Jin Xu is a postdoctoral research fellow at the Smithsonian Conservation Biology Institute's Conservation Ecology Center. Her current project aims to model tree and bird biodiversity at the continental and global scales using statistical analysis methods and machine learning algorithms from passive and active remote sensing data.
Xu’s graduate research focused on scale effect and uncertainty analysis for shrub willow biophysical parameter estimation from satellite and unmanned aerial system (UAS) data. She developed new approaches for estimating shrub willow biophysical parameters across time, space, and scale and demonstrated that better use of remote sensing technology can increase the efficiency of shrub willow management practices.
Xu, Jin., Quackenbush, Lindi. J., Volk, Timothy. A., & Stehman, Stephen. V. (2022). Shrub willow canopy chlorophyll concentration estimation from unmanned aerial system (UAS) data: Estimation and uncertainty analysis across time, space, and scales. International Journal of Applied Earth Observation and Geoinformation, 108: 102737. DOI: 10.1016/ j.jag.2022.102737.
Wang, Shufeng., Volk, Timothy. A., & Xu, Jin. (2021). Variability in growth and cadmium accumulation capacity among willow hybrids and their parents: implications for yield-based selection of Cd-efficient cultivars. Journal of Environmental Management, 299: 113643. DOI: 10.1016/ j.jenvman.2021.113643.
Xu, Jin., Volk, Timothy. A., Quackenbush, Lindi. J., & Stehman, Stephen. V. (2021). Estimation of shrub willow leaf chlorophyll concentration across different growth stages using a hand-held chlorophyll meter to monitor plant health and production. Biomass & Bioenergy, 150: 106132. DOI: 10.1016/j.biombioe.2021.106132.
Xu, Jin., Quackenbush, Lindi. J., Volk, Timothy. A., & Im, Jungho. (2020). Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions. Remote Sensing, 12(18), 293 DOI: 10.3390/rs12182934
Xu, Jin., Meng, Jihua., & Quackenbush, Lindi. J. (2019). Use of remote sensing to predict the optimal harvest date of corn. Field Crops Research, 236, 1-13. DOI: 10.1016/ j.fcr.2019.03.003.
Meng, Jihua., Xu, Jin., & You, Xingzhi. (2015). Optimizing soybean harvest data using HJ-1 satellite imagery. Precision Agriculture, 16(2), 164-179. DOI: 10.1007/s11119-014-9368-3.