Bio
Daryl Yang joined Oak Ridge National Laboratory in 2024 as a Liane B. Russell Distinguished Staff Fellow (DSF). He is working in the Plant-Soil Interactions group in the Environmental Sciences Division, Biological and Environmental Systems Science Directorate (BESSD). Daryl’s overall research is in integrating novel Earth observations, ecological theories, and process models to understand the interconnections between ecosystem dynamics and climate change. He will focus his DSF research on combining multiscale remote-sensing and numeric models to understand fire-driven ecosystem transition and its impacts on soil-land-atmosphere interactions across Arctic and boreal ecosystems. His work is expected to improve our ability to monitor and model fire-impacted ecosystems in the circumpolar Arctic. Daryl is a member and task lead of DOE’s Next Generation Ecosystem Experiment Arctic (NGEE Arctic) project. He is also a co-lead of the Terrestrial Ecosystem Community of Practice for Interagency Arctic Research Policy Committee (IARPC), a User Working Group member of ORNL’s Distributed Active Archive Center (DAAC) and NASA’s Spectral Imaging Working Group.
Daryl received his Ph.D in Ecology and Evolution from Stony Brook University, during which he was a Future Investigator in NASA’s Earth and Space Science and Technology (FINESST) program.
Professional Experience
2024 - Present: Distinguished Staff Fellow, Oak Ridge National Laboratory
2022 - 2023: NASA FINESST Fellow, Stony Brook University, Brookhaven National Laboratory
2018 - 2022: Research Assistant, Stony Brook University, Brookhaven National Laboratory
2017 - 2018: Teaching Assistant, Stony Brook University
Awards
Department of Energy’s NGEE-Arctic project Early Career Excellence Award (2022)
Future Investigator in NASA Earth and Space Science and Technology Award (2022)
Brookhaven National Laboratory Dr. Mow Shiah Lin Scholarship (2022)
Stony Brook University John Dunn Award (2021)
First Place Poster Award for PECORA 21/ISRSE 38 Conference (2019)
Stony Brook University Recruitment Fellowship (2017)
“Zhou Ting Ru” Academic Excellence Award, Beijing Normal University (2017)
First Academic Scholarship, Beijing Normal University (2014 - 2017)
National Undergraduate Scholarship, Ministry of Education of China (2014)
Education
2023 Ph.D. in Ecology and Evolution; Stony Brook University, Stony Brook, NY; Brookhaven National Laboratory, Upton, NY, USA.
2017 M.S. in Global Environmental Change, Beijing Normal University, Beijing, China
2014 B.S. in Surveying and Mapping Engineering, Central South University, Changsha, China
Professional Service
Co-lead of the Terrestrial Ecosystem Community of Practice for Interagency Arctic Research Policy Committee (IARPC)
User Working Group Member of ORNL's Distributed Active Archive Center (DAAC)
Member of NASA ABoVE’s Spectra Imaging Working Group
Reviewer for Global Change Biology, Remote Sensing of Environment, Global Ecology and Biogeography, Earth’s Future, ISPRS Journal of Photogrammetry and Remote sensing, International Journal of Applied Earth Observation and Geoinformation, Polar Research, Geoscience and Remote Sensing
Other Publications
Berner, L. T., Orndahl, K. M., Rose, M., Tamstorf, M., Arndal, M. F., Alexander, H. D., Humphreys, E. R., Loranty, M. M., Ludwig, S. M., Nyman, J., Juutinen, S., Aurela, M., Happonen, K., Mikola, J., Mack, M. C., Vankoughnett, M. R., Iversen, C. M., Salmon, V. G., Yang, D., … Goetz, S. J. (2024). The Arctic Plant Aboveground Biomass Synthesis Dataset. Scientific Data, 11(1), 305. https://doi.org/10.1038/s41597-024-03139-w
Lin, Z., Cheng, K. H., Yang, D., Xu, F., Song, G., Meng, R., Wang, J., Zhu, X., Ng, M., & Wu, J. (2024). Ecoregion-wise fractional mapping of tree functional composition in temperate mixed forests with sentinel data: Integrating time-series spectral and radar data. Remote Sensing of Environment, 304, 114026. https://doi.org/10.1016/j.rse.2024.114026
Song, G., Wang, J., Zhao, Y., Yang, D., Lee, C. K. F., Guo, Z., Detto, M., Alberton, B., Morellato, P., Nelson, B., & Wu, J. (2024). Scale matters: Spatial resolution impacts tropical leaf phenology characterized by multi-source satellite remote sensing with an ecological-constrained deep learning model. Remote Sensing of Environment, 304, 114027. https://doi.org/10.1016/j.rse.2024.114027
Frost, G.V., Macander, M.J., Bhatt, U.S., Berner, L.T., Bjerke, J.W., Epstein, H.E., Forbes, B.C., Lara, M.J., Magnússon R.Í., Montesano, P.M, Phoenix, G.K., Serbin, S.P., Tømmerik, H., Waigl, C., Walker, D.A., and Yang, D. 2023. Tundra Greenness. Arctic Report Card 2023, R.L. Thoman, T.A. Moon, and M.L. Druckenmiller (eds.). https://doi.org/10.25923/s86a-jn24
Schore, A. I. G., Fraterrigo, J. M., Salmon, V. G., Yang, D., & Lara, M. J. (2023). Nitrogen fixing shrubs advance the pace of tall-shrub expansion in low-Arctic tundra. Communications Earth & Environment, 4(1), 421. https://doi.org/10.1038/s43247-023-01098-5
Yang, D., McMahon, A., Hantson, W., Anderson, J., & Serbin, S. P. (2023). PiCAM: A Raspberry Pi-based open-source, low-power camera system for monitoring plant phenology in Arctic environments. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.14231
Frost, G.V., Macander, M.J., Bhatt, U.S., Berner, L.T., Bjerke, J.W., Epstein, H.E., Forbes, B.C., Goetz, S.J., Lara M.J., Park, T., Phoenix, G.K., Serbin, S.P., Tommervik, H., Walker, D.A., Yang, D., 2023. State of the Climate in 2022 – The Arctic. Bulletin of American Meteorological Society. https://doi.org/10.1175/BAMS-D-23-0079.1
Wang, J., Song, G., Liddell, M., Morellato, P., Lee, C., Yang, D., Alberton, B., Detto, M., Ma, X., Zhao, Y., Yeung, H., Zhang, H., Ng, M., Nelson, B., Heute, A. An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2022.113429
Yang, D., Morrison, B.D., Hanston, W., McMahon, A., Baskaran, L., Hayes, D.J., Miller, C.E., Serbin, S.P., 2023. Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska. Remote Sens Environ 286, 113430. https://doi.org/10.1016/j.rse.2022.113430
Frost, G.V., Macander, M.J., Bhatt, U.S., Berner, L.T., Bjerke, J.W., Epstein, H.E., Forbes, B.C., Goetz, S.J., Lara M.J., Park, T., Phoenix, G.K., Serbin, S.P., Tommervik, H., Walker, D.A., Yang, D., 2022, NOAA 2022 Arctic Report Card – Tundra Greenness. 10.25923/mhrv-gr76
Frost, G.V., Macander, M.J., Bhatt, U.S., Berner, L.T., Bjerke, J.W., Epstein, H.E., Forbes, B.C., Goetz, S.J., Lara M.J., Park, T., Phoenix, G.K., Serbin, S.P., Tommervik, H., Walker, D.A., Yang, D., 2022. State of the Climate in 2021 – The Arctic. Bulletin of American Meteorological Society. https://www.ametsoc.org/index.cfm/ams/
Yang, D., Morrison, B.D., Davidson, K.J., Lamour, J., Li, Q., Nelson, P.R., Hantson, W., Hayes, D.J., Swetnam, T.L., McMahon, A., Anderson, J., Ely, K.S., Rogers, A., Serbin, S.P., 2022. Remote Sensing from Unoccupied Aerial Systems: Opportunities to Enhance Arctic Plant Ecology in a Changing Climate. Journal of Ecology. https://doi.org/10.1111/1365-2745.13976
Nelson, P.R., Maguire, A.J., Pierrat, Z., Orcutt, E.L., Yang, D., Serbin, S.P., Frost, G.V., Macander, M.J., Magney, T.S., Thompson, D.R., Wang, J., Oberbauer, S.F., Zesati, S.A.V., Davidson, S.J., Epstein, H., Unger, S., Campbell, P.K.E., Carmon, N., Velez-Reyes, M., Huemmrich, K.F., 2022. Remote Sensing of Tundra Ecosystems using High Spectral Resolution Reflectance: Opportunities and Challenges. https://doi.org/10.1002/essoar.10508585.1
Frost, G.V., Macander, M.J., Bhatt, U.S., Berner, L.T., Bjerke, J.W., Epstein, H.E., Forbes, B.C., Goetz, S.J., Lara M.J., Park, T., Phoenix, G.K., Serbin, S.P., Tommervik, H., Walker, D.A., Yang, D., 2021, NOAA 2021 Arctic Report Card – Tundra Greeness. DOI: 10.25923/8n78-wp73
Yang, D., Morrison, B.D., Hantson, W., Breen, A.L., McMahon, A., Li, Q., Salmon, V.G., Hayes, D.J., Serbin, S.P., 2021. Landscape-scale characterization of Arctic tundra vegetation composition, structure, and function with a multi-sensor unoccupied aerial system. Environ Res Lett. https://doi.org/10.1088/1748-9326/ac1291
Wang, J., Yang, D., Chen, S., Zhu, X., Wu, S., Bogonovich, M., Guo, Z., Zhu, Z., Wu, J., 2021. Automatic cloud and cloud shadow detection in tropical areas for PlanetScope satellite images. Remote Sens Environ 264, 112604. https://doi.org/10.1016/j.rse.2021.112604
Burnett, A.C., Anderson, J., Davidson, K.J., Ely, K.S., Lamour, J., Li, Q., Morrison, B.D., Yang, D., Rogers, A., Serbin, S.P., 2021. A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression. J Exp Bot erab295-. https://doi.org/10.1093/jxb/erab295
Liu, X., Guo, L., Cui, X., Butnor, J.R., Boyer, E.W., Yang, D., Chen, J., Fan, B., 2021. An Automatic Processing Framework for In Situ Determination of Ecohydrological Root Water Content by Ground-Penetrating Radar. Ieee T Geosci Remote PP, 1–15. https://doi.org/10.1109/tgrs.2021.3065066
Burnett, A.C., Serbin, S.P., Lamour, J., Anderson, J., Davidson, K.J., Yang, D., Rogers, A., 2021. Seasonal trends in photosynthesis and leaf traits in scarlet oak. Tree Physiol. https://doi.org/10.1093/treephys/tpab015
Ely, K.S., Rogers, A., Agarwal, … Yang, D., 2021. A reporting format for leaf-level gas exchange data and metadata. Ecol Inform 101232. https://doi.org/10.1016/j.ecoinf.2021.101232
Yang, D., Meng, R., Morrison, B.D., McMahon, A., Hantson, W., Hayes, D.J., Breen, A.L., Salmon, V.G., Serbin, S.P., 2020. A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra. Remote Sens-basel 12, 2638. https://doi.org/10.3390/rs12162638
Wang, J., Yang, D., Detto, M., Nelson, B.W., Chen, M., Guan, K., Wu, S., Yan, Z., Wu, J., 2020. Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest. Remote Sens Environ 246, 111865. https://doi.org/10.1016/j.rse.2020.111865
Meng, R., Yang, D., McMahon, A., Hantson, W., Hayes, D., Breen, A., Serbin, S., 2019. A UAS Platform for Assessing Spectral, Structural, and Thermal Patterns of Arctic Tundra Vegetation. Igarss 2019 - 2019 Ieee Int Geoscience Remote Sens Symposium 9113–9116. https://doi.org/10.1109/igarss.2019.8897953
Liu, X., Cui, X., Guo, L., Chen, J., Li, W., Yang, D., Cao, X., Chen, X., Liu, Q., Lin, H., 2019. Non-invasive estimation of root zone soil moisture from coarse root reflections in ground-penetrating radar images. Plant Soil 436, 623–639. https://doi.org/10.1007/s11104-018-03919-5
Guo, Z., Yang, D., Chen, J., Cui, X., 2018. A new index for mapping the ‘blue steel tile’ roof dominated industrial zone from Landsat imagery. Remote Sens Lett 9, 578–586. https://doi.org/10.1080/2150704x.2018.1452057
Yang, D., Chen, J., Zhou, Y., Chen, Xiang, Chen, Xuehong, Cao, X., 2017. Mapping plastic greenhouse with medium spatial resolution satellite data: Development of a new spectral index. Isprs J Photogramm 128, 47–60. https://doi.org/10.1016/j.isprsjprs.2017.03.002
Yang, D., Chen, X., Chen, J., Cao, X., 2017. Multiscale Integration Approach for Land Cover Classification Based on Minimal Entropy of Posterior Probability. Ieee J Sel Top Appl 10, 1105–1116. https://doi.org/10.1109/jstars.2016.2615073
Yang, D., Sun, S., Chen, J., Liu, X., 2016. Analysis for the spatial and temporal patterns of plasticulture in Shandong province, China with remotely sensed data. 2016 Fifth Int Conf Agro-geoinformatics Agro-geoinformatics 1–4. https://doi.org/10.1109/agro-geoinformatics.2016.7577663
Chen, X., Yang, D., Chen, J., Cao, X., 2015. An improved automated land cover updating approach by integrating with downscaled NDVI time series data. Remote Sens Lett 6, 29–38. https://doi.org/10.1080/2150704x.2014.998793
Cai, Q., Liu, N., Dai, W., Yang, D., 2015. The Robust Kalman Filtering with Continuous Variable Equivalent Weight Function. Journal of Geodesy and Geodynamics.