Microwave and millimeter wave signals reflectance of soil carbon content

Agricultural and forestry biomass can be converted to biochar through
pyrolysis gasification, making it a significant carbon source for soil.
Applying biochar to soil is a carbon-negative process that helps combat
climate change, sustain soil biodiversity, and regulate water cycling.
However, quantifying soil carbon content conventionally is time-consuming,
labor-intensive, imprecise, and expensive, making it difficult to
accurately measure in-field soil carbon's effect on storage water and
nutrients. To address this challenge, for the first time, we report on
extensive lab tests demonstrating non-intrusive methods for sensing soil
carbon and related smart biochar applications, such as differentiating
between biochar types from various biomass feedstock species, monitoring
soil moisture, and biochar water retention capacity using portable
microwave and millimeter wave sensors and machine learning. The datasets
provide details on the microwave and millimeter wave reflectance signals.
We validated our quantification method using supervised machine learning
algorithms by collecting real soil mixed with known biochar contents in
the field.

农林业生物质可通过热解气化（pyrolysis gasification）转化为生物炭（biochar），成为土壤的重要碳源。将生物炭施用于土壤属于负碳过程，有助于应对气候变化、维持土壤生物多样性并调节水循环。然而，传统土壤碳含量定量分析方法耗时耗力、精度欠佳且成本高昂，难以精准测定田间土壤碳对储水与养分的影响。为解决这一难题，本研究首次通过大规模实验室测试证实，可采用非侵入式方法实现土壤碳传感及相关智能生物炭应用：例如区分不同生物质原料物种制备的生物炭类型、监测土壤湿度，以及结合便携式微波与毫米波传感器（portable microwave and millimeter wave sensors）与机器学习（machine learning）技术测定生物炭保水能力。本数据集包含微波与毫米波反射信号的详细信息。我们通过采集田间添加已知生物炭含量的真实土壤样本，结合监督机器学习算法（supervised machine learning algorithms）对所提出的定量方法进行了验证。