{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T17:38:26Z","timestamp":1770831506492,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2014,5,28]],"date-time":"2014-05-28T00:00:00Z","timestamp":1401235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Regression models based on spectral indices are typically empirical formulae enabling the mapping of biophysical parameters derived from Earth Observation (EO) data. Due to its empirical nature, it remains nevertheless uncertain to what extent a selected regression model is the most appropriate one, until all band combinations and curve fitting functions are assessed. This paper describes the application of a Spectral Index (SI) assessment toolbox in the Automated Radiative Transfer Models Operator (ARTMO) package. ARTMO enables semi-automatic retrieval and mapping of biophysical parameters from optical remote sensing observations. The SI toolbox facilitates the assessment of biophysical parameter retrieval accuracy of established as well as new and generic SIs. For instance, based on the SI formulation used, all possible band combinations of formulations with up to ten bands can be defined and evaluated. Several options are available in the SI assessment: calibration\/validation data partitioning, the addition of noise and the definition of curve fitting models. To illustrate its functioning, all two-band combinations according to simple ratio (SR) and normalized difference (ND) formulations as well as various fitting functions (linear, exponential, power, logarithmic, polynomial) have been assessed. HyMap imaging spectrometer (430\u20132490 nm) data obtained during the SPARC-2003 campaign in Barrax, Spain, have been used to extract leaf area index (LAI) and leaf chlorophyll content (LCC) estimates. For both SR and ND formulations the most sensitive regions have been identified for two-band combinations of green (539\u2013570 nm) with longwave SWIR (2421\u20132453 nm) for LAI (r2: 0.83) and far-red (692 nm) with NIR (1340 nm) or shortwave SWIR (1661\u20131686 nm) for LCC (r2: 0.93). Polynomial, logarithmic and linear fitting functions led to similar best correlations, though spatial differences emerged when applying the functions to HyMap imagery. We suggest that a systematic SI assessment is a strong requirement in the quality assurance approach for accurate biophysical parameter retrieval.<\/jats:p>","DOI":"10.3390\/rs6064927","type":"journal-article","created":{"date-parts":[[2014,5,28]],"date-time":"2014-05-28T11:10:36Z","timestamp":1401275436000},"page":"4927-4951","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["On the Semi-Automatic Retrieval of Biophysical Parameters Based on Spectral Index Optimization"],"prefix":"10.3390","volume":"6","author":[{"given":"Juan","family":"Rivera","sequence":"first","affiliation":[{"name":"Image Processing Laboratory (IPL), Parc Cient\u00edfic, Universitat de Val\u00e8ncia, 46980 Paterna, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6313-2081","authenticated-orcid":false,"given":"Jochem","family":"Verrelst","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), Parc Cient\u00edfic, Universitat de Val\u00e8ncia, 46980 Paterna, Spain"}]},{"given":"Jes\u00fas","family":"Delegido","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), Parc Cient\u00edfic, Universitat de Val\u00e8ncia, 46980 Paterna, Spain"}]},{"given":"Frank","family":"Veroustraete","sequence":"additional","affiliation":[{"name":"Department of Bioscience Engineering, Faculty of Sciences, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerpen, Belgium"}]},{"given":"Jos\u00e9","family":"Moreno","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), Parc Cient\u00edfic, Universitat de Val\u00e8ncia, 46980 Paterna, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2014,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1080\/014311698215586","article-title":"Combining agricultural crop models and satellite observations: From field to regional scales","volume":"19","author":"Moulin","year":"1998","journal-title":"Int. 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