综 述｜Review
遥感陆地碳通量研究进展与挑战
To cite this article:
Wang, T., Zhang, Y., Yue, C., Wang, Y., Wang, X., Lyu, G., … Piao, S. (2024).Progress and challenges in remotely sensed terrestrial carbon fluxes. Geo-Spatial Information Science, 28(1), 1–21. https://doi.org/10.1080/10095020.2024.2336599
陆地生态系统碳循环参数建模
Terrestrial ecosystem Carbon cycle parameters modeling
1
基于多光谱无人机的
叶面积指数与叶片叶绿素含量遥感协同反演研究
To cite this article:
Guo, X., Wang, R., Chen, J. M., Cheng, Z., Zeng, H., Miao, G., … Niu, J. (2023).Synergetic inversion of leaf area index and leaf chlorophyll content using multi-spectral remote sensing data. Geo-Spatial Information Science, 28(1), 22–35. https://doi.org/10.1080/10095020.2023.2251540
2
基于遥感和TPO调查数据的年度伐木产品估算
To cite this article:
Gong, W., Huang, C., Zhao, F., & Lu, J. (2024).Estimation of annual harvested wood products based on remote sensing and TPO survey data. Geo-Spatial Information Science, 28(1), 36–48. https://doi.org/10.1080/10095020.2024.2313329
3
茂密森林低估GEDI光斑LAI的改进
To cite this article:
Liang, L., Shang, R., Chen, J. M., Xu, M., & Zeng, H. (2023).Improved estimation of the underestimated GEDI footprint LAI in dense forests. Geo-Spatial Information Science, 28(1), 49–64. https://doi.org/10.1080/10095020.2023.2286377
4
全球与区域总初级生产力估算的
最优参数方案比较分析研究
To cite this article:
9Li, Y., Guan, X., & Huanfeng, S. (2024).Optimal parameter schemes for global and regional gross primary productivity estimation: a comparative analysis. Geo-Spatial Information Science, 28(1), 65–84. https://doi.org/10.1080/10095020.2024.2416658
5
表征尺度误差对山地植被总初级生产力空间降尺度的影响
To cite this article:
Xie, X., & Li, A. (2023).Characterizing the effect of scaling errors on the spatial downscaling of mountain vegetation gross primary productivity. Geo-Spatial Information Science, 28(1), 85–96. https://doi.org/10.1080/10095020.2023.2265149
6
一种从单株树木到景观尺度估算地上生物量，
并对次生林多尺度空间不确定性进行量化的框架
To cite this article:
Ma, Y., Im, J., Zhen, Z., & Zhao, Y. (2025).A framework for upscaling aboveground biomass from an individual tree to landscape level and qualifying the multiscale spatial uncertainties for secondary forests. Geo-Spatial Information Science, 28(1), 97–116. https://doi.org/10.1080/10095020.2024.2449447
Greenhouse gas and related components monitoring
1
区域尺度下高分辨率网格化
人为 CO2 排放清单编制的先进方法
To cite this article:
Xu, M., Han, G., Pei, Z., Yu, H., Li, S., & Gong, W. (2024).Advanced method for compiling a high-resolution gridded anthropogenic CO2 emission inventory at a regional scale. Geo-Spatial Information Science, 28(1), 117–130. https://doi.org/10.1080/10095020.2024.2425182
2
全球卫星XCO2产品比对与评估
To cite this article:
Yang, H., Li, T., Wu, J., & Zhang, L. (2023).Inter-comparison and evaluation of global satellite XCO2 products. Geo-Spatial Information Science, 28(1), 131–144. https://doi.org/10.1080/10095020.2023.2252017
基于机器学习的新算法
New algorithms based on machine learning
1
基于Sentinel-2时间序列数据和深度学习的
亚热带地区植被分类
To cite this article:
Zhang, M., Li, D., Li, G., & Lu, D. (2024).Vegetationification in a subtropical region with Sentinel-2 time series data and deep learning. Geo-Spatial Information Science, 28(1), 145–163. https://doi.org/10.1080/10095020.2024.2336604
2
使用多源卫星遥感数据和街景图像进行城市树种分类
To cite this article:
Wang, S., Liu, C., Wei, S., & Tang, H. (2025).Urban tree speciesification using multisource satellite remote sensing data and street view imagery. Geo-Spatial Information Science, 28(1), 164–184. https://doi.org/10.1080/10095020.2024.2439380
3
森林语义（ForestSemantic）：
近距离感知森林语义数据集
To cite this article:
Liang, X., Qi, H., Deng, X., Chen, J., Cai, S., Zhang, Q., … Hyyppä, J. (2024).ForestSemantic: a dataset for semantic learning of forest from close-range sensing. Geo-Spatial Information Science, 28(1), 185–211. https://doi.org/10.1080/10095020.2024.2313325
4
EFFC-Net：
遥感灾害图像中的轻量级全卷积神经网络
To cite this article:
Yuan, J., Ma, X., Zhang, Z., Xu, Q., Han, G., Li, S., … Cai, X. (2023).EFFC-Net: lightweight fully convolutional neural networks in remote sensing disaster images. Geo-Spatial Information Science, 28(1), 212–223. https://doi.org/10.1080/10095020.2023.2183145
基于遥感的应用和分析
Remote sensing-based applications and analysis
1
植被恢复与气候变化
对武夷山地区植被生产力趋势及年际变化的影响
To cite this article:
He, J., Yang, M., Liu, J., Chen, J. M., & Xie, X. (2024).Impacts of vegetation greening and climate change on trend and interannual variability in vegetation productivity in the Wuyi Mountain region. Geo-Spatial Information Science, 28(1), 224–245. https://doi.org/10.1080/10095020.2024.2362754
2
基于遥感生态指标的甘南青藏高寒草地生态健康评价
To cite this article:
Du, Z., Ji, X., Liu, J., Zhao, W., He, Z., Jiang, J., … Gao, J. (2024).Ecological health assessment of Tibetan alpine grasslands in Gannan using remote sensed ecological indicators. Geo-Spatial Information Science, 28(1), 246–264. https://doi.org/10.1080/10095020.2024.2311862
3
2001—2020年东北地区植被生长
对冻土退化程度的内部响应
To cite this article:
Yang, H., Xing, Y., Chang, X., Wang, J., Li, Y., Tang, J., & Wang, D. (2024).Internal response of vegetation growth to degrees of permafrost degradation in Northeast China from 2001 to 2020. Geo-Spatial Information Science, 28(1), 265–283. https://doi.org/10.1080/10095020.2024.2363618
4
2001—2020年中国干旱时间尺度及特征
对总初级生产力的影响
To cite this article:
Qiao, L., & Xia, H. (2024).The impact of drought time scales and characteristics on gross primary productivity in China from 2001 to 2020. Geo-Spatial Information Science, 28(1), 284–302. https://doi.org/10.1080/10095020.2024.2315279
