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"通过近距离传感估计土壤表面粗度,以在现场规模实施土壤侵蚀建模"

Giovanni Matranga¹, Francesco Palazzi¹, Antonio Leanza²

¹Institute of Sciences and Technologies for Sustainable Energy and Mobility (STEMS), National Research Council of Italy (CNR), 10135, Torino, Italy.

Environmental research

|October 2, 2023

概括

一种新的RGB-深度摄像头方法准确地测量了土壤表面粗度 (SSR) 和树冠覆盖 (CC). 这改进了MMF和RUSLE等土壤侵蚀模型,优于葡萄园土壤损失预测的传统方法.

科学领域:

土壤科学土壤科学
农业工程农业工程
遥感遥感遥感遥感

背景情况:

土壤表面粗度 (SSR) 显著影响水文过程和土壤侵蚀.
准确的SSR测量对于土壤侵蚀建模至关重要.
传统的SSR测量方法往往耗时且不准确.

研究的目的:

引入和验证一种新的RGB-深度摄像机技术,用于测量SSR和天花板覆盖 (CC).
评估空间显式SSR数据对土壤侵蚀模型性能的影响.
评估摄像机数据对土壤侵蚀因子计算的有用性.

主要方法:

使用RGB-深度摄像头生成高分辨率的数字海拔模型 (DEM) 用于SSR量化.
从摄像头图像中测量了天花板覆盖面 (CC).
在摩根-摩根-芬尼 (MMF) 模型中整合衍生SSR和CC指数,用于沉积物产量预测.
模型预测与意大利葡萄园的长期土壤侵蚀数据进行了比较.
在RUSLE模型中应用C因子的衍生值.

主要成果:

货币货币基金模型在预测土壤损失方面表现令人满意或良好,特别是在使用空间化SSR数据时.
与统一的SSR值相比,空间化SSR数据改善了模型性能,无论是耕地还是永久地面覆盖的地块.
来自相机的测量结果对于获得RUSLE C因子很有用,为表格值提供了替代方案.

结论:

RGB-深度摄像机技术为测量SSR和CC提供了可靠和高效的方法.
空间显式土壤粗度数据显著提高了土壤侵蚀模型的准确性.
这种新的方法为农业环境中的土壤侵蚀评估和管理提供了实际应用.

关键词:

侵蚀建模侵蚀建模货币货币基金组织 (MMF) 是一个MMF.在近距离感应.拉塞尔 (Russle) 是一个土壤侵蚀导致土壤侵蚀.土壤粗度土壤粗度

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