Applications of Machine Learning and Remote Sensing in Soil and Water Conservation

Ye Inn Kim, Woo Hyeon Park, Yongchul Shin, Jin Woo Park, Bernie Engel, Young Jo Yun, Won Seok Jang

Research output: Contribution to journalReview articlepeer-review

2 Scopus citations

Abstract

The application of machine learning (ML) and remote sensing (RS) in soil and water conservation has become a powerful tool. As analytical tools continue to advance, the variety of ML algorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on the research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review of the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applications in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the fields of soil and water conservation.

Original languageEnglish
Article number183
JournalHydrology
Volume11
Issue number11
DOIs
StatePublished - Nov 2024

Keywords

  • data-driven decision-making
  • environmental analysis
  • machine learning
  • remote sensing
  • resource management
  • soil conservation
  • water conservation

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