44 citations as recorded by crossref.

1. Assessing the uncertainty of deep learning soil spectral models using Monte Carlo dropout J. Padarian et al. https://doi.org/10.1016/j.geoderma.2022.116063
2. Research Path on Climate Change Accounting M. Uula https://doi.org/10.58968/as.v3i1.607
3. Performance of in situ vs laboratory mid-infrared soil spectroscopy using local and regional calibration strategies I. Greenberg et al. https://doi.org/10.1016/j.geoderma.2021.115614
4. Quantification techniques of soil organic carbon: an appraisal A. Kanagaraj et al. https://doi.org/10.1007/s44211-025-00746-4
5. Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century R. Viscarra Rossel et al. https://doi.org/10.1111/ejss.13271
6. 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. https://doi.org/10.1111/gcb.14815
7. Spatial modelling approach and accounting method affects soil carbon estimates and derived farm-scale carbon payments S. Beka et al. https://doi.org/10.1016/j.scitotenv.2022.154164
8. Soil apparent electrical conductivity‐directed sampling design for advancing soil characterization in agricultural fields M. Longo et al. https://doi.org/10.1002/vzj2.20060
9. Soil carbon simulation confounded by different pool initialisation J. Lee & R. Viscarra Rossel https://doi.org/10.1007/s10705-019-10041-0
10. Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review T. Angelopoulou et al. https://doi.org/10.3390/rs11060676
11. Outlook on the knowledge gaps to conserve and increase soil organic carbon stocks Å. Almås et al. https://doi.org/10.3897/soils4europe.e149027
12. On soil health and the pivotal role of sensing Y. Hu et al. https://doi.org/10.5194/soil-12-227-2026
13. Performance of field‐scale lab vs in situ visible/near‐ and mid‐infrared spectroscopy for estimation of soil properties I. Greenberg et al. https://doi.org/10.1111/ejss.13180
14. Deep transfer learning of global spectra for local soil carbon monitoring Z. Shen et al. https://doi.org/10.1016/j.isprsjprs.2022.04.009
15. The Economic and Policy Challenges of Climate-Smart Agriculture A. Smith & A. Swanson https://doi.org/10.1086/733661
16. 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. https://doi.org/10.1016/j.geoderma.2020.114401
17. Preliminary assessment of the knowledge gaps to conserve and increase soil organic carbon stocks Å. Almås et al. https://doi.org/10.3897/soils4europe.e118635
18. A review of the global soil property maps for Earth system models Y. Dai et al. https://doi.org/10.5194/soil-5-137-2019
19. Enhancing carbon sequestration potential of lowland rice agroecosystems for environmentally clean production system: A review S. Das et al. https://doi.org/10.1016/j.csag.2025.100054
20. Airborne radiometric data: in search of the lost peatlands, Anglesey, North Wales D. Beamish & J. White https://doi.org/10.1016/j.jenvrad.2025.107858
21. From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review T. Angelopoulou et al. https://doi.org/10.3390/su12020443
22. Current and emerging methodologies for estimating carbon sequestration in agricultural soils: A review A. Nayak et al. https://doi.org/10.1016/j.scitotenv.2019.02.125
23. Assessing the response of soil carbon in Australia to changing inputs and climate using a consistent modelling framework J. Lee et al. https://doi.org/10.5194/bg-18-5185-2021
24. Characterization of an In-Situ Soil Organic Carbon (SOC) via a Smart-Electrochemical Sensing Approach V. Dhamu et al. https://doi.org/10.3390/s24041153
25. Advancing Soil Organic Carbon Prediction: A Comprehensive Review of Technologies, AI, Process‐Based and Hybrid Modelling Approaches Z. Ding et al. https://doi.org/10.1002/advs.202504152
26. Quantifying terrestrial carbon in the context of climate change: a review of common and novel technologies and methods S. Gameiro et al. https://doi.org/10.1186/s13021-025-00316-1
27. 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. https://doi.org/10.3390/rs15082053
28. 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 https://doi.org/10.3390/land12081580
29. Application of a Handheld Near Infrared Spectrophotometer to Farm‐Scale Soil Carbon Monitoring J. Sanderman et al. https://doi.org/10.1111/ejss.70053
30. Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy L. Lotfollahi et al. https://doi.org/10.1016/j.jenvman.2023.118854
31. Toprak Organik Karbonu ve Toprak Organik Karbon Stokları üzerine 1970-2021 Yılları Arasında Yapılan Araştırmaların Bibliyometrik Analizi E. ÇOMAKLI https://doi.org/10.31590/ejosat.929666
32. In‐field soil spectroscopy in Vis–NIR range for fast and reliable soil analysis: A review C. Piccini et al. https://doi.org/10.1111/ejss.13481
33. Accuracies and costs of prediction and mapping soil properties using proximal sensors: A systematic review C. Lozano-Fondón et al. https://doi.org/10.1016/j.compag.2025.111378
34. Precise method for measuring soil carbon A. Zontikov https://doi.org/10.1016/j.apradiso.2023.110661
35. Simulating the attenuation of in situ 40K gamma rays to monitor soil properties R. McTaggart https://doi.org/10.1007/s11368-025-04151-2
36. 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. https://doi.org/10.3390/s24092784
37. Characterization and detailed mapping of C by spectral sensor for soils of the Western Plateau of São Paulo K. Fernandes et al. https://doi.org/10.1038/s41598-024-66369-3
38. A Comparative Assessment of Sentinel-2 and UAV-Based Imagery for Soil Organic Carbon Estimations Using Machine Learning Models I. El-Jamaoui et al. https://doi.org/10.3390/s25175281
39. Carbon offsets in agriculture: linking soil organic carbon and measurement, reporting and verification framework with financial reporting K. Medeckytė-Žydelė https://doi.org/10.1108/JFRA-03-2025-0214
40. Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring P. Baumann et al. https://doi.org/10.5194/soil-7-525-2021
41. Quantification and depth distribution analysis of carbon to nitrogen ratio in forest soils using reflectance spectroscopy A. Gholizadeh et al. https://doi.org/10.1016/j.iswcr.2022.06.004
42. SATELLITES TO AGRICULTURAL FIELDS: THE ROLE OF REMOTE SENSING IN PRECISION AGRICULTURE M. FAKHAR & M. KHALID https://doi.org/10.54112/basrj.v2023i1.14
43. Choosing the appropriate methodology to monitor soil organic carbon (SOC) in croplands: aligning methods with evolving monitoring reporting verification (MRV) frameworks A. Ihasusta et al. https://doi.org/10.1080/17583004.2026.2638317
44. Soil moisture effects on predictive VNIR and MIR modeling of soil organic carbon and clay content M. Seidel et al. https://doi.org/10.1016/j.geoderma.2022.116103