通过将无人机纹理特征与新型三维纹理索引融合,估计新疆南部冬季小麦的土壤水分含量
Tao Sun1,2, Zhijun Li1,2, Zijun Tang1,2
1Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling 712100, China.
Plants (Basel, Switzerland)
|October 16, 2025
概括
本研究引入了一种新的无人机多谱方法,使用3D纹理指数准确估计干旱冬季小麦田的土壤水分含量 (SMC). 多源数据融合显著提高了SMC估计的准确性,有助于精确灌.
科学领域:
- 农业遥感 农业遥感
- 土壤科学 土壤科学
- 地理空间分析是什么
背景情况:
- 实时监测土壤水分含量 (SMC) 对冬季小麦产量安全至关重要,特别是在干旱地区.
- 目前用于SMC估计的遥感方法需要在干旱条件下进行改进.
研究的目的:
- 开发和验证无人机基于多光谱的土壤湿度估计方法,使用新的3D纹理指数用于干旱的耕地.
- 评估不同质地指数和机器学习模型的性能,以在不同土壤深度评估SMC.
主要方法:
- 在冬季小麦联合阶段采集高分辨率无人机多光谱图像.
- 提取和构建传统的,2D和3D纹理索引.
- 使用随机森林 (RF),PLSR和BPNN与单源和多源融合输入的土壤水分含量 (SMC) 模型的开发.
主要成果:
- 与射频相结合的3D纹理指数,特别是DTTI,在浅层SMC估计方面表现强 (R2 = 0.827).
- 多源数据融合 (纹理特征 + 2D + 3D索引) 与射频进一步改进了浅层SMC估计 (R2 = 0.890).
- 与传统的纹理特征相比,融合显著提高了所有土壤深度的准确性.
结论:
- 新的3D纹理指数有效地捕捉了树冠的空间结构,对浅层土壤水分动态敏感.
- 多源数据融合是一种可行的策略,可以大幅提高基于遥感的SMC估计的准确性.
- 拟议的方法为干旱的农业环境中精确灌和水分监测提供了一种实用方法.
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