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Narrow band spectroscopy can distinguish the phases of water by wavelength and the concentration by the depth of the absorption of water in vapor, liquid, and solid phases in the solar reflected spectrum. Wavelengths near 900–1100, 1200–1400, and 1300–1900 nm show that absorption spectra for the three phases of water overlap but maxima are displaced in wavelength by about 34, 67, and 130 nm, respectively. Data provided by Robert O. Green, Jet Propulsion Laboratory. 

Narrow band spectroscopy can distinguish the phases of water by wavelength and the concentration by the depth of the absorption of water in vapor, liquid, and solid phases in the solar reflected spectrum. Wavelengths near 900–1100, 1200–1400, and 1300–1900 nm show that absorption spectra for the three phases of water overlap but maxima are displaced in wavelength by about 34, 67, and 130 nm, respectively. Data provided by Robert O. Green, Jet Propulsion Laboratory. 

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Canopy water content is a dynamic quantity that depends on the balance between water losses from transpiration and water uptake from the soil. Absorption of short-wave radiation by water is determined by various frequencies that match overtones of fundamental bending and stretching molecular transitions. Leaf water potential and relative water cont...

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... (Roberts et al., 1998; Sims and Gamon, 2002). The problem with the analogy to the Beer–Lam- bert Law is the need to account for multiple scattering within a leaf (Zhang et al., 2011). In Fig. 2, a fresh leaf of Zea mays had an LWC of 0.16 kg m –2 ; however, regressions of –log(R) with respect to the absorption coefficient resulted in LWC of 0.61 kg m –2 at 970 nm, 0.62 kg m –2 at 1200 nm, and 0.43 kg m –2 at 1600 nm. Additional terms that account for multiple scattering could be used in regression equations to improve the fit (Zhang et al., 2011); yet added interaction terms do not give an unambiguous estimate of LWC. Nonethe - less, the regression equations are useful, because with a good prediction of leaf reflectance based on LWC, the residual between the predicted and measured leaf reflectance shows spectral features related to leaf dry matter content (Gao and Goetz, 1994; Ramoelo et al., 2011; Wang et al., 2011). Thus, spectroscopy estimates are not very useful for directly determining LWC per se; but the regression equations may be used to unmask other spectral features in leaves. A recent paper by Féret et al. (2011) shows how the PROSPECT leaf radiative transfer model can be used to identify wavelengths that will produce improved indexes. In the 1990s, more sophisticated radiative transfer mod- els began to be developed that accounted for changing leaf biochemistry and structure, including leaf water content (see review by Jacquemoud et al., 2009). The simplest models considered the leaf as a single scat- tering and absorbing plane-parallel layer (e.g, Allen et al., 1969), while the most complicated models consider the full three-dimensional structure and biochemistry of the cells and tissues that form the leaf (Govaerts et al., 1996; Ustin et al., 2001). At a minimum, physically realistic models require detailed information about the refractive index and the specific absorption coefficients of leaf constituents. Today’s models were built on the earlier studies of Allen et al. (1969), who represented a compact leaf as an absorbing plate with rough surfaces producing diffusion. This approach was extended to non-compact leaves by treating them as layers of plates (N) separated by N−1 air spaces (Allen et al., 1970). The widely used and validated PROSPECT model (Leaf Optical Properties Spectra) (Jacquemoud and Baret, 1990) accurately simulates the hemispherical reflectance and transmittance of various types of plant leaves (fresh monocot and dicot leaves, senescent and dry leaves) over the solar spectrum from 400 to 2500 nm (Jacquemoud et al., 2009). After 20 years of use, PROSPECT inputs the concentrations of chlorophyll a+b, carotenoids, EWT, dry matter (DM), and the N structural parameter (the internal structure causing scat- tering) and predicts leaf reflectance or, inversely, inputs reflectance and predicts leaf chemistry. It is often linked with one of the SAIL family of models (Verhoef, 1984) as PROSAIL (Jacquemoud et al., 2009) that calculate hemispherical and directional reflectance of an homo - geneous vegetation canopy (Fig. 3). The SAIL model assumes horizontally homogeneous layers and calculates canopy reflectance from input pa - rameters, including the sun/viewing geometry, soil and canopy leaf angles and spectral reflectance, and canopy structure. A “Markov‐Chain Canopy Reflectance Model” (MCRM) (Kuusk, 1995a,b) with additions to simulate row crop structure and “Forest Light Inter- action Model” (FLIM) (Rosema et al., 1992) address discontinuous canopies and have already been tested theoretically and shown to successfully obtain CWC for crops and forest canopies, respectively (Cheng et al., 2008). “SAIL combined with a geometric model” (GeoSAIL) (Huemmrich, 2001) was also tested in forest canopies (Kötz et al., 2004). A higher level of complexity is introduced in three-dimensional vegeta- tion structure models (Pinty et al., 2004) to understand heterogeneous canopies. For example, the “Forest Light Interaction Model” (FLIGHT) (North, 1996) was suc - cessfully applied to estimate CWC (Kötz et al., 2004). 15 Although these models are closer to reality they require a large number of input variables, making them harder to parameterize (with real data). Figure 3 shows that the band locations for MODIS and Landsat only partially match the location of the main spectral absorption features in plants. Many bands are too wide to effectively measure changes in spectral shape and depth. Given the advantage of contiguous narrow-band spectral coverage in the 400 to 2500 nm region demonstrated in imaging spectroscopy stud- ies, a spectral fitting technique originally designed for improved atmospheric calibration for Advanced Vis- ible Infrared Imaging Spectrometer (AVIRIS) data was developed to simultaneously estimate water vapor and liquid water (Green et al., 1991, 1993; Roberts et al., 1997). Based on the spectral separation between water vapor absorption and liquid water absorption features (Fig. 1), this technique is able to retrieve liquid water over a pixel concurrent with water vapor estimates. The analysis has been applied to airborne imaging spectrom- eters including AVIRIS (Fig. 4), and to Hyperion data on the EO-1 satellite. Figure 4 shows the simultaneous 16 quantification of water vapor and liquid water made by fitting AVIRIS-measured radiance to a radiance spectrum of water vapor and liquid water absorption generated by moderate spectral resolution atmospheric transmittance algorithm (MODTRAN) (Green et al., 1993; Roberts et al., 1997, 1998). This physically based liquid water is expressed as the equivalent water thick- ness (EWT), which at the leaf level is the volume of water, expressed as depth per unit area. When the area is a pixel, it becomes the equivalent depth of water over the area of a pixel that is required to fit the water absorp - tion modeled in the atmospheric calibration procedure (Green et al., 1991, 1993; Roberts et al., 1997). Allen et al. (1969) and Gausman et al. (1970) de - rived the expression for LWC as the thickness of a slab of water that accounts for the radiative properties of a leaf over the 1400–2500 nm range, and validated from measurements on corn and cotton leaves. Narrow-band airborne imaging spectroscopy has been used to quan- tify EWT and CWC by measuring the depth and area of water absorption features in the 940–1000 nm interval in the reflected NIR spectrum (Green et al., 1991; Gao and Goetz, 1995; Jacquemoud et al., 1995, 1996; Rob - erts et al., 1997; Zhang et al., 1997; Sanderson et al., 1998; Ustin et al., 1998a; Datt, 1999; Serrano et al., 2000; Cheng et al., 2008). Subsequent studies have shown the potential for this technique to retrieve EWT, and to use it to monitor vegetation water content (Roberts et al., 1997; Ustin et al., 1998b; Serrano et al., 2000). When applied to images, the leaf EWT multiplied by leaf area index (LAI) yields CWC (Hunt, 1991), as shown in radiative transfer modeling studies (Cheng et al., 2006b). Roberts et al. (1997) used CWC to monitor temporal and spatial variation in water content in herbaceous, shrub, and conifer vegetation. Ustin et al. (1998b) compared dif- ferent methods for estimating CWC and showed good estimates for chaparral shrubs and detected seasonal differences, although Serrano et al. (2000) later found significant but moderate correlations (0.30–0.41) with index methods, including the simple water index (WI, Table 2), the normalized difference water index (NDWI, Table 2) compared to the method of Green et al. (1991; 1993) and Roberts et al. (1997). Cheng et al. (2006b) utilized three linked leaf-canopy radiative transfer models to simulate AVIRIS-equivalent spectra using the MODTRAN based code implemented in Atmospheric Correction Now (ACORN) software (ImSpec LLC, ), in Mode 1.5, for three architecturally different vegetation scenarios. Retrievals of CWC from these synthetic spectra were shown to be sensitive to changes in the EWT (input into the simulation). The major sources of uncertainty were shown to be from leaf dry matter (DM) and soil background in retrievals of CWC (Daughtry and Hunt, 2008). The potential use of AVIRIS CWC for validating retrievals from MODIS data was also demonstrated at three study sites ranging from crops to woodland and to forest ecosystems (Cheng et al., 2006b). MODIS, with its morning and afternoon overpasses, provides the required high temporal resolution SWIR bands that are located close to CWC absorptions. Zarco- Tejada et al. (2003) showed that MODIS could estimate CWC from leaf EWT and LAI and that it was possible to accurately follow changes in canopy water content even in low-water-content chaparral shrublands, based on a time series of MODIS data extending over the sum- mer drought (~6 months) in the California Coast Range compared to field-measured fuel moisture content (FMC) This radiative transfer method was further de- veloped by Trombetti et al. (2008) to produce monthly CWC maps for the continental United States. Inversion of radiative transfer models (Jacquemoud and Baret, 1990; Zarco-Tejada et al., 2003; Riaño et al., 2005; Yebra et al., 2008) can be used to account for dif - ferences in the types of leaves and canopy structure that are found in different vegetation biomes. These models generally identify the best fit between the model and empirical data, which is evaluated using a merit func- tion. The whole spectrum or only part of a spectrum can be used to establish the best fit merit function (Jacque - moud et al., 1996). Even vegetation indexes have been used for this purpose (Zarco-Tejada et al., 2003). These methods for retrieving water content can be applied to broad scales with different environmental features, but they are computationally very expensive (Fang and Liang, 2003). Methods proposed to solve the computa- tional limitations include using a Look Up Table (LUT) (Schaepman et al., 2005) or applying a machine learn- ing ...
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... affects the harmonics of the O–H bond stretching frequency (Collins, 1925; Dorsey, 1940; Luck, 1963; Tam and Patel, 1979) with temperatures between 0 and 95 oC causing shifts to shorter wavelengths of 20–45 nm, depending on wavelength. Curcio and Petty (1951) noted the shift in absorption position for different states of water, that is, between water vapor, liquid water, and ice, as shown in more recent measurements by Robert O. Green, Jet Propulsion Laboratory ( Fig. 1). In contrast to absorption by photosynthetic pigments in the visible spectrum, water absorption has dominant im- pacts on reflectance and transmittance of green leaves in the near-infrared (NIR) and shortwave-infrared (SWIR) regions (Gates et al., 1965; Knipling, 1970). Both excess and deficiency of water affect leaf reflectance. Knipling (1970) showed that NIR (700–1400 nm) reflectance declines significantly when leaf cells are saturated with water. This result demonstrated the importance of mul- tiple scattering within the leaf’s air cavities, observed by earlier work on leaf optics (Willstättler and Stoll, 1913). Knipling (1970) also described the impact of leaf dehydration and senescence on reflectance: reflectance increases broadly across the wavelength interval from 1600 to 2500 nm. Direct physiological measurements of leaf water content often rely on leaf relative water content (RWC; Table 1) due to its sensitivity to change. RWC shows a non-linear relationship to leaf water potential ( ψ ), a measure of the energy required to extract water from the soil and transport it to the leaf surface. This measure is considered by plant physiologists to provide a good indication of the water deficit (drought) that a leaf is experiencing. ψ is widely used to quantify the response of plants to drought and is inversely related to RWC. Carter (1991) expanded on the work of Knipling (1970) with a detailed examination of wavelength-dependent response to varying RWC, measured by the reflectance difference between the leaf at RWC = 100% and at vary - ing levels of dehydration. Sensitivity varied with leaf anatomical type but for sensitive species at low RWC, reflectance increased by as much as 70% at wavelengths of 1420 and 1900 nm. Carter (1993) later identified wavelengths at 1412, 1978, 2004, and 2401 nm to be most sensitive to changes in RWC. However, thicker leaves with greater leaf water content (LWC) do not show much response at these wavelengths because of water’s large absorption coefficients (Fig. 1). Figure 2 illustrates the relationship between spectral reflectance of a corn ( Zea mays) leaf (medium thickness) and the strength of the specific absorption coefficient for water. In early field studies of plant water status, Hardisky et al. (1983) were the first to propose and test a normal - ized difference water index using Landsat TM bands 4 and 5 (see Fig. 3) , which they termed the Infrared Index (currently it is referred to as the Normalized Difference Infrared Index (NDII; Table 2)). Hunt et al. (1987) and Hunt and Rock (1989) developed and tested a Leaf Water Stress Index (LWSI; Table 2), which was de - rived from the Beer–Lambert law to be equal to RWC. However, Hunt and Rock (1989) showed that the LWSI was not practical because of the difficulty in measuring some of the parameters. Furthermore, Hunt and Rock (1989) showed there is a log-linear relationship between the Moisture Stress Index (MSI; Table 2) and LWC for leaves of different species from crops to desert suc- culents. Cohen (1991a,b) examined Landsat TM ...
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... affects the harmonics of the O–H bond stretching frequency (Collins, 1925; Dorsey, 1940; Luck, 1963; Tam and Patel, 1979) with temperatures between 0 and 95 oC causing shifts to shorter wavelengths of 20–45 nm, depending on wavelength. Curcio and Petty (1951) noted the shift in absorption position for different states of water, that is, between water vapor, liquid water, and ice, as shown in more recent measurements by Robert O. Green, Jet Propulsion Laboratory ( Fig. 1). In contrast to absorption by photosynthetic pigments in the visible spectrum, water absorption has dominant im- pacts on reflectance and transmittance of green leaves in the near-infrared (NIR) and shortwave-infrared (SWIR) regions (Gates et al., 1965; Knipling, 1970). Both excess and deficiency of water affect leaf reflectance. Knipling (1970) showed that NIR (700–1400 nm) reflectance declines significantly when leaf cells are saturated with water. This result demonstrated the importance of mul- tiple scattering within the leaf’s air cavities, observed by earlier work on leaf optics (Willstättler and Stoll, 1913). Knipling (1970) also described the impact of leaf dehydration and senescence on reflectance: reflectance increases broadly across the wavelength interval from 1600 to 2500 nm. Direct physiological measurements of leaf water content often rely on leaf relative water content (RWC; Table 1) due to its sensitivity to change. RWC shows a non-linear relationship to leaf water potential ( ψ ), a measure of the energy required to extract water from the soil and transport it to the leaf surface. This measure is considered by plant physiologists to provide a good indication of the water deficit (drought) that a leaf is experiencing. ψ is widely used to quantify the response of plants to drought and is inversely related to RWC. Carter (1991) expanded on the work of Knipling (1970) with a detailed examination of wavelength-dependent response to varying RWC, measured by the reflectance difference between the leaf at RWC = 100% and at vary - ing levels of dehydration. Sensitivity varied with leaf anatomical type but for sensitive species at low RWC, reflectance increased by as much as 70% at wavelengths of 1420 and 1900 nm. Carter (1993) later identified wavelengths at 1412, 1978, 2004, and 2401 nm to be most sensitive to changes in RWC. However, thicker leaves with greater leaf water content (LWC) do not show much response at these wavelengths because of water’s large absorption coefficients (Fig. 1). Figure 2 illustrates the relationship between spectral reflectance of a corn ( Zea mays) leaf (medium thickness) and the strength of the specific absorption coefficient for water. In early field studies of plant water status, Hardisky et al. (1983) were the first to propose and test a normal - ized difference water index using Landsat TM bands 4 and 5 (see Fig. 3) , which they termed the Infrared Index (currently it is referred to as the Normalized Difference Infrared Index (NDII; Table 2)). Hunt et al. (1987) and Hunt and Rock (1989) developed and tested a Leaf Water Stress Index (LWSI; Table 2), which was de - rived from the Beer–Lambert law to be equal to RWC. However, Hunt and Rock (1989) showed that the LWSI was not practical because of the difficulty in measuring some of the parameters. Furthermore, Hunt and Rock (1989) showed there is a log-linear relationship between the Moisture Stress Index (MSI; Table 2) and LWC for leaves of different species from crops to desert suc- culents. Cohen (1991a,b) examined Landsat TM ...

Citations

... 7 Water is the main determinant in the shortwave infrared region, and its characteristic absorption leads to two weak and two signicant water absorbing bands appearing at 970, 1250, 1460 and 1940 nm, respectively. 8,9 Many physical models have been proposed to simulate the spectral reectance and spectral transmittance of the leaf, such as "plate model", "stochastic model", "N-ux model" and "radiation transfer model". [10][11][12][13] Among these models, the "plate model" needs the fewest parameters and can be inverted to obtain the chemical constituent information of leaves. ...
Article
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Water is the main determinant of the leaf spectral characteristics in the shortwave infrared region, whereas only changing the water content in the PROSPECT model cannot accurately describe the solar spectrum reflectance and transmittance of the dehydrated leaf. To elucidate the effects of water loss, the solar spectrum reflectances and transmittances of the Osmanthus fragrans leaves in the fresh state, natural air-dry state and oven-dry state were measured, and the leaf parameters were predicted by the PROSPECT model inversion. The results revealed that the first effect was to increase the brown pigment content, which led to an increase in leaf absorption and change of the leaf absorption characteristics, and correspondingly, in the visible region, both the reflected and transmitted radiations were decreased and the reflection peak shifted towards a long wavelength. The other two effects were to increase the leaf structure index and refractive index, which resulted in an enhancement of the reflected radiation and an attenuation of the transmitted radiation over the range from 400 to 2500 nm. These findings suggest that if people consider the changes of leaf pigment content, structure and refractive index when water is lost from an actual leaf, it will be expected to improve the monitoring accuracy of the leaf water content based on leaf spectral remote sensing technology.
... The hollow located at 739 nm corresponds to the region of the rededge and more precisely to its inflection point (maximum of the first derivative). The hollow at 970 nm corresponds to the water absorption (Osborne et al., 1993;Ustin et al., 2012). The presence of the two peaks at 945 nm and 1040 nm express an enlargement of the hollow at 970 nm. ...
Article
Spectroscopy is today and for two decades strongly used in many fields (pharmacy, agriculture, process, medicine…). This use in a very large number of applications is linked to the great spectral richness of the measurement and therefore to the large amount of accessible chemical information. For plant breeding, spectral reflectance in the visible and near-infrared range (VIS–NIR) embeds a lot of information about vegetation (pigments, structure, water, etc.). Discriminatory power between genotypes can be greatly improved by using high spectral resolution. NIR spectroscopy is still limited in the field for phenotyping compared to existing imaging solutions that are easier to implement. In this study, we will address the potential of high spectral resolution data by using NIR spectroscopy to describe phenotypic responses of maize genotypes to water stress. To that end, data acquired following an experimental design with water-deficient environment are processed using an analysis of variance method adapted to multivariate data called REP-ASCA. For each factor, this method gives its significance, the loadings describing the impacted spectral regions and the scores to classify observations. For a date with proven water stress, the treatment and genotype factors and the interaction term are significant with a p-value threshold at 0.05. Treatment term loadings highlight the spectral regions impacted by the change in irrigation while those of the genotype factor allows to group genotypes according to the yield potential regardless the irrigation. The interaction term loadings are used as a phenotyping trait related to water stress response. Based on this signature, tolerant genotypes are differentiated from sensitive genotypes according to a ranking based on final yield (R = 0.81). This spectral signature was then applied to another environment with a moderate water deficit. For most genotypes, we were able to recover the ranking previously established by the stressed environment (R = 0.60).
... Several remote sensing techniques allow monitoring VWC or proxy measures of VWC with different levels of precision. These measurements cover a wide range of the electromagnetic spectrum, ranging from optical spectral imaging (Asner et al., 2016;Ustin et al., 2012) to thermal infrared imaging (Jones et al., 2009), to active (radar) and passive (radiometer) microwave sensing (Konings et al., 2019;Vermunt et al., 2020;Kim et al., 2012). Microwave frequencies are arguably the most useful for systematic measurement of VWC because of their all-time observational capabilities during day and night and irrespective of cloud cover, and the ability to penetrate beyond the top few millimeters of the forest canopy. ...
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Droughts in a warming climate have become more common and more extreme, making understanding forest responses to water stress increasingly pressing. Analysis of water stress in trees has long focused on water potential in xylem and leaves, which influences stomatal closure and water flow through the soil-plant-atmosphere continuum. At the same time, changes of vegetation water content (VWC) are linked to a range of tree responses, including fluxes of water and carbon, mortality, flammability, and more. Unlike water potential, which requires demanding in situ measurements, VWC can be retrieved from remote sensing measurements, particularly at microwave frequencies using radar and radiometry. Here, we highlight key frontiers through which VWC has the potential to significantly increase our understanding of forest responses to water stress. To validate remote sensing observations of VWC at landscape scale and to better relate them to data assimilation model parameters, we introduce an ecosystem-scale analogue of the pressure-volume curve, the non-linear relationship between average leaf or branch water potential and water content commonly used in plant hydraulics. The sources of variability in these ecosystem-scale pressure-volume curves and their relationship to forest response to water stress are discussed. We further show to what extent diel, seasonal, and decadal dynamics of VWC reflect variations in different processes relating the tree response to water stress. VWC can also be used for inferring belowground conditions – which are difficult to impossible to observe directly. Lastly, we discuss how a dedicated geostationary spaceborne observational system for VWC, when combined with existing datasets, can capture diel and seasonal water dynamics to advance the science and applications of global forest vulnerability to future droughts.
... The results indicate that leaf wetness in a rainforest cannot be simply described in terms of a wetness duration, as 50.4 % of the time the canopy is between 5 and 95 % wet, and the canopy is only completely dry for 34 % of the time (Fig. 3). This has significant implications for interpreting eddy covariance fluxes (Hong et al., 2014;van Dijk et al., 2015) and satellite-derived measurements of canopy moisture content (Konings et al., 2017;Ustin et al., 2012). In contrast to the results of Aparecido et al. (2016), the analysis suggested that leaves at the top of the canopy spend more time wet than those at the bottom (Fig. 4) although, perhaps counterintuitively, not because of dew-related wetness which was variable throughout the canopy profile (Fig. 5), but because of rain (Fig. 7). ...
Article
Canopy wetness is a common condition that influences photosynthesis, the leaching or uptake of solutes, the water status and energy balance of canopies, and the interpretation of eddy covariance and remote sensing data. While often treated as a binary variable, ‘wet’ or ‘dry’, forest canopies are often partially wet, requiring the use of a continuous description of wetness. Minor precipitation events such as dew, that wet a fraction of the canopy, have been found to contribute to dry season foliar water uptake in the Eastern Amazon, and are fundamentally important to the canopy energy balance. However, few studies have reported the spatial and temporal distribution of canopy wetness, or the relative contribution of dew to leaf wetness, for forest ecosystems. In this study, we use two canopy profiles of leaf wetness sensors, coupled with meteorological data, to address fundamental questions about spatial and temporal variation of leaf wetness in an Eastern Amazonian rainforest. We also investigate how well meteorological tower data can predict canopy wetness using two models, one empirical and one that is physically-based. The results show that the canopy is 100% dry only for 34% of the time, otherwise being between 5% and 100% wet. Dew accounts for 20% or 43% of total annual leaf wetness, and 36% or 50% of canopy wetness in dry season, excluding or including dew events that co-occur with rain, respectively. Wetness duration was higher at the top than bottom of the canopy, mainly because of rain events, whilst dew formation was strongly dependent on the local canopy structure and varied horizontally through the canopy. The best empirical model accounted for 55% of the variance in canopy wetness, while the physical model accounted for 48% of the variance. We discuss future modelling improvements of the physical model to increase its predictive capacity.
... Shorter crop trees without understory produce stronger relationships between optical remote sensing and canopy water content [55,56]. This latter variable relates to LFMC after accounting for specific leaf area and leaf area index [57]. Another remarkable aspect is that forest canopy LFMC (foliar moisture) becomes important for crown fire modeling but not for surface fires [58]. ...
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... Les loadings de la composante principale sont présentés sur la D'un autre côté, les deux pics situés à 935 nm et 1040 nm entourent le creux situé à 970 nm correspondant à l'absorption de l'eau (Osborne et al., 1993;Ustin et al., 2012). La présence de ces deux pics montre un effet d'élargissement du creux situé à 970 nm. ...
Thesis
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L’objectif de la thèse est d’explorer le potentiel d’un couplage entre un capteur de haute résolution spectrale/faible résolution spatiale et un capteur à faible résolution spectrale et forte résolution spatiale pour la sélection variétale. Ce système est étudié dans le cadre du phénotypage du maïs en conditions de stress hydrique. L’étude est organisée de la manière suivante : 1. Dans un premier temps, il s’agissait de vérifier l’hypothèse selon laquelle l’utili-sation d’une forte résolution spectrale apporte un plus pour le phénotypage dans le cadre de la sélection variétale. Pour cela, deux campagnes expérimentales ont été réalisées en 2017 et 2018. Des spectres ont été acquis au champ en utilisant la spectroscopie visible et proche-infrarouge selon un plan d’expérience comptant au total 10 génotypes connus pour leur tolérance face au stress hydrique. Cette partie montre qu’il est possible de caractériser les comportements des génotypes en situation de stress hydrique tout en décrivant précisément les régions spectrales responsables de cette classification. 2. L’utilisation d’un spectromètre en extérieur induit un manque de répétabilité des mesures. Les conclusions des analyses réalisées sur des spectres portant cette erreur peuvent alors être faussées. Une méthode a donc été développée pour réduire l’erreur de répétabilité à travers l’utilisation d’une série de répétitions de mesures additionnelles au plan d’expérience. Cette méthode modifie l’algorithme d’analyse de variance ASCA en introduisant des projections orthogonales dans l’espace des spectres, en complément des projections orthogonales dans l’espace des individus, réalisées naturellement par l’analyse de variance. 3. L’objectif de la dernière partie était de réaliser le couplage d’un capteur à haute ré-solution spectrale et faible résolution spatiale (spectromètre Vis-NIR) avec un cap-teur à faible résolution spectrale et haute résolution spatiale (une caméra RGB) à l’aide d’algorithmes de pan-sharpening pour reconstituer une image hyperspectrale de test. Cette partie comportait deux étapes : une approche par simulation pour comparer les algorithmes de pan-sharpening à l’aide d’une image hyperspectrale et une partie pour proposer une méthode pour reconstruire une image hyperspectrale à partir de la solution de couplage proposée
... Vegetation water content enables the synthesis of CO 2 into carbohydrates, determines hydraulic pressure for opening leaf stomata, and maintain the structural integrity, which further affect the energy flow and material circulation of an entire ecosystem (Swain et al., 2012;Zygielbaum et al., 2009;Yi et al., 2014;Neinavaz et al., 2017). Water content is a critical factor determining not only the primary productivity and health of vegetation (Delucia et al., 2014;Argyrokastritis et al., 2015;Pôças et al., 2015), but also the biomass burning processes (Raymond et al., 2011;Ustin et al., 2012). As a result, estimating and monitoring leaf water content becomes a vital topic of climate change, carbon cycle, ecosystem service, and plant health (Swain et al., 2012;Ramachandiram and Pazhanivelan, 2015). ...
... (3). Spectral canopy water absorption derivatives: Derivative analysis was used to measure the wavelength position and magnitude of the NIR water absorption edges [90], abbreviated here as Wtr1EdgeWvl and Wtr1EdgeMag, the canopy water absorption features between 958-1073 nm and 1105-1168 nm, abbreviated as Wtr1AbAr and Wtr2AbAr, respectively, and the physically-derived equivalent water thickness (EWT; the depth of water/per pixel area) [90][91][92]. (4). Principal component analysis (PCA) [93] was performed both using the full spectral range, as well as the independent regions: visible, near infrared, and shortwave infrared which was done to summarize significant information in all three regions of the spectrum. ...
... These combinations can be different in each tree; therefore, over the 500 runs the relative strength of each metric on model performance was established. [18,91] Normalized Difference Water Index (NDWI); [87] Fractional Cover form the first returns (FC-1rtn); [18] Normalized Difference Infrared Index (NDII); [88] Leaf Area Index (LAI); [92] Cellulose Absorption Index (CAI); [89] Vegetation Vertical Profile integral (VVIint) [93,94] Wavelength positon of the NIR water absorption edge (Wtr1EdgeWvl), [90] Clumping Index [23] Magnitude positon of the NIR water absorption edge (Wtr1EdgeMag), [90] Canopy water absorption feature between 958-1073 nm (Wtr1AbAr), [90] Canopy water absorption feature between 1105-1168 nm (Wtr2AbAr), [90] Equivalent Water Thickness (EWT); [90][91][92] Principal Component: PC1, PC2, PC1_visible, PC2_visible, PC1_NIR, PC2_NIR, PC1_SWIR1 and PC1_SWIR2 [93]. ...
... These combinations can be different in each tree; therefore, over the 500 runs the relative strength of each metric on model performance was established. [18,91] Normalized Difference Water Index (NDWI); [87] Fractional Cover form the first returns (FC-1rtn); [18] Normalized Difference Infrared Index (NDII); [88] Leaf Area Index (LAI); [92] Cellulose Absorption Index (CAI); [89] Vegetation Vertical Profile integral (VVIint) [93,94] Wavelength positon of the NIR water absorption edge (Wtr1EdgeWvl), [90] Clumping Index [23] Magnitude positon of the NIR water absorption edge (Wtr1EdgeMag), [90] Canopy water absorption feature between 958-1073 nm (Wtr1AbAr), [90] Canopy water absorption feature between 1105-1168 nm (Wtr2AbAr), [90] Equivalent Water Thickness (EWT); [90][91][92] Principal Component: PC1, PC2, PC1_visible, PC2_visible, PC1_NIR, PC2_NIR, PC1_SWIR1 and PC1_SWIR2 [93]. ...
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Accurate information about ecosystem structure and biogeochemical properties is essential to providing better estimates ecosystem functioning. Airborne LiDAR (light detection and ranging) is the most accurate way to retrieve canopy structure. However, accurately obtaining both biogeochemical traits and structure parameters requires concurrent measurements from imaging spectrometers and LiDARs. Our main objective was to evaluate the use of imaging spectroscopy (IS) to provide vegetation structural information. We developed models to estimate structural variables (i.e., biomass, height, vegetation heterogeneity and clumping) using IS data with a random forests model from three forest ecosystems (i.e., an oak-pine low elevation savanna, a mixed conifer/broadleaf mid-elevation forest, and a high-elevation montane conifer forest) in the Sierra Nevada Mountains, California. We developed and tested general models to estimate the four structural variables with accuracies greater than 75%, for the structurally and ecologically different forest sites, demonstrating their applicability to a diverse range of forest ecosystems. The model R2 for each structural variable was least in the conifer/broadleaf forest than either the low elevation savanna or the montane conifer forest. We then used the structural variables we derived to discriminate site-specific, ecologically meaningful descriptions of canopy structural types (CST). Our CST results demonstrate how IS data can be used to create comprehensive and easily interpretable maps of forest structural types that capture their major structural features and trends across different vegetation types in the Sierra Nevada Mountains. The mixed conifer/broadleaf forest and montane conifer forest had the most complex structures, containing six and five CSTs respectively. The identification of CSTs within a site allowed us to better identify the main drivers of structural variability in each ecosystem. CSTs in open savanna were driven mainly by differences in vegetation cover; in the mid-elevation mixed forest, by the combination of biomass and canopy height; and in the montane conifer forest, by vegetation heterogeneity and clumping.
... Previous studies have concentrated on the effect of arbuscular mycorrhizal inoculation on crop LWC and crop LWC estimation from remote sensing data [6,[26][27][28] . However, few studies have been carried out to study the spectral response characteristics of the arbuscular mycorrhizal inoculated soybean under drought stress, and no optical LWC retrieval models have yet been developed specifically for the inoculated soybean. ...
... Hyperspectral imaging, or imaging spectroscopy, refers to the acquisition of coregistered images over contiguous narrow spectral bands (Schaepman et al., 2009). Hyperspectral remote sensing has proven its utility in a wide range of Earth system science domains, among others those focused on greenhouse gases (Roberts et al., 2010;Dennison et al., 2013), plants (Johnson et al., 1994;Roberts et al., 1998;Asner et al., 2000;Somers et al., 2010;Ustin et al., 2012), minerals (Hook et al., 1991;van der Meer and Bakker, 1997;Baugh et al., 1998), snow and ice (Painter et al., 2003;Dozier et al., 2009), coastal and inland water (Hoogenboom et al., 1998;Salem et al., 2005;Kudela et al., 2015), urban environments (Roessner et al., 2001;Roberts et al., 2012), and fire (Dennison and Roberts, 2009;Schepers et al., 2014;Veraverbeke et al., 2014b). These studies were conducted based on airborne hyperspectral (AHS) remote sensing, often based on data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) or the Airborne Prism Experiment (APEX) (Itten et al., 2008). ...