Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors

By Guang Zheng, L. Monika Moskal. Italian National Conference on Sensors, 2009

568 citations recorded by the source corpus.

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels.

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/338c5e71690a5971c75f951a0348da0cdf44daf1

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Citations in the corpus

New Critical Nitrogen Curve Based on Leaf Area Index for Winter Wheat (2014, background): “Because the LAI remains consistent when the spatial resolution changes, LAI estimates from remote sensing provide a meaningful biophysical parameter and a convenient and ecologically relevant variable for multiscale and multitemporal research ranging from the scale of leaves to fields and regions (Z”
Linking plant strategies and plant traits derived by radiative transfer modelling (2017, background): “Therefore, LAI is a dimensionless quantity that complies with the spatial constraints of Earth Observation data (Asner et al. 2003) and can be mapped across a range of spatial scales with relatively high accuracy (Garrigues et al. 2008; Zheng & Moskal 2009).”
Quantifying the impact of woody material on leaf area index estimation from hemispherical photography using 3D canopy simulations (2016, methodology): “The accurate retrieval of α is then a 44 critical step in the estimation of LAI from ubiquitous indirect LAI estimation methods (see 45 extensive reviews by (Bréda, 2003; Jonckheere et al., 2004; Zheng & Moskal, 2009)).”
Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data (2015, methodology): “Various methods for LAI determination exist, differing in the type of measurement methodology and the requirements of the technical equipment (Bréda 2003; Zheng and Moskal 2009).”
Differentiating plant functional types using reflectance: which traits make the difference? (2018, background): “For example, as LAI is a dimensionless quantity it complies with the spatial constraints of EO-data and can be mapped across a range of spatial scales with relatively high accuracy (Garrigues et al. 2008; Zheng and Moskal 2009).”
Relationship between tree row LIDAR-volume and leaf area density for fruit orchards and vineyards obtained with a LIDAR 3D Dynamic Measurement System (2013, background): “…2007; 65 Lopez-Lozano et al., 2011) 66 67 The leaf area can be calculated by direct measurement which involves a lot of time and 68 expense or it can be indirectly estimated using remote sensors and without any physical 69 contact with the leaves (Zheng and Moskal, 2009; Rosell and Sanz, 2012).”
Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study (2018, background): “A review about LAI retrieval including other examples of applications is provided in [150].”
Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content (2011, background): “Concerning physical models, experimental studies using RTMs have shown great flexibility in retrieving plant cover variables, because of being able to parameterize these models to a wide range of land cover situations and sensor configurations [21,22].”
Sources of variability in canopy reflectance and the convergent properties of plants. (2011, background): “As an illustration, Zygielbaum et al. (2009) collected leaf spectra from maize (Zea mays) plants over a 7-d period during which water stress was induced by withholding water.”
An improved theoretical model of canopy gap probability for Leaf Area Index estimation in woody ecosystems (2015, background): “Pgap estimates for LAI calculation are ubiquitous and are derived from instruments across all platforms, thus enabling LAI estimation from the local to global scale; see reviews by Bréda (2003), Weiss et al. (2004), and Zheng and Moskal (2009).”