All articles published by MDPI are made immediately available worldwide under an open access license. No special
permission is required to reuse all or part of the article published by MDPI, including figures and tables. For
articles published under an open access Creative Common CC BY license, any part of the article may be reused without
permission provided that the original article is clearly cited. For more information, please refer to
https://www.mdpi.com/openaccess.
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature
Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for
future research directions and describes possible research applications.
Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive
positive feedback from the reviewers.
Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world.
Editors select a small number of articles recently published in the journal that they believe will be particularly
interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the
most exciting work published in the various research areas of the journal.
Li, H.; Yan, K.; Gao, S.; Song, W.; Mu, X.
Revisiting the Performance of the Kernel-Driven BRDF Model Using Filtered High-Quality POLDER Observations. Forests2022, 13, 435.
https://doi.org/10.3390/f13030435
AMA Style
Li H, Yan K, Gao S, Song W, Mu X.
Revisiting the Performance of the Kernel-Driven BRDF Model Using Filtered High-Quality POLDER Observations. Forests. 2022; 13(3):435.
https://doi.org/10.3390/f13030435
Chicago/Turabian Style
Li, Hanliang, Kai Yan, Si Gao, Wanjuan Song, and Xihan Mu.
2022. "Revisiting the Performance of the Kernel-Driven BRDF Model Using Filtered High-Quality POLDER Observations" Forests 13, no. 3: 435.
https://doi.org/10.3390/f13030435
APA Style
Li, H., Yan, K., Gao, S., Song, W., & Mu, X.
(2022). Revisiting the Performance of the Kernel-Driven BRDF Model Using Filtered High-Quality POLDER Observations. Forests, 13(3), 435.
https://doi.org/10.3390/f13030435
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
