You can also find everything on my Google Scholar profile.


Taylor S.D., Browning D.M. 2022. Classification of Daily Crop Phenology in PhenoCams Using Deep Learning and Hidden Markov Models. Remote Sensing. 14(2):286. [Preprint, Code, Data & Code Archive]

Taylor, S.D., Browning, D.M., Baca, R.A., Gao, F. 2021. Constraints and Opportunities for Detecting Land Surface Phenology in Drylands. Journal of Remote Sensing. [Preprint, Code, Data & Code Archive]

Browning, D. M., Russell, E. S., Ponce-Campos, G. E., Kaplan, N., Richardson, A. D., Seyednasrollah, B., … Taylor, S. D. 2021. Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework. Ecological Indicators, 131, 108147.

Taylor, Shawn D., and Dawn M. Browning. 2021. Multi-Scale Assessment of a Grassland Productivity Model. Biogeosciences 18 (6): 2213–20. [Code, Data & Code Archive]

Prince, K., Taylor, S., & Angelini, C. 2020. A global, cross-system meta-analysis of polychlorinated biphenyl biomagnification. Environmental Science & Technology.

Taylor, S.D., Marconi, S. 2020. Rethinking global carbon storage potential of trees. A comment on Bastin et al. (2019). Annals of Forest Science 77, 23. [Code, Data & Code Archive, Preprint]

Taylor, S.D., White, E.P., 2019. Automated data-intensive forecasting of plant phenology throughout the United States. Ecological Applications. [Code, Code Archive, Preprint]

Taylor S.D. 2019. Estimating flowering transition dates from status-based phenological observations: a test of methods. PeerJ 7:e7720 [Code, Code Archive, Preprint]

Taylor, S.D., J.M. Meiners, K. Riemer, M.C. Orr, E.P White. 2019. Comparison of large-scale citizen science data and long-term study data for phenology modeling. Ecology [Preprint,Code]

Harris, D.J., S.D. Taylor, E.P. White. 2018. Forecasting biodiversity in breeding birds using best practices. PeerJ, 6:e4278 [Code, Code Archive, Preprint]

White, E.P., G.M. Yenni, S.D. Taylor, E.M. Christensen, E.K. Bledsoe, J.L. Simonis, S.K.M. Ernest. 2018. Developing an automated iterative near-term forecasting system for an ecological study. Methods in Ecology and Evolution [Website, Data, Code, Preprint]


Taylor, SD and White, EP, 2020. Influence of climate forecasts, data assimilation, and uncertainty propagation on the performance of near-term phenology forecasts. bioRxiv,
[Code, Data & Code Archive]

Taylor, SD and Guralnick, RP, 2019. Opportunistically collected photographs can be used to estimate large-scale phenological trends. bioRxiv, 794396. [Data & Code Archive]

Taylor, SD. 2018. “NEON NIST Data Science Evaluation Challenge: Methods and Results of Team Shawn.” PeerJ Preprints 6: e26967v1.