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Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States

Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States
Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States

The Sentinel-2 Level 2 Prototype Processor (SL2P) is made available to users for the retrieval of vegetation biophysical variables including leaf area index (LAI) from Multispectral Instrument (MSI) data within the Sentinel Application Platform (SNAP). A limited number of validation exercises have indicated SL2P LAI retrievals frequently meet user requirements over agricultural environments, but perform comparatively poorly over heterogeneous canopies such as forests. Recently, a modified version of SL2P was developed, using the directional area scattering factor (DASF) to constrain retrievals as an alternative to regularisation (SL2P-D). Whilst SL2P makes use of prior information on expected canopy conditions, SL2P-D is trained using uniform distributions of input parameters to define radiative transfer model (RTM) simulations. Using in situ measurements available through the Copernicus Ground Based Observations for Validation (GBOV) service, we performed an extensive validation of SL2P and SL2P-D LAI retrievals over 19 sites throughout the United States. For effective LAI (LAI e), SL2P demonstrated good overall performance (RMSD = 0.50, NRMSD = 31%, bias = −0.10), with all LAI retrievals meeting the Sentinels for Science (SEN4SCI) uncertainty requirements over homogeneous canopies (cultivated crops, grasslands, pasture/hay and shrub/scrub), whilst underestimation occurred over heterogeneous canopies (deciduous forest, evergreen forest, mixed forest, and woody wetlands). SL2P-D retrievals demonstrated reduced bias, slightly improving overall performance when compared with SL2P (RMSD = 0.48, NRMSD = 30%, bias = −0.05), indicating its retrieval approach appears to offer some advantages over regularisation using prior information, especially at LAI e > 3. Additionally, SL2P-D resulted in 32% more valid retrievals than SL2P, with the largest differences observed at LAI e < 1. Validation against in situ measurements of LAI as opposed to LAI e yielded similar patterns but poorer performance (RMSD = 1.08 to 1.13, NRMSD = 49% to 52%, bias = −0.64 to −0.68) because the RTM used by SL2P and SL2P-D does not account for foliage clumping. In addition to the retrievals themselves, we examined the relationship between predicted uncertainties and observed differences in retrieved and in situ LAI. With respect to LAI e, SL2P's predicted uncertainties were conservative, underestimating observed differences in only 35% of cases, whilst those for LAI were unbiased.

GBOV, LAI, MSI, NEON, SL2P, SL2P-D
0924-2716
71-87
Brown, Luke
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Fernandes, Richard
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Djamai, Najib
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Meier, Courtney
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Gobron, Nadine
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Morris, Harry
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Canisius, Francis
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Bai, Gabriele
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Lerebourg, Christophe
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Lanconelli, Christian
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Clerici, Marco
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Dash, Jadunandan
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Brown, Luke
3f3ee47e-ee1f-4a44-a223-36059b69ce92
Fernandes, Richard
4664475b-0fc6-467b-b72f-540885f2b087
Djamai, Najib
ee1fce82-773d-485e-ae0e-556ae557fbf0
Meier, Courtney
6f0a0c09-0e94-4678-acd0-748d46d3a0dc
Gobron, Nadine
e6aee853-16d6-4aad-bbf0-5f9b75121297
Morris, Harry
d7b9d1e5-e105-40a3-9f5d-7c3e4531b32c
Canisius, Francis
e3278569-a1a5-4565-bf4c-ea3f681c10fa
Bai, Gabriele
34496139-cb2c-4248-974f-a3349ded983b
Lerebourg, Christophe
44727f9e-aae3-493c-b3db-0dbf59c2e73d
Lanconelli, Christian
345ab5f0-317d-4a1c-a522-ac6e243d1125
Clerici, Marco
f02b61d8-d3c8-42c8-893f-82a4c40ad752
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8

Brown, Luke, Fernandes, Richard, Djamai, Najib, Meier, Courtney, Gobron, Nadine, Morris, Harry, Canisius, Francis, Bai, Gabriele, Lerebourg, Christophe, Lanconelli, Christian, Clerici, Marco and Dash, Jadunandan (2021) Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States. ISPRS Journal of Photogrammetry and Remote Sensing, 175, 71-87. (doi:10.1016/j.isprsjprs.2021.02.020).

Record type: Article

Abstract

The Sentinel-2 Level 2 Prototype Processor (SL2P) is made available to users for the retrieval of vegetation biophysical variables including leaf area index (LAI) from Multispectral Instrument (MSI) data within the Sentinel Application Platform (SNAP). A limited number of validation exercises have indicated SL2P LAI retrievals frequently meet user requirements over agricultural environments, but perform comparatively poorly over heterogeneous canopies such as forests. Recently, a modified version of SL2P was developed, using the directional area scattering factor (DASF) to constrain retrievals as an alternative to regularisation (SL2P-D). Whilst SL2P makes use of prior information on expected canopy conditions, SL2P-D is trained using uniform distributions of input parameters to define radiative transfer model (RTM) simulations. Using in situ measurements available through the Copernicus Ground Based Observations for Validation (GBOV) service, we performed an extensive validation of SL2P and SL2P-D LAI retrievals over 19 sites throughout the United States. For effective LAI (LAI e), SL2P demonstrated good overall performance (RMSD = 0.50, NRMSD = 31%, bias = −0.10), with all LAI retrievals meeting the Sentinels for Science (SEN4SCI) uncertainty requirements over homogeneous canopies (cultivated crops, grasslands, pasture/hay and shrub/scrub), whilst underestimation occurred over heterogeneous canopies (deciduous forest, evergreen forest, mixed forest, and woody wetlands). SL2P-D retrievals demonstrated reduced bias, slightly improving overall performance when compared with SL2P (RMSD = 0.48, NRMSD = 30%, bias = −0.05), indicating its retrieval approach appears to offer some advantages over regularisation using prior information, especially at LAI e > 3. Additionally, SL2P-D resulted in 32% more valid retrievals than SL2P, with the largest differences observed at LAI e < 1. Validation against in situ measurements of LAI as opposed to LAI e yielded similar patterns but poorer performance (RMSD = 1.08 to 1.13, NRMSD = 49% to 52%, bias = −0.64 to −0.68) because the RTM used by SL2P and SL2P-D does not account for foliage clumping. In addition to the retrievals themselves, we examined the relationship between predicted uncertainties and observed differences in retrieved and in situ LAI. With respect to LAI e, SL2P's predicted uncertainties were conservative, underestimating observed differences in only 35% of cases, whilst those for LAI were unbiased.

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Accepted/In Press date: 24 February 2021
e-pub ahead of print date: 13 March 2021
Published date: May 2021
Keywords: GBOV, LAI, MSI, NEON, SL2P, SL2P-D

Identifiers

Local EPrints ID: 447890
URI: http://eprints.soton.ac.uk/id/eprint/447890
ISSN: 0924-2716
PURE UUID: e0cd66fc-30b0-4824-89b3-1b3cb0ed5d98
ORCID for Luke Brown: ORCID iD orcid.org/0000-0003-4807-9056
ORCID for Jadunandan Dash: ORCID iD orcid.org/0000-0002-5444-2109

Catalogue record

Date deposited: 25 Mar 2021 18:26
Last modified: 21 May 2024 01:52

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Contributors

Author: Luke Brown ORCID iD
Author: Richard Fernandes
Author: Najib Djamai
Author: Courtney Meier
Author: Nadine Gobron
Author: Harry Morris
Author: Francis Canisius
Author: Gabriele Bai
Author: Christophe Lerebourg
Author: Christian Lanconelli
Author: Marco Clerici
Author: Jadunandan Dash ORCID iD

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