基于日光诱导叶绿素荧光估算湿地植被冠层蒸腾量的方法
By using a sunlight-induced chlorophyll fluorescence method, combined with a wetland canopy resistance model and a deep neural network, the wetland model was optimized, solving the problems of accuracy and applicability in wetland canopy transpiration estimation and achieving high-precision wetland transpiration monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 中国气象局沈阳大气环境研究所
- Filing Date
- 2025-11-24
- Publication Date
- 2026-07-17
AI Technical Summary
Existing wetland canopy transpiration estimation techniques suffer from a lack of physical basis in the models, poor universality, and inability to adapt to different wetland vegetation types or environmental stresses, resulting in low estimation accuracy and weak applicability.
A method based on sunlight-induced chlorophyll fluorescence was used, combined with a wetland canopy resistance model, a deep neural network model, and an isotope method/water balance method, to obtain the true values of canopy resistance and transpiration. Coupling factors were screened through a deep neural network to optimize the semi-mechanistic model, and a wetland model was constructed to output simulated transpiration values.
This method enables high-precision evapotranspiration estimation in wetland environments, reduces operating costs and spatial heterogeneity errors, and enhances the universality and accuracy of the method.
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Figure CN121562399B_ABST
Abstract
Citation Information
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