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Christine Chang
Research Plant Physiologist


Publications (Clicking on the reprint icon Reprint Icon will take you to the publication reprint.)
Effect of far-red light on biomass accumulation, plant morphology, and phytonutrient composition of ruby streaks mustard at microgreen, baby leaf, and flowering stages - (Peer Reviewed Journal)
Teng, Z., Luo, Y., Sun, J., Pearlstein, D.J., Oehler, M., Fitzwater, J.D., Zhou, B., Hussan, M.A., Chang, C.Y., Chen, P., Wang, Q., Fonseca, J.M. 2024. Effect of far-red light on biomass accumulation, plant morphology, and phytonutrient composition of ruby streaks mustard at microgreen, baby leaf, and flowering stages. Journal of Agricultural and Food Chemistry. 72(17):9587–9598. https://doi.org/10.1021/acs.jafc.3c06834.
Rapid response of nonstructural carbohydrate allocation and photosynthesis to short photoperiod, low temperature, or elevated CO2 in Pinus strobus - (Peer Reviewed Journal)
Chang, C.Y., Unda, F., Mansfield, S.D., Ensminger, I. 2023. Rapid response of nonstructural carbohydrate allocation and photosynthesis to short photoperiod, low temperature, or elevated CO2 in Pinus strobus. Physiologia Plantarum. 175(6). Article e14095. https://doi.org/10.1111/ppl.14095.
Transforming remotely-sensed SIF to ecosystem structure, functions, and service: Part II - Harnessing data - ()
Sun, Y., Wen, J., Gu, J., Van Der Tol, C., Porcar-Castell, A., Joiner, J., Chang, C.Y., Magney, T.S., Wang, L., Hu, L., Rascher, U., Zarco-Tejada, P., Barrett, C.B., Lai, J., Han, J. 2023. Transforming remotely-sensed SIF to ecosystem structure, functions, and service: Part II - Harnessing data. Global Change Biology. 29(11):2893-2925. https://doi.org/10.1111/gcb.16646.
From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I—Harnessing theory - ()
Sun, Y., Wen, J., Gu, L., Van Der Tol, C., Porcar-Castell, A., Joiner, J., Chang, C.Y., Magney, T.D., Wang, L., Hu, L., Rascher, U., Zarco-Tejada, P., Barrett, C.B., Lai, J., Han, J. 2023. Transforming ecosystem structure, function, and service: Part I—Harnessing theory. Global Change Biology. 29(11):2926-2952. https://doi.org/10.1111/gcb.16634.
A precise and atmospherically-robust method for ground-based retrieval of red and far-red sun-induced chlorophyll fluorescence - (Peer Reviewed Journal)
Naethe, P., Tommaso, J., Chang, C.Y., Burkart, A., Migliavacca, M., Guanter, L., Rascher, U. 2022. A precise and atmospherically-robust method for ground-based retrieval of red and far-red sun-induced chlorophyll fluorescence. Agricultural and Forest Meteorology. 325:109152. https://doi.org/10.1016/j.agrformet.2022.109152.
The physiological basis for estimating photosynthesis from Chl a fluorescence - (Peer Reviewed Journal)
Han, J., Chang, C.Y., Gu, L., Zhang, Y., Meeker, E.W., Magney, T.S., Walker, A.P., Wen, J., Kira, O., Mcnaull, S., Sun, Y. 2022. The physiological basis for estimating photosynthesis from Chl a fluorescence. New Phytologist. 234; 1206-1219. https://doi.org/10.1111/nph.18045.
Unpacking the drivers of diurnal dynamics of sun-induced chlorophyll fluorescence (SIF): Canopy structure, plant physiology, instrument configuration and retrieval methods - (Peer Reviewed Journal)
Chang, C.Y., Wen, J., Han, J., Kira, O., Levonne, J., Melkonian, J., Riha, S.J., Skovira, J., Ng, S., Gu, L., Wood, J.D., Naethe, P., Sun, Y. 2021. Unpacking the drivers of diurnal dynamics of sun-induced chlorophyll fluorescence (SIF): Canopy structure, plant physiology, instrument configuration and retrieval methods. Remote Sensing of Environment. 265:112672. https://doi.org/10.1016/j.rse.2021.112672.
A minimally disruptive method for measuring water potential in-planta using hydrogel nanoreporters - (Peer Reviewed Journal)
Jain, P., Liu, W., Zhu, S., Chang, C.Y., Melkonian, J., Rockwell, F.E., Pauli, D., Sun, Y., Holbrook, M.N., Riha, S.J., Gore, M.A., Stroock, A.D. 2021. A minimally disruptive method for measuring water potential in-planta using hydrogel nanoreporters. Proceedings of the National Academy of Sciences(PNAS). 118(23):e2008276118. https://doi.org/10.1073/pnas.2008276118.
Fusion of Multispectral Aerial Imagery and Vegetation Indices for Machine Learning-Based Ground Classification - (Peer Reviewed Journal)
Zhang, Y., Wen, Y., Sun, Y., Chang, C.Y., Yu, J., Zhang, W. 2021. Fusion of Multispectral Aerial Imagery and Vegetation Indices for Machine Learning-Based Ground Classification. Remote Sensing. 13:1411. https://doi.org/10.3390/rs13081411.