Yuchi Ma

All Publications

• 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

• 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