Bio

As a postdoctoral scholar in Earth System Science at Stanford University with a Ph.D. in Agricultural Engineering and a minor in Machine Learning from the University of Wisconsin-Madison (UW-Madison), my research is dedicated to developing and applying precision sensing technologies and Geospatial AI to advance scalable Earth observation, environmental monitoring, and data-driven decision support. My research program develops sensing-to-decision frameworks that connect the physical and computational dimensions of the Earth system. I contribute across three tightly linked areas: (1) remote sensing strategies for fine-scale environmental and agroecosystem observation; (2) GeoAI methods that improve model generalization across space, sensors, and time; and (3) science applications that translate these methods into actionable insights on land management, climate resilience, and sustainability.

My research has resulted in 8 first-authored and 17 co-authored publications in leading journals, including Nature Sustainability and Remote Sensing of Environment. The impact of my work is reflected in 2 first-authored papers recognized as Web of Science Highly Cited Papers (Top 1%) and 1 first-authored paper designated as a Top Cited Paper in Remote Sensing of Environment (2025). Beyond academia, the real-world impact of my research is evident: my models have been adopted by USDA and Google X, demonstrating their practical value to both government and industry.

Besides, I have taught 3 courses, including one semester as the Lecturer of Record in Geography at UW-Madison. For service, I have served as reviewers for over 30 journals and convened agroecosystem- and AI-related sessions at the AGU and AAG meetings. In addition, I have actively secured internal and external funding, serving as PI or Co-PI on multiple awarded projects. These leadership and collaborative roles have allowed me to build enduring connections with top researchers from academic institutions and private sectors, extending my professional network beyond Stanford. More details are listed in my CV.

Honors & Awards

  • Early Career Scholars Award in Remote Sensing, American Association of Geographers (2026)
  • Remote Sensing of Environment Top Cited Article, Elsevier (2025)
  • Biological Systems Engineering Graduate Student of the Year, American Society of Agricultural and Biological Engineers (2023)
  • Woods Postdoctoral Fellows, Stanford University (2023)
  • Thomsen Wisconsin Distinguished Graduate Fellowship, UW-Madison (2022)
  • Lecturer Fellowship, UW-Madison (2021)

Stanford Advisors

Contact

  • Academic
    yuchima@stanford.edu
    University - Scholar Department: Department of Earth System Science Position: Postdoctoral Scholar

Additional Info

Links

All Publications

  • Reduced Crop Yield Stability Is More Likely to Be Associated With Heat Than With Moisture Extremes in the US Midwest EARTHS FUTURE Liu, W., Zhou, J., Luo, Y., Chen, S., Ma, Y. 2025; 13 (9)

    View details for DOI 10.1029/2024EF005172

    View details for Web of Science ID 001562442500001

  • The mixed effects of recent cover crop adoption on US cropland productivity NATURE SUSTAINABILITY Lobell, D. B., Di Tommaso, S., Zhou, Q., Ma, Y., Specht, J., Guan, K. 2025

    View details for DOI 10.1038/s41893-025-01599-5

    View details for Web of Science ID 001538711800001

  • Advancing Corn Yield Mapping in Kenya Through Transfer Learning REMOTE SENSING Bohra, A., Nottmeyer, S., Ren, C., Chen, S., Ma, Y. 2025; 17 (10)

    View details for DOI 10.3390/rs17101717

    View details for Web of Science ID 001496101900001

  • Learning county from pixels: corn yield prediction with attention-weighted multiple instance learning INTERNATIONAL JOURNAL OF REMOTE SENSING Wang, X., Ma, Y., Xu, Y., Huang, Q., Yang, Z., Zhang, Z. 2025

    View details for DOI 10.1080/01431161.2025.2459215

    View details for Web of Science ID 001420414300001

  • Subfield-level crop yield mapping without ground truth data: A scale transfer framework REMOTE SENSING OF ENVIRONMENT Ma, Y., Liang, S., Myers, D., Swatantran, A., Lobell, D. B. 2024; 315

    View details for DOI 10.1016/j.rse.2024.114427

    View details for Web of Science ID 001316677400001

  • Quantifying Global Wetland Methane Emissions With In Situ Methane Flux Data and Machine Learning Approaches. Earth's future Chen, S., Liu, L., Ma, Y., Zhuang, Q., Shurpali, N. J. 2024; 12 (11): e2023EF004330

    Abstract

    Wetland methane (CH4) emissions have a significant impact on the global climate system. However, the current estimation of wetland CH4 emissions at the global scale still has large uncertainties. Here we developed six distinct bottom-up machine learning (ML) models using in situ CH4 fluxes from both chamber measurements and the Fluxnet-CH4 network. To reduce uncertainties, we adopted a multi-model ensemble (MME) approach to estimate CH4 emissions. Precipitation, air temperature, soil properties, wetland types, and climate types are considered in developing the models. The MME is then extrapolated to the global scale to estimate CH4 emissions from 1979 to 2099. We found that the annual wetland CH4 emissions are 146.6 ± 12.2 Tg CH4 yr-1 (1 Tg = 1012 g) from 1979 to 2022. Future emissions will reach 165.8 ± 11.6, 185.6 ± 15.0, and 193.6 ± 17.2 Tg CH4 yr-1 in the last two decades of the 21st century under SSP126, SSP370, and SSP585 scenarios, respectively. Northern Europe and near-equatorial areas are the current emission hotspots. To further constrain the quantification uncertainty, research priorities should be directed to comprehensive CH4 measurements and better characterization of spatial dynamics of wetland areas. Our data-driven ML-based global wetland CH4 emission products for both the contemporary and the 21st century shall facilitate future global CH4 cycle studies.

    View details for DOI 10.1029/2023EF004330

    View details for PubMedID 39619149

    View details for PubMedCentralID PMC11607141

  • Unequal impact of climate warming on meat yields of global cattle farming COMMUNICATIONS EARTH & ENVIRONMENT Liu, W., Zhou, J., Ma, Y., Chen, S., Luo, Y. 2024; 5 (1)

    View details for DOI 10.1038/s43247-024-01232-x

    View details for Web of Science ID 001155907000001

  • Self-supervised pre-training for large-scale crop mapping using Sentinel-2 time series ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING Xu, Y., Ma, Y., Zhang, Z. 2024; 207: 312-325

    View details for DOI 10.1016/j.isprsjprs.2023.12.005

    View details for Web of Science ID 001153362500001

  • Transfer learning in environmental remote sensing REMOTE SENSING OF ENVIRONMENT Ma, Y., Chen, S., Ermon, S., Lobell, D. B. 2024; 301

    View details for DOI 10.1016/j.rse.2023.113924

    View details for Web of Science ID 001127637200001

https://orcid.org/0000-0002-0412-9851