Improvement of Drought Risk Index model using Ocean temperature change, vegetation health, and surface soil moisture in Thailand's agricultural areas

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment Pub Date : 2025-08-01 DOI:10.1016/j.rsase.2025.101723

Soravis Supavetch , Aphisit Phonchob , Woranut Chansury , Panu Nueangjumnong , Sirilux Noikeaing , Anuphao Aobpaet

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引用次数: 0

Abstract

Thailand, located in Southeast Asia, is among the countries most severely affected by climate variability, particularly droughts driven by the El Niño–Southern Oscillation (ENSO). Effective drought monitoring is crucial for agricultural management and disaster mitigation. This research aims to enhance the Drought Risk Index (DRI) model by integrating key parameters: the Ocean Niño Index (ONI) representing ENSO phases, the Vegetation Health Index (VHI), and Surface Soil Moisture (SSM) derived from SMAP satellite data. The study employed ONI data to categorise climatic conditions into higher and lower Pacific Ocean temperature periods, coupled with weekly VHI from the Suomi-NPP satellite and monthly averaged SSM data to recalibrate and improve the drought sensitivity of the existing DRI model. Results indicate that incorporating ENSO-related parameters significantly enhances the ability of the DRI to detect agricultural drought conditions, particularly during severe drought events, as observed in 2019. However, the short data series (2019–2022) poses limitations in long-term drought trend analysis and potential overfitting of model parameters. Nevertheless, the improved DRI model provides greater accuracy for drought monitoring and can effectively support decision-making processes for drought resilience and recovery in Thailand's agricultural sector.

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基于海洋温度变化、植被健康和表层土壤湿度的泰国农业区干旱风险指数模型改进

泰国位于东南亚，是受气候变率影响最严重的国家之一，特别是受厄尔Niño-Southern涛动（ENSO）驱动的干旱影响最严重。有效的干旱监测对农业管理和减灾至关重要。本研究旨在通过整合代表ENSO阶段的海洋Niño指数（ONI）、植被健康指数（VHI）和SMAP卫星数据获得的地表土壤湿度（SSM）等关键参数来增强干旱风险指数（DRI）模型。该研究利用ONI数据将气候条件划分为太平洋温度较高和较低的时期，并结合Suomi-NPP卫星的每周VHI和月平均SSM数据重新校准并改进了现有DRI模型的干旱敏感性。结果表明，纳入enso相关参数可显著提高DRI检测农业干旱状况的能力，特别是在2019年观测到的严重干旱事件期间。然而，短数据序列（2019-2022年）在长期干旱趋势分析和模型参数可能过拟合方面存在局限性。尽管如此，改进后的DRI模型为干旱监测提供了更高的准确性，并可以有效地支持泰国农业部门抗旱能力和恢复的决策过程。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Remote Sensing Applications-Society and Environment Multiple-

CiteScore

8.00

自引率

8.50%

发文量

204

审稿时长

65 days

期刊介绍： The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems