Spectroscopic determination of leaf water content using continuous wavelet analysis

Cheng, Tao; Rivard, Benoît; Sánchez‐Azofeifa, Arturo

doi:10.1016/j.rse.2010.11.001

Cited by 231 publications

(137 citation statements)

References 66 publications

Supporting

Mentioning

123

Contrasting

Unclassified

Order By: Relevance

“…Others have found species to have unique signatures based on their surface and structural characteristics (e.g., da Luz, 2006). PC 2 in our study was most strongly correlated with the SWIR region, which contains information related to water, dry matter and internal leaf structure (Cheng et al, 2011(Cheng et al, , 2014Kokaly & Clark, 1999). As such, this component was strongly correlated with species and cPFT, and to a lesser extent, with leaf form and leaf duration.…”

Section: Drivers Of Leaf Spectral Variabilitysupporting

confidence: 59%

Leaf spectral clusters as potential optical leaf functional types within California ecosystems

Roth

Casas

Huesca

et al. 2016

Remote Sensing of Environment

View full text Add to dashboard Cite

a b s t r a c tOur ability to measure and map plant function at multiple ecological scales is critical for understanding current and future changes in Earth's ecosystems and the global carbon budget. Conventional plant functional types (cPFTs) based on a few productivity-related traits have been previously used to simplify and represent major differences in global plant functions, but more recent research has directly focused on the use of functional trait information. Still, sampling limitations have constrained efforts to truly understand the variance and covariance of functional traits globally. Reflectance spectra offer a fast, repeatable, simultaneous measurement of a wide variety of leaf functional traits and could be used to optically define leaf functional types. To evaluate this concept, we measured leaf reflectance from a wide range of species in a diverse set of ecosystems across central and northern California, including observations from multiple individuals, sites, and seasons. Using principal components analysis, we analyzed spectral variation in relation to categorical attributes such as species and cPFTs, as well as to a set of functional trait metrics calculated from the spectra. We found the first three principal components (PCs) to be weakly related to categorical attributes and more strongly related to spectrally-derived functional metrics. Each PC was more strongly associated with different portions of the spectrum and contained different functional information. We applied a hybrid clustering algorithm to the PC coordinates of the observations to define potential optical leaf functional types. Twelve spectral clusters were identified, and these did not correspond directly to either single cPFTs or species. However, each cluster had a unique functional metric profile. Clusters represented both inter-and intra-species and cPFT functional differences driven by taxonomy, trait evolution and environmental responses, demonstrating their value as optical leaf functional types and the value of the clustering approach used here for defining optical types from leaf spectra. Our findings support the notion that cPFTs do not adequately capture differences in leaf function. They demonstrate that spectral measurements can be used to improve both the definition of PFTs as well as our knowledge regarding the covariance of functional traits within these classes.

show abstract

Section: Drivers Of Leaf Spectral Variabilitysupporting

confidence: 59%

Leaf spectral clusters as potential optical leaf functional types within California ecosystems

Roth

Casas

Huesca

et al. 2016

Remote Sensing of Environment

View full text Add to dashboard Cite

show abstract

“…Reflectance in the NIR spectrum (740-1300 nm) is most sensitive to leaf internal structure changes (Jacquemoud and Baret 1990) and is insensitive to moisture variation (Elvidge and Lyon 1985), except in conditions leading to leaf dehydration which therefore affects leaf structure (Girard and Girard 2010;Jensen 2007). Recent studies confirmed the high sensitivity of the SWIR band to moisture variation in vegetation and soil (Ceccato et al 2001;Cheng et al 2013;Cheng, Rivard, and Sánchez-Azofeifa 2011;Hunt et al 2011;Liu et al 2012;Hunt and Rock 1989).…”

Section: Satellite Images and Their Processingmentioning

confidence: 68%

Monitoring surface water content using visible and short-wave infrared SPOT-5 data of wheat plots in irrigated semi-arid regions

Benabdelouahab

Balaghi²,

Hadria³

et al. 2015

International Journal of Remote Sensing

View full text Add to dashboard Cite

Irrigated agriculture is an important strategic sector in arid and semi-arid regions. Given the large spatial coverage of irrigated areas, operational tools based on satellite remote sensing can contribute to their optimal management. The aim of this study was to evaluate the potential of two spectral indices, calculated from SPOT-5 highresolution visible (HRV) data, to retrieve the surface water content values (from bare soil to completely covered soil) over wheat fields and detect irrigation supplies in an irrigated area. These indices are the normalized difference water index (NDWI) and the moisture stress index (MSI), covering the main growth stages of wheat. These indices were compared to corresponding in situ measurements of soil moisture and vegetation water content in 30 wheat fields in an irrigated area of Morocco, during the 2012-2013 and 2013-2014 cropping seasons. NDWI and MSI were highly correlated with in situ measurements at both the beginning of the growing season (sowing) and at full vegetation cover (grain filling). From sowing to grain filling, the best correlation (R 2 = 0.86; p < 0.01) was found for the relationship between NDWI values and observed soil moisture values. These results were validated using a k-fold crossvalidation methodology; they indicated that NDWI can be used to estimate and map surface water content changes at the main crop growth stages (from sowing to grain filling). NDWI is an operative index for monitoring irrigation, such as detecting irrigation supplies and mitigating wheat water stress at field and regional levels in semi-arid areas.

show abstract

“…The inversion of PROSPECT‐D was performed using wavelet transformations and a look‐up‐table approach (Blackburn ; Blackburn and Ferwerda; Cheng et al. ; Ali et al. ; Li et al.…”

Section: Methodsmentioning

confidence: 99%

Differentiating plant functional types using reflectance: which traits make the difference?

Kattenborn

Fassnacht

Schmidtlein

2018

Remote Sens Ecol Conserv

View full text Add to dashboard Cite

Abiotic ecosystem properties together with plant species interaction create differences in structural and physiological traits among plant species. Certain plant traits cause a spatial and temporal variation in canopy reflectance that enables the differentiation of plant functional types, using earth observation data. However, it often remains unclear which traits drive the differences in reflectance between plant functional types, since the spectral regions in which electromagnetic radiation is influenced by certain plant traits are often overlapping. The present study aims to assess the relative (statistical) contributions of plant traits to the separability of plant functional groups using their reflectance. We apply the radiative transfer model PROSAIL to simulate optical canopy reflectance of 38 herbaceous plant species based on field-measured traits such as leaf area index, leaf inclination distribution, chlorophyll content, carotenoid content, water and dry matter content. These traits of the selected grassland species were measured in an outdoor plant experiment. The 38 species differed in growth form and strategy types according to Grime 0 s CSR model and hence represented a broad range of plant functioning. We determined the relative (statistical) contribution of each plant trait for separating plant functional groups via reflectance. Therein we used a separation into growth forms, that is graminoids and herbs, and into CSR strategy types. Our results show that the relative contribution of plant traits to differentiate between the examined plant functional types (PFT) groups using canopy reflectance depends on the PFT scheme applied. Plant traits describing the canopy structure were more important in this regard than leaf traits. Accordingly, leaf area index (LAI) and leaf inclination showed consistently high importance for separating the examined PFT groups. This indicates that the role of canopy structure for spectrally differentiating PFT might have been underestimated.

show abstract

Spectroscopic determination of leaf water content using continuous wavelet analysis

Cited by 231 publications

References 66 publications

Leaf spectral clusters as potential optical leaf functional types within California ecosystems

Leaf spectral clusters as potential optical leaf functional types within California ecosystems

Monitoring surface water content using visible and short-wave infrared SPOT-5 data of wheat plots in irrigated semi-arid regions

Differentiating plant functional types using reflectance: which traits make the difference?

Contact Info

Product

Resources

About