Optics in Remote Sensing

Walther, Thomas; Fry, Edward S.

doi:10.1007/978-3-319-31903-2_9

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…Based on the atmospheric window within the microwave portion of the spectrum [49], radar systems emit long wavelength electromagnetic energy and identify the range to the target by capturing reflected waves [27]. Lidar systems are based on an application of light amplification by stimulated emission of radiation (laser) [50] and actively emit shorter wavelength energy (such as green or near infrared wavelengths). This section discusses the application of active remote sensing to forest and crop LAI estimation from terrestrial, airborne, and satellite platforms.…”

Section: Active Remote Sensingmentioning

confidence: 99%

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Quackenbush

Volk

et al. 2020

Remote Sensing

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Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation.

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Section: Active Remote Sensingmentioning

confidence: 99%

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Quackenbush

Volk

et al. 2020

Remote Sensing

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“…6,7 There are numerous applications in lighting and display backlighting technologies. 3,4,[8][9][10][11][12] This is due to the capability of phosphors to produce high-quality light in the spectrum between far-red and blue wavelengths. [13][14][15] They are also environmentally friendly materials.…”

Section: Introductionmentioning

confidence: 99%

A theoretical spectroscopy study of the photoluminescence properties of narrow band Eu²⁺-doped phosphors containing multiple candidate doping centers. Prediction of an unprecedented narrow band red phosphor

Shafei,

Strobel,

Schmidt

et al. 2024

Phys. Chem. Chem. Phys.

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Optics in Remote Sensing

Cited by 2 publications

References 24 publications

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

A theoretical spectroscopy study of the photoluminescence properties of narrow band Eu²⁺-doped phosphors containing multiple candidate doping centers. Prediction of an unprecedented narrow band red phosphor

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Optics in Remote Sensing

Cited by 2 publications

References 24 publications

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

A theoretical spectroscopy study of the photoluminescence properties of narrow band Eu2+-doped phosphors containing multiple candidate doping centers. Prediction of an unprecedented narrow band red phosphor

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A theoretical spectroscopy study of the photoluminescence properties of narrow band Eu²⁺-doped phosphors containing multiple candidate doping centers. Prediction of an unprecedented narrow band red phosphor