GIScience & Remote Sensing
Volume 61, 2024 - Issue 1
Open access
483
Views
0
CrossRef citations to date
0
Altmetric
Research Article
The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China’s evergreen coniferous forests
Yiru ZhangSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, ChinaView further author information
, Binbin HeSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, ChinaCorrespondence[email protected]
View further author information
, View further author information
Rui ChenSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, ChinaView further author information
, Hongguo ZhangSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, ChinaView further author information
, Chunquan FanSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, ChinaView further author information
, Jianpeng YinSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, ChinaView further author information
& Yanxi LiSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, ChinaView further author information
show all
Article: 2345438
|
Received 05 Dec 2023, Accepted 16 Apr 2024, Published online: 26 Apr 2024
Reprints and Permissions
Permission is granted subject to the terms of the License under which the work was published. Permission will be required if your reuse is not covered by the terms of the License.
To request a reprint or commercial or derivative permissions for this article, please click on the relevant link below.
For more information please visit our Permissions help page.
Related research
People also read lists articles that other readers of this article have read.
Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.
Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.