Designing a System for Digital Soil Assessments by Integrating Soil Sensor Technologies with Unmanned Aerial Systems

土壤计量学是土壤科学研究的一个新兴领域，它利用遥感、大数据分析和机器学习等现有技术来发现土壤变异的环境控制因素，并支持可持续的土壤管理。我的土壤景观分析与建模实验室是加拿大在该领域的领先实验室，我们的研究在国内和国际上都有影响。对土壤变异性和土壤安全的深刻理解与我们解决粮食安全、水安全、减缓气候变化和可持续资源管理问题的能力息息相关；43 .提供准确的土壤信息与8项联合国可持续发展目标（SDG）有关。因此，研究计划的长期愿景和目标是创新技术和应用数据驱动的方法，以确保使用计步法在多个空间尺度（如农场、区域和国家尺度）实现可持续的土壤管理。为实现可持续发展目标2：零饥饿，不断采用精准农业技术，以降低管理成本和农业对环境的影响。为了最大限度地发挥这些技术的潜力，需要对田间土壤变异性进行准确和精确的测量和建模，以开发预测性土壤图（psm），通过逐地优化农业投入（如肥料、水和种子）的效率，为可持续土壤管理实践和可变速率技术提供信息。农场规模的计步器研究通常集中在地面传感器上。然而，无人机（uav）的出现，加上传感器精度的提高、成本的降低和尺寸的减小，为彻底改变土壤变异的建模和监测提供了一个独特的机会。本项目的短期目标如下：1。整合、优化和评估无人机上的多个传感器，以便在野外尺度上预测土壤属性，并将其与地面传感器调查的有效性进行比较。2. 评估和比较地质统计学和机器学习技术，以便在对比种植制度和景观的情况下生成关键土壤健康指标的高分辨率psm。该项目将创新一个自动化传感器平台，使用安装在无人机系统上的高光谱、激光雷达和伽马辐射传感器，对农业试验区进行高分辨率调查。利用一系列机器学习方法，传感器平台将预测土壤健康指数的空间格局。这项研究将通过最小化农业投入成本和环境影响而产生经济和环境效益。加拿大人将受益于记录环境产品和服务的成本效益工具；此外，本研究提供了评估农民产生碳抵消的再生农业实践的手段。

Pedometrics is an emerging field of soil science research that leverages existing technologies in remote sensing, Big Data analytics, and machine-learning to discover the environmental controls on soil variability and support sustainable soil management. My Soil-Landscape Analysis & Modelling Laboratory is the leading Canadian lab in this field, and our research has had national and international impact. A strong understanding of soil variability and soil security is anchored to our ability to address food security, water security, climate change mitigation, and sustainable resource management;43 and the provision of accurate soil information is linked to eight UN Sustainable Development Goals (SDG). Hence, the long-term vision and objective of the research program is to innovate technologies and apply data-driven approaches to ensure sustainable soil management across multiple spatial scales (e.g. farm, regional, and national-scales) using pedometric approaches. To address SDG 2: Zero Hunger, precision agriculture technologies are continuously adopted to reduce management costs and the environmental impacts of agriculture. Maximizing the potential of these technologies requires accurate and precise measurement and modelling of field-scale soil variability to develop predictive soil maps (PSMs) that inform sustainable soil management practices and variable rate technologies by optimizing the efficiency of agricultural inputs (e.g. fertilizers, water, and seeds) on a location-by-location basis. Farm-scale, pedometrics research has typically focused on ground-based sensors. However, the emergence of unmanned aerial vehicles (UAVs), coupled with the increasing accuracy and decreasing cost and size of sensors, provides a unique opportunity to revolutionize the modelling and monitoring of soil variability. The short-term objectives of this project are as follows: 1. To integrate, optimize, and evaluate multiple sensors on a UAV in order to predict soil attributes at the field-scale and to compare its effectiveness against ground-based sensor surveys. 2. To evaluate and compare geostatistical and machine learning techniques for producing high-resolution PSMs of key soil health indicators over contrasting cropping systems and landscapes. This project will innovate an automated sensor platform using hyperspectral, LiDAR, and gamma radiometric sensors that are mounted on a UAV system to perform high-resolution surveys of agricultural test areas. Using a range of machine-learning approaches, the sensor platform will predict the spatial patterns of soil health indices. This research will have economic and environmental benefits by minimizing agricultural input costs and environmental impacts. Canadians will benefit from cost-effective tools that document environmental goods and services; furthermore, this research provides the means to assess regenerative agricultural practices that generate carbon offsets by farmers.