DOI: 10.6071/m3m092
title: Microwave and millimeter wave signals reflectance of soil carbon content

description: 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.
description: The microwave sensor operated at a sampling frequency of 10.24
 GHz, and each sample was measured 10 times to mitigate random error.
 Similarly, the millimeter-wave sensor, functioning within the 62 GHz to 69
 GHz frequency range, replicated the microwave sensor's sampling
 instances to diminish error probabilities. We established 150 sampling
 points within the specified frequency band, setting the resolution
 bandwidth (RBW) at 100 kHz to balance sweep time efficiency and noise
 reduction. Additionally, samples were consistently positioned less than 2
 centimeters beneath the direct millimeter-wave radar sensor for
 measurement consistency. Soil samples were obtained
 from almond orchards and carefully filtered to remove large particles like
 stones. The biochar used was derived from a variety of sources, including
 100% orchard prunings, walnut shells, pure wood, almond shells, and
 pinewood, designated as samples 1 through 5, respectively. In subsequent
 experiments, the soil was evenly spread to a weight of 50 grams, and the
 biochar was allocated at 2 grams, constituting 20% of the soil's
 weight. The materials were thoroughly combined prior to testing. For the
 millimeter-wave experimental setup, we prepared new soil and biochar
 mixtures with variable ratios ranging from 0% to 100% biochar
 content. Additionally, the microwave data encapsulated
 a total of 8192 attributes per signal, whereas the millimeter-wave data
 comprised 1024 attributes.
description: Please use Matlab or Numpy to load mat files. 
description: # Microwave and Millimeter wave signals reflectance of Soil Carbon Content
 ## Reference Information Authors: Di An, YangQuan Chen Data Created:
 04/10/2023 Data Modified: 05/08/2023 Suggested Citations: Dataset: An, Di;
 Chen, YangQuan (2023), Microwave and millimeter wave signals reflectance
 of soil carbon content, Dryad, Dataset, https://doi.org/10.6071/M3M092
 Corresponding publication: An, D., & Chen, Y. (2023). Non-intrusive
 soil carbon content quantification methods using machine learning
 algorithms: A comparison of microwave and millimeter wave radar sensors.
 Journal of Automation and Intelligence, 2(3), 152-166. ## Data and File
 Overview There are a total of 10 datasets: 5 for microwave and 5 for
 millimeterwave, respectively. The name meaning as follows:
 "amount_biochar_microwave",
 "final_soilContent_low_millimeterwave" = Different Soil Carbon
 Amount Recognition "type_biochar_microwave",
 "final_biochar_low_millimeterwave" = Different Types of Soil
 Carbon Classification "soil_moisture_microwave",
 "final_moisture_low_millimeterwave" = Soil Moisture Study and
 Comparison "Mos_C_microwave",
 "final_moistureCC_low_millimeterwave" = Soil Moisture Influenced
 by Carbon Content Added "multiple_containers_microwave",
 "millimeterwave_mul_containers" = Sensing Robustness for Soil
 Carbon Content Percentage ### Setup * Unpacking instructions: n/a *
 Relationships between files/folders: they are in different experiments
 sections * Recommended software/tools: &gt; Matlab 2020 # File/Folder
 Details-Microwave ## Details for: amount\_biochar\_microwave.mat *
 Description: This is for classifying different amount of biochar mixed
 with soil using microwave sensor. * Format(s): .mat * Size(s): 6,149 KB *
 Dimensions: * Data: 50 rows x 8192 cols * label: 50 rows x 1 cols *
 Variables: * amount_biochar: * type: table * description: combined
 sampling data with label together. Can be used in Classifier App directly.
 * d_2: raw data * y: Total 5 classes, each label has 10 sampling,
 therefore totally it has 50 labels. ## Details for:
 type\_biochar\_microwave.mat * Description: This is for classifying
 different types of biochar using microwave sensor. * Format(s): .mat *
 Size(s): 6,140 KB * Dimensions: * Data: 50 rows x 8192 cols * label: 50
 rows x 1 cols * Variables: * type_biochar: * type: table * description:
 combined sampling data with label together. Can be used in Classifier App
 directly. * d_2: raw data * y: Total 5 classes, each label has 10
 sampling, therefore totally it has 50 labels. ## Details for:
 Mos\_C\_microwave.mat * Description: This is for classifying different
 soil moisture affected by carbon content added * Format(s): .mat *
 Size(s): 6,150 KB * Dimensions: * Data: 50 rows x 8192 cols * label: 50
 rows x 1 cols * Variables: * mos_carbon: * type: table * description:
 combined sampling data with label together. Can be used in Classifier App
 directly. * d_2: raw data * y: Total 5 classes, each label has 10
 sampling, therefore totally it has 50 labels. ## Details for:
 multiple\_contains\_microwave.mat * Description: This is for investigating
 robustness sensing for soil carbon content percentage * Format(s): .mat *
 Size(s): 61,322 KB * Dimensions: * Data: 500 rows x 8192 cols * label: 500
 rows x 1 cols * Variables: * mulitle_container: * type: table *
 description: combined sampling data with label together. Can be used in
 Classifier App directly. * d_2: raw data * y: Total 5 classes, each label
 has 10 sampling, therefore totally it has 50 labels. ## Details for:
 soil\_moisture\_microwave.mat * Description: This is for classifying
 different soil moisture levels using microwave sensor. * Format(s): .mat *
 Size(s): 6,148 KB * Dimensions: * Data: 50 rows x 8192 cols * label: 50
 rows x 1 cols * Variables: * moisture_soil: * type: table * description:
 combined sampling data with label together. Can be used in Classifier App
 directly. * d_2: raw data * y: Total 5 classes, each label has 10
 sampling, therefore totally it has 50 labels. # File/Folder
 Details-Millimeter wave ## Details for:
 final\_soilContent\_low\_millimeterwave.mat * Description: This is for
 classifying different amount of biochar mixed with soil using millimeter
 wave sensor. * Format(s): .mat * Size(s): 2,134 KB * Dimensions: * Data:
 140 rows x 1024 cols * label: 140 rows x 1 cols * Variables: *
 soilCarbon_low: * type: table * description: combined sampling data with
 label together. Can be used in Classifier App directly. *
 new_soilContent_low: raw data * y: Total 7 classes, each label has 20
 sampling, therefore totally it has 140 labels. ## Details for:
 final\_biochar\_low\_millimeterwave.mat * Description: This is for
 classifying different types of biochar using millimeter wave sensor. *
 Format(s): .mat * Size(s): 1,857 KB * Dimensions: * Data: 120 rows x 1024
 cols * label: 120 rows x 1 cols * Variables: * newBiochar: * type: table *
 description: combined sampling data with label together. Can be used in
 Classifier App directly. * new_biochar_low: raw data * y: Total 6 classes,
 each label has 20 sampling, therefore totally it has 120 labels. ##
 Details for: final\_moistureCC\_low\_millimeterwave.mat * Description:
 This is for classifying different soil moisture affected by carbon content
 added using millimeter wave sensor. * Format(s): .mat * Size(s): 2,475 KB
 * Dimensions: * Data: 160 rows x 1024 cols * label: 160 rows x 1 cols *
 Variables: * new_Mosi: * type: table * description: combined sampling data
 with label together. Can be used in Classifier App directly. *
 new_moistureC_low: raw data * y: Total 8 classes, each label has 20
 sampling, therefore totally it has 160 labels. ## Details for:
 mm\_multi\_containers.mat * Description: This is for investigating
 robustness sensing for soil carbon content percentage using millimeter
 wave sensor. * Format(s): .mat * Size(s): 3,279 KB * Dimensions: * Data:
 500 rows x 300 cols * label: 500 rows x 1 cols * Variables: *
 mm_mult_container: * type: table * description: combined sampling data
 with label together. Can be used in Classifier App directly. *
 mul_contain_mm: raw data in double types, can be used directly. *
 multi_containers_mm: raw data in complex number, easy for doing PDP plots.
 * y: Total 5 classes, each label has 100 sampling, therefore totally it
 has 500 labels. ## Details for: final\_moisture\_low\_millimeterwave.mat *
 Description: This is for classifying different soil moisture levels using
 microwave sensor. * Format(s): .mat * Size(s): 2,166 KB * Dimensions: *
 Data: 140 rows x 8192 cols * label: 140 rows x 1 cols * Variables: *
 new_Mosi: * type: table * description: combined sampling data with label
 together. Can be used in Classifier App directly. * new_moisture_low: raw
 data * y: Total 7 classes, each label has 20 sampling, therefore totally
 it has 140 labels.