{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T21:50:04Z","timestamp":1768513804787,"version":"3.49.0"},"reference-count":69,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 \u2018forest\u2019 and PROSAIL for \u2018short vegetation\u2019 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.<\/jats:p>","DOI":"10.3390\/rs12111788","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T09:19:27Z","timestamp":1591089567000},"page":"1788","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes"],"prefix":"10.3390","volume":"12","author":[{"given":"Abebe Mohammed","family":"Ali","sequence":"first","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 217, 7500 AE Enschede, The Netherlands"},{"name":"Department of Geography and Environmental Studies, Wollo University, 1145 Dessie, Ethiopia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7512-0574","authenticated-orcid":false,"given":"Roshanak","family":"Darvishzadeh","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 217, 7500 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7446-8429","authenticated-orcid":false,"given":"Andrew","family":"Skidmore","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 217, 7500 AE Enschede, The Netherlands"},{"name":"Department of Environmental Science, Macquarie University, NSW 2106, Australia"}]},{"given":"Marco","family":"Heurich","sequence":"additional","affiliation":[{"name":"Department of Visitor Management and National Park Monitoring, Bavarian Forest National Park, 94481 Grafenau, Germany"},{"name":"Chair of Wildlife Ecology and Wildlife Management, University of Freiburg, Tennenbacher Stra\u00dfe 4, D-79106 Freiburg, Germany"}]},{"given":"Marc","family":"Paganini","sequence":"additional","affiliation":[{"name":"European Space Agency - ESRIN, Via Galileo Galilei, Casella Postale 64, 00044 Frascati (R.M.), Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3865-1912","authenticated-orcid":false,"given":"Uta","family":"Heiden","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), The Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), M\u00fcnchener Str. 20, 82234 We\u00dfling, Germany"}]},{"given":"Sander","family":"M\u00fccher","sequence":"additional","affiliation":[{"name":"Wageningen Environmental Research, Wageningen University, and Research, 47, 6700 AA Wageningen, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3983","DOI":"10.1093\/jxb\/ert208","article-title":"Chlorophyll fluorescence analysis: A guide to good practice and understanding some new applications","volume":"64","author":"Murchie","year":"2013","journal-title":"J. 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