Articles | Volume 4, issue 2
https://doi.org/10.5194/soil-4-101-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-4-101-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
15 May 2018
Review article |
| 15 May 2018
| 15 May 2018
Proximal sensing for soil carbon accounting
Jacqueline R. England
CORRESPONDING AUTHOR
CSIRO Land and Water, Private Bag 10, Clayton South, VIC 3169, Australia
Raphael A. Viscarra Rossel
CSIRO Land and Water, Bruce E. Butler Laboratory, P.O. Box 1700, Canberra, ACT 2601, Australia
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29 citations as recorded by crossref.
- Assessing the uncertainty of deep learning soil spectral models using Monte Carlo dropout J. Padarian et al. 10.1016/j.geoderma.2022.116063
- Performance of in situ vs laboratory mid-infrared soil spectroscopy using local and regional calibration strategies I. Greenberg et al. 10.1016/j.geoderma.2021.115614
- Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century R. Viscarra Rossel et al. 10.1111/ejss.13271
- How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal P. Smith et al. 10.1111/gcb.14815
- Spatial modelling approach and accounting method affects soil carbon estimates and derived farm-scale carbon payments S. Beka et al. 10.1016/j.scitotenv.2022.154164
- Soil apparent electrical conductivity‐directed sampling design for advancing soil characterization in agricultural fields M. Longo et al. 10.1002/vzj2.20060
- Soil carbon simulation confounded by different pool initialisation J. Lee & R. Viscarra Rossel 10.1007/s10705-019-10041-0
- Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review T. Angelopoulou et al. 10.3390/rs11060676
- Performance of field‐scale lab vs in situ visible/near‐ and mid‐infrared spectroscopy for estimation of soil properties I. Greenberg et al. 10.1111/ejss.13180
- Deep transfer learning of global spectra for local soil carbon monitoring Z. Shen et al. 10.1016/j.isprsjprs.2022.04.009
- Strategies to improve the prediction of bulk soil and fraction organic carbon in Brazilian samples by using an Australian national mid-infrared spectral library C. Briedis et al. 10.1016/j.geoderma.2020.114401
- Preliminary assessment of the knowledge gaps to conserve and increase soil organic carbon stocks Å. Almås et al. 10.3897/soils4europe.e118635
- A review of the global soil property maps for Earth system models Y. Dai et al. 10.5194/soil-5-137-2019
- From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review T. Angelopoulou et al. 10.3390/su12020443
- Current and emerging methodologies for estimating carbon sequestration in agricultural soils: A review A. Nayak et al. 10.1016/j.scitotenv.2019.02.125
- Assessing the response of soil carbon in Australia to changing inputs and climate using a consistent modelling framework J. Lee et al. 10.5194/bg-18-5185-2021
- Characterization of an In-Situ Soil Organic Carbon (SOC) via a Smart-Electrochemical Sensing Approach V. Dhamu et al. 10.3390/s24041153
- Prediction of Soil Properties in a Field in Typical Black Soil Areas Using in situ MIR Spectra and Its Comparison with vis-NIR Spectra J. Yin et al. 10.3390/rs15082053
- Biochar as Soil Amendment: The Effect of Biochar on Soil Properties Using VIS-NIR Diffuse Reflectance Spectroscopy, Biochar Aging and Soil Microbiology—A Review V. Tsolis & P. Barouchas 10.3390/land12081580
- Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy L. Lotfollahi et al. 10.1016/j.jenvman.2023.118854
- Toprak Organik Karbonu ve Toprak Organik Karbon Stokları üzerine 1970-2021 Yılları Arasında Yapılan Araştırmaların Bibliyometrik Analizi E. ÇOMAKLI 10.31590/ejosat.929666
- In‐field soil spectroscopy in Vis–NIR range for fast and reliable soil analysis: A review C. Piccini et al. 10.1111/ejss.13481
- Precise method for measuring soil carbon A. Zontikov 10.1016/j.apradiso.2023.110661
- Predicting Soil Organic Matter, Available Nitrogen, Available Phosphorus and Available Potassium in a Black Soil Using a Nearby Hyperspectral Sensor System S. Wan et al. 10.3390/s24092784
- Characterization and detailed mapping of C by spectral sensor for soils of the Western Plateau of São Paulo K. Fernandes et al. 10.1038/s41598-024-66369-3
- Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring P. Baumann et al. 10.5194/soil-7-525-2021
- Quantification and depth distribution analysis of carbon to nitrogen ratio in forest soils using reflectance spectroscopy A. Gholizadeh et al. 10.1016/j.iswcr.2022.06.004
- SATELLITES TO AGRICULTURAL FIELDS: THE ROLE OF REMOTE SENSING IN PRECISION AGRICULTURE M. FAKHAR & M. KHALID 10.54112/basrj.v2023i1.14
- Soil moisture effects on predictive VNIR and MIR modeling of soil organic carbon and clay content M. Seidel et al. 10.1016/j.geoderma.2022.116103
29 citations as recorded by crossref.
- Assessing the uncertainty of deep learning soil spectral models using Monte Carlo dropout J. Padarian et al. 10.1016/j.geoderma.2022.116063
- Performance of in situ vs laboratory mid-infrared soil spectroscopy using local and regional calibration strategies I. Greenberg et al. 10.1016/j.geoderma.2021.115614
- Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century R. Viscarra Rossel et al. 10.1111/ejss.13271
- How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal P. Smith et al. 10.1111/gcb.14815
- Spatial modelling approach and accounting method affects soil carbon estimates and derived farm-scale carbon payments S. Beka et al. 10.1016/j.scitotenv.2022.154164
- Soil apparent electrical conductivity‐directed sampling design for advancing soil characterization in agricultural fields M. Longo et al. 10.1002/vzj2.20060
- Soil carbon simulation confounded by different pool initialisation J. Lee & R. Viscarra Rossel 10.1007/s10705-019-10041-0
- Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review T. Angelopoulou et al. 10.3390/rs11060676
- Performance of field‐scale lab vs in situ visible/near‐ and mid‐infrared spectroscopy for estimation of soil properties I. Greenberg et al. 10.1111/ejss.13180
- Deep transfer learning of global spectra for local soil carbon monitoring Z. Shen et al. 10.1016/j.isprsjprs.2022.04.009
- Strategies to improve the prediction of bulk soil and fraction organic carbon in Brazilian samples by using an Australian national mid-infrared spectral library C. Briedis et al. 10.1016/j.geoderma.2020.114401
- Preliminary assessment of the knowledge gaps to conserve and increase soil organic carbon stocks Å. Almås et al. 10.3897/soils4europe.e118635
- A review of the global soil property maps for Earth system models Y. Dai et al. 10.5194/soil-5-137-2019
- From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review T. Angelopoulou et al. 10.3390/su12020443
- Current and emerging methodologies for estimating carbon sequestration in agricultural soils: A review A. Nayak et al. 10.1016/j.scitotenv.2019.02.125
- Assessing the response of soil carbon in Australia to changing inputs and climate using a consistent modelling framework J. Lee et al. 10.5194/bg-18-5185-2021
- Characterization of an In-Situ Soil Organic Carbon (SOC) via a Smart-Electrochemical Sensing Approach V. Dhamu et al. 10.3390/s24041153
- Prediction of Soil Properties in a Field in Typical Black Soil Areas Using in situ MIR Spectra and Its Comparison with vis-NIR Spectra J. Yin et al. 10.3390/rs15082053
- Biochar as Soil Amendment: The Effect of Biochar on Soil Properties Using VIS-NIR Diffuse Reflectance Spectroscopy, Biochar Aging and Soil Microbiology—A Review V. Tsolis & P. Barouchas 10.3390/land12081580
- Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy L. Lotfollahi et al. 10.1016/j.jenvman.2023.118854
- Toprak Organik Karbonu ve Toprak Organik Karbon Stokları üzerine 1970-2021 Yılları Arasında Yapılan Araştırmaların Bibliyometrik Analizi E. ÇOMAKLI 10.31590/ejosat.929666
- In‐field soil spectroscopy in Vis–NIR range for fast and reliable soil analysis: A review C. Piccini et al. 10.1111/ejss.13481
- Precise method for measuring soil carbon A. Zontikov 10.1016/j.apradiso.2023.110661
- Predicting Soil Organic Matter, Available Nitrogen, Available Phosphorus and Available Potassium in a Black Soil Using a Nearby Hyperspectral Sensor System S. Wan et al. 10.3390/s24092784
- Characterization and detailed mapping of C by spectral sensor for soils of the Western Plateau of São Paulo K. Fernandes et al. 10.1038/s41598-024-66369-3
- Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring P. Baumann et al. 10.5194/soil-7-525-2021
- Quantification and depth distribution analysis of carbon to nitrogen ratio in forest soils using reflectance spectroscopy A. Gholizadeh et al. 10.1016/j.iswcr.2022.06.004
- SATELLITES TO AGRICULTURAL FIELDS: THE ROLE OF REMOTE SENSING IN PRECISION AGRICULTURE M. FAKHAR & M. KHALID 10.54112/basrj.v2023i1.14
- Soil moisture effects on predictive VNIR and MIR modeling of soil organic carbon and clay content M. Seidel et al. 10.1016/j.geoderma.2022.116103
Latest update: 14 Dec 2024
Short summary
Proximal sensing can be used for soil C accounting, but the methods need to be standardized and procedural guidelines developed to ensure proficient measurement and accurate reporting. This is particularly important if there are financial incentives for landholders to adopt practices to sequester C. We review sensing for C accounting and discuss the requirements for the development of new soil C accounting methods based on sensing, including requirements for reporting, auditing and verification.
Proximal sensing can be used for soil C accounting, but the methods need to be standardized and...
