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Hongbin Liu

Researcher at Hong Kong University of Science and Technology

Publications -  388
Citations -  10841

Hongbin Liu is an academic researcher from Hong Kong University of Science and Technology. The author has contributed to research in topics: Environmental science & Biology. The author has an hindex of 48, co-authored 308 publications receiving 7735 citations. Previous affiliations of Hongbin Liu include Ocean University of China & University of Hawaii.

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Journal ArticleDOI

Environmental controlling mechanisms on bacterial abundance in the South China Sea inferred from generalized additive models (GAMs)

TL;DR: In this paper, Hong Kong University Grant Council through the Area of Excellence program [AoE/P-04/04], Hong Kong Research Grant Council General Research Fund [661809, 661610, 661911], TUYF Charitable Trust [TUYFC10SC08]
Book ChapterDOI

Water Quality in Irrigated Paddy Systems

Abstract: Irrigated paddy rice (Oryza sativa L.) is a staple food for roughly half of the world’s population. Concerns over water quality have arisen in recent decades, particularly in China, which is the largest rice-producing country in the world and has the most intensive use of nutrients and water in rice production. On the one hand, the poor water quality has constrained the use of water for irrigation to paddy systems in many areas of the world. On the other hand, nutrient losses from paddy production systems contribute to contamination and eutrophication of freshwater bodies. Here, we review rice production, water requirement, water quality issues, and management options to minimize nutrient losses from paddy systems. We conclude that management of nutrient source, rate, timing, and placement should be combined with the management of irrigation and drainage water to reduce nitrogen and phosphorus losses from paddies. More research is needed to identify cost-effective monitoring approaches and mitigation options, and relevant extension and policy should be enforced to achieve water quality goals. The review is preliminarily based on China’s scenario, but it would also provide valuable information for other rice-producing countries.
Journal ArticleDOI

Identification of Soil Texture Classes Under Vegetation Cover Based on Sentinel-2 Data With SVM and SHAP Techniques

TL;DR: In this paper , the authors investigated the usefulness of Sentinel-2 data for predicting soil texture class using an interpretable machine learning (ML) strategy, and proposed three support vector machines with different input parameters.
Journal ArticleDOI

Effects of biochar application with fertilizer on soil microbial biomass and greenhouse gas emissions in a peanut cropping system

TL;DR: In all, biochar can improve soil quality, and enhance soil carbon sequestration as well as peanut yields, and both the net global warming potential (GWP) and the greenhouse gas intensity (GHGI) were significantly decreased compared to that without biochar amendment.