Transfer learning in environmental remote sensing

Y Ma, S Chen, S Ermon, DB Lobell - Remote Sensing of Environment, 2024 - Elsevier
Abstract Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …

Toward impact-based monitoring of drought and its cascading hazards

A AghaKouchak, LS Huning, M Sadegh, Y Qin… - Nature Reviews Earth & …, 2023 - nature.com
Growth in satellite observations and modelling capabilities has transformed drought
monitoring, offering near-real-time information. However, current monitoring efforts focus on …

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

L Liu, W Zhou, K Guan, B Peng, S Xu, J Tang… - Nature …, 2024 - nature.com
Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-
relevant scales is critical to mitigating climate change and ensuring sustainable food …

Applying knowledge-guided machine learning to slope stability prediction

T Pei, T Qiu, C Shen - Journal of Geotechnical and …, 2023 - ascelibrary.org
Slope stability prediction is an important task in geotechnical engineering which can be
achieved through physics-based or data-driven approaches. Physics-based approaches …

Knowledge-guided machine learning: Current trends and future prospects

A Karpatne, X Jia, V Kumar - ar** high spatiotemporal resolution LAI
J Zhou, Q Yang, L Liu, Y Kang, X Jia, M Chen… - ISPRS Journal of …, 2023 - Elsevier
Leaf area index (LAI) is an important variable for characterizing vegetation structure.
Contemporary satellite-based LAI products with moderate spatial resolution, such as those …

Using knowledge-guided machine learning to assess patterns of areal change in waterbodies across the contiguous united states

HL Wander, MJ Farruggia, S La Fuente… - Environmental …, 2024 - ACS Publications
Lake and reservoir surface areas are an important proxy for freshwater availability.
Advancements in machine learning (ML) techniques and increased accessibility of remote …

Enhancing spectroscopy-based fruit quality control: A knowledge-guided machine learning approach to reduce model uncertainty

J Yang, Z Sun, S Tian, H Jiang, J Feng, KC Ting… - Postharvest Biology and …, 2024 - Elsevier
Spectroscopy-based techniques have made remarkable advancements in their application
to fruit quality control but encounter challenges of high model uncertainty arising from …

Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision

Q Yang, L Liu, J Zhou, M Rogers, Z ** - Computers and Electronics in …, 2024 - Elsevier
Monitoring and modeling the growth of strawberries at the individual fruit level can open up
new opportunities for yield prediction, fruit grading and supply chain optimization. However …