Remote Sensing Indices for Drought Characterization in Northeast Thailand: Provisional Descriptive Reference Points and Implications for Drought Monitoring
- 1Research Unit of Geoinformatics for Spatial Management, Department of Geoinformatics, Faculty of Informatics, Mahasarakham University, Maha Sarakham 44150, Thailand
- 2Program in Animal Science, Faculty of Agricultural Technology, Sakon Nakhon Rajabhat University, Sakon Nakhon 47000, Thailand
- 3Department of Civil & Architectural Engineering & Mechanics, University of Arizona, 1209 E. Second St., Tucson, AZ 85721, USA
Abstract
Drought is a recurring agricultural hazard in Northeast Thailand’s floodplain environments, yet the actual values of remote sensing indices at confirmed drought locations remain poorly characterized. This study characterized six remote sensing indices (NDVI, VCI, SMI, NDMI, MNDWI, NSMI) at 541 agricultural drought-reporting locations in the Chi River Basin, Maha Sarakham Province, across three years representing different ENSO phases (La Niña 2020, El Niño 2023, neutral 2024). Drought-reporting frequency was classified based on village-level alert frequency: high frequency (six alerts, n = 100 villages) and low–moderate frequency (≤3 alerts, n = 441 villages). Sentinel-2 imagery was processed for the January–May dry season. Due to non-independence of observations (repeated measurements and spatial autocorrelation), analyses focused on descriptive statistics and effect sizes (Cohen’s d) rather than formal hypothesis testing. Results revealed remarkably small mean differences between frequency classes (0.008–0.024), with uniformly small effect sizes (Cohen’s d = 0.20–0.22). VCI and MNDWI showed negligible differences (Cohen’s d = 0.124 and 0.094, respectively). Index values at high-frequency locations showed stability across years (CV < 7% for all indices except NDMI), with limited year-to-year variation. SMI and NSMI were perfectly correlated (r = 1.00), indicating mathematical redundancy. Provisional descriptive reference points were derived from the three-year dataset (NDVI ≈ 0.21, VCI ≈ 0.52, SMI ≈ 0.41, MNDWI ≈ −0.33 at high-frequency locations), but these are descriptive summaries only and require validation with longer time series before they can be considered for operational use. These findings demonstrate that individual remote sensing indices have limited discriminatory power in this sandy soil floodplain environment, where local factors—soil properties, topography, and irrigation access—dominate over regional climate forcing. Five policy-relevant observations are proposed, including re-evaluation of threshold-based early warning systems and prioritized irrigation investments based on static vulnerability factors. This study contributes to SDG 2 (Zero Hunger), SDG 6 (Clean Water), and SDG 13 (Climate Action) through improved understanding of drought monitoring limitations in floodplain environments.
Related
- Multi-scale characterization of fault zones in the Groningen gas field: Implications for fault reactivation
- Thermal-state transition pathways and spatial differentiation of permafrost in Northeast China
- Methane bursts during spring thaw in the permafrost region of Northeast China
- Late Holocene paleoenvironmental and paleoclimatic changes recorded in a sediment core from Sidi Ali El Mekki lagoon (Northeast Tunisia)
- Loss of snow persistence during late season drives snow-season shortening in Northeast China under global warming
BibTeX
@article{prasertsri2026remote,
title = {{Remote Sensing Indices for Drought Characterization in Northeast Thailand: Provisional Descriptive Reference Points and Implications for Drought Monitoring}},
author = {Narueset Prasertsri and Patiwat Littidej and Benjamabhorn Pumhirunroj and D. C. Slack},
journal = {Sustainability},
year = {2026},
month = jul,
doi = {10.3390/su18147490},
url = {https://doi.org/10.3390/su18147490},
note = {CC BY},
}
RIS
TY - JOUR
TI - Remote Sensing Indices for Drought Characterization in Northeast Thailand: Provisional Descriptive Reference Points and Implications for Drought Monitoring
AU - Narueset Prasertsri
AU - Patiwat Littidej
AU - Benjamabhorn Pumhirunroj
AU - D. C. Slack
PY - 2026
DA - 2026/07/22
JO - Sustainability
DO - 10.3390/su18147490
UR - https://doi.org/10.3390/su18147490
AB - Drought is a recurring agricultural hazard in Northeast Thailand’s floodplain environments, yet the actual values of remote sensing indices at confirmed drought locations remain poorly characterized. This study characterized six remote sensing indices (NDVI, VCI, SMI, NDMI, MNDWI, NSMI) at 541 agricultural drought-reporting locations in the Chi River Basin, Maha Sarakham Province, across three years representing different ENSO phases (La Niña 2020, El Niño 2023, neutral 2024). Drought-reporting frequency was classified based on village-level alert frequency: high frequency (six alerts, n = 100 villages) and low–moderate frequency (≤3 alerts, n = 441 villages). Sentinel-2 imagery was processed for the January–May dry season. Due to non-independence of observations (repeated measurements and spatial autocorrelation), analyses focused on descriptive statistics and effect sizes (Cohen’s d) rather than formal hypothesis testing. Results revealed remarkably small mean differences between frequency classes (0.008–0.024), with uniformly small effect sizes (Cohen’s d = 0.20–0.22). VCI and MNDWI showed negligible differences (Cohen’s d = 0.124 and 0.094, respectively). Index values at high-frequency locations showed stability across years (CV < 7% for all indices except NDMI), with limited year-to-year variation. SMI and NSMI were perfectly correlated (r = 1.00), indicating mathematical redundancy. Provisional descriptive reference points were derived from the three-year dataset (NDVI ≈ 0.21, VCI ≈ 0.52, SMI ≈ 0.41, MNDWI ≈ −0.33 at high-frequency locations), but these are descriptive summaries only and require validation with longer time series before they can be considered for operational use. These findings demonstrate that individual remote sensing indices have limited discriminatory power in this sandy soil floodplain environment, where local factors—soil properties, topography, and irrigation access—dominate over regional climate forcing. Five policy-relevant observations are proposed, including re-evaluation of threshold-based early warning systems and prioritized irrigation investments based on static vulnerability factors. This study contributes to SDG 2 (Zero Hunger), SDG 6 (Clean Water), and SDG 13 (Climate Action) through improved understanding of drought monitoring limitations in floodplain environments.
SN - 2071-1050
ER -