{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T06:57:51Z","timestamp":1778309871433,"version":"3.51.4"},"reference-count":92,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,8,22]],"date-time":"2019-08-22T00:00:00Z","timestamp":1566432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["Discovery Grant [RGPIN-386183]"],"award-info":[{"award-number":["Discovery Grant [RGPIN-386183]"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003579","name":"University of Toronto","doi-asserted-by":"publisher","award":["Graduate Expansion Funds"],"award-info":[{"award-number":["Graduate Expansion Funds"]}],"id":[{"id":"10.13039\/501100003579","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Different types of methods have been developed to retrieve vegetation attributes from remote sensing data, including conventional empirical regressions (i.e., linear regression (LR)), advanced empirical regressions (e.g., multivariable linear regression (MLR), partial least square regression (PLSR)), machine learning (e.g., random forest regression (RFR), decision tree regression (DTR)), and radiative transfer modelling (RTM, e.g., PROSAIL). Given that each algorithm has its own strengths and weaknesses, it is essential to compare them and evaluate their effectiveness. Previous studies have mainly used single-date multispectral imagery or ground-based hyperspectral reflectance data for evaluating the models, while multi-seasonal hyperspectral images have been rarely used. Extensive spectral and spatial information in hyperspectral images, as well as temporal variations of landscapes, potentially influence the model performance. In this research, LR, PLSR, RFR, and PROSAIL, representing different types of methods, were evaluated for estimating vegetation chlorophyll content from bi-seasonal hyperspectral images (i.e., a middle- and a late-growing season image, respectively). Results show that the PLSR and RFR generally performed better than LR and PROSAIL. RFR achieved the highest accuracy for both images. This research provides insights on the effectiveness of different models for estimating vegetation chlorophyll content using hyperspectral images, aiming to support future vegetation monitoring research.<\/jats:p>","DOI":"10.3390\/rs11171979","type":"journal-article","created":{"date-parts":[[2019,8,23]],"date-time":"2019-08-23T10:15:07Z","timestamp":1566555307000},"page":"1979","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Evaluating Empirical Regression, Machine Learning, and Radiative Transfer Modelling for Estimating Vegetation Chlorophyll Content Using Bi-Seasonal Hyperspectral Images"],"prefix":"10.3390","volume":"11","author":[{"given":"Bing","family":"Lu","sequence":"first","affiliation":[{"name":"Department of Geography, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhong","family":"He","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/0034-4257(95)00234-0","article-title":"Leaf optical properties with explicit description of its biochemical composition: Direct and inverse problems","volume":"56","author":"Fourty","year":"1996","journal-title":"Remote Sens. 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