在干旱的绿洲棉花田中,无人机对二次盐化的高光谱分析:FOD特征选择和SOA-RF特征选择的影响
Zeyuan Wang1,2,3, Jianli Ding1,2,3, Jiao Tan1,2,3
1College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China.
Frontiers in plant science
|March 5, 2024
概括
二次盐化阻碍了干旱地区的农业. 这项研究使用无人机超光谱遥感和先进的算法来准确监测土壤盐度,提高棉花产量和促进可持续农业.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 环境科学 环境科学
背景情况:
- 二次盐化是干旱地区的主要农业制约因素,由灌灌加剧.
- 在这些条件下,有效地在现场范围内监测土壤盐度是具有挑战性的.
研究的目的:
- 开发一种高精度,高效的无人机超谱遥感方法,用于监测土壤盐度.
- 用先进的特征提取和机器学习算法优化土壤盐度预测模型.
主要方法:
- 无人机的高光谱图像使用竞争性自适应重量化采样 (CARS),灰色水平共发生矩阵 (GLCM),Boruta和分数顺序差异化 (FOD) 的亮度颜色指数 (BCI) 进行处理.
- 开发了三种建模策略,整合了光谱,纹理和三频段指数.
- 使用海随机森林优化算法 (SOA-RF) 来预测土壤电导率 (EC) 和空间分布.
主要成果:
- 分数顺序差异化 (1.9 顺序最佳) 增强的光谱特征提取.
- 整合三带指数显著提高了模型准确性 (R2 = 0.9476).
- 与传统的随机森林相比,SOA-RF模型显示出更高的准确性和稳定性.
结论:
- 拟议的SOA-RF模型准确而非侵入性地监测用膜覆盖灌滴滴灌下土壤盐度.
- 这种方法对于改善棉花种植,提高产量和支持干旱地区可持续农业发展至关重要.
- 该研究为区域防治和控制土壤化提供了宝贵的参考资料.
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