Retrieval of chlorophyll content and LAI of crops using hyperspectral techniques: application to PROBA/CHRIS data

Delegido, Jesús; Fernández, Gallardo; Gandía, S.; Moreno, José

doi:10.1080/01431160802238401

Cited by 49 publications

(26 citation statements)

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“…Hyperspectral sensors can obtain spectral features based on radiation from visible to near-infrared wavelengths, and advancements in technology have increased the number of features that can be collected [17][18][19]. Spectral features and indices are important variables for estimating hyperspectral crop parameters [20][21][22][23][24][25][26], and many studies have estimated AGB using a combination of narrow spectral bands and vegetation indices [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models

et al. 2017

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Correct estimation of above-ground biomass (AGB) is necessary for accurate crop growth monitoring and yield prediction. We estimated AGB based on images obtained with a snapshot hyperspectral sensor (UHD 185 firefly, Cubert GmbH, Ulm, Baden-Württemberg, Germany) mounted on an unmanned aerial vehicle (UAV). The UHD 185 images were used to calculate the crop height and hyperspectral reflectance of winter wheat canopies from hyperspectral and panchromatic images. We constructed several single-parameter models for AGB estimation based on spectral parameters, such as specific bands, spectral indices (e.g., Ratio Vegetation Index (RVI), NDVI, Greenness Index (GI) and Wide Dynamic Range VI (WDRVI)) and crop height and several models combined with spectral parameters and crop height. Comparison with experimental results indicated that incorporating crop height into the models improved the accuracy of AGB estimations (the average AGB is 6.45 t/ha). The estimation accuracy of single-parameter models was low (crop height only: R 2 = 0.50, RMSE = 1.62 t/ha, MAE = 1.24 t/ha; R 670 only: R 2 = 0.54, RMSE = 1.55 t/ha, MAE = 1.23 t/ha; NDVI only: R 2 = 0.37, RMSE = 1.81 t/ha, MAE = 1.47 t/ha; partial least squares regression R 2 = 0.53, RMSE = 1.69, MAE = 1.20), but accuracy increased when crop height and spectral parameters were combined (partial least squares regression modeling: R 2 = 0.78, RMSE = 1.08 t/ha, MAE = 0.83 t/ha; verification: R 2 = 0.74, RMSE = 1.20 t/ha, MAE = 0.96 t/ha). Our results suggest that crop height determined from the new UAV-based snapshot hyperspectral sensor can improve AGB estimation and is advantageous for mapping applications. This new method can be used to guide agricultural management.

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Section: Introductionmentioning

confidence: 99%

Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models

et al. 2017

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“…Barrax (Albacete, España) fue uno de los dos lugares seleccionados para la realización de la campaña Sen3Exp. La campaña de Barrax se llevó a cabo del 20 al 24 de Junio de 2009 y se tomaron medidas conducentes a la estimación de paráme-tros biofísicos de la vegetación, como el LAI, la fracción de cobertura vegetal verde (FCover), la fracción de radiación fotosintéticamente activa (fAPAR), y los contenidos de agua y clorofila en la vegetación (Delegido et al, 2008). En total fueron caracterizadas 46 ESU's (unidades elementales de muestreo) correspondientes a 14 campos y 9 cultivos diferentes, de las que 31 fueron utilizadas en el presente estudio.…”

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Influencia del ángulo de observación en la estimación del índice de área foliar (LAI) mediante imágenes PROBA/CHRIS

et al. 2016

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Resumen: La estimación de variables biofísicas como el Índice de Área Foliar (LAI) mediante técnicas de teledetección es objeto de numerosos estudios, ya que de su conocimiento se puede extraer valiosa información sobre el estado de la vegetación. En este trabajo se estudia la estimación del LAI mediante imágenes multiangulares PROBA/CHRIS, analizando el comportamiento de la reflectividad medida en sus 5 ángulos de observación, en las longitudes de onda de 665 y 705 nm correspondientes a la banda de absorción de la clorofila y la reflectividad de la vegetación en el Red-Edge, respectivamente. El Índice de Diferencia Normalizada (NDI) calculado en estas longitudes de onda, mostró una buena correlación con el LAI, lo que permite su estimación por teledetección y su aplicabilidad a las imágenes del recientemente lanzado Sentinel 2 de la ESA, pues incorpora como novedad varias bandas en el Red-Edge. En esta investigación se analiza la influencia en la geometría de adquisición del NDI calibrando la relación entre este índice y el LAI para cada uno de los 5 ángulos de observación de PROBA/CHRIS. Con ello, se ha obtenido una relación capaz de proporcionar el LAI a partir del ángulo de observación y del índice NDI.Palabras clave: LAI, multiangular, NDI, PROBA/CHRIS, Red-Edge, Sentinel-2.Influence of observation angle in leaf area index (LAI) estimation using PROBA/CHRIS images Abstract: The estimation of biophysical variables, such as the Leaf Area Index (LAI), using remote sensing techniques, is still the subject of numerous studies, since these variables allow obtaining valuable information on the vegetation status. In this work, we estimate LAI from multiangular PROBA/CHRIS images, by analyzing the reflectance measured in its 5 observation angles, for the bands centered in 665 and 705 nm. These wavelengths correspond to the chlorophyll absorption band and the Red-Edge region, respectively. The Normalized Difference Index (NDI) calculated from this wavelengths, shows good correlation with LAI and allows its remote sensing estimation and its applicability to the recently launched ESA Sentinel 2, thanks to its new bands in the Red-Edge. This research analyzed the influence on the acquisition geometry in the NDI, calibrating the relationship between this index and the LAI for each of the five observation angles in the PROBA / CHRIS images. As a result, we have obtained a relationship capable of providing LAI from the viewing angle and the NDI index.

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“…In addition, in recent years, satellite images have made it possible to retrieve chlorophyll concentrations, carotenoid content, and the leaf area index (LAI) (Delegido et al 2008;Kempeneer et al 2008;Wu et al 2010;Panigada et al 2010;Simic et al 2011;Croft et al 2013;Zarco-Tejada et al 2013). For instance, Kempeneer et al (2008) reported that the FLIGHT model offered a robust technique for extracting chlorophyll content quantitatively from an airborne hyperspectral line-scanner radiometer at the canopy level.…”

Section: Introductionmentioning

confidence: 98%

“…Estimating and evaluating chlorophyll concentration can be helpful for managing forest, agricultural, and grassland ecological systems. Over the past few decades, hyperspectral remote sensing has become a valuable tool for estimating a wide variety of biophysical components (Jacquemoud et al 1995;Villalobos et al 1995;Thorp et al 2012;Avtar et al 2013;Delegido et al 2013) and biochemical components (Curran 1989;Delegido et al 2008;Botha et al 2010;Nichol and Grace 2010;Cheng et al 2013;Rotbart et al 2013). Rapid foliar biochemical concentration determination by remote sensing technology has developed since the 1980s.…”

Section: Introductionmentioning

confidence: 99%

Predicting Cherry Leaf Chlorophyll Concentrations Based on Foliar Reflectance Spectra Variables

Xia

Peng

2014

J Indian Soc Remote Sens

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This paper assesses the capability of hyperspectral remote sensing to estimate chlorophyll a (Chl a), chlorophyll b (Chl b), and chlorophyll a + b (Chl a + b) concentration at cherry leaf scale using a variety of spectral variables during the growth season. A field experiment was conducted in cherry orchard. The leaf reflectance spectra of cherry plants were acquired within 350-1050 nm wavelengths. A variety of spectral variables were mathematically computed based on the leaf spectra and transformation of reflectance spectra. The relationships between all of spectral variables and chlorophyll concentration were discussed. Estimating Chl a, Chl b, and Chl a + b concentration by stepwise linear regression method and curve estimation method were carried out. Results demonstrated that the best spectral variable for prediction of chlorophyll concentration was the new spectral variable (the first derivative of log (1/R 741 ) and D 751 /D 511 ), with the root mean square error prediction (RMSEP) of 4.802 mg L −1 for Chl a concentration, 1.659 mg L −1 for Chl b concentration, and 6.419 mg L −1 for Chl a + b concentration. It should be noted that spectral variables such as D 715 /D 705 , EBFR, D 705 /D 722 , and BND showed a good performance with the RMSEP of 5. 768, 7.838, 12.146, and 14.437 mg L −1 for Chl a concentration, 1.795, 1.985, 1.765, and 3.164 mg L −1 for Chl b concentration, and 7. 935, 11.49, 17.99, and 21.79 mg L −1 for Chl a + b concentration respectively. Further investigation is needed to evaluate the effectiveness of such techniques on other orchard varieties or at the canopy level.

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Retrieval of chlorophyll content and LAI of crops using hyperspectral techniques: application to PROBA/CHRIS data

Cited by 49 publications

References 48 publications

Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models

Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models

Influencia del ángulo de observación en la estimación del índice de área foliar (LAI) mediante imágenes PROBA/CHRIS

Predicting Cherry Leaf Chlorophyll Concentrations Based on Foliar Reflectance Spectra Variables

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