Jing Guo （Ph.D.）
SEX: Female
NATIONALITY: China
PRESENT POSITION: Ph.D
DATE OF BIRTH: 07th Aug 1999
CONTRACT DETAILS
Address: Aerospace Information Research Institute, Chinese Academy of Sciences
No.9 Dengzhuang South Road, Haidian District, Beijing 100094, P.R.China.
Email: guojing211@mails.ucas.ac.cn
Mobile:+86-18847653503
ACADEMIC AND PROFESSIONAL INTERESTS
（Main research interests or main skills）
Jing Guo: Ph.D, working in Aerospace Information Research Institute, Chinese Academy of Sciences. The main research interests cover Quantitative estimation of vegetation variables and pests using multi-platform remote sensing data; Land use/cover change detection; Study on variable fertilization in precision agriculture using remote sensing technology. The major analysis software used contain ERDAS Imagine, & ENVI image analysis systems, ESRI ArcGIS analysis software, SPSS statistical analysis software.
ACADEMIC QUALIFICATIONS
PhD. Aerospace Information Research Institute, The Chinese Academy of Sciences, China Remote Sensing; Grasshopper; Habitat Monitoring; Landscape; Zoology; Land use and cover; Agriculture; Grassland; pest and disease; spatial Analysis; Change Detection (2021-Now)
BSc. (Hons). Yunnan University, China Geography Information Science (2017-2021)
PUBLICATIONS
[1] Guo J; Huang W; Dong Y; Lin K; Yue F; et al. A weight-adaptive updated method for grasshopper habitat mapping at the national scale using remote sensing: Combined with spatial heterogeneity and landscape. ISPRS Journal of Photogrammetry and Remote Sensing. 2025, 227, 84-98. https://doi.org/10.1016/j.isprsjprs.2025.05.033. IF=12.2
[2] Guo J; Huang W; Dong Y; Lin K; Zhou Y; et al. Spatiotemporal monitoring of grasshopper habitats using multi-source data: Combined with landscape and spatial heterogeneity. International Journal of Applied Earth Observation and Geoinformation. 2024, 130, 103838. https://doi.org/10.1016/j.jag. IF=8.6
[3] Guo, J.; Lu, L.; Dong, Y.; Huang, W.; Zhang, B.; et al. Spatiotemporal Distribution and Main Influencing Factors of Grasshopper Potential Habitats in Two Steppe Types of Inner Mongolia, China. Remote Sens. 2023, 15, 866. https://doi.org/10.3390/rs15030866. IF=4.2
[4] Guo, J.; Zhao, L.; Huang, W.; Dong, Y.; Geng, Y. Study on the Forming Mechanism of the High-Density Spot of Locust Coupled with Habitat Dynamic Changes and Meteorological Conditions Based on Time-Series Remote Sensing Images. Agronomy. 2022, 12, 1610. https://doi.org/10.3390/agronomy12071610. IF=3.2
[5] Hu, B.; Huang, W.; Hao, Z; Guo, J.; Huang, Y.; Cheng, X.; et al. Invasion of Pine Wilt Disease: A threat to forest carbon storage in China. Ecological Indicators. 2024, 169, 112819. https://doi.org/10.1016/j.ecolind. IF=7.6
[6] Hao, Z.; Huang, W.; Zhang, B.; Chen, Y.; Fang, G.; Guo, J.; et al. Improved pest and disease spread model for simulating the spread of invasive species: A case study of pine wilt disease, Computers and Electronics in Agriculture. 2025, 237, 110561. https://doi.org/10.1016/j.compag. IF=8.9
[7] Zhao, M.; Dong, Y.; Huang, W.; Ruan, C.; Guo, J. Regional-Scale Monitoring of Wheat Stripe Rust Using Remote Sensing and Geographical Detectors. Remote Sens. 2023, 15, 4631. https://doi.org/10.3390/rs15184631.
[8] Zhang, Y.; Dong, Y.; Huang, W.; Guo, J.; Wang, N.; Ding, X. Extraction and Analysis of Grasshopper Potential Habitat in Hulunbuir Based on the Maximum Entropy Model. Remote Sens. 2024, 16, 746. https://doi.org/10.3390/rs16050746.
[9] Cheng, X.; Huang, M.; Guo, A.; Huang, W.; Cai, Z.; Dong, Y.; Guo, J. et al. Early Detection of Rubber Tree Powdery Mildew by Combining Spectral and Physicochemical Parameter Features. Remote Sens. 2024, 16, 1634. https://doi.org/10.3390/rs16091634.
[10] Huang, Y.; Dong, Y.; Huang, W.; Guo, J.; Hao, Z.; Zhao, M.; et al. Predicting the Global Potential Suitable Distribution of Fall Armyworm and Its Host Plants Based on Machine Learning Models. Remote Sens. 2024, 16, 2060. https://doi.org/10.3390/rs16122060.
ORAL PRESENTATIONS（请列出重要的会议报告，尤其特邀报告）
[1] Guo J., Huang W., Dong Y., et al. (2025) Spatiotemporal monitoring of grasshopper habitats using multi-source data: Combined with landscape and spatial heterogeneity. IGRASS 2025, Brisbane, Australia., August 2025.