The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594
A large and persistent carbon sink in the world’s forests
Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A., Phillips, O. L., Shvidenko, A., Lewis, S. L., Canadell, J. G., et al. (2011) · Science · doi:10.1126/science.1201609 · Cited by 8,458
Self-citationForests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests
Bonan, G. B. (2008) · Science · doi:10.1126/science.1155121 · Cited by 6,238
Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate
Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rödenbeck, C., Arain, M. A., Baldocchi, D., Bonan, G. B., et al. (2010) · Science · doi:10.1126/science.1184984 · Cited by 2,831
Benchmark map of forest carbon stocks in tropical regions across three continents
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A., Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1019576108 · Cited by 2,353
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The carbon balance of terrestrial ecosystems in China
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Self-citationChanges in Forest Biomass Carbon Storage in China Between 1949 and 1998
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Self-citation
Cited by
Zhao, K., Wulder, M. A., Hu, T., Bright, R., Wu, Q., Qin, H., Li, Y., Toman, E., Mallick, B., Zhang, X., et al. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.04.034 · Cited by 514
Self-citationLiu, X., Su, Y., Hu, T., Yang, Q., Liu, B., Deng, Y., Tang, H., Tang, Z., Fang, J., & Guo, Q. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112844 · Cited by 270
Self-citationThe Importance of Consistent Global Forest Aboveground Biomass Product Validation
Duncanson, L., Armston, J., Disney, M., Avitabile, V., Barbier, N., Calders, K., Carter, S., Chave, J., Herold, M., Crowther, T. W., et al. (2019) · Surveys in Geophysics · doi:10.1007/s10712-019-09538-8 · Cited by 213
Metadata
- Authors
- Su, Y., Guo, Q., Xue, B., Hu, T., Alvarez, O., Tao, S., & Fang, J.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2016-02
- Volume
- 173
- Pages
- 187-199
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W2193261635
- Cited by
- 263
- References
- 61
- FWCI
- 10.36
- Type
- journal article
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data. https://app.cochat.ai/papers/10.1016/j.rse.2015.12.002
- The paper
- Su, Y., Guo, Q., Xue, B., Hu, T., Alvarez, O., Tao, S., & Fang, J. (2016). Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2015.12.002
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
