What does this research mean for the field?

Satellite-based wildfire monitoring and prediction are transitioning from classical statistical methods toward multi-source data integration and spatiotemporal deep learning frameworks to better capture complex fire dynamics. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to review satellite-based wildfire indicators and spatiotemporal modeling techniques while identifying future research challenges.

July 26, 2026EarthOpen Access

A Comprehensive Survey of Satellite-Based Wildfire Indicators and Spatiotemporal Modeling Approaches: Past, Present, and Future

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Authors

SNSri Nurdiati MNMohamad Khoirun Najib EKElis Khatizah

Discussion

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Overview

Comprehensive survey reviews satellite-derived wildfire indicators and modeling approaches, highlighting future research directions.

Key Points

The aim is to review satellite-based wildfire indicators and spatiotemporal modeling techniques while identifying future research challenges.
Survey of wildfire datasets derived from MODIS, VIIRS, and other platforms
Synthesis of modeling approaches from statistical regression to deep learning architectures
Case study focused on the Indonesian region
Highlighting the transition to multi-source data integration for wildfire dynamics
Identification of key challenges such as uncertainty quantification in predictive frameworks
Advancement of scalable real-time wildfire intelligence systems

Cite This Study

Nurdiati et al. (2026) studied this question.

synapsesocial.com/papers/6a65a422d3aea3239cd76ebb https://doi.org/10.3390/earth7040121

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July 26, 2026EarthOpen Access

A Comprehensive Survey of Satellite-Based Wildfire Indicators and Spatiotemporal Modeling Approaches: Past, Present, and Future

View Full Paper

Ask AI

Bookmark

Authors

SNSri Nurdiati MNMohamad Khoirun Najib EKElis Khatizah

Discussion

Member takes

Overview

Comprehensive survey reviews satellite-derived wildfire indicators and modeling approaches, highlighting future research directions.

Key Points

The aim is to review satellite-based wildfire indicators and spatiotemporal modeling techniques while identifying future research challenges.
Survey of wildfire datasets derived from MODIS, VIIRS, and other platforms
Synthesis of modeling approaches from statistical regression to deep learning architectures
Case study focused on the Indonesian region
Highlighting the transition to multi-source data integration for wildfire dynamics
Identification of key challenges such as uncertainty quantification in predictive frameworks
Advancement of scalable real-time wildfire intelligence systems

Cite This Study

Nurdiati et al. (2026) studied this question.

synapsesocial.com/papers/6a65a422d3aea3239cd76ebb https://doi.org/10.3390/earth7040121

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