Optimizing LUT-based inversion of leaf chlorophyll from hyperspectral lidar data : Role of cost functions and regulation strategies
(2021) In International Journal of Applied Earth Observation and Geoinformation 105.- Abstract
Hyperspectral lidar (HSL) is a novel remote sensing technology that provides spectral information in addition to spatial features. This unprecedented data source leads to new possibilities for monitoring leaf biochemistry. Inversion of physically based radiative transfer models (RTMs) is a popular method for deriving leaf physiological traits due to its robustness and generalization capability. However, owing to the active nature of the HSL system, RTM inversion using the backscattered reflectance spectra may face new problems. Thus, optimization strategies for RTM inversion based on HSL measurements need to be studied. In this paper, several regulation strategies for lookup table (LUT)-based PROSPECT model inversions were explored for... (More)
Hyperspectral lidar (HSL) is a novel remote sensing technology that provides spectral information in addition to spatial features. This unprecedented data source leads to new possibilities for monitoring leaf biochemistry. Inversion of physically based radiative transfer models (RTMs) is a popular method for deriving leaf physiological traits due to its robustness and generalization capability. However, owing to the active nature of the HSL system, RTM inversion using the backscattered reflectance spectra may face new problems. Thus, optimization strategies for RTM inversion based on HSL measurements need to be studied. In this paper, several regulation strategies for lookup table (LUT)-based PROSPECT model inversions were explored for an HSL system. In particular, the influences of i) different cost functions, ii) multiple best solutions (1–1000), iii) different LUT sizes (100–100000), and iv) spectral domains for leaf chlorophyll (Chl) retrieval were analyzed. An evaluation against an experimental dataset of rice leaves indicated that i) least-squares estimation (LSE) provided better estimates than seven alternative cost functions when more than 200 solutions were taken; ii) accuracy in leaf Chl retrieval increased up until 200 solutions where after it stabilized; iii) the impact of LUT size became insignificant after 1000; and iv) the red edge was the spectral domain that had the largest impact on the inversion performance. The optimal performance of leaf Chl estimation reached R2 of 0.58 and RMSE of 0.69 between the z-scores from retrieved and measured leaf Chl. The practical application of combining RTM with HSL data will facilitate the detection of leaf-level biochemistry and advance research on terrestrial carbon cycle modeling.
(Less)
- author
- Sun, Jia
LU
; Shi, Shuo ; Wang, Lunche ; Li, Haiyan ; Wang, Shaoqiang ; Gong, Wei and Tagesson, Torbern LU
- organization
- publishing date
- 2021-12-25
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Hyperspectral lidar, Leaf chlorophyll, Lookup table (LUT), Model inversion, PROSPECT model
- in
- International Journal of Applied Earth Observation and Geoinformation
- volume
- 105
- article number
- 102602
- publisher
- Elsevier
- external identifiers
-
- scopus:85121683490
- ISSN
- 1569-8432
- DOI
- 10.1016/j.jag.2021.102602
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2021
- id
- 10002d5f-74ae-4fdd-a374-ddac77fdf134
- date added to LUP
- 2022-01-27 18:22:12
- date last changed
- 2023-02-21 10:19:05
@article{10002d5f-74ae-4fdd-a374-ddac77fdf134, abstract = {{<p>Hyperspectral lidar (HSL) is a novel remote sensing technology that provides spectral information in addition to spatial features. This unprecedented data source leads to new possibilities for monitoring leaf biochemistry. Inversion of physically based radiative transfer models (RTMs) is a popular method for deriving leaf physiological traits due to its robustness and generalization capability. However, owing to the active nature of the HSL system, RTM inversion using the backscattered reflectance spectra may face new problems. Thus, optimization strategies for RTM inversion based on HSL measurements need to be studied. In this paper, several regulation strategies for lookup table (LUT)-based PROSPECT model inversions were explored for an HSL system. In particular, the influences of i) different cost functions, ii) multiple best solutions (1–1000), iii) different LUT sizes (100–100000), and iv) spectral domains for leaf chlorophyll (Chl) retrieval were analyzed. An evaluation against an experimental dataset of rice leaves indicated that i) least-squares estimation (LSE) provided better estimates than seven alternative cost functions when more than 200 solutions were taken; ii) accuracy in leaf Chl retrieval increased up until 200 solutions where after it stabilized; iii) the impact of LUT size became insignificant after 1000; and iv) the red edge was the spectral domain that had the largest impact on the inversion performance. The optimal performance of leaf Chl estimation reached R<sup>2</sup> of 0.58 and RMSE of 0.69 between the z-scores from retrieved and measured leaf Chl. The practical application of combining RTM with HSL data will facilitate the detection of leaf-level biochemistry and advance research on terrestrial carbon cycle modeling.</p>}}, author = {{Sun, Jia and Shi, Shuo and Wang, Lunche and Li, Haiyan and Wang, Shaoqiang and Gong, Wei and Tagesson, Torbern}}, issn = {{1569-8432}}, keywords = {{Hyperspectral lidar; Leaf chlorophyll; Lookup table (LUT); Model inversion; PROSPECT model}}, language = {{eng}}, month = {{12}}, publisher = {{Elsevier}}, series = {{International Journal of Applied Earth Observation and Geoinformation}}, title = {{Optimizing LUT-based inversion of leaf chlorophyll from hyperspectral lidar data : Role of cost functions and regulation strategies}}, url = {{http://dx.doi.org/10.1016/j.jag.2021.102602}}, doi = {{10.1016/j.jag.2021.102602}}, volume = {{105}}, year = {{2021}}, }