Zhang et al. (2026) Drought monitoring based on remote sensing and multiple data fusion

Identification

• Journal: Elsevier eBooks
• Year: 2026
• Date: 2026-01-01
• Authors: Xiang Zhang, Xin Liu
• DOI: 10.1016/b978-0-443-38345-8.00010-7

Research Groups

National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan, P.R. China

Short Summary

This chapter emphasizes the critical need for effective agricultural drought monitoring indices, driven by the increasing frequency of extreme drought events and their severe impacts, advocating for quantitative assessments using multisource data.

Objective

• To establish effective agricultural drought monitoring indices to monitor agricultural drought.

Study Configuration

• Spatial Scale: Not specified in the provided text, but implies global relevance due to "global climate change" and "worldwide" impacts.
• Temporal Scale: Not specified in the provided text, but implies monitoring over crop growth periods and long-term trends.

Methodology and Data

• Models used: Mathematical models for constructing drought indices (specific models not detailed in the provided text).
• Data sources: Multisource data (meteorological, hydrological, agricultural variables).

Main Results

• Not applicable; the provided text is an introductory chapter, not a results section.

Contributions

• Emphasizes the critical need for and scientific shift towards quantitative agricultural drought monitoring using multisource data and advanced indices, particularly highlighting the role of remote sensing and data fusion.

Funding

• Not specified in the provided text.

Citation

@article{Zhang2026Drought,
  author = {Zhang, Xiang and Liu, Xin},
  title = {Drought monitoring based on remote sensing and multiple data fusion},
  journal = {Elsevier eBooks},
  year = {2026},
  doi = {10.1016/b978-0-443-38345-8.00010-7},
  url = {https://doi.org/10.1016/b978-0-443-38345-8.00010-7}
}