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Embedded physical constraints in machine learning to enhance vegetation phenology prediction

Created2025-02-04|Updated2025-02-04
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Author: ALTNT
Link: http://blog.705553939.xyz/2025/02/04/crop_classification/Crop%20classification/2024_PCNNs/
Copyright Notice: All articles in this blog are licensed under CC BY-NC-SA 4.0 unless stating additionally.
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Contents
  1. 1. 摘要
  2. 2. 一、引言
  3. 3. 2、Study area and datasets
    1. 3.1. 2.1 Study area
    2. 3.2. 2.2. Data acquisition
      1. 3.2.1. MOD09Q1数据集:
      2. 3.2.2. PhenoCam dataset
      3. 3.2.3. 美国国家物候网络(USA - NPN)数据集:
      4. 3.2.4. Daymet数据集:
  4. 4. 3、方法
    1. 4.1. 3.1 数据预处理
    2. 4.2. 3.2. 用于植被物候预测的脉冲耦合神经网络(PCNNs)模型
      1. 4.2.1. 3.2.1. 神经网络架构
      2. 4.2.2. 3.2.2. 物理约束架构
      3. 4.2.3. 3.2.3. 反馈机制架构
    3. 4.3. 3.3. 模型评估
    4. 4.4. 3.4. 趋势与关系分析
  5. 5. 4.结果
    1. 5.1. 4.1. 预测结果的空间模式
    2. 5.2. 4.2. 四种不同植被类型的物候
    3. 5.3. 4.3. 20 年来植被物候时间趋势比较
    4. 5.4. 4.4. 气象变量与植被物候之间的关系
  6. 6. 5.讨论
    1. 6.1. 5.1. 机器学习模型中的物理约束
    2. 6.2. 5.2. 植被物候中气象变量的选择
    3. 6.3. 5.3.优势和局限性
  7. 7. 6.结论
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