Abstract

Soil carbon (C) plays a key role in mitigating and adapting to global climate change. In-situ soil C measurement has faced many challenges including those related to aerial coverage, economics, accuracy, and availability. The concept of paying for C credits to farmers and ranchers who sequester C has necessitated availability of improved methods for in-situ measurement of soil C at large scale. The objective of this review is to i) synthesize the existing knowledge on methods of soil C measurement, (ii) discuss their pros and cons (iii) review key factors affecting soil C measurement, and (iv) propose integrated data driven method of soil C measurement using Machine Learning (ML)/Artificial Intelligence (AI) approach. Lab and in-situ techniques of soil C determination are expensive, time consuming and lack scale. Although, remote sensing (RS) technique is used to predict soil C maps at large scale, it also lacks accuracy and requires high technical knowledge of image processing. Soil C measurements are affected by key soil physical properties such as color, texture, moisture content, bulk density etc. Thus, these factors must be considered while developing innovative methods for soil C determination. A prototype handheld device is proposed to measure these four properties along with Near Infrared (NIR) reflectance of soil that store data in cloud using Wi-Fi signals. A data driven model is proposed that can use the data from handheld devices and integrate with drone imagery to create soil C map of the entire field and satellite imagery for the entire region. This model uses data from in-situ soil C measurement technique in integrated form and soil C map can be updated every time the handheld device is used at different locations of the field.

摘要

土壤碳 (C) 在减缓和适应全球气候变化方面发挥着关键作用。现场土壤碳测量面临许多挑战，包括与空中覆盖、经济性、准确性和可用性相关的挑战。向封存 C 的农民和牧场主支付 C 信用额度的概念需要有改进的方法来大规模现场测量土壤 C。本综述的目的是：i）综合现有的土壤碳测量方法知识，（ii）讨论它们的优缺点（iii）回顾影响土壤碳测量的关键因素，以及（iv）提出土壤数据驱动的综合方法使用机器学习 (ML)/人工智能 (AI) 方法进行 C 测量。实验室和现场土壤碳测定技术昂贵、耗时且缺乏规模。尽管遥感（RS）技术用于预测大尺度土壤碳图，但它也缺乏准确性，并且需要很高的图像处理技术知识。土壤碳测量受颜色、质地、水分含量、容重等关键土壤物理特性的影响。因此，在开发土壤碳测定的创新方法时必须考虑这些因素。提出了一种原型手持设备来测量这四个特性以及土壤的近红外 (NIR) 反射率，这些土壤使用 Wi-Fi 信号将数据存储在云中。提出了一种数据驱动模型，可以使用来自手持设备的数据并与无人机图像集成，以创建整个田地的土壤 C 地图和整个区域的卫星图像。每次在田间不同位置使用手持设备时，都可以更新集成形式的原位土壤 C 测量技术和土壤 C 图。