Assessing Soil Organic Carbon in Semi-Arid Agricultural Soils Using UAVs and Machine Learning: A Pathway to Sustainable Water and Soil Resource Management
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area and Field Sampling
2.2. Remote Sensing Image
2.3. Development of Spectral Characteristic Indices
2.4. Model Performance Evaluation
2.5. Modelling of the Organic Carbon Content
2.6. Multiple Linear Regression Models
2.7. Calculation of the Variance Inflation Factor (VIF)
2.8. Random Forest
2.9. Support Vector Regression
3. Results
3.1. Relationship Between SOC and Multispectral Sensor Data
3.2. Development of the Inversion Model for SOC
3.3. Spatial of Predicted SOM Map
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
MDPI | Multidisciplinary Digital Publishing Institute |
DOAJ | Directory of open access journals |
TLA | Three-letter acronym |
LD | Linear dichroism |
SOC | Soil organic carbon |
UAVs | Unmanned aerial vehicles |
PLS | Partial least squares regression |
RF | Random forest |
SVM | Support vector machine |
RMSE | Root-mean-square error |
VIF | Variance inflation factor (VIF) |
MAE | Mean absolute error |
R2 | Coefficient of determination |
OC | Organic carbon |
PCR | Principal Component Regression |
RR | Ridge Regression |
References
- Hemamali, D.D.A.E.; Vitharana, U.W.A.; Balasooriya, B.L.W.K.; Attanayake, C.P.; Dandeniya, W.S.; Nimanthi, S.I. Impact of agricultural land use on soil organic carbon sequestration at sub-catchment scale. Trop. Agric. Res. 2020, 31, 13. [Google Scholar] [CrossRef]
- Lal, R. Soil Carbon Sequestration Impacts on Global Climate Change and Food Security. Science 2004, 304, 1623–1627. [Google Scholar] [CrossRef]
- Lienin, P.; Kleyer, M. Plant trait responses to the environment and effects on ecosystem properties. Basic Appl. Ecol. 2012, 13, 301–311. [Google Scholar] [CrossRef]
- Kumari, V.; Laik, R.; Poonia, S.; Nath, D. Regulation of soil organic carbon stock with physical properties in alluvial soils of Bihar. Environ. Conserv. J. 2022, 23, 309–314. [Google Scholar] [CrossRef]
- Komolafe, A.A.; Olorunfemi, I.E.; Oloruntoba, C.; Akinluyi, F.O. Spatial prediction of soil nutrients from soil, topography and environmental attributes in the northern part of Ekiti State, Nigeria. Remote Sens. Appl. Soc. Environ. 2020, 21, 100450. [Google Scholar] [CrossRef]
- Ngatia, L.W.; Moriasi, D.; Iii, J.M.G.; Fu, R.; Gardner, C.S.; Taylor, R.W. Land Use Change Affects Soil Organic Carbon: An Indicator of Soil Health. Available online: www.intechopen.com (accessed on 12 May 2024).
- Wang, Y.; Xu, Y.; Pei, J.; Li, M.; Shan, T.; Zhang, W.; Wang, J. Below ground residues were more conducive to soil organic carbon accumulation than above ground ones. Appl. Soil Ecol. 2020, 148, 103509. [Google Scholar] [CrossRef]
- Huang, X.; Ibrahim, M.M.; Luo, Y.; Jiang, L.; Chen, J.; Hou, E. Land Use Change Alters Soil Organic Carbon: Constrained Global Patterns and Predictors. Earth’s Future 2024, 12, e2023EF004254. [Google Scholar] [CrossRef]
- Ramesh, T.; Bolan, N.S.; Kirkham, M.B.; Wijesekara, H.; Kanchikerimath, M.; Rao, C.S.; Sandeep, S.; Rinklebe, J.; Ok, Y.S.; Choudhury, B.U.; et al. Soil organic carbon dynamics: Impact of land use changes and management practices: A review. Adv. Agron. 2019, 156, 1–107. [Google Scholar] [CrossRef]
- Xiao, J.; Chevallier, F.; Gomez, C.; Guanter, L.; Hicke, J.A.; Huete, A.R.; Ichii, K.; Ni, W.; Pang, Y.; Rahman, A.F.; et al. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years. Remote Sens. Environ. 2019, 233, 111383. [Google Scholar] [CrossRef]
- Wang, G.; Zhang, X.; Liu, X.; Gao, H.; Pan, Y. Stabilization of micaceous residual soil with industrial and agricultural byproducts: Perspectives from hydrophobicity, water stability, and durability enhancement. Constr. Build. Mater. 2024, 430, 136450. [Google Scholar] [CrossRef]
- Taylor, J.A.; Jacob, F.; Galleguillos, M.; Prevot, L.; Guix, N.; Lagacherie, P. The utility of remotely-sensed vegetative and terrain covariates at different spatial resolutions in modelling soil and watertable depth (for digital soil mapping). Geoderma 2013, 193, 83–93. [Google Scholar] [CrossRef]
- Wang, K.; Qi, Y.; Guo, W.; Zhang, J.; Chang, Q. Retrieval and mapping of soil organic carbon using sentinel-2A spectral images from bare cropland in autumn. Remote Sens. 2021, 13, 1072. [Google Scholar] [CrossRef]
- Rossel, R.A.V.; Taylor, H.J.; McBratney, A.B. Multivariate calibration of hyperspectral-ray energy spectra for proximal soil sensing. Eur. J. Soil Sci. 2007, 58, 343–353. [Google Scholar] [CrossRef]
- Zhu, Y.L.; Wang, D.Y.; Zhang, H.; Shi, P. Soil organic carbon content retrieved by UAV-borne high resolution spectrometer. Trans. Chin. Soc. Agric. Eng. 2021, 37, 66–72. [Google Scholar] [CrossRef]
- Odebiri, O.; Mutanga, O.; Odindi, J.; Peerbhay, K.; Dovey, S.; Ismail, R. Estimating soil organic carbon stocks under commercial forestry using topo-climate variables in KwaZulu-Natal, South Africa. S. Afr. J. Sci. 2020, 116, 71–78. [Google Scholar] [CrossRef]
- Vaudour, E.; Gilliot, J.M.; Bel, L.; Lefevre, J.; Chehdi, K. Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra. Int. J. Appl. Earth Obs. Geoinf. 2016, 49, 24–38. [Google Scholar] [CrossRef]
- Crucil, G.; Castaldi, F.; Aldana-Jague, E.; van Wesemael, B.; Macdonald, A.; Van Oost, K. Assessing the Performance of UAS-Compatible Multispectral and Hyperspectral Sensors for Soil Organic Carbon Prediction. Sustainability 2019, 11, 1889. [Google Scholar] [CrossRef]
- Van Huynh, C.; Pham, T.G.; Nguyen, L.H.K.; Nguyen, H.T.; Nguyen, P.T.; Le, Q.N.P.; Tran, P.T.; Nguyen, M.T.H.; Tran, T.T.A. Application GIS and remote sensing for soil organic carbon mapping in a farm-scale in the hilly area of central Vietnam. Air, Soil Water Res. 2022, 15, 11786221221114777. [Google Scholar] [CrossRef]
- Xu, S.; Zhao, Y.; Wang, M.; Shi, X. Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–NIR spectroscopy. Geoderma 2018, 310, 29–43. [Google Scholar] [CrossRef]
- Faria, O.C.O.; Torres, G.N.; Di Raimo, L.A.D.L.; Couto, E.G. Estimate of carbon stock in the soil via diffuse reflectance spectroscopy (vis/nir) air and orbital remote sensing. Rev. Caatinga 2023, 36, 675–689. [Google Scholar] [CrossRef]
- Odebiri, O.; Mutanga, O.; Odindi, J. Deep learning-based national scale soil organic carbon mapping with Sentinel-3 data. Geoderma 2022, 411, 115695. [Google Scholar] [CrossRef]
- Sarmadian, F.; Keshavarzi, A.; Amiri, G.Z.; Javadikia, H. Mapping of Spatial Variability of Soil Organic Carbon Based on Radial Basis Functions Method. Available online: https://www.researchgate.net/publication/262327712 (accessed on 18 December 2024).
- Chan, T.; Gomez, C.A.; Kothikar, A.; Baiz, P. Joint Study of Above Ground Biomass and Soil Organic Carbon for Total Carbon Estimation using Satellite Imagery in Scotland. May 2022. Available online: http://arxiv.org/abs/2205.04870 (accessed on 5 October 2023).
- Mondal, B.P.; Sekhon, B.S.; Sahoo, R.N.; Paul, P. ViS-NIR reflectance spectroscopy for assessment of soil organic carbon in a rice-wheat field of Ludhiana district of Punjab. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2019, XLII-3/W6, 417–422. [Google Scholar] [CrossRef]
- Feilhauer, H.; Asner, G.P.; Martin, R.E. Multi-method ensemble selection of spectral bands related to leaf biochemistry. Remote Sens. Environ. 2015, 164, 57–65. [Google Scholar] [CrossRef]
- Jin, Y.; Yang, X.; Qiu, J.; Li, J.; Gao, T.; Wu, Q.; Zhao, F.; Ma, H.; Yu, H.; Xu, B. Remote sensing-based biomass estimation and its spatio-temporal variations in temperate Grassland, Northern China. Remote Sens. 2014, 6, 1496–1513. [Google Scholar] [CrossRef]
- Zhang, H.; Wu, P.; Yin, A.; Yang, X.; Zhang, M.; Gao, C. Prediction of soil organic carbon in an intensively managed reclamation zone of eastern China: A comparison of multiple linear regressions and the random forest model. Sci. Total Environ. 2017, 592, 704–713. [Google Scholar] [CrossRef] [PubMed]
- Parsaie, F.; Firouzi, A.F.; Mousavi, S.R.; Rahmani, A.; Sedri, M.H.; Homaee, M. Large-scale digital mapping of topsoil total nitrogen using machine learning models and associated uncertainty map. Environ. Monit. Assess. 2021, 193, 1–15. [Google Scholar] [CrossRef]
- Rostaminia, M.; Rahmani, A.; Mousavi, S.R.; Taghizadeh-Mehrjardi, R.; Maghsodi, Z. Spatial prediction of soil organic carbon stocks in an arid rangeland using machine learning algorithms. Environ. Monit. Assess. 2021, 193, 1–17. [Google Scholar] [CrossRef]
- El Jamaoui, I.; Sánchez, M.J.M.; Sirvent, C.P.; Mana, A.A.; López, S.M. Machine learning-driven modeling for soil organic carbon estimation from multispectral drone imaging: A case study in Corvera, Murcia (Spain). Model. Earth Syst. Environ. 2024, 10, 3473–3494. [Google Scholar] [CrossRef]
- Shan, J.; Zhao, J.; Liu, L.; Zhang, Y.; Wang, X.; Wu, F. A novel way to rapidly monitor microplastics in soil by hyperspectral imaging technology and chemometrics. Environ. Pollut. 2018, 238, 121–129. [Google Scholar] [CrossRef]
- Forkuor, G.; Hounkpatin, O.K.L.; Welp, G.; Thiel, M. High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models. PLoS ONE 2017, 12, e0170478. [Google Scholar] [CrossRef]
- Zhu, C.; Ding, J.; Zhang, Z.; Wang, Z. Exploring the potential of UAV hyperspectral image for estimating soil salinity: Effects of optimal band combination algorithm and random forest. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 2022, 279, 121416. [Google Scholar] [CrossRef] [PubMed]
- Santos, E.P.D.; Moreira, M.C.; Fernandes-Filho, E.I.; Demattê, J.A.M.; Dionizio, E.A.; Silva, D.D.D.; Cruz, R.R.P.; Moura-Bueno, J.M.; Santos, U.J.D.; Costa, M.H. Sentinel-1 imagery used for estimation of soil organic carbon by dual-polarization SAR vegetation indices. Remote Sen. 2023, 15, 5464. [Google Scholar] [CrossRef]
- Takata, Y.; Funakawa, S.; Akshalov, K.; Ishida, N.; Kosaki, T. Regional evaluation of the spatio-temporal variation in soil organic carbon dynamics for rainfed cereal farming in northern Kazakhstan. Soil Sci. Plant Nutr. 2008, 54, 794–806. [Google Scholar] [CrossRef]
- Zhang, Y.; Yu, J.; Dong, X.; Zhong, P. Multi-task support vector machine with pinball loss. Eng. Appl. Artif. Intell. 2021, 106, 104458. [Google Scholar] [CrossRef]
- Song, B.; Park, K. remote sensing Detection of Aquatic Plants Using Multispectral UAV Imagery and Vegetation Index. Remote Sens. 2020, 12, 387. [Google Scholar] [CrossRef]
- Xu, Y.; Shrestha, V.; Piasecki, C.; Wolfe, B.; Hamilton, L.; Millwood, R.J.; Mazarei, M.; Stewart, C.N. Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery. Plants 2021, 10, 2726. [Google Scholar] [CrossRef]
- Dawson, C.; Abrahart, R.; See, L. HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environ. Model. Softw. 2007, 22, 1034–1052. [Google Scholar] [CrossRef]
- Mohammed, S.A.; Solomatine, D.P.; Hrachowitz, M.; Hamouda, M.A. Impact of Dataset Size on the Signature-Based Calibration of a Hydrological Model. Water 2021, 13, 970. [Google Scholar] [CrossRef]
- Boafo, D.K.; Kraisornpornson, B.; Panphon, S.; Owusu, B.E.; Amaniampong, P.N. Effect of organic soil amendments on soil quality in oil palm production. Appl. Soil Ecol. 2020, 147, 103358. [Google Scholar] [CrossRef]
- Raiter, K.G.; Hawlena, D. Managing multiple uncertainties in species distribution modelling. Divers. Distrib. 2024, 30, e13857. [Google Scholar] [CrossRef]
- Liu, X.; Zhu, C.; Yu, K.; Li, W.; Luo, Y.; Dai, Y.; Wang, H. Accurate Determination of Moisture Content in Flavor Microcapsules Using Headspace Gas Chromatography. Polymers 2022, 14, 3002. [Google Scholar] [CrossRef] [PubMed]
- Schumacher, B.A. Methods for the Determination of Total Organic Carbon (TOC) in Soils and Sediments. April 2002. Available online: https://www.researchgate.net/publication/292706836 (accessed on 4 November 2024).
- Were, K.; Bui, D.T.; Dick, Ø.B.; Singh, B.R. A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecol. Indic. 2015, 52, 394–403. [Google Scholar] [CrossRef]
- Chen, F.; Feng, P.; Harrison, M.T.; Wang, B.; Liu, K.; Zhang, C.; Hu, K. Cropland carbon stocks driven by soil characteristics, rainfall and elevation. Sci. Total Environ. 2022, 862, 160602. [Google Scholar] [CrossRef]
- Zhang, Z.; Ding, J.; Wang, J.; Ge, X. Prediction of soil organic matter in northwestern China using fractional-order derivative spectroscopy and modified normalized difference indices. CATENA 2020, 185, 104257. [Google Scholar] [CrossRef]
- Biney, J.K.M.; Saberioon, M.; Borůvka, L.; Houška, J.; Vašát, R.; Agyeman, P.C.; Coblinski, J.A.; Klement, A. Exploring the suitability of uas-based multispectral images for estimating soil organic carbon: Comparison with proximal soil sensing and spaceborne imagery. Remote Sens. 2021, 13, 308. [Google Scholar] [CrossRef]
- Kim, J.H. Multicollinearity and misleading statistical results. Korean J. Anesthesiol. 2019, 72, 558–569. [Google Scholar] [CrossRef] [PubMed]
- Strobl, C.; Malley, J.; Tutz, G. An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random Forests. Psychol. Methods 2009, 14, 323–348. [Google Scholar] [CrossRef]
- Tziachris, P.; Aschonitis, V.; Chatzistathis, T.; Papadopoulou, M. Assessment of spatial hybrid methods for predicting soil organic matter using DEM derivatives and soil parameters. CATENA 2019, 174, 206–216. [Google Scholar] [CrossRef]
- Cutler, A. Remembering Leo Breiman 1. Ann. Appl. Stat. 2010, 4, 1621–1633. [Google Scholar] [CrossRef]
- Meng, X.; Bao, Y.; Liu, J.; Liu, H.; Zhang, X.; Zhang, Y.; Wang, P.; Tang, H.; Kong, F. Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data. Int. J. Appl. Earth Obs. Geoinformation 2020, 89, 102111. [Google Scholar] [CrossRef]
- Reyna Bowen, J.L.; Vera Montenegro, L.; Delgado Moreira, M.I. Optimizing soil analysis in precision agriculture: Evaluating alternative methods for SOC prediction. J. Ecol. Eng. 2025, 26, 322–331. [Google Scholar] [CrossRef] [PubMed]
- Dou, X.; Wang, X.; Liu, H.; Zhang, X.; Meng, L.; Pan, Y.; Yu, Z.; Cui, Y. Prediction of soil organic matter using multi-temporal satellite images in the Songnen Plain, China. Geoderma 2019, 356, 113896. [Google Scholar] [CrossRef]
- Liu, Y.; Jiang, Q.; Fei, T.; Wang, J.; Shi, T.; Guo, K.; Li, X.; Chen, Y. Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes. Remote Sens. 2014, 6, 4305–4322. [Google Scholar] [CrossRef]
- Li, Z.; Yang, Y.; Gu, S.; Tang, B.; Zhang, J. Research on theprediction of several soil properties in heihe river basin based on remote sensing images. Sustainability 2021, 13, 13930. [Google Scholar] [CrossRef]
- Jia, S.; Li, H.; Wang, Y.; Tong, R.; Li, Q. Hyperspectral imaging analysis for the classification of soil types and the determination of soil total nitrogen. Sensors 2017, 17, 2252. [Google Scholar] [CrossRef] [PubMed]
- De Morais, C.P.; McMeekin, K.; Nault, C. Scalable solution for agricultural soil organic carbon measurements using laser-induced breakdown spectroscopy. Sci. Rep. 2024, 14, 15272. [Google Scholar] [CrossRef]
- Zhu, S.-P.; Huang, H.-Z.; Peng, W.; Wang, H.-K.; Mahadevan, S. Probabilistic Fatigue Life Updating for Railway Bridges Based on Local Inspection and Repair. Sensors 2017, 17, 936. [Google Scholar] [CrossRef]
- Zhang, Z.; Zhu, L. A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications. Drones 2023, 7, 398. [Google Scholar] [CrossRef]
- Zhou, J.; Zhang, J.; Chen, Y.; Qin, G.; Cui, B.; Lu, Z.; Wu, J.; Huang, X.; Thapa, P.; Li, H.; et al. Blue carbon gain by plant invasion in saltmarsh overcompensated carbon loss by land reclamation. Carbon Res. 2023, 2, 39. [Google Scholar] [CrossRef]
- Wang, S.; Wang, Z.; Heinonsalo, J.; Zhang, Y.; Liu, G. Soil organic carbon stocks and dynamics in a mollisol region: A 1980s–2010s study. Sci. Total Environ. 2022, 807, 150910. [Google Scholar] [CrossRef]
- Jin, H.; Xie, X.; Pu, L.; Jia, Z.; Xu, F. Mapping Soil Organic Matter Using Different Modeling Techniques in the Dryland Agroecosystem of Huang-Huai-Hai Plain, Eastern China. Remote Sens. 2023, 15, 4945. [Google Scholar] [CrossRef]
- Hu, X.; Li, X. Information extraction of subsided cultivated land in high-groundwater-level coal mines based on unmanned aerial vehicle visible bands. Environ. Earth Sci. 2019, 78, 413. [Google Scholar] [CrossRef]
Blue | Red | Green | Red Edge | Nir | OC | |
---|---|---|---|---|---|---|
compter | 76 | 76 | 76 | 76 | 76 | 76 |
MST | 0.030954 | 0.029485 | 0.029714 | 0.028618 | 0.030962 | 0.535191 |
Min | 0.029856 | 0.078989 | 0.053327 | 0.062899 | 0.102499 | 1.062703 |
25% | 0.051323 | 0.107848 | 0.076627 | 0.099781 | 0.129896 | 1.866545 |
50% | 0.068521 | 0.130638 | 0.090413 | 0.123422 | 0.150972 | 2.256125 |
75% | 0.099709 | 0.151322 | 0.118497 | 0.140109 | 0.172126 | 2.565112 |
max | 0.144179 | 0.213147 | 0.170175 | 0.197603 | 0.24138 | 3.256402 |
Variable | VIF |
---|---|
Red | 1.101 |
Blue | 12.2 |
Nir | 1.069 |
Red_edge | 1.201 |
Green | 13.25 |
Variable | R-Squared | Coefficient | Std. Error | t-Statistic | Probability |
---|---|---|---|---|---|
Blue | 0.66 | −0.586961 | 13.0199 | −0.450817 | 0.65365 |
Red Edge | 0.82 | 15.5247 | 30.6159 | 0.00130 | 0.61384 |
Green | 0.77 | 12.4869 | 21.706 | 0.575275 | 0.56712 |
Red | 0.83 | 0.0636 | 27.8802 | 0.001282 | 0.99845 |
Nir | 0.82 | −5.2746 | 11.9071 | 0.00103 | 0.65927 |
Model | R2 | MAE | MSE | RMSE | EVE | |
---|---|---|---|---|---|---|
SVR | training | 0.76 | 0.21 | 0.07 | 0.27 | 0.76 |
Test | 0.38 | 0.25 | 0.12 | 0.77 | 0.57 | |
RFR | Training | 0.92 | 0.19 | 0.05 | 0.22 | 0.71 |
Test | 0.89 | 0.22 | 0.1 | 0.31 | 0.67 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
El-Jamaoui, I.; Delgado-Iniesta, M.J.; Martínez Sánchez, M.J.; Pérez Sirvent, C.; Martínez López, S. Assessing Soil Organic Carbon in Semi-Arid Agricultural Soils Using UAVs and Machine Learning: A Pathway to Sustainable Water and Soil Resource Management. Sustainability 2025, 17, 3440. https://doi.org/10.3390/su17083440
El-Jamaoui I, Delgado-Iniesta MJ, Martínez Sánchez MJ, Pérez Sirvent C, Martínez López S. Assessing Soil Organic Carbon in Semi-Arid Agricultural Soils Using UAVs and Machine Learning: A Pathway to Sustainable Water and Soil Resource Management. Sustainability. 2025; 17(8):3440. https://doi.org/10.3390/su17083440
Chicago/Turabian StyleEl-Jamaoui, Imad, María José Delgado-Iniesta, Maria José Martínez Sánchez, Carmen Pérez Sirvent, and Salvadora Martínez López. 2025. "Assessing Soil Organic Carbon in Semi-Arid Agricultural Soils Using UAVs and Machine Learning: A Pathway to Sustainable Water and Soil Resource Management" Sustainability 17, no. 8: 3440. https://doi.org/10.3390/su17083440
APA StyleEl-Jamaoui, I., Delgado-Iniesta, M. J., Martínez Sánchez, M. J., Pérez Sirvent, C., & Martínez López, S. (2025). Assessing Soil Organic Carbon in Semi-Arid Agricultural Soils Using UAVs and Machine Learning: A Pathway to Sustainable Water and Soil Resource Management. Sustainability, 17(8), 3440. https://doi.org/10.3390/su17083440