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Abstract Machine learning (ML) has been broadly applied for vadose zone applications in recent years. This article provides a comprehensive review of such developments. ML applications for variables corresponding to different complex vadose zone processes are summarized mostly in a prediction context. By analyzing and assessing these applications, we discovered extensive usages of classic ML models with relatively limited applications of deep learning (DL) approaches in general. We also recognized a lack of benchmark datasets for soil property research as well as limited integration of physics‐based vadose zone principles into the ML approaches. To facilitate this interdisciplinary research of ML in vadose zone characterization and processes, a paradigm of knowledge‐guided machine learning is suggested along with other data‐driven and ML model‐based research suggestions to advance future research.
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Bringing automated, remote‐sensed, machine learning methods to monitoring crop landscapes at scale
Abstract This article provides an overview of how recent advances in machine learning and the availability of data from earth observing satellites can dramatically improve our ability to automatically map croplands over long periods and over large regions. It discusses three applications in the domain of crop monitoring where machine learning (ML) approaches are beginning to show great promise. For each application, it highlights machine learning challenges, proposed approaches, and recent results. The article concludes with discussion of major challenges that need to be addressed before ML approaches will reach their full potential for this problem of great societal relevance.
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- Award ID(s):
- 1838159
- PAR ID:
- 10244780
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Agricultural Economics
- Volume:
- 50
- Issue:
- S1
- ISSN:
- 0169-5150
- Page Range / eLocation ID:
- p. 41-50
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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