The relationship of hyper-spectral vegetation indices with leaf area index (LAI) over the growth cycle of wheat and chickpea at 3nm spectral resolution

Gupta, Rajendra Kumar; Vijayan, D.; Prasad, T.S.

doi:10.1016/j.asr.2003.02.091

Cited by 9 publications

(9 citation statements)

References 5 publications

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“…The relationship observed could be considered as a relatively weak relationship (R 2 values were in the range of 0.20 to 0.32). Similar results were reported by (Gupta et al, 2006) where lower R 2 values of 0.20 to 0.53 were obtained for the relationship between wheat crop LAI and spectral indices, such as Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI). In contrast, (Haboudane et al, 2004) Statistically significant relationship between LAI and NDII only for February 17th measurements (at wheat crop booting growth stage) was revealed.…”

Section: Discussionsupporting

confidence: 69%

In-Season Assessment of Wheat Crop Health Using Vegetation Indices Based on Ground Measured Hyper Spectral Data

Al-Gaadi¹

2014

American Journal of Agricultural and Biological Sciences

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Section: Discussionsupporting

confidence: 69%

In-Season Assessment of Wheat Crop Health Using Vegetation Indices Based on Ground Measured Hyper Spectral Data

Al-Gaadi¹

2014

American Journal of Agricultural and Biological Sciences

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“…Similar R² and nRMSE values have been reported in forests (Banskota et al, 2013;Schlerf and Atzberger, 2006) and agro-ecosystems (Gupta et al, 2006;Haboudane et al, 2004). Although hyperspectral data has also proven successful in the estimation of grass and shrub SLA or LMA (Ball et al, 2015;Casas et al, 2014), and grassland vegetation height (Capolupo et al, 2015), these applications are unique.…”

Section: Overall Trait Estimation Performance In Grass-and Shrubland supporting

confidence: 60%

The functional characterization of grass- and shrubland ecosystems using hyperspectral remote sensing: trends, accuracy and moderating variables

Cleemput

Vanierschot

Fernández-Castilla

et al. 2018

Remote Sensing of Environment

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Hyperspectral remote sensing is increasingly being recognized as a powerful tool to map ecosystem properties and functions through time and space. However, general information on the accuracy of this technology to assess the vegetation's biophysical and-chemical trait composition, and on the variables which are mediating this accuracy, is often lacking so far. Here, we addressed this knowledge gap for grass-and shrubland ecosystems and applied novel three-level meta-analytical regression equations to 77 studies that validated hyperspectral remote sensing data with field observations. Our results showed that the accuracy of hyperspectral sensors is generally high, but strongly depends on the trait being studied (leaf area index: R² = 0.79 and nRMSE = 0.19, chlorophyll: R² = 0.77 and nRMSE = 0.21, carotenoids: R² = 0.80 and nRMSE = 0.29, phosphorus: R² = 0.76 and nRMSE = 0.14, nitrogen: R² = 0.74 and nRMSE =0.09, water: R² = 0.69 and nRMSE = 0.13, and lignin content: R² = 0.64 and nRMSE = 0.26). Moreover, they indicated that the use of multivariate signal processing techniques could improve these estimation accuracies (adjusted p < 0.06 for LAI, chlorophyll and nitrogen). Finally, estimations from air-and spaceborne imaging spectrometers, allowing for functional mapping at broader spatial scales, were found to be as accurate as estimations from ground-based spectral measurements. Despite these promising findings, we revealed that leaf morphological properties (e.g. specific leaf area and leaf dry matter content) and biochemical traits which are not growth-related (e.g. lignin and cellulose) remain underexplored in grass-and shrublands. Moreover there was a strong publication bias towards R² for assessing model performance. Our findings foster and direct further methodological and technological developments for a more accurate and complete functional characterization of these ecosystems worldwide.

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“…The relationship between hyperspectral vegetation indices and LAI has been examined for wheat and chickpea over their growth cycles [97]. Not only did the examination focus on the leaf level, but also the canopy structural variables inversely estimated based on a reflectance model from hyperspectral remote sensing data [98].…”

Section: Measurement Methods and Sensorsmentioning

confidence: 99%

“…Hyperspectral remote sensing datasets have a fine spectral resolution allowing for the detection of physiological characteristics such as accurate chlorophyll a and b content estimation [ 96 ]. The relationship between hyperspectral vegetation indices and LAI has been examined for wheat and chickpea over their growth cycles [ 97 ]. Not only did the examination focus on the leaf level, but also the canopy structural variables inversely estimated based on a reflectance model from hyperspectral remote sensing data [ 98 ].…”

Section: Measurement Methods and Sensorsmentioning

confidence: 99%

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

Zheng

Moskal

2009

Sensors

459

273

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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.

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The relationship of hyper-spectral vegetation indices with leaf area index (LAI) over the growth cycle of wheat and chickpea at 3nm spectral resolution

Cited by 9 publications

References 5 publications

In-Season Assessment of Wheat Crop Health Using Vegetation Indices Based on Ground Measured Hyper Spectral Data

In-Season Assessment of Wheat Crop Health Using Vegetation Indices Based on Ground Measured Hyper Spectral Data

The functional characterization of grass- and shrubland ecosystems using hyperspectral remote sensing: trends, accuracy and moderating variables

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

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