• realizar Ia inversión del modelo de transferencia radiativa basándose en Ia inversión de Ia pareja "hojas-capa superior" para los valores de Cab y LAI mateniendo fijo el parámetro estructural (N), el contenido hídrico (Cw) y el Ia cantidad de materia seca (Cm), específicos de cada vegetación,• make the inversion of the radiative transfer model based on the investment of the pair "upper layer sheets" for the values of Cab and LAI keeping fixed the structural parameter (N), the water content (Cw) and the amount of matter dry (cm), specific to each vegetation,
• obtener el espectro teórico para condiciones de no estrés a partir del resultado de Ia inversión del modelo realizada en el paso anterior,• obtain the theoretical spectrum for non-stress conditions from the result of the inversion of the model made in the previous step,
• obtener el sPRI que es el índice PRI de no estrés modelizado a partir del espectro teórico obtenido en el paso anterior,• obtain the sPRI which is the PRI index of non-stress modeled from the theoretical spectrum obtained in the previous step,
• definir una línea base sPRI que delimita Ia región espectral por encima de Ia cuál se considera que hay estrés,• define a sPRI baseline that delimits the spectral region above which it is considered to be stress,
• extraer Ia reflectancia de cada copa de Ia imagen tomada por Ia cámara multiespectral (2), • calcular el PRI de Ia vegetación captada, y• extract the reflectance of each cup from the image taken by the multispectral camera (2), • calculate the PRI of the captured vegetation, and
• comparar el PRI obtenido en el paso anterior con el sPRI obtenido anteriormente.• compare the PRI obtained in the previous step with the sPRI obtained previously.
4. Método de caracterización de vegetación según reivindicación 3 caracterizado porque Ia caracterización de Ia vegetación viene dada por Ia comparación sistemática del sPRI (modelizado, no estrés) con el PRI de cada árbol extraído de Ia imagen, en el que si el PRI > sPRI se considera que dicha vegetación está hídricamente estresada. 4. Vegetation characterization method according to claim 3 characterized in that the characterization of the vegetation is given by Ia Systematic comparison of the sPRI (modeled, not stress) with the PRI of each tree extracted from the image, in which if the PRI> sPRI it is considered that said vegetation is hydraulically stressed.
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US11436824B2 (en) 2017-12-05 2022-09-06 Jiangsu University Water stress detection method for tomatoes in seedling stage based on micro-CT and polarization-hyperspectral imaging multi-feature fusion
RU2746690C1 (en) * 2020-05-07 2021-04-19 Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский Нижегородский государственный университет им. Н.И. Лобачевского» System for measuring the photochemical reflectance index (pri) in plants

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