Authored over 150 research articles published in high-impact journals under Nature, AGU, and other leading academic publishers, and holds more than 60 national invention patents. Research outcomes have been widely recognized and featured by major media outlets including the Chinese Academy of Sciences, CCTV News, China Science Daily, China Daily, Science and Technology Daily, China News Service, and China.org.cn.

Eco-hydrological remote sensing;

Eco-hydrological carbon coupling and global change； 

Spatial big data and intelligent mining

Received six prestigious awards, including theDayu Water Science and Technology Awardand theEnvironmental Protection Science and Technology Award. The project titled"Remote Sensing-Based Assessment and Early Warning of Water Resources (Environmental) Carrying Capacity" (TZBX20043)has been designated as akey technology for nationwide promotion. In addition, more thanten policy briefsderived from this work have provided important support for decision-making by theChinese Academy of Sciences,national ministries, and other governmental agencies. 

[1] Bo Y, Li X, Liu K, Wang S*, Li D, Xu Y, Wang M. Hybrid theory‐guided data driven framework for calculating irrigation water use of three staple cereal crops in China. Water Resources Research. 2024 Mar;60(3):e2023WR035234.

[2] Liu K, Bo Y, Li X, Wang S*, Zhou G. Uncovering current and future variations of irrigation water use across China using machine learning. Earth's Future. 2024 Mar;12(3):e2023EF003562.

[3] Liu K, Li X, Wang S*, Zhou G. Past and future adverse response of terrestrial water storages to increased vegetation growth in drylands. npj Climate and Atmospheric Science. 2023 Aug 11;6(1):113.

[4] Liu K, Li X, Wang S*, Zhang X. Unrevealing past and future vegetation restoration on the Loess Plateau and its impact on terrestrial water storage. Journal of Hydrology. 2023 Feb 1;617:129021.

[5] Liu K, Li X, Wang S*, Zhang H. A robust gap-filling approach for European Space Agency Climate Change Initiative (ESA CCI) soil moisture integrating satellite observations, model-driven knowledge, and spatiotemporal machine learning. Hydrology and Earth System Sciences. 2023 Jan 30;27(2):577-98.