Min Chen
Assistant Professor
Russell Labs
1630 Linden Drive
Madison, WI 53706
Phone:
E-mail: mchen392@wisc.edu
Expertise
Terrestrial ecosystem/land surface modeling, remote sensing theory and applications, human-Earth system interactions
Education/Experience
Education
| Degree |
Institution | Major Field | Granted |
| PhD | Purdue University | Earth & Atmospheric Sciences | 2013 |
| MS | Beijing Normal University | Remote Sensing and GIS | 2008 |
| BS | Beijing Normal University | Computer Science | 2005 |
Professional Experience
| Institution | Title | Years |
| Department of Global Ecology, Carnegie Institution for Science at Stanford University | Barbara McClintock Fellow | 2015-2016 |
| Department of Organismic and Evolutionary Biology, Harvard University | Postdoctoral Fellow | 2013-2015 |
Research
Arctic-Boreal ecosystem changes and the socioeconomic impacts
We are leading a project that investigates the critically important but poorly understood ecosystem changes in the Arctic-Boreal ecosystems, as well as the associated socioeconomic impacts. We are using a variety of observations in an integrated framework consisting of an advanced land surface model (the Community Land Model, CLM), a sophisticated integrated assessment model (the Global Change Assessment Model, GCAM), and the Data Assimilation Research Testbed (DART) to quantify how the observation data obtained by the Arctic Boreal Vulnerability Experiment (ABoVE) can constrain the terrestrial component of Human-Earth system models, characterize uncertainties in their projections, and assess the socioeconomic impacts of such improved projections at regional and global scales.
Remote sensing of Solar-induced Chlorophyll Fluorescence
One of the overarching goals of vegetation remote sensing is to provide spatially resolved information to support simulation of the photosynthesis rate of the terrestrial biosphere. The recent successes of solar induced fluorescence (SIF) retrievals as a proxy for GPP across different vegetation types and with different environmental or physiological limitations, showing that there is potentially more information in remote sensing data than previously thought. Similar to other optical signals, SIF is reabsorbed and scattered within the canopy, and these radiative transfer processes contribute to the remotely-sensed SIF signals. Some key questions remain to be answered, such as: What is the role of canopy structure in the correlation between remotely-sensed SIF and vegetation photosynthesis? And, how can we minimize the sun-sensor geometry effects on SIF? Can we retrieve leaf-scale SIF and related parameters (e.g., light use efficiency for SIF, namely SIF yield)? Further, if we can do this, will these advances have follow-on impacts on our ability to use remote sensing for other purposes?
We are leading a project funded by NASA's Remote Sensing Theory Program to pursue answers to the above questions. We are developing a novel leaf-canopy radiative transfer model to improve our understanding of radiative transfer as relate to SIF, and in turn obtain new insights of remote sensing of vegetation properties.
Wildfire prediction and its role in the Earth system
Recent wildfires have caused enormous environmental hazards and economic losses. We are working towards using advanced machine learning techniques, Earth system modeling to better understand the patterns, drivers and impacts of the wildfires. The work has been supported by AmFam Data Initiative.
Global monitoring system for wetland CH4 emission
Wetlands are highly dynamic terrestrial-aquatic interfaces widely distributed across tropical, temperate, and high latitude ecosystems. As the largest natural source to the atmosphere, wetlands are responsible for 30% of global methane (CH4) emissions, which accounts for about 25% of cumulative anthropogenic radiative forcing since the industrial revolution. The complexity of wetland CH4 production and consumption processes makes it challenging to upscale and estimate the net emission flux at large scales. We are working to develop a prototype monitoring system of global wetland CH4 emissions and to improve understanding of how the physical environment (i.e., temperature, water, and air pressure, etc.) and vegetation activity affect the spatial and temporal dynamics of CH4 emissions, in collaboration with scientists at Lawrence Berkeley National Laboratory and the University of Illinois at Chicago.
Courses Taught
Fall 2021 – FWE550 Forest Ecology
Number of Credits: 3.0
Instructor: Phil Townsend
Co-Instructor: Natalie Queally, Eric Kruger, and Min Chen
Publications
Lab members in Bold
2025
(95) Liu, H.; Xiao, J.; Hao, D.; Li, F.; Ji, F.; Chen, M. Importance of viewing angle: hotspot effect improves the ability of satellites to track terrestrial photosynthesis. Remote Sens. Environ. 2025, 317, 114492. https://doi.org/10.1016/j.rse.2024.114492
(94) Gao, L.; Guan, K.; Jiang, C.; Lu, X.; Wang, S.; Ainsworth E.A.; Wu, X.; Chen, M. Incorporating environmental stress improves estimation of photosynthesis from NIRvP in US Great Plains pasturelands and Midwest croplands. Remote Sens. Environ. 2025, 316, 114516. https://doi.org/10.1016/j.rse.2024.114516.
2024
(93) Li, F.; Zhu Q.; Yuan, K.; Ji, F.; Paul, A.; Lee, P.; Radeloff, V. C.; Chen, M. Projecting large fires in the western US with an interpretable and accurate hybrid machine learning method. Earths Future 2024. 12, e2024EF004588. https://doi.org/10.1029/2024EF004588
(92) Zhu, Q.; Yuan, K.; Li, F.; Riley, W. J.; Hoyt, A.; Jackson, R.; McNicol, G.; Chen, M.; Knox, S. H.; Briner, O.; Beerling, D.; Gedney, N.; Hopcroft, P. O.; Ito, A.; Jain, A. K.; Jensen, K.; Kleinen, T.; Li, T.; Liu, X.; McDonald, K. C.; Melton, J. R.; Miller, P. A.; Müller, J.; Peng, C.; Poulter, B.; Qin, Z.; Peng, S.; Tian, H.; Xu, X.; Yao, Y.; Xi, Y.; Zhang, Z.; Zhang, W.; Zhu, Q.; Zhuang, Q. Critical Needs to Close Monitoring Gaps in Pan-Tropical Wetland CH4 Emissions. Environ. Res. Lett. 2024, 19 (11), 114046. https://doi.org/10.1088/1748-9326/ad8019.
(91) Dashti, H.; Chen, M., Smith, W. K.; Zhao, K.; Moore, D.J.P. Ecosystems Disturbance Recovery: What It Was or What It Could Have Been? Geophysical Research Letters 2024 51, e2024GL109219. https://doi.org/10.1029/2024GL109219
(90) , , , , & (2024). Impacts of forest management-induced productivity changes on future land use and land cover change. Earth’s Future, 12, e2024EF004878. https://doi.org/10.1029/2024EF004878
(89) , , , , , , , , & (2024). A holistic big data approach to understand and manage increasing pollen-induced respiratory allergies under global change. Global Change Biology, 30, e17451. https://doi.org/10.1111/gcb.17451
(88) Qin, Z., Zhu, Y., Canadell, J.G., Chen, M., Li, T., Mishra, U., Yuan, W., (2024), Global spatially explicit carbon emissions from land-use change over the past six decades (1961–2020). One Earth. https://doi.org/10.1016/j.oneear.2024.04.002
(87) Ji, F., Li, F., Hao, D., Shiklomanov, A.N., Yang, X., Townsend, P.A., Dashti, H., Nakaji, T., Kovach, K.R., Liu, H., Luo, M. and Chen, M. (2024), Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset. New Phytologist, https://doi.org/10.1111/nph.19807
(86) Yuan, K.*, Li, F.* (co-first author), McNicol, G., Chen, M., Hoyt, A., Knox, S., Riley, W. J., Jackson, R., & Zhu, Q. (2024), Boreal–Arctic wetland methane emissions modulated by warming and vegetation activity. Nature Climate Change. https://doi.org/10.1038/s41558-024-01933-3
(85) McManamay, R.A., Vernon, C.R., Chen, M., Thompson, I., Khan, Z. & Narayan, K.B. (2024) Dynamic urban land extensification is projected to lead to imbalances in the global land-carbon equilibrium. Commun Earth Environ 5, 70. https://doi.org/10.1038/s43247-024-01231-y
(84) Wheeler, K. I., Dietze, M. C., LeBauer, D., Peters, J. A., Richardson, A. D., Ross, A. A., Thomas, R. Q., Zhu, K., Bhat, U., Munch, S., Buzbee, R. F., Chen, M., Goldstein, B., Guo, J., Hao, D., Jones, C., Kelly-Fair, M., Liu, H., Malmborg, C., … Zachmann, L. (2024). Predicting spring phenology in deciduous broadleaf forests: NEON phenology forecasting community challenge. Agricultural and Forest Meteorology, 345, 109810. https://doi.org/10.1016/j.agrformet.2023.109810
(83) , , , , , , , , , , & (2024). Global impacts of vegetation clumping on regulating land surface heat fluxes. Agricultural and Forest Meteorology, 345, https://doi.org/10.1016/j.agrformet.2023.109820.
2023
(82) Zhou, J., Yang, Q., Liu, L., Kang, Y., Jia, X., Chen, M., Ghosh, R., Xu, S., Jiang, C., Guan, K., Kumar, V., & Jin, Z. (2023). A deep transfer learning framework for mapping high spatiotemporal resolution LAI. ISPRS Journal of Photogrammetry and Remote Sensing, 206, 30–48. https://doi.org/10.1016/j.isprsjprs.2023.10.017
(81) , , , , , & (2023). Uncertain spatial pattern of future land use and land cover change and its impacts on terrestrial carbon cycle over the Arctic–Boreal region of North America. Earth’s Future, 11, e2023EF003648. https://doi.org/10.1029/2023EF003648
(80) Zeng, Y., Hao, D., Park, T., Zhu, P., Huete, A., Myneni, R., Knyazikhin, Y., Qi, J., Nemani, R. R., Li, F., Huang, J., Gao, Y., Li, B., Ji, F., Köhler, P., Frankenberg, C., Berry, J. A., & Chen, M. Structural complexity biases vegetation greenness measures. Nature Ecology & Evolution (2023). https://doi.org/10.1038/s41559-023-02187-6
, , , , , , Bansal, S., Chang, K., Chen, M. et al. (2023). Upscaling wetland methane emissions from the FLUXNET-CH4 eddy covariance network (UpCH4 v1.0): Model development, network assessment, and budget comparison. AGU Advances, 4, e2023AV000956. https://doi.org/10.1029/2023AV000956
(78) Qiu, H., Zhang, X., Yang, A., Wickland, K. P., Stets, E. G. & Chen. M., Watershed carbon yield derived from gauge observations and river network connectivity in the United States. Sci Data 10, 278 (2023). https://doi.org/10.1038/s41597-023-02162-7
(77) Yang, J. X., You, Y., Blackwell, W., Da, C., Kalnay, E., Grassotti, C., Liu, Q. (Mark), Ferraro, R., Meng, H., Zou, C.-Z., Ho, S.-P., Yin, J., Petkovic, V., Hewison, T., Posselt, D., Gambacorta, A., Draper, D., Misra, S., Kroodsma, R., & Chen, M. (2023). SatERR: A Community Error Inventory for Satellite Microwave Observation Error Representation and Uncertainty Quantification. Bulletin of the American Meteorological Society, 1(aop). https://doi.org/10.1175/BAMS-D-22-0207.1
(76) Khan, Z., Thompson, I., Vernon, C.R., Graham, N.T., Wild, T.B., Chen, M.. Global monthly sectoral water use for 2010–2100 at 0.5° resolution across alternative futures. Scientific Data 10, 201 (2023). https://doi.org/10.1038/s41597-023-02086-2
(75) Li, F., Zhu, Q., Riley, W. J., Zhao, L., Xu, L., Yuan, K., Chen, M., Wu, H., Gui, Z., Gong, J., and Randerson, J. T. (2023) : AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics, Geosci. Model Dev., 16, 869–884, https://doi.org/10.5194/gmd-16-869-2023
(74) Yourek, M., Liu, M., Scarpare, F. V., Rajagopalan, K., Malek, K., Boll, J., Huang, M., Chen, M., & Adam, J. C. (2023). Downscaling global land-use/cover change scenarios for regional analysis of food, energy, and water subsystems. Frontiers in Environmental Science, 11. https://www.frontiersin.org/articles/10.3389/fenvs.2023.1055771
(73) Qiu, H., Hao, D., Zeng, Y., Zhang, X., & Chen, M. (2023). Global and northern-high-latitude net ecosystem production in the 21st century from CMIP6 experiments. Earth System Dynamics, 14(1), 1–16. https://doi.org/10.5194/esd-14-1-2023
2022
(72) , , , , , , , , , , & (2022). Vegetation clumping modulates global photosynthesis through adjusting canopy light environment. Global Change Biology, 00, 1– 16. https://doi.org/10.1111/gcb.16503
(71) Chen, M. (2022) Acceleration of vegetation phenological changes. Global Change Biology, 00, 1– 2. https://doi.org/10.1111/gcb.16430
(70) Yuan, K., Zhu, Q., Li, F., Riley, W. J., Torn, M., Chu, H., McNicol, G., Chen, M., Knox, S., Delwiche, K., Wu, H., Baldocchi, D., Ma, H., Desai, A. R., Chen, J., Sachs, T., Ueyama, M., Sonnentag, O., Helbig, M., … Jackson, R. (2022). Causality guided machine learning model on wetland CH4 emissions across global wetlands. Agricultural and Forest Meteorology, 324, 109115. https://doi.org/10.1016/j.agrformet.2022.109115
(69) Wu, G.; Guan, K.; Jiang, C.; Kimm, H.; Miao, G.; Bernacchi, C.J.; Moore, C.E., Ainsworth, E.A.; Yang, X.; Berry, J.A.; Frankenberg, C.; Chen, M. Attributing differences of solar-induced chlorophyll fluorescence (SIF)-gross primary production (GPP) relationships between two C4 crops: corn and miscanthus, Agricultural and Forest Meteorology, 2022, 323, 109046, ISSN 0168-1923, https://doi.org/10.1016/j.agrformet.2022.109046.
(68) Zeng, Y.; Hao, D.; Huete, A.; Dechant, B.; Berry, J.; Chen, J.M.; Joiner, J.; Frankenberg, C.; Bond-Lamberty, B.; Ryu, Y.; Xiao, J.; Asrar, G.; Chen, M. Optical vegetation indices for monitoring terrestrial ecosystems globally. Nature Reviews Earth & Environment. 2022, https://doi.org/10.1038/s43017-022-00298-5 Click to read
(67) Li, L.; Li, X.; Asrar, G.; Zhou, Y.; Chen, M.; Zeng, Y.; Li, X.; Li, F.; Luo, M.; Sapkota, A.; Hao, D. Detection and attribution of long-term and fine-scale changes in spring phenology over urban areas: A case study in New York State. International Journal of Applied Earth Observation and Geoinformation. 2022, 110, 102815. https://doi.org/10.1016/j.jag.2022.102815
(66) Hao, D.; Zeng, Y.; Zhang, Z.; Zhang, Y.; Qiu, H.; Biriukova, K.; Celesti, M.; Rossini, M.; Zhu, P.; Asrar, G. R.; Chen, M. Adjusting Solar-Induced Fluorescence to Nadir-Viewing Provides a Better Proxy for GPP. ISPRS J. Photogramm. Remote Sens. 2022, 186, 157–169. https://doi.org/10.1016/j.isprsjprs.2022.01.016.
, , , , , , & (2022). The diurnal dynamics of gross primary productivity using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite-R Series at an oak savanna ecosystem. Journal of Geophysical Research: Biogeosciences, 127, e2021JG006701. https://doi.org/10.1029/2021JG006701
(64) Zeng, Y.; Chen, M.; Hao, D.; Damm, A.; Badgley, G.; Rascher, U.; Johnson, J. E.; Dechant, B.; Siegmann, B.; Ryu, Y.; Qiu, H.; Krieger, V.; Panigada, C.; Celesti, M.; Miglietta, F.; Yang, X.; Berry, J. A. Combining Near-Infrared Radiance of Vegetation and Fluorescence Spectroscopy to Detect Effects of Abiotic Changes and Stresses. Remote Sens. Environ. 2022, 270, 112856. https://doi.org/10.1016/j.rse.2021.112856.
(63) Li, L.; Hao, D.; Li, X.; Chen, M.; Zhou, Y.; Jurgens, D.; Asrar, G.; Sapkota, A. Satellite-Based Phenology Products and in-Situ Pollen Dynamics: A Comparative Assessment. Environmental Research 2022, 204, 111937. https://doi.org/10.1016/j.envres.2021.111937.
2021
(62) Kimm, H.; Guan, K.; Jiang, C.; Miao, G.; Wu, G.; Suyker, A. E.; Ainsworth, E. A.; Bernacchi, C. J.; Montes, C. M.; Berry, J.; Yang, X.; Frankenberg, C.; Chen, M.; Koehler, P. A Physiological Signal Derived from Sun-Induced Chlorophyll Fluorescence Quantifies Crop Physiological Response to Environmental Stresses in the U.S. Corn Belt. Environ. Res. Lett. 2021. https://doi.org/10.1088/1748-9326/ac3b16.
(61) Cao, B.; Yu, L.; Li, X.; Chen, M.; Li, X.; Hao, P.; Gong, P. A 1-Km Global Cropland Dataset from 10000 BCE to 2100 CE. Earth System Science Data 2021, 13 (11), 5403–5421. https://doi.org/10.5194/essd-13-5403-2021.
(60) Qiu, H.; Qi, J.; Lee, S.; Moglen, G. E.; McCarty, G. W.; Chen, M.; Zhang, X. Effects of Temporal Resolution of River Routing on Hydrologic Modeling and Aquatic Ecosystem Health Assessment with the SWAT Model. Environmental Modelling & Software 2021, 146, 105232. https://doi.org/10.1016/j.envsoft.2021.105232.
(59) Zeng, Y.; Hao, D.; Badgley, G.; Damm, A.; Rascher, U.; Ryu, Y.; Johnson, J.; Krieger, V.; Wu, S.; Qiu, H.; Liu, Y.; Berry, J. A.; Chen, M. Estimating Near-Infrared Reflectance of Vegetation from Hyperspectral Data. Remote Sensing of Environment 2021, 267, 112723. https://doi.org/10.1016/j.rse.2021.112723.
(58) Wolf, J.; Chen, M.; Asrar, G. R. Global Rangeland Primary Production and Its Consumption by Livestock in 2000–2010. Remote Sensing 2021, 13 (17), 3430. https://doi.org/10.3390/rs13173430.
(57) Yang, X.; Xu, X.; Stovall, A.; Chen, M.; Lee, J.‐E. Recovery: Fast and Slow – vegetation response during the 2012‐2016 California Drought. Journal of Geophysical Research: Biogeosciences, 126, e2020JG005976. https://doi.org/10.1029/2020JG005976
(56) Wang, X.; Wang, C.; Wu, J.; Miao, G.; Chen, M.; Chen, S.; Wang, S.; Guo, Z.; Wang, Z.; Wang, B.; Li, J.; Zhao Y.; Wu, X.; Zhao, C.; Lin, W.; Zhang, Y.; Liu, L.. Intermediate aerosol loading enhances photosynthetic activity of croplands. Geophysical Research Letters, 48, e2020GL091893. https://doi.org/10.1029/2020GL091893
(55) Wang, B.; Wang, Z.; Wang, C.; Wang, X.; Li, J.; Jia, Z.; Li, P.; Wu, J.; Chen, M.; Liu, L. Field evidence reveals conservative water use of poplar saplings under high aerosol conditions. J Ecol. 2021; 00: 1– 13. https://doi.org/10.1111/1365‐2745.13633
(54) Hao, D.; Asrar, G. R.; Zeng, Y.; Yang, X.; Li, X.; Xiao, J.; Guan, K.; Wen, J.; Xiao, Q.; Berry, J. A.; Chen, M. Potential of hotspot solar-induced chlorophyll fluorescence for better tracking terrestrial photosynthesis. Global Change Biology. 2021, https://doi.org/10.1111/gcb.15554.
(53) Hao, D.; Zeng, Y.; Qiu, H.; Biriukova, K.; Celesti, M.; Migliavacca, M.; Rossini, M.; Asrar, G. R.; Chen, M. Practical Approaches for Normalizing Directional Solar-Induced Fluorescence to a Standard Viewing Geometry. Remote Sens. Environ. 2021, 255, 112171. https://doi.org/10.1016/j.rse.2020.112171.
(52) Yuan, K.; Zhu, Q.; Zheng, S.; Zhao, L.; Chen, M.; Riley, W. J.; Cai, X.; Ma, H.; Li, F.; Wu, H.; Chen, L. Deforestation Reshapes Land-Surface Energy-Flux Partitioning. Environ. Res. Lett. 2021. https://doi.org/10.1088/1748-9326/abd8f9.
(51) Liu, Y.; Chen, D.; Mouatadid, S.; Lu, X.; Chen, M.; Cheng, Y.; Xie, Z.; Jia, B.; Wu, H.; Gentine, P. Development of a Daily Multi-Layer Cropland Soil Moisture Dataset for China Using Machine Learning and Application to Cropping Patterns. J. Hydrometeorol. 2021, 22 (2). https://doi.org/10.1175/JHM-D-19-0301.1.
(50) Wu, S.; Zeng, Y.; Hao, D.; Liu, Q.; Li, J.; Chen, X.; Asrar, G. R.; Yin, G.; Wen, J.; Yang, B.; Zhu, P.; Chen, M. Quantifying Leaf Optical Properties with Spectral Invariants Theory. Remote Sens. Environ. 2021, 253, 112131. https://doi.org/10.1016/j.rse.2020.112131.
2020
(49) Chen, M.; Caldeira, K. Climate Change as an Incentive for Future Human Migration. Earth Syst. Dyn. 2020, 11 (4), 875–883. https://doi.org/10.5194/esd-11-875-2020.
(48) Chen, M.; Vernon, C. R.; Graham, N. T.; Hejazi, M.; Huang, M.; Cheng, Y.; Calvin, K. Global Land Use for 2015–2100 at 0.05° Resolution under Diverse Socioeconomic and Climate Scenarios. Sci. Data 2020, 7 (1), 320. https://doi.org/10.1038/s41597-020-00669-x.
(47) Cheng, Y.; Huang, M.; Chen, M.; Guan, K.; Bernacchi, C.; Peng, B.; Tan, Z. Parameterizing Perennial Bioenergy Crops in Version 5 of the Community Land Model Based on Site-Level Observations in the Central Midwestern United States. J. Adv. Model. Earth Syst. 2020, 12 (1), e2019MS001719. https://doi.org/10.1029/2019MS001719.
(46) Graham, N. T.; Hejazi, M. I.; Chen, M.; Davies, E. G. R.; Edmonds, J. A.; Kim, S. H.; Turner, S. W. D.; Li, X.; Vernon, C. R.; Calvin, K.; Miralles-Wilhelm, F.; Clarke, L.; Kyle, P.; Link, R.; Patel, P.; Snyder, A. C.; Wise, M. A. Humans Drive Future Water Scarcity Changes across All Shared Socioeconomic Pathways. Environ. Res. Lett. 2020, 15 (1), 014007. https://doi.org/10.1088/1748-9326/ab639b.
(45) Hao, D.; Asrar, G. R.; Zeng, Y.; Zhu, Q.; Wen, J.; Xiao, Q.; Chen, M. DSCOVR/EPIC-Derived Global Hourly and Daily Downward Shortwave and Photosynthetically Active Radiation Data at 0.1° × 0.1° Resolution. Earth Syst. Sci. Data 2020, 12 (3), 2209–2221. https://doi.org/10.5194/essd-12-2209-2020.
(44) Ito, A.; Reyer, C. P. O.; Gädeke, A.; Ciais, P.; Chang, J.; Chen, M.; François, L.; Forrest, M.; Hickler, T.; Ostberg, S.; Shi, H.; Thiery, W.; Tian, H. Pronounced and Unavoidable Impacts of Low-End Global Warming on Northern High-Latitude Land Ecosystems. Environ. Res. Lett. 2020, 15 (4), 044006. https://doi.org/10.1088/1748-9326/ab702b.
(43) Wang, J.; Yang, D.; Detto, M.; Nelson, B. W.; Chen, M.; Guan, K.; Wu, S.; Yan, Z.; Wu, J. Multi-Scale Integration of Satellite Remote Sensing Improves Characterization of Dry-Season Green-up in an Amazon Tropical Evergreen Forest. Remote Sens. Environ. 2020, 246, 111865. https://doi.org/10.1016/j.rse.2020.111865.
(42) Wang, C.; Guan, K.; Peng, B.; Chen, M.; Jiang, C.; Zeng, Y.; Wu, G.; Wang, S.; Wu, J.; Yang, X.; Frankenberg, C.; Köhler, P.; Berry, J.; Bernacchi, C.; Zhu, K.; Alden, C.; Miao, G. Satellite Footprint Data from OCO-2 and TROPOMI Reveal Significant Spatio-Temporal and Inter-Vegetation Type Variabilities of Solar-Induced Fluorescence Yield in the U.S. Midwest. Remote Sens. Environ. 2020, 241, 111728. https://doi.org/10.1016/j.rse.2020.111728.
(41) Weber, M.; Hao, D.; Asrar, G. R.; Zhou, Y.; Li, X.; Chen, M. Exploring the Use of DSCOVR/EPIC Satellite Observations to Monitor Vegetation Phenology. Remote Sens. 2020, 12 (15), 2384. https://doi.org/10.3390/rs12152384.
(40) Wu, G.; Guan, K.; Jiang, C.; Peng, B.; Kimm, H.; Chen, M.; Yang, X.; Wang, S.; Suyker, A. E.; Bernacchi, C. J.; Moore, C. E.; Zeng, Y.; Berry, J. A.; Cendrero-Mateo, M. P. Radiance-Based NIRv as a Proxy for GPP of Corn and Soybean. Environ. Res. Lett. 2020, 15 (3), 034009. https://doi.org/10.1088/1748-9326/ab65cc.
(39) Zeng, Y.; Badgley, G.; Chen, M.; Li, J.; Anderegg, L. D. L.; Kornfeld, A.; Liu, Q.; Xu, B.; Yang, B.; Yan, K.; Berry, J. A. A Radiative Transfer Model for Solar Induced Fluorescence Using Spectral Invariants Theory. Remote Sens. Environ. 2020, 240, 111678. https://doi.org/10.1016/j.rse.2020.111678.
(38) Zeng, Y.; Li, J.; Liu, Q.; Huete, A. R.; Xu, B.; Yin, G.; Fan, W.; Ouyang, Y.; Yan, K.; Hao, D.; Chen, M. A Radiative Transfer Model for Patchy Landscapes Based on Stochastic Radiative Transfer Theory. IEEE Trans. Geosci. Remote Sens. 2020, 58 (4), 2571–2589. https://doi.org/10.1109/TGRS.2019.2952377.
2019
(37) Bond-Lamberty, B.; Dorheim, K.; Cui, R.; Horowitz, R.; Snyder, A.; Calvin, K.; Feng, L.; Hoesly, R.; Horing, J.; Kyle, G. P.; Link, R.; Patel, P.; Roney, C.; Staniszewski, A.; Turner, S.; Chen, M.; Feijoo, F.; Hartin, C.; Hejazi, M.; Iyer, G.; Kim, S.; Liu, Y.; Lynch, C.; McJeon, H.; Smith, S.; Waldhoff, S.; Wise, M.; Clarke, L. Gcamdata: An R Package for Preparation, Synthesis, and Tracking of Input Data for the GCAM Integrated Human-Earth Systems Model. J. Open Res. Softw. 2019, 7 (1), 6. https://doi.org/10.5334/jors.232.
(36) Chen, M.; Vernon, C. R.; Huang, M.; Calvin, K. V.; Kraucunas, I. P. Calibration and Analysis of the Uncertainty in Downscaling Global Land Use and Land Cover Projections from GCAM Using Demeter (v1.0.0). Geosci. Model Dev. 2019, 12 (5), 1753–1764. https://doi.org/10.5194/gmd-12-1753-2019.
(35) Hao, D.; Asrar, G. R.; Zeng, Y.; Zhu, Q.; Wen, J.; Xiao, Q.; Chen, M. Estimating Hourly Land Surface Downward Shortwave and Photosynthetically Active Radiation from DSCOVR/EPIC Observations. Remote Sens. Environ. 2019, 232, 111320. https://doi.org/10.1016/j.rse.2019.111320.
(34) Huang, Z.; Hejazi, M.; Tang, Q.; Vernon, C. R.; Liu, Y.; Chen, M.; Calvin, K. Global Agricultural Green and Blue Water Consumption under Future Climate and Land Use Changes. J. Hydrol. 2019, 574, 242–256. https://doi.org/10.1016/j.jhydrol.2019.04.046.
(33) Zeng, Y.; Badgley, G.; Dechant, B.; Ryu, Y.; Chen, M.; Berry, J. A. A Practical Approach for Estimating the Escape Ratio of Near-Infrared Solar-Induced Chlorophyll Fluorescence. Remote Sens. Environ. 2019, 232, 111209. https://doi.org/10.1016/j.rse.2019.05.028.
2018
(32) Bond-Lamberty, B.; Bailey, V. L.; Chen, M.; Gough, C. M.; Vargas, R. Globally Rising Soil Heterotrophic Respiration over Recent Decades. Nature 2018, 560 (7716), 80–83. https://doi.org/10.1038/s41586-018-0358-x.
(31) Guan, K.; Good, S. P.; Caylor, K. K.; Medvigy, D.; Pan, M.; Wood, E. F.; Sato, H.; Biasutti, M.; Chen, M.; Ahlström, A.; Xu, X. Simulated Sensitivity of African Terrestrial Ecosystem Photosynthesis to Rainfall Frequency, Intensity, and Rainy Season Length. Environ. Res. Lett. 2018, 13 (2), 025013. https://doi.org/10.1088/1748-9326/aa9f30.
(30) Peng, B.; Guan, K.; Chen, M.; Lawrence, D. M.; Pokhrel, Y.; Suyker, A.; Arkebauer, T.; Lu, Y. Improving Maize Growth Processes in the Community Land Model: Implementation and Evaluation. Agric. For. Meteorol. 2018, 250–251, 64–89. https://doi.org/10.1016/j.agrformet.2017.11.012.
(29) Richardson, A. D.; Hufkens, K.; Milliman, T.; Aubrecht, D. M.; Chen, M.; Gray, J. M.; Johnston, M. R.; Keenan, T. F.; Klosterman, S. T.; Kosmala, M.; Melaas, E. K.; Friedl, M. A.; Frolking, S. Tracking Vegetation Phenology across Diverse North American Biomes Using PhenoCam Imagery. Sci. Data 2018, 5 (1), 180028. https://doi.org/10.1038/sdata.2018.28.
(28) Sihi, D.; Davidson, E. A.; Chen, M.; Savage, K. E.; Richardson, A. D.; Keenan, T. F.; Hollinger, D. Y. Merging a Mechanistic Enzymatic Model of Soil Heterotrophic Respiration into an Ecosystem Model in Two AmeriFlux Sites of Northeastern USA. Agric. For. Meteorol. 2018, 252, 155–166. https://doi.org/10.1016/j.agrformet.2018.01.026.
(27) Vernon, C. R.; Page, Y. L.; Chen, M.; Huang, M.; Calvin, K. V.; Kraucunas, I. P.; Braun, C. J. Demeter – A Land Use and Land Cover Change Disaggregation Model. J. Open Res. Softw. 2018, 6 (1), 15. https://doi.org/10.5334/jors.208.
(26) Wang, X.; Wu, J.; Chen, M.; Xu, X.; Wang, Z.; Wang, B.; Wang, C.; Piao, S.; Lin, W.; Miao, G.; Deng, M.; Qiao, C.; Wang, J.; Xu, S.; Liu, L. Field Evidences for the Positive Effects of Aerosols on Tree Growth. Glob. Change Biol. 2018, 24 (10), 4983–4992. https://doi.org/10.1111/gcb.14339.
2017
(25) Chen, M.; Rafique, R.; Asrar, G. R.; Bond-Lamberty, B.; Ciais, P.; Zhao, F.; Reyer, C. P. O.; Ostberg, S.; Chang, J.; Ito, A.; Yang, J.; Zeng, N.; Kalnay, E.; West, T.; Leng, G.; Francois, L.; Munhoven, G.; Henrot, A.; Tian, H.; Pan, S.; Nishina, K.; Viovy, N.; Morfopoulos, C.; Betts, R.; Schaphoff, S.; Steinkamp, J.; Hickler, T. Regional Contribution to Variability and Trends of Global Gross Primary Productivity. Environ. Res. Lett. 2017, 12 (10), 105005. https://doi.org/10.1088/1748-9326/aa8978.
(24) Lu, X.; Chen, M.; Liu, Y.; Miralles, D. G.; Wang, F. Enhanced Water Use Efficiency in Global Terrestrial Ecosystems under Increasing Aerosol Loadings. Agric. For. Meteorol. 2017, 237–238, 39–49. https://doi.org/10.1016/j.agrformet.2017.02.002.
(23) Wu, J.; Serbin, S. P.; Xu, X.; Albert, L. P.; Chen, M.; Meng, R.; Saleska, S. R.; Rogers, A. The Phenology of Leaf Quality and Its Within-Canopy Variation Is Essential for Accurate Modeling of Photosynthesis in Tropical Evergreen Forests. Glob. Change Biol. 2017, 23 (11), 4814–4827. https://doi.org/10.1111/gcb.13725.
2016 and before
(22) Carbone, M. S.; Richardson, A. D.; Chen, M.; Davidson, E. A.; Hughes, H.; Savage, K. E.; Hollinger, D. Y. Constrained Partitioning of Autotrophic and Heterotrophic Respiration Reduces Model Uncertainties of Forest Ecosystem Carbon Fluxes but Not Stocks. J. Geophys. Res. Biogeosciences 2016, 121 (9), 2476–2492. https://doi.org/10.1002/2016JG003386.
(21) Chen, M.; Melaas, E. K.; Gray, J. M.; Friedl, M. A.; Richardson, A. D. A New Seasonal-Deciduous Spring Phenology Submodel in the Community Land Model 4.5: Impacts on Carbon and Water Cycling under Future Climate Scenarios. Glob. Change Biol. 2016, 22 (11), 3675–3688. https://doi.org/10.1111/gcb.13326.
(20) Liu, S.; Chen, M.*; Zhuang, Q. Direct Radiative Effects of Tropospheric Aerosols on Changes of Global Surface Soil Moisture. Clim. Change 2016, 136 (2), 175–187. https://doi.org/10.1007/s10584-016-1611-7.
(19) Liu, S.; Zhuang, Q.; Chen, M.; Gu, L. Quantifying Spatially and Temporally Explicit CO2 Fertilization Effects on Global Terrestrial Ecosystem Carbon Dynamics. Ecosphere 2016, 7 (7), e01391. https://doi.org/10.1002/ecs2.1391.
(18) Jin, Z.; Zhuang, Q.; Dukes, J. S.; He, J.-S.; Sokolov, A. P.; Chen, M.; Zhang, T.; Luo, T. Temporal Variability in the Thermal Requirements for Vegetation Phenology on the Tibetan Plateau and Its Implications for Carbon Dynamics. Clim. Change 2016, 138 (3), 617–632. https://doi.org/10.1007/s10584-016-1736-8.
(17) Chen, M.; Zhuang, Q. Evaluating Aerosol Direct Radiative Effects on Global Terrestrial Ecosystem Carbon Dynamics from 2003 to 2010. Tellus B Chem. Phys. Meteorol. 2014, 66 (1), 21808. https://doi.org/10.3402/tellusb.v66.21808.
(16) Chen, M.; Zhuang, Q.; He, Y. An Efficient Method of Estimating Downward Solar Radiation Based on the MODIS Observations for the Use of Land Surface Modeling. Remote Sens. 2014, 6 (8), 7136–7157. https://doi.org/10.3390/rs6087136.
(15) Liu, S.; Chen, M.*; Zhuang, Q. Aerosol Effects on Global Land Surface Energy Fluxes during 2003–2010. Geophys. Res. Lett. 2014, 41 (22), 7875–7881. https://doi.org/10.1002/2014GL061640.
(14) Hayes, D. J.; Kicklighter, D. W.; McGuire, A. D.; Chen, M.; Zhuang, Q.; Yuan, F.; Melillo, J. M.; Wullschleger, S. D. The Impacts of Recent Permafrost Thaw on Land–Atmosphere Greenhouse Gas Exchange. Environ. Res. Lett. 2014, 9 (4), 045005. https://doi.org/10.1088/1748-9326/9/4/045005.
(13) He, Y.; Zhuang, Q.; McGuire, A. D.; Liu, Y.; Chen, M. Alternative Ways of Using Field-Based Estimates to Calibrate Ecosystem Models and Their Implications for Carbon Cycle Studies. J. Geophys. Res. Biogeosciences 2013, 118 (3), 983–993. https://doi.org/10.1002/jgrg.20080.
(12) Chen, M.; Zhuang, Q. Modelling Temperature Acclimation Effects on the Carbon Dynamics of Forest Ecosystems in the Conterminous United States. Tellus B Chem. Phys. Meteorol. 2013, 65 (1), 19156. https://doi.org/10.3402/tellusb.v65i0.19156.
(11) Zhuang, Q.; Qin, Z.; Chen, M. Biofuel, Land and Water: Maize, Switchgrass or Miscanthus ? Environ. Res. Lett. 2013, 8 (1), 015020. https://doi.org/10.1088/1748-9326/8/1/015020.
(10) Zhuang, Q.; Chen, M.; Xu, K.; Tang, J.; Saikawa, E.; Lu, Y.; Melillo, J. M.; Prinn, R. G.; McGuire, A. D. Response of Global Soil Consumption of Atmospheric Methane to Changes in Atmospheric Climate and Nitrogen Deposition. Glob. Biogeochem. Cycles 2013, 27 (3), 650–663. https://doi.org/10.1002/gbc.20057.
(9) Liu, Y.; Zhuang, Q.; Chen, M.; Pan, Z.; Tchebakova, N.; Sokolov, A.; Kicklighter, D.; Melillo, J.; Sirin, A.; Zhou, G.; He, Y.; Chen, J.; Bowling, L.; Miralles, D.; Parfenova, E. Response of Evapotranspiration and Water Availability to Changing Climate and Land Cover on the Mongolian Plateau during the 21st Century. Glob. Planet. Change 2013, 108, 85–99. https://doi.org/10.1016/j.gloplacha.2013.06.008.
(8) Chen, M.; Zhuang, Q. Spatially Explicit Parameterization of a Terrestrial Ecosystem Model and Its Application to the Quantification of Carbon Dynamics of Forest Ecosystems in the Conterminous United States. Earth Interact. 2012, 16 (5), 1–22. https://doi.org/10.1175/2012EI400.1.
(7) Qin, Z.; Zhuang, Q.; Chen, M. Impacts of Land Use Change Due to Biofuel Crops on Carbon Balance, Bioenergy Production, and Agricultural Yield, in the Conterminous United States. GCB Bioenergy 2012, 4 (3), 277–288. https://doi.org/10.1111/j.1757-1707.2011.01129.x.
(6) Zhuang, Q.; Lu, Y.; Chen, M. An Inventory of Global N2O Emissions from the Soils of Natural Terrestrial Ecosystems. Atmos. Environ. 2012, 47, 66–75. https://doi.org/10.1016/j.atmosenv.2011.11.036.
(5) Zhu, X.; Zhuang, Q.; Chen, M.; Sirin, A.; Melillo, J.; Kicklighter, D.; Sokolov, A.; Song, L. Rising Methane Emissions in Response to Climate Change in Northern Eurasia during the 21st Century. Environ. Res. Lett. 2011, 6 (4), 045211. https://doi.org/10.1088/1748-9326/6/4/045211.
(4) Chen, M.; Zhuang, Q.; Cook, D. R.; Coulter, R.; Pekour, M.; Scott, R. L.; Munger, J. W.; Bible, K. Quantification of Terrestrial Ecosystem Carbon Dynamics in the Conterminous United States Combining a Process-Based Biogeochemical Model and MODIS and AmeriFlux Data. Biogeosciences 2011, 8 (9), 2665–2688. https://doi.org/10.5194/bg-8-2665-2011.
(3) Mcguire, A. D.; Hayes, D. J.; Kicklighter, D. W.; Manizza, M.; Zhuang, Q.; Chen, M.; Follows, M. J.; Gurney, K. R.; Mcclelland, J. W.; Melillo, J. M.; Peterson, B. J.; Prinn, R. G. An Analysis of the Carbon Balance of the Arctic Basin from 1997 to 2006. Tellus B Chem. Phys. Meteorol. 2010, 62 (5), 455–474. https://doi.org/10.1111/j.1600-0889.2010.00497.x.
(2) Huang, H.; Chen, M.; Liu, Q.; Liu, Q.; Zhang, Y.; Zhao, L.; Qin, W. A Realistic Structure Model for Large-Scale Surface Leaving Radiance Simulation of Forest Canopy and Accuracy Assessment. Int. J. Remote Sens. 2009, 30 (20), 5421–5439. https://doi.org/10.1080/01431160903130911.
(1) Chen, M; Jiang, Y.; Xi, X; Liu, Z. Application of Kernel Independent Component Analysis in Image Processing. Application Research of Computers. 2008. doi:cnki:sun:jsyj.02008-01-097 (In Chinese with English Abstract)