Journal
REMOTE SENSING
Volume 5, Issue 7, Pages 3280-3304Publisher
MDPI AG
DOI: 10.3390/rs5073280
Keywords
biophysical parameters; LUT-based inversion; cost functions; radiative transfer models; PROSAIL; Sentinel-2
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Funding
- Spanish Ministry for Science and Innovation [AYA2010-21432-C02-01, CSD2007-00018]
- Natural Environment Research Council [earth010002] Funding Source: researchfish
- NERC [earth010002] Funding Source: UKRI
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Lookup-table (LUT)-based radiative transfer model inversion is considered a physically-sound and robust method to retrieve biophysical parameters from Earth observation data but regularization strategies are needed to mitigate the drawback of ill-posedness. We systematically evaluated various regularization options to improve leaf chlorophyll content (LCC) and leaf area index (LAI) retrievals over agricultural lands, including the role of (1) cost functions (CFs); (2) added noise; and (3) multiple solutions in LUT-based inversion. Three families of CFs were compared: information measures, M-estimates and minimum contrast methods. We have only selected CFs without additional parameters to be tuned, and thus they can be immediately implemented in processing chains. The coupled leaf/canopy model PROSAIL was inverted against simulated Sentinel-2 imagery at 20 m spatial resolution (8 bands) and validated against field data from the ESA-led SPARC (Barrax, Spain) campaign. For all 18 considered CFs with noise introduction and opting for the mean of multiple best solutions considerably improved retrievals; relative errors can be twice reduced as opposed to those without these regularization options. M-estimates were found most successful, but also data normalization influences the accuracy of the retrievals. Here, best LCC retrievals were obtained using a normalized L-1-estimate function with a relative error of 17.6% (r(2): 0.73), while best LAI retrievals were obtained through non-normalized least-squares estimator (LSE) with a relative error of 15.3% (r(2): 0.74).
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