Shawn Serbin

First name
Shawn
Last name
Serbin

2022

  • Lamour, Julien, et al. “New Calculations for Photosynthesis Measurement Systems: What’s the Impact for Physiologists and Modelers?”. New Phytologist, vol. 233, no. 2, 2022, pp. 592-8, https://doi.org/https://doi.org/10.1111/nph.17762.
  • Rogers, Alistair, et al. “Reducing Model Uncertainty of Climate Change Impacts on High Latitude Carbon Assimilation”. Global Change Biology, vol. 28, no. 4, 2022, pp. 1222-47, https://doi.org/https://doi.org/10.1111/gcb.15958 .

2021

  • Burnett, Angela C., et al. “A Best-Practice Guide to Predicting Plant Traits from Leaf-Level Hyperspectral Data Using Partial Least Squares Regression”. Journal of Experimental Botany, vol. 72, no. 18, 2021, pp. 6175-89, https://doi.org/10.1093/jxb/erab295.
  • Ely, Kim S., et al. “A Reporting Format for Leaf-Level Gas Exchange Data and Metadata”. Ecological Informatics, vol. 61, 2021, p. 101232, https://doi.org/10.1016/j.ecoinf.2021.101232.
  • Fer, Istem, et al. “Beyond Ecosystem Modeling: A Roadmap to Community Cyberinfrastructure for Ecological data‐model Integration”. Global Change Biology, vol. 27, no. 1, 2021, pp. 13-26, https://doi.org/10.1111/gcb.15409.
  • Yang, Dedi, et al. “Landscape-Scale Characterization of Arctic Tundra Vegetation Composition, Structure, and Function With a Multi-Sensor Unoccupied Aerial System”. Environmental Research Letters, vol. 16, no. 8, 2021, p. 085005, https://doi.org/10.1088/1748-9326/ac1291.
  • Cawse-Nicholson, Kerry, et al. “NASA’s Surface Biology and Geology Designated Observable: A Perspective on Surface Imaging Algorithms”. Remote Sensing of Environment, vol. 257, 2021, p. 112349, https://doi.org/10.1016/j.rse.2021.112349.
  • Rogers, Alistair, et al. “Triose Phosphate Utilization Limitation: An Unnecessary Complexity in Terrestrial Biosphere Model Representation of Photosynthesis”. New Phytologist, 2021, https://doi.org/10.1111/nph.17092.

2020

  • Yang, Dedi, et al. “A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra”. Remote Sensing, vol. 12, no. 16, 2020, p. 2638, https://doi.org/10.3390/rs12162638.

2019

  • Serbin, Shawn P., et al. “From the Arctic to the Tropics: Multibiome Prediction of Leaf Mass Per Area Using Leaf Reflectance”. New Phytologist, vol. 224, 2019, pp. 1557-68, https://doi.org/10.1111/nph.16123.
  • Rogers, Alistair, et al. “Terrestrial Biosphere Models May Overestimate Arctic Carbon Dioxide Assimilation If They Do Not Account for Decreased Quantum Yield and Convexity at Low Temperature”. New Phytologist, vol. 223, no. 223, 2019, pp. 167-79, https://doi.org/10.1111/nph.15750.
  • Burnett, Angela C., et al. “The ‘one‐point method’ for Estimating Maximum Carboxylation Capacity of Photosynthesis: A Cautionary Tale”. Plant, Cell & Environment, vol. 42, no. 8, 2019, pp. 2472-81, https://doi.org/10.1111/pce.13574.

2017

  • Rogers, Alistair, et al. “A Roadmap for Improving the Representation of Photosynthesis in Earth System Models”. New Phytologist, vol. 213, no. 1, 2017, pp. 22-42, https://doi.org/10.1111/nph.14283.
  • Lewin, Keith F., et al. “A Zero-Power Warming Chamber for Investigating Plant Responses to Rising Temperature”. Biogeosciences, vol. 14, no. 18, 2017, pp. 4071-83, https://doi.org/10.5194/bg-14-4071-2017.
  • Rogers, Alistair, et al. “Terrestrial Biosphere Models Underestimate Photosynthetic Capacity and Carbon Dioxide Assimilation in the Arctic”. New Phytologist, vol. 216: 1090-1103, no. 4, 2017, pp. 1090-03, https://doi.org/10.1111/nph.14740.

2016

  • De Kauwe, Martin G., et al. “A Test of the ‘one-Point method’ for Estimating Maximum Carboxylation Capacity from Field-Measured, Light-Saturated Photosynthesis”. New Phytologist, no. 3, 2016, pp. 1130-44, https://doi.org/10.1111/nph.13815.