Sheng Wang

Sheng Wang

Research Assistant Professor University of Illinois sheng12@illinois.edu shengwang12@gmail.com

I am a Research Assistant Professor at Department of Natural Resources and Environmental Sciences (NRES) and a Research Scientist at Institute for Sustainability, Energy, and Environment (ISEE), University of Illinois Urbana-Champaign (UIUC). My research focuses on utilizing hyperspectral, thermal, and multispectral data collected from manned/unmanned airborne systems, satellites, and proximal sensing to assess vegetation functional traits and ecosystem ecohydrological processes. My overarching research goal is to develop and apply novel sensing techniques with process-based models and data-driven algorithms for ecosystem monitoring and modeling to secure environmental sustainability.

In the past years, I have designed and tested operational manned/unmanned airborne-based monitoring systems to map high-resolution ecosystem functional traits, nutrients, productivity, water use, soil moisture, energy budget and water use efficiency. Along with sensing techniques, I also use process-based models, machine learning algorithms, and field measurements to advance the understanding of key ecohydrological processes across spatial and temporal scales in natural and managed ecosystems. Before joining UIUC, I received a Ph.D. degree in environmental engineering from Technical University of Denmark (DTU) in 2019.

Recruitment: We are recruiting highly motivated Ph.D. students and postdocs with full support in the coming year. If you are interested, please feel free to reach out to me by email.

Research Interest

Sustainability, Conservation Science, Agriculture, Ecohydrology, Manned/Unmanned Airborne Systems, Imaging Spectroscopy, Machine Learning, Ecosystem Modeling, Radiative Transfer modeling

Education

Professional Experience

  • 2021 - Present: Research Assitant Professor, Department of Natural Resources and Environmental Sciences, UIUC
  • 2021 - Present: Research Scientist, Institute for Sustainability, Energy, and Environment, UIUC
  • 2019 - 2020: Postdoc researcher, Institute for Sustainability, Energy, and Environment, UIUC

Selected Awards

  • 2020, Top Reviewer for Remote Sensing of Environment
  • 2019, Young Researcher Award, DTU
  • 2019, Outstanding Self-finance Student Abroad, Ministry of Education, China
  • 2018, European Union COST action travel grant to University of Twente
  • 2017, Otto Moensted travel grant, Denmark
  • 2016, European Union COST action travel grant to Spanish National Research Council
  • 2015, Graduation with honors, Beijing Municipality
  • 2015, Graduation with honors, CAS
  • 2015, Graduation with honors, Institute of Geographic Sciences and Natural Resources (IGSNRR), CAS
  • 2015, Dean’s scholarship, IGSNRR, CAS

Journal Reviewer

Proceedings of the National Academy of Sciences of the United States of America, Remote Sensing of Environment, Nature Scientific Report, Agricultural and Forest Meteorology, Journal of Experimental Botany, The Plant Journal, Biogeosciences, Precision Agriculture, Ecological Applications, European Journal of Agronomy, European Journal of Remote Sensing, Aerospace, Water Resource Research, Remote Sensing, Forests, Atmosphere, Journal of Hydrology, Journal of Hydrometeorology, Electronics, Applied Sciences, Sensors, Sustainability, International Journal of Digital Earth, Environmental Research Letter, Information Fusion, Earth System Science Data

Publications

* corresponding author

  1. Liu, L., Zhou, W., Guan, K., Peng, B., Xu, S., Tang, J., Zhu, Q., Till, J., Jia, X., Jiang, C., Wang, S., Qin, Z., Kong, H., Grant, R., Mezbahuddin, S., Kumar, V., and Jin, Z., 2023. Knowledge-based artificial intelligence significantly improved agroecosystem carbon cycle quantification. Nature Communications.
  2. Zheng, H., Zhang, C., Guan, K., Deng, Y., Wang., S., Rhoads, B., Margenot, A., Zhou, S., Wang S.*, 2023. Segment Any Stream: Scalable Water Extent Detection with the Segment Anything Model. Conference on Neural Information Processing Systems (NeurIPS) Computational Sustainability (CompSust).
  3. Zhou, Q., Wang, S.*, Guan, K.. Advancing airborne hyperspectral data processing and applications for sustainable agriculture using RTM-based machine learning. In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, pp. 1269-1272. IEEE, 2023. https://doi.org/10.1109/IGARSS52108.2023.10283455
  4. Potash, E., Guan, K., Margenot, A., Lee, D., Boe, A., Douglass, M, Heaton, E., Jang, C., Jin, V., Li, N., Mitchell, R., Namoi, N., Schmer, M., Wang, S., Zumpf, C., Multi-site Evaluation of Stratified and Balanced Sampling for Estimating Soil Organic Carbon Stocks in Agricultural Fields. Geoderma. https://doi.org/10.1016/j.geoderma.2023.116587
  5. Ye, L., Guan, K., Qin, Z., Wang, S., Zhou., W., Peng, B., Grant, R., Tang, J., Hu, T., Jin, Z. Improved quantification of cover crop biomass and ecosystem services through remote sensing based model-data fusion. Environmental Research Letter. https://doi.org/10.1088/1748-9326/ace4df
  6. Guan, K., Jin, Z., Peng, B., Tang, J., DeLucia, E.H., West, P., Jiang, C., Wang, S., Kim, T., Zhou, W. and Griffis, T., 2023. A scalable framework for quantifying field-level agricultural carbon outcomes. Earth-Science Reviews, p.104462. https://doi.org/10.1016/j.earscirev.2023.104462
  7. Wang, S.*, Guan K.*, Zhang, C., Jiang, C., Zhou, Q., Li, K., Qin, Z., Ainsworth, E.A., He, J., Wu, J., Schaefer, D., Gentry, L., Margenot, A., Herzberger, L., 2023. Airborne hyperspectral imaging of cover crops through radiative transfer process-guided machine learning. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2022.113386
  8. Zhou, Q., Wang, S.*, Liu, N., Townsend, P., Jiang, C., Peng, B., Verhoef, W., Guan, K., 2023. Towards operational atmospheric correction of airborne hyperspectral imaging spectroscopy: Algorithm evaluation, key parameter analysis, and machine learning emulators. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2022.11.016
  9. Wang, S.*, Guan, K.*, Zhang, C., Zhou, Q., Wang, S., Wu, X., Jiang, C., Peng, B., Mei, W., Li, K., Li, Z., Yang, Y., Zhou, W., Ma, Z. 2023. Cross-scale sensing of field-level crop residue fraction and tillage intensity: integrating field photos, airborne hyperspectral imaging, and satellite data. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2022.113366
  10. Feng, S., Qiu, J., Crow, W.T., Mo, X., Liu, S., Wang, S., Gao, L., Wang, X. and Chen, S., 2023. Improved estimation of vegetation water content and its impact on L-band soil moisture retrieval over cropland. Journal of Hydrology, p.129015. https://doi.org/10.1016/j.jhydrol.2022.129015
  11. Zhou, Q., Guan, K.*, Wang, S.*, Jiang, C., Huang, Y., Peng, B., Chen, Z., Wang, S., Hipple, J., Schaefer, D., Qin, Z., Stroebel, S., Coppess, J., Khanna, M., Cai, Y., 2022. Recent rapid increase of cover crop adoption across the U.S. Midwest detected by fusing multi-source satellite data. Geophysical Research Letter. https://doi.org/10.1029/2022GL100249
  12. Deines, J., Guan, K., Lopez, B., White, C., Wang, S., Lobell, D., 2022. Recent cover crop adoption is associated with small maize and soybean yield losses in the United States. Global Change Biology. https://doi.org/10.1111/gcb.16489
  13. Wu, G., Guan, K., Jiang, C., Kim, H., Wang, S., Bernacchi, C., Suyker, A., Yang, X., Magney, T., Frankenberg, C. 2022. Difference in seasonal peak timing of soybean SIF and GPP explained by canopy structure and chlorophyll content. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2022.113104
  14. Wu, J., Ainsworth, E.A., Wang, S., Guan, K., He. J., 2022. Distribution-Informed Neural Networks for Domain Adaptation Regression. The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022)
  15. Zhou, W., Guan, K., Peng, B., Margenot, A., Lee, D., Jin, Z., Grant, R., DeLucia, E., Qin, Z., Wander, M., Wang, S., 2022. How does uncertainty of soil organic carbon stock affect cropland carbon budgets and soil carbon credit calculation for the U.S. Midwest? Geoderma
  16. Qiu, J., Crow, W.T., Wang, S., Dong, J., Li, Y., Garcia, M. and Shangguan, W., 2022. Microwave-based soil moisture improves estimates of vegetation response to drought in China. Science of The Total Environment, p.157535. https://doi.org/10.1016/j.scitotenv.2022.157535
  17. Wang, S.*, Guan, K.*, Zhang, C., Lee, D., Margenot, A, J., Ge, Y., Peng, J., Zhou, W., Zhou, Q., and Huang, Y. 2022. Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2022.112914
  18. Potash, E., Guan, K., Margenot, A., Lee, D., Delucia, E., Wang, S., and Jang, C. 2022. How to estimate soil organic carbon stocks of agricultural fields? Perspectives using ex-ante evaluation. Geodema. https://doi.org/10.1016/j.geoderma.2021.115693
  19. Wu, J., Ainsworth, E.A., Wang, S., Guan, K., He. J., 2021. Adaptive Transfer Learning for Plant Phenotyping. MLCAS. https://arxiv.org/abs/2201.05261
  20. Wang, S*, Guan, K.*, Wang, Z., Ainsworth, E.A., Zheng, T., Townsend, P.A., Liu, N., Nafziger, E.D., Masters, M.D., Li, K., Wu, G., Jiang, C. 2021. Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling. International Journal of Applied Earth Observation and Geoinformation. https://doi.org/10.1016/j.jag.2021.102617
  21. Köppl, C.J., Malureanu, R., Dam-Hansen, C., Wang, S., Jin, H., Barchiesi, S., Sandí, J.M.S., Munoz-Carpena, R., Johnson, M., Durán-Quesada, A.M. and Bauer-Gottwein, P., 2021. Hyperspectral Reflectance Measurements from UAS under Intermittent Clouds: Correcting Irradiance Measurements for Sensor Tilt. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2021.112719
  22. Acharya, B.S., Bhandari, M., Bandini, F., Pizarro, A., Perks, M., Joshi, D.R., Wang, S., Dogwiler, T., Ray, R.L., Kharel, G. and Sharma, S. (2021). Unmanned Aerial Systems in Hydrology and Water Resource Management: Applications, Challenges and Perspectives. Water Resources Research. https://doi.org/10.1029/2021WR029925
  23. Li, K., Guan, K., Jiang, C., Wang, S., Peng, B. and Cai, Y., 2021. Evaluation of four new land surface temperature (LST) products in the US Corn Belt: ECOSTRESS, GOES-R, Landsat, and Sentinel-3. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 14, pp.9931-9945.
  24. Jiang, C., Guan, K., Wu, G., Peng, B. and Wang, S., 2021. A daily, 250 m and real-time gross primary productivity product (2000–present) covering the contiguous United States. Earth System Science Data, 13(2), pp.281-298. https://doi.org/10.5194/essd-2020-36.
  25. Wang, S.*, Guan, K.*, Wang, Z., Ainsworth, E.A., Zheng, T., Townsend, P.A., Li, K., Moller, C., Wu, G. and Jiang, C., 2021. Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy. Journal of Experimental Botany, 72(2), pp.341-354. https://doi.org/10.1093/jxb/eraa432.
  26. Wang, S.*, Garcia, M.*, Ibrom, A. and Bauer-Gottwein, P., 2020. Temporal interpolation of land surface fluxes derived from remote sensing–results with an unmanned aerial system. Hydrology and Earth System Sciences, 24(7), pp.3643-3661. https://doi.org/10.5194/hess-24-3643-2020.
  27. Wang, C., Guan, K., Peng, B., Chen, M., Jiang, C., Zeng, Y., Wu, G., Wang, S., Wu, J., Yang, X. and Frankenberg, C., 2020. Satellite footprint data from OCO-2 and TROPOMI reveal significant spatio-temporal and inter-vegetation type variabilities of solar-induced fluorescence yield in the US Midwest. Remote Sensing of Environment, 241, p.111728. https://doi.org/10.1016/j.rse.2020.111728.
  28. Wu, G., Guan, K., Jiang, C., Peng, B., Kimm, H., Chen, M., Yang, X., Wang, S., Suyker, A.E., Bernacchi, C.J. and Moore, C.E., 2020. Radiance-based NIRv as a proxy for GPP of corn and soybean. Environmental Research Letters, 15(3), p.034009. https://doi.org/10.1088/1748-9326/ab65cc..
  29. Wang, S.*, Garcia, M.*, Bauer-Gottwein, P., Jakobsen, J., Zarco-Tejada, P.J., Bandini, F., Paz, V.S. and Ibrom, A., 2019. High spatial resolution monitoring land surface energy, water and CO2 fluxes from an Unmanned Aerial System. Remote Sensing of Environment, 229, pp.14-31. https://doi.org/10.1016/j.rse.2019.03.040.
  30. Wang, S.*, Baum, A., Zarco-Tejada, P.J., Dam-Hansen, C., Thorseth, A., Bauer-Gottwein, P., Bandini, F. and Garcia, M.*, 2019. Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition. ISPRS Journal of Photogrammetry and Remote Sensing, 155, pp.58-71. https://doi.org/10.1016/j.isprsjprs.2019.06.017.
  31. Wang, S.*, Ibrom, A., Bauer-Gottwein, P. and Garcia, M., 2018. Incorporating diffuse radiation into a light use efficiency and evapotranspiration model: An 11-year study in a high latitude deciduous forest. Agricultural and Forest Meteorology, 248, pp.479-493. https://doi.org/10.1016/j.agrformet.2017.10.023.
  32. Wang, S.*, Garcia, M.*, Ibrom, A., Jakobsen, J., Josef Köppl, C., Mallick, K., Looms, M.C. and Bauer-Gottwein, P., 2018. Mapping root-zone soil moisture using a temperature–vegetation triangle approach with an unmanned aerial system: Incorporating surface roughness from structure from motion. Remote Sensing, 10(12), p.1978. https://doi.org/10.3390/rs10121978.
  33. Bandini, F., Olesen, D., Jakobsen, J., Kittel, C.M.M., Wang, S., Garcia, M. and Bauer-Gottwein, P., 2018. Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an unmanned aerial vehicle. Hydrology and Earth System Sciences, 22(8), pp.4165-4181. https://doi.org/10.5194/hess-22-4165-2018.
  34. Bandini, F., Lopez-Tamayo, A., Merediz-Alonso, G., Olesen, D., Jakobsen, J., Wang, S., Garcia, M. and Bauer-Gottwein, P., 2018. Unmanned aerial vehicle observations of water surface elevation and bathymetry in the cenotes and lagoons of the Yucatan Peninsula, Mexico. Hydrogeology Journal, 26(7), pp.2213-2228. https://doi.org/10.1007/s10040-018-1755-9.
  35. Liu, S., Ding, W., Mo, X., Wang, S., Liu, C., Luo, X., He, D., Bajracharya, S., Shrestha, A., Agrawal, N., 2017. Climate Change and Its Impact on Runoff in Lancang and Nujiang River Basins. Advances in Climate Change Research. 13(4): 356-365. https://doi.org/10.12006/j.issn.1673-1719.2016.212.
  36. Christian, K., Bandini, F., Wang, S., Garcia, M., Bauer-Gottwein, P., Applying drones for thermal detection of contaminated groundwater influx (Grindsted Å). Appendix in Anvendelse af drone til termisk kortlægning af forureningsudstrømning. Report of Drone System (Henrik Grosen, Sune Nielsen), edited by Miljøstyrelsen. 2016.
  37. Qiu, J., Gao, Q., Wang, S., and Su, Z., 2016. Comparison of temporal trends from multiple soil moisture data sets and precipitation: The implication of irrigation on regional soil moisture trend. International Journal of Applied Earth Observation and Geoinformation, 48, pp.17-27. https://doi.org/10.1016/j.jag.2015.11.012.
  38. Wang, S., Liu, S., Mo, X., Peng, B., Qiu, J., Li, M., Liu, C., Wang, Z. and Bauer-Gottwein, P., 2015. Evaluation of remotely sensed precipitation and its performance for streamflow simulations in basins of the southeast Tibetan Plateau. Journal of Hydrometeorology, 16(6), pp.2577-2594. https://doi.org/10.1175/JHM-D-14-0166.1.
  39. Mo, X., Liu, S., Lin, Z., Wang, S., and Hu, S., 2015. Trends in land surface evapotranspiration across China with remotely sensed NDVI and climatological data for 1981–2010. Hydrological Sciences Journal, 60(12), pp.2163-2177. https://doi.org/10.1080/02626667.2014.950579.
  40. Liu, S., Wang, S., Wang, Y., Li, M., Huang, M., Peng, G., Xiao, Z., 2014. Exploring the relationship between polar motion and runoff. Advances in Meteorological Science and Technology. 4(3):6-12. https://doi.org/10.3969/j.issn.2095-1973.2014.03.001.
  41. Wang, S. and LIU, S., 2013. Exploring the relationship between polar motion and a natural river’s runoff based on Granger causality. IAHS-AISH publication, 360, pp.131-138. https://dio.org/iahs.info.15559.25-131-138-360-22.

Updated on May 20, 2023