Intelligent Remote Sensing of Ecological Environment | Views : 0 下载量: 426 CSCD: 0
  • Export

  • Share

  • Collection

  • Album

    • Accurately retrieving vegetation phenology at high spatial and temporal resolutions based on GEE and multi-source remote sensing data fusion

    • In the field of vegetation phenology research, experts used the Google Earth Engine platform and ESTARFM algorithm to fuse Landsat 8 images with MODIS products, generating high-resolution EVI sequences. They verified the improvement effect of their phenological monitoring ability compared to MODIS data, and explored the factors that affect the fusion effect, providing theoretical support and data reference for refined vegetation dynamic monitoring and ecosystem research.
    • Vol. 28, Issue 11, Pages: 2910-2926(2024)   

      Published: 07 November 2024

    • DOI: 10.11834/jrs.20232646     

    移动端阅览

  • Song J,Zhang Z and Han J C. 2024. Accurately retrieving vegetation phenology at high spatial and temporal resolutions based on GEE and multi-source remote sensing data fusion. National Remote Sensing Bulletin, 28(11):2910-2926 DOI: 10.11834/jrs.20232646.
  •  
  •  
Alert me when the article has been cited
提交

相关作者

FU Yongshuo 北京师范大学 水科学研究院
ZHU Wenquan 北京师范大学 地理科学学部, 遥感科学国家重点实验室
XIE Zhiying 北京师范大学 水科学研究院
Yi LUO 兰州大学 资源环境学院
Xuanlong MA 兰州大学 资源环境学院
Xiaodan WU 兰州大学 资源环境学院
Qiaoyun XIE 悉尼科技大学 生命科学学院
Zhengyang ZHANG 兰州大学 资源环境学院

相关机构

College of Water Sciences, Beijing Normal University
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University
College of Earth and Environmental Sciences, Lanzhou University
School of Life Sciences, University of Technology Sydney
兰州大学 资源环境学院
0