Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index

Pasqualotto, Nieves; Delegido, Jesús; Wittenberghe, Shari Van; Verrelst, Jochem; Rivera, Juan Pablo; Moreno, José

doi:10.1016/j.jag.2018.01.002

Cited by 49 publications

(41 citation statements)

References 71 publications

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“…Alternatively, some authors proposed to calculate finite integrals of specific spectral regions, typically covering a part of the visible and the red-edge region for LAI or chlorophyll content estimations, into a (normalized) index (Broge and Leblanc 2001;Delegido et al 2010;Malenovský et al 2006Malenovský et al , 2015Mutanga et al 2005;Oppelt and Mauser 2004). Likewise, in a recent study of Pasqualotto et al (2018) this method exploited the water absorption spectral regions to quantify canopy water content. In these studies, integration-based indices were demonstrated to perform superior to classical vegetation indices, as they exploit more optimally absorption regions embedded in spectroscopic data than indices relying on a reflectance intensity of few individual bands (Kováč et al 2013).…”

Section: Parametric Approaches Based On Spectral Shapes and Spectralmentioning

confidence: 99%

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

et al. 2018

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An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2)

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Section: Parametric Approaches Based On Spectral Shapes and Spectralmentioning

confidence: 99%

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

et al. 2018

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“…In recent studies, mostly parametric regression models based on vegetation indices [3,49], derivative- [5,23], or integration-based [50] indices have been applied to retrieve crop canopy water content information from hyperspectral data. Verrelst, et al [74] obtained very good CWC correlation on SPARC03 data (R 2 = 0.95) by applying Gaussian process regression with integrated sequential backward band removal.…”

Section: Dependency Of Canopy Water Detection On Canopy Structurementioning

confidence: 99%

“…Studies that aimed at separating all three phases of water were not designed to quantify canopy water content in absolute terms and therefore accurate measurements were not carried out [45][46][47][48]. On the other hand, studies which derived water content explicitly by applying the Beer-Lambert law often relied on the assumption that upscaling leaf EWT to canopy water content (CWC) could be done by a simple multiplication with the leaf area index (LAI) (see references [5,23,25,49,50]). In other publications, biomass sampling strategies have not been designed to deduce the single water components of a canopy that an optical sensor can actually detect (e.g., references [21,[51][52][53]).…”

Section: Introductionmentioning

confidence: 99%

Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data

et al. 2018

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Quantitative equivalent water thickness on canopy level (EWT canopy ) is an important land surface variable and retrieving EWT canopy from remote sensing has been targeted by many studies. However, the effect of radiative penetration into the canopy has not been fully understood. Therefore, in this study the Beer-Lambert law is applied to inversely determine water content information in the 930 to 1060 nm range of canopy reflectance from measured winter wheat and corn spectra collected in 2015, 2017, and 2018. The spectral model was calibrated using a look-up-table (LUT) of 50,000 PROSPECT spectra. Internal model validation was performed using two leaf optical properties datasets (LOPEX93 and ANGERS). Destructive in-situ measurements of water content were collected separately for leaves, stalks, and fruits. Correlation between measured and modelled water content was most promising for leaves and ears in case of wheat, reaching coefficients of determination (R 2 ) up to 0.72 and relative RMSE (rRMSE) of 26% and in case of corn for the leaf fraction only (R 2 = 0.86, rRMSE = 23%). These findings indicate that, depending on the crop type and its structure, different parts of the canopy are observed by optical sensors. The results from the Munich-North-Isar test sites indicated that plant compartment specific EWT canopy allows us to deduce more information about the physical meaning of model results than from equivalent water thickness on leaf level (EWT) which is upscaled to canopy water content (CWC) by multiplication of the leaf area index (LAI). Therefore, it is suggested to collect EWT canopy data and corresponding reflectance for different crop types over the entire growing cycle. Nevertheless, the calibrated model proved to be transferable in time and space and thus can be applied for fast and effective retrieval of EWT canopy in the scope of future hyperspectral satellite missions.

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“…[8][9][10] More recent HSI studies, however, have focused on detection of solid materials, typically using reflected visible or near-infrared wavelengths. 11,12 The solid-phase studies have grown at an exponential pace, particularly for vegetation [13][14][15] as well as for rocks, minerals, and other geological specimens. 11,[16][17][18][19] To support these geological studies, the ASTER spectral library database, for example, has grown to include over 2300 spectra for a wide variety of materials including minerals, rocks, vegetation, soils, and manmade materials covering the wavelength range 0.4 to 15.4 μm.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging of minerals in the longwave infrared: the use of laboratory directional-hemispherical reference measurements for field exploration data

Myers

Johnson

Gallagher

et al. 2019

J. Appl. Rem. Sens.

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Hyperspectral imaging (HSI) continues to grow as a method for remote detection of vegetation, materials, minerals, and pure chemicals. We have used a longwave infrared (7.7 to 11.8 μm) imaging spectrometer in a static outdoor experiment to collect HSI data from 24 minerals and background materials to determine the efficacy with which HSI can remotely detect and distinguish both pure minerals and mineral mixtures at a 45-deg tilt angle relative to ground using two different backgrounds. Measurements were obtained separately for the minerals and materials mounted directly on both a bare plywood board and a board coated with aluminum foil: 19 powders (3 mixtures and 16 pure mineral powders) held in polyethylene bottle lids as well as five samples in rock form were taped directly to the boards. The primary goal of the experiment was to demonstrate that a longwave infrared library of solids and minerals collected as directional-hemispherical reflectance spectra in the laboratory could be used directly for HSI field identification along with simple algorithms for a rapid survey of the target materials. Prior to the experiment, all 24 mineral/inorganic samples were measured in the laboratory using a Fourier transform infrared spectrometer equipped with a gold-coated integrating sphere; the spectra were assimilated as part of a larger reference library of 21 pure minerals, 3 mixtures, and the polyethylene lid. Principal component analysis with mean-centering was used in an exploratory analysis of the HSI images and showed that, for the aluminum-coated board, the first principal component captured the difference between the signal that resembled a blackbody and the highly reflective aluminum background. In contrast, the second, third, and fourth principal components were able to discriminate the materials including phosphates, silicates, carbonates, and the mixtures. Results from generalized least squares target detection clearly showed that laboratory reference spectra of minerals could be utilized as targets with high fidelity for field detection.

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Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index

Cited by 49 publications

References 71 publications

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data

Hyperspectral imaging of minerals in the longwave infrared: the use of laboratory directional-hemispherical reference measurements for field exploration data

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