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.
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.
the Creative Commons Attribution 4.0 License.
A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022
Qiangqiang Sun
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Ping Zhang
National Geomatics Center of China, Ministry of Natural Resources, Beijing, 100830, China
Xin Jiao
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Xin Lin
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Wenkai Duan
China Agricultural University Library, China Agricultural University, Beijing, 100193, China
Su Ma
Institute of Environmental Information, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
Qidi Pan
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Lu Chen
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Yongxiang Zhang
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Shucheng You
Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing, China
Shunxi Liu
China Land Survey and Planning Institute, Ministry of Natural Resources, Beijing, China
Jinmin Hao
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Hong Li
CORRESPONDING AUTHOR
Institute of plant nutrition and resources, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
Technology innovation Center of land engineering, Ministry of Natural Resources, Beijing, China
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Cited
6 citations as recorded by crossref.
- Improved mapping of perennial crop types based on intra-annual biophysical changing patterns of spectral endmembers X. Gao et al. 10.1016/j.rse.2025.115059
- A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022 Q. Sun et al. 10.5194/essd-16-1333-2024
- Impact of data density and endmember definitions on long-term trends in ground cover fractions across European grasslands K. Lewińska et al. 10.1016/j.rse.2025.114736
- A Full-Life-Cycle Modeling Framework for Cropland Abandonment Detection Based on Dense Time Series of Landsat-Derived Vegetation and Soil Fractions Q. Sun et al. 10.3390/rs17132193
- Spatiotemporal prediction of soil organic carbon density in Europe (2000–2022) using earth observation and machine learning X. Tian et al. 10.7717/peerj.19605
- A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022 Q. Sun et al. 10.5194/essd-16-1333-2024
5 citations as recorded by crossref.
- Improved mapping of perennial crop types based on intra-annual biophysical changing patterns of spectral endmembers X. Gao et al. 10.1016/j.rse.2025.115059
- A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022 Q. Sun et al. 10.5194/essd-16-1333-2024
- Impact of data density and endmember definitions on long-term trends in ground cover fractions across European grasslands K. Lewińska et al. 10.1016/j.rse.2025.114736
- A Full-Life-Cycle Modeling Framework for Cropland Abandonment Detection Based on Dense Time Series of Landsat-Derived Vegetation and Soil Fractions Q. Sun et al. 10.3390/rs17132193
- Spatiotemporal prediction of soil organic carbon density in Europe (2000–2022) using earth observation and machine learning X. Tian et al. 10.7717/peerj.19605
Latest update: 19 Oct 2025
Short summary
To provide multifaceted changes under climate change and anthropogenic impacts, we estimated monthly vegetation and soil fractions in 2001–2022, providing an accurate estimate of surface heterogeneous composition, better than vegetation index and vegetation continuous-field products. We find a greening trend on Earth except for the tropics. A combination of interactive changes in vegetation and soil can be adopted as a valuable measurement of climate change and anthropogenic impacts.
To provide multifaceted changes under climate change and anthropogenic impacts, we estimated...
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