Data-Driven Soil Water Content Estimation at Multiple Depths Using SFCW GPR
Data-Driven Soil Water Content Estimation at Multiple Depths Using SFCW GPR
复制标题
DOI：
10.1109/orss58323.2023.10161940
复制
发表时间：
2023-04
期刊：
影响因子：
--
通讯作者：
Vincent Filardi;Allen Cheung;Ruba Khan;Oren Mangoubi;Majid Moradikia;S. Zekavat;B. Wilson;Radwin Askari;D. Petkie
中科院分区：
文献类型：
--
作者：
Vincent Filardi;Allen Cheung;Ruba Khan;Oren Mangoubi;Majid Moradikia;S. Zekavat;B. Wilson;Radwin Askari;D. Petkie
本文基于伍斯特理工学院（WPI）进行的广泛测量活动，利用探地雷达（GPR）和机器学习（ML），为多个根区深度的土壤含水量（SWC）估算提供了一种具有成本效益的解决方案。SWC特征对于最佳的工业化农业灌溉至关重要，进而影响水资源保护和缓解土壤质量退化。准确预测根区土壤的地下水位和SWC对精准农业是非常宝贵的。对不同地下深度的SWC进行高分辨率建模，可能会提高灌溉效率和玉米等作物的产量。玉米的水足迹高达7680亿立方米，估计占世界每日卡路里摄入量的5%。利用地下探针进行地下土壤表征的传统方法是侵入性的、昂贵的和劳动密集型的。我们的方法使用信号处理原理和ML，将其应用于2022年10月20日至30日收集的51个实际现场测量数据集，生成了多个根级深度壤土土壤含水量的准确和精确表征。我们将ML算法应用于步进频率连续波（SFCW） GPR信号采集的预处理数据，并提取了与多个深度的SWC预测相关的最相关特征。我们使用这些提取的特征，在我们的现场数据的四个根区深度上实现了低至6%的平均绝对百分比误差。本研究在0.4至2.0 GHz频率范围内进行，并提供了对根区SWC表征关键频率的分析。
This paper provides a cost-effective solution to Soil Water Content (SWC) estimation at multiple root-zone depths using Ground Penetrating Radar (GPR) and Machine Learning (ML) based on an extensive measurement campaign conducted at Worcester Polytechnic Institute (WPI). SWC characterization is critical for optimal industrial farming irrigation and, in turn, impacts water conservation and the mitigation of soil quality degradation. Accurate prediction of the water table and SWC of the root-zone soil is invaluable for precision farming. High-resolution modeling of SWC at varying sub-surface depths can potentially increase irrigation efficiency and the yield of crops such as maize, which has a massive water footprint upwards of 768 billion cubic meters and accounts for an estimated 5% percent of the world’s daily calorie intake. Traditional methods of subsurface soil characterization by subsurface probes are invasive, costly, and labor-intensive. Our approach generates an accurate and precise characterization of the soil water content of loamy soil at multiple root level depths using Signal Processing principles and ML applied to a small dataset of size 51 of real field measurements collected between October 20th to 30th 2022. We applied ML algorithms to the preprocessed data collected by a Stepped Frequency Continuous Wave (SFCW) GPR signal and extracted the most relevant features related to SWC prediction at multiple depths. We used these extracted features to achieve a mean absolute percentage error as low as 6% across the four root-zone depths of our field data. This study was conducted within the 0.4 to 2.0 GHz frequency range, and provides an analysis of frequencies key to root-zone SWC characterization.