Sheng Wang

About Sheng Wang

Sheng Wang, With an exceptional h-index of 19 and a recent h-index of 18 (since 2020), a distinguished researcher at University of Illinois at Urbana-Champaign, specializes in the field of Sustainability, Climate-Smart Agriculture, Ecohydrology, Remote Sensing, Ecosystem Modeling.

His recent articles reflect a diverse array of research interests and contributions to the field:

Earlier spring greening in Northern Hemisphere terrestrial biomes enhanced net ecosystem productivity in summer

Quantifying Evapotranspiration and Gross Primary Productivity Across Europe Using Radiative Transfer Process-Guided Machine Learning

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems

Segment Any Stream: Scalable Water Extent Detection with the Segment Anything Model

Systems and methods for quantifying agroecosystem variables through multi-tier scaling from ground data, to mobile platforms, and to satellite observations

Multi-site evaluation of stratified and balanced sampling of soil organic carbon stocks in agricultural fields

Improved quantification of cover crop biomass and ecosystem services through remote sensing-based model–data fusion

Advancing Airborne Hyperspectral Data Processing and Applications for Sustainable Agriculture Using RTM-Based Machine Learning

Sheng Wang Information

University

Position

___

Citations(all)

1065

Citations(since 2020)

983

Cited By

284

hIndex(all)

19

hIndex(since 2020)

18

i10Index(all)

26

i10Index(since 2020)

26

Email

University Profile Page

Google Scholar

Sheng Wang Skills & Research Interests

Sustainability

Climate-Smart Agriculture

Ecohydrology

Remote Sensing

Ecosystem Modeling

Top articles of Sheng Wang

Earlier spring greening in Northern Hemisphere terrestrial biomes enhanced net ecosystem productivity in summer

Communications Earth & Environment

2024/3/11

Quantifying Evapotranspiration and Gross Primary Productivity Across Europe Using Radiative Transfer Process-Guided Machine Learning

2024/3/7

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems

Nature Communications

2024/1/8

Segment Any Stream: Scalable Water Extent Detection with the Segment Anything Model

2023/12/15

Systems and methods for quantifying agroecosystem variables through multi-tier scaling from ground data, to mobile platforms, and to satellite observations

2023/10/19

Multi-site evaluation of stratified and balanced sampling of soil organic carbon stocks in agricultural fields

Geoderma

2023/10/1

Improved quantification of cover crop biomass and ecosystem services through remote sensing-based model–data fusion

Environmental Research Letters

2023/8/14

Advancing Airborne Hyperspectral Data Processing and Applications for Sustainable Agriculture Using RTM-Based Machine Learning

2023/7/16

Knowledge-based Artificial Intelligence for Agroecosystem Carbon Budget and Crop Yield Estimation

Authorea Preprints

2023/7/3

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

2023/2/1

Improved estimation of vegetation water content and its impact on L-band soil moisture retrieval over cropland

Journal of Hydrology

2023/2/1

Airborne hyperspectral imaging of cover crops through radiative transfer process-guided machine learning

Remote Sensing of Environment

2023/2/1

Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Remote Sensing of Environment

2023/2/1

Recent cover crop adoption is associated with small maize and soybean yield losses in the United States

Global change biology

2023/2

How does uncertainty of soil organic carbon stock affect the calculation of carbon budgets and soil carbon credits for croplands in the US Midwest?

Geoderma

2023/1/1

Detecting tillage practices in the US Midwest using multi-source satellite data

AGU Fall Meeting Abstracts

2022/12

Distribution-Informed Neural Networks for Domain Adaptation Regression

2022/11/28

Recent rapid increase of cover crop adoption across the US Midwest detected by fusing multi‐source satellite data

Geophysical Research Letters

2022/11/28

Microwave-based soil moisture improves estimates of vegetation response to drought in China

Science of The Total Environment

2022/11/25

See List of Professors in Sheng Wang University(University of Illinois at Urbana-Champaign)

Co-Authors

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