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【目的】作物涝渍灾害是威胁全球粮食安全的主要农业气象灾害之一，其发生本质在于土壤水分过饱和引发根际氧浓度下降，导致根系有氧呼吸受阻、生理功能障碍，最终造成产量损失。迫切需要系统研究作物涝渍灾害的致灾机理、判定指标及监测预警技术。【方法】在致灾机理层面，明确了以降水为主导触发因子、地形与土壤属性决定空间分异、作物耐渍性调控灾损程度的致灾因子体系；在判定指标层面，将现有指标体系归纳为基于地下水位埋深的胁迫指数和基于土壤根层含水量的涝渍胁迫指数两类，分析了各自的理论基础与适用边界；在遥感监测层面，梳理了光学、热红外及微波遥感在涝渍灾害识别中的应用现状与局限性。【结果】研究表明，当前尚未形成成熟的作物涝渍灾害动态遥感监测指标体系，其根本制约在于：单一环境参数难以表征多因子协同胁迫的复杂性；现有指标未纳入作物生育期特异性响应及品种间耐渍性遗传差异；土壤温度对渍害临界阈值的动态调控作用及作物受渍后恢复能力尚未被量化；表层土壤水分与根层土壤含水量间的时空异质性导致遥感监测的物理基础薄弱；农田混合像元效应严重制约指数的空间代表性。【结论】基于上述分析，提出未来研究应重点围绕构建涝害与渍害融合的综合性辨识特征量、发展基于物理机制与机器学习相结合的根层土壤水分反演模型、建立涵盖生育期动态权重与品种特异性校正的多维指标体系，以及构建基于多源遥感数据融合的动态监测体系等方向展开，为作物涝渍灾害精细化监测预警提供理论依据与技术支撑。
Abstract：[Objective] Crop waterlogging disasters are among the major agrometeorological disasters threatening global food security. Their occurrence is primarily attributed to soil water supersaturation， which reduces rhizosphere oxygen concentration， thereby impeding aerobic respiration， disrupting root physiological functions， and ultimately resulting in yield losses. There is an urgent need to systematically investigate the disaster-inducing mechanisms， diagnostic indicators， and monitoring and early-warning technologies for crop waterlogging. [Methods] At the level of disaster-inducing mechanisms， a tripartite framework was identified in which precipitation served as the dominant triggering factor， topography and soil properties governed spatial differentiation， and crop waterlogging tolerance modulated the extent of damage. In terms of diagnostic indicators， existing indicator systems were categorized into two types: stress indices based on groundwater table depth and waterlogging stress indices based on root-zone soil moisture content， and their respective theoretical foundations and applicability ranges were analyzed. With respect to remote sensing-based monitoring， the current applications and limitations of optical， thermal infrared， and microwave remote sensing for identifying crop waterlogging disasters were reviewed. [Results] The results indicated that a mature dynamic remote sensing monitoring indicator system for crop waterlogging had not yet been developed. The fundamental constraints were as follows: (1) individual environmental parameters were inadequate for characterizing the complexity of multi-factor synergistic stress. (2) Existing indicators failed to incorporate growth-stage-specific responses and genetic differences in waterlogging tolerance among varieties. (3) The dynamic regulatory effects of soil temperature on critical thresholds for waterlogging damage and the post-waterlogging recovery capacity of crops had not been quantified. (4) The spatiotemporal heterogeneity between surface soil moisture and root-zone soil moisture content weakened the physical basis of remote sensing-based monitoring. (5) The mixed-pixel effect in agricultural landscapes severely compromised the spatial representativeness of the indices. [Conclusion] Based on these analyses， future research should focus on the following priority directions: constructing an integrated diagnostic signature that combines waterlogging and submergence stress; developing root-zone soil moisture retrieval models that integrate physical mechanisms with machine learning; establishing multidimensional indicator systems that incorporate growth-stage dynamic weights and variety-specific calibration; and building dynamic monitoring frameworks based on multi-source remote sensing data fusion. These efforts are expected to provide a theoretical foundation and technical support for fine-scale monitoring and early warning of crop waterlogging disasters.
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基本信息:
中图分类号:TP79;S422
引用信息:
[1]雷添杰,王嘉宝,童飞,等.多源遥感视角下作物涝渍灾害监测预警技术研究进展与展望[J].水利水电技术(中英文)().
基金信息:
国家重点研发计划项目（2023YFD2300300）
2026-08-31
2026-08-31
2026-08-31