基于改进的CASA模型的作物净初级生产率估计方法的研究
Wanning Li1, Zhuo Wang1, Chunling Chen1
1College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China.
Frontiers in plant science
|November 19, 2025
概括
这项研究通过使用卫星数据和人工智能改进光合作用活性辐射 (FPAR) 分数计算来提高作物净初级生产率 (NPP) 估计. 这种精细的模型显著提高了农业监测和碳循环研究的准确性.
科学领域:
- 生态生态学 生态生态学
- 遥感 遥感 遥感 遥感
- 农业科学 农业科学
背景情况:
- 净初级生产率 (NPP) 对生态系统碳平衡和农业生产率评估至关重要.
- 准确地估计农业中的核能发电是个挑战,特别是在大型规模.
- 由于关键参数计算的局限性,现有的模型往往难以准确估计.
研究的目的:
- 改进CASA模型中的光合作用活性辐射 (FPAR) 分数估计,以提高核电站的准确性.
- 利用遥感数据开发一种用于大规模作物核电站监测的新方法.
- 提高核电站评估的可靠性,用于农业决策和碳循环研究.
主要方法:
- 利用了高分辨率的哨兵-2卫星图像.
- 采用递归特征消除算法来识别FPAR相关的植被指数.
- 使用卷积神经网络 (CNN) 估计的FPAR.
- 将改进的FPAR估计纳入结合农业与环境 (CASA) 模型.
主要成果:
- 实现了FPAR估计的显著降低 根平均平方误差 (RMSE) 从0.2040到0.0020.
- 证明FPAR的预测误差在0.0001到0.0092之间,平均绝对误差 (MAE) 低于0.01.
- 与现场数据相比,将核电站估计的平均绝对百分比误差 (MAPE) 从28.92%降至20.31%.
- 在测试样本中达到15%至25%的MAPE值,表明可靠性提高.
结论:
- 新的FPAR估计方法在CASA模型中显著提高了NPP准确性.
- 优化的模型显示了使用遥感进行大规模作物核电站监测的巨大潜力.
- 这种进步为农业管理和碳循环研究提供了强有力的支持.
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