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Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes

Ali, Abebe and Darvishzadeh, Roshanak and Skidmore, Andrew and Heurich, Marco and Paganini, Marc and Heiden, Uta and Mücher, S. (2020) Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes. Remote Sensing, 12 (1788), pp. 1-23. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs12111788 ISSN 2072-4292

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Abstract

Accurate measurement of canopy chlorophyll content (CCC) is essential for the understanding of terrestrial ecosystem dynamics through monitoring and evaluating properties such as carbon and water flux, productivity, light use efficiency as well as nutritional and environmental stresses. Information on the amount and distribution of CCC helps to assess and report biodiversity indicators related to ecosystem processes and functional aspects. Therefore, measuring CCC continuously and globally from earth observation data is critical to monitor the status of the biosphere. However, generic and robust methods for regional and global mapping of CCC are not well defined. This study aimed at examining the spatiotemporal consistency and scalability of selected methods for CCC mapping across biomes. Four methods (i.e., radiative transfer models (RTMs) inversion using a look-up table (LUT), the biophysical processor approach integrated into the Sentinel application platform (SNAP toolbox), simple ratio vegetation index (SRVI), and partial least square regression (PLSR)) were evaluated. Similarities and differences among CCC products generated by applying the four methods on actual Sentinel-2 data in four biomes (temperate forest, tropical forest, wetland, and Arctic tundra) were examined by computing statistical measures and spatiotemporal consistency pairwise comparisons. Pairwise comparison of CCC predictions by the selected methods demonstrated strong agreement. The highest correlation (R2 = 0.93, RMSE = 0.4371 g/m2) was obtained between CCC predictions of PROSAIL inversion by LUT and SNAP toolbox approach in a wetland when a single Sentinel-2 image was used. However, when time-series data were used, it was PROSAIL inversion against SRVI (R2 = 0.88, RMSE = 0.19) that showed greatest similarity to the single date predictions (R2 = 0.83, RMSE = 0.17 g/m2) in this biome. Generally, the CCC products obtained using the SNAP toolbox approach resulted in a systematic over/under-estimation of CCC. RTMs inversion by LUT (INFORM and PROSAIL) resulted in a non-biased, spatiotemporally consistent prediction of CCC with a range closer to expectations. Therefore, the RTM inversion using LUT approaches particularly, INFORM for ‘forest’ and PROSAIL for ‘short vegetation’ ecosystems, are recommended for CCC mapping from Sentinel 2 data for worldwide mapping of CCC. Additional validation of the two RTMs with field data of CCC across biomes is required in the future.

Item URL in elib:https://elib.dlr.de/135121/
Document Type:Article
Title:Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Ali, AbebeUniversity of Twente, Department of Natural Resources (ITC)UNSPECIFIED
Darvishzadeh, Roshanakr.darvish (at) utwente.nlUNSPECIFIED
Skidmore, Andrewa.k.skidmore (at) utwente.nlUNSPECIFIED
Heurich, MarcoNationalparkverwaltung Bayerischer WaldUNSPECIFIED
Paganini, Marcmarc.paganini (at) esa.intUNSPECIFIED
Heiden, Utauta.heiden (at) dlr.dehttps://orcid.org/0000-0002-3865-1912
Mücher, S.sander.mucher (at) wur.nlUNSPECIFIED
Date:1 June 2020
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:12
DOI :10.3390/rs12111788
Page Range:pp. 1-23
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:canopy chlorophyll content (CCC); SNAP toolbox; INFORM; PROSAIL; SRVI; PLSR; Sentinel-2
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Heiden, Dr.rer.nat. Uta
Deposited On:04 Jun 2020 11:18
Last Modified:05 Jun 2020 12:34

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