Sujono et al. (2026) Remote sensing-based early warning for agricultural flood damage mitigation in recurrent flood-prone areas

Identification

• Journal: Natural Hazards
• Year: 2026
• Date: 2026-03-26
• Authors: Imam Sujono, Robert Kurniawan, Prana Ugiana Gio, Rezzy Eko Caraka, Sri Kuswantono Wongsonadi
• DOI: 10.1007/s11069-026-08046-4

Research Groups

• Statistical Computing Department, Polytechnic Statistics STIS, Jakarta, Indonesia
• Department of Mathematics, Universitas Sumatera Utara, Medan, Indonesia
• Research Center for Data and Information Sciences, Research Organization for Electronics and Informatics, National Research and Innovation Agency (BRIN), Bandung, West Java, Indonesia
• School of Economics and Business, Telkom University, Bandung, West Java, Indonesia
• Department of Community Education, Faculty of Education, State University of Jakarta, Rawamangun, Jakarta, Indonesia

Short Summary

This study develops a remote sensing-based early warning system for agricultural flood damage mitigation in recurrent flood-prone areas, using Sentinel-1 SAR data and a localized change detection approach in Demak Regency, Indonesia, to identify high-risk zones and estimate potential crop losses.

Objective

• To identify and analyze agricultural areas repeatedly affected by flooding using Sentinel-1 SAR data and a sub-area-based change detection approach.
• To generate flood-related data (extent, dynamics, severity, epicenters, potential agricultural losses) to support the development of disaster risk reduction strategies in rural, agriculture-dependent regions.

Study Configuration

• Spatial Scale: Demak Regency, Central Java Province, Indonesia, covering 14 subdistricts and 249 villages, with a focus on agricultural areas.
• Temporal Scale: Analysis of flood events from December 2023 to April 2024 (5-month period), with reference images from June to July 2023.

Methodology and Data

• Models used:
  • Change Detection (CD) method with a localized thresholding technique (k=1.5).
  • Post-processing for flood maps: removal of permanent water, steep terrain (slopes > 5 degrees), and isolated pixels.
  • Non-parametric bootstrap resampling (1000 samples) for 95% confidence interval estimation of flood area.
  • Agricultural Potential Loss (APL) estimation based on flood area, rice productivity, and harvested rice grain price.
• Data sources:
  • Satellite: Sentinel-1 SAR (Interferometric Wide Swath (IW) mode, VV and VH polarization, 10 m spatial resolution).
  • Reference maps: FloodScan flood maps (Verisk Atmospheric and Environmental Research).
  • Ancillary data: WWF DEM (for slope mask), JRC Global Surface Water (GSW) data (for permanent water bodies), BIG (agricultural area map).
  • Platform: Google Earth Engine (GEE).
  • Socio-economic data: BPS-Statistics of Demak Regency (GRDP, rice harvest area, production, productivity), BPS-Statistics of Central Java Province (rice prices).
  • Precipitation data: MODIS satellite imagery.
  • Ground truth/reports: Official government reports, online news platforms, Regional Disaster Management Agency of Demak Regency.

Main Results

• The localized thresholding technique (k=1.5) for change detection achieved high accuracy, with Intersection over Union (IoU) values ranging from 80% to 86% across subdistricts, outperforming a global threshold.
• The most severe agricultural flooding occurred in March 2024, covering 9128.17 hectares, affecting nearly 50% of agricultural land in some subdistricts.
• Flood epicenters are primarily located along the main river, with high-risk zones concentrated in the eastern and central regions of Demak Regency.
• Weak river embankments and high precipitation (averaging 294.17 mm from November 2023 to April 2024) are significant contributing factors to flood frequency and severity.
• The estimated potential loss to rice production due to agricultural flooding in March 2024 was over IDR 390.50 billion, with a range of IDR 244.72 billion to IDR 468.60 billion considering price variability. This loss is equivalent to 6.22% of the 2023 Gross Regional Domestic Product (GRDP) of the agriculture, forestry, and fisheries sector in Demak Regency.
• A strong positive correlation (Pearson coefficient = 0.75) was found between flood severity and potential economic loss.

Contributions

• Introduces and validates a sub-area-based change detection approach with localized thresholding for enhanced precision in flood extent delineation in heterogeneous and extensive agricultural areas.
• Integrates statistical validation using bootstrapped confidence intervals to provide robust estimates of flood extent uncertainty.
• Generates detailed spatiotemporal flood data (extent, dynamics, severity, epicenters, frequency) and agricultural potential loss estimations at subdistrict and village levels, crucial for targeted disaster risk reduction strategies.
• Proposes context-specific flood mitigation strategies (reinforcing embankments, river normalization, and disaster relief assistance) directly informed by the remote sensing analysis, contributing to food security and climate adaptation efforts (SDGs 2, 11, 12, 13, 15).

Funding

• We received no financial support for the research.

Citation

@article{Sujono2026Remote,
  author = {Sujono, Imam and Kurniawan, Robert and Gio, Prana Ugiana and Caraka, Rezzy Eko and Wongsonadi, Sri Kuswantono},
  title = {Remote sensing-based early warning for agricultural flood damage mitigation in recurrent flood-prone areas},
  journal = {Natural Hazards},
  year = {2026},
  doi = {10.1007/s11069-026-08046-4},
  url = {https://doi.org/10.1007/s11069-026-08046-4}
}