A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022

Sun, Qiangqiang; Zhang, Ping; Jiao, Xin; Lin, Xin; Duan, Wenkai; Ma, Su; Pan, Qidi; Chen, Lu; Zhang, Yongxiang; You, Shucheng; Liu, Shunxi; Hao, Jinmin; Li, Hong; Sun, Danfeng

doi:https://doi.org/10.5194/essd-16-1333-2024

Articles | Volume 16, issue 3

https://doi.org/10.5194/essd-16-1333-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-16-1333-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 3

Data description paper

|

14 Mar 2024

Data description paper |

| 14 Mar 2024

A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022

Qiangqiang Sun, Ping Zhang, Xin Jiao, Xin Lin, Wenkai Duan, Su Ma, Qidi Pan, Lu Chen, Yongxiang Zhang, Shucheng You, Shunxi Liu, Jinmin Hao, Hong Li, and Danfeng Sun

Supplement

https://doi.org/10.5194/essd-16-1333-2024-supplement

Data sets

A global estimate of monthly vegetation and soil fractions from spatio-temporally adaptive spectral mixture analysis during 2001-2022 Qiangqiang Sun and Danfeng Sun https://doi.org/10.57760/sciencedb.13287

Model code and software

qiangsunpingzh/GEE_mesma: GEE (GEE) Q. Sun https://doi.org/10.5281/zenodo.10796386

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To provide multifaceted changes under climate change and anthropogenic impacts, we estimated...