Abstract
Soil organic carbon (SOC) is a key parameter for soil quality. As one of the major grain-producing regions of China, Jilin Province plays a critical role in ensuring national food security, making cropland SOC monitoring essential. Based on satellite remote sensing observations, this study reveals an overall 5.14% decline in SOC across croplands in Jilin Province over the past seven years. Losses were most pronounced in the west, while the central and eastern areas remained relatively stable. Conventional SOC estimation methods largely rely on machine learning, which can lack physical interpretability and reproducibility. PLSR-based SOC models achieved validation R2 values of 0.40–0.61 with corresponding RMSEs of 0.30–0.38 across MODIS Terra, Landsat OLI, and Sentinel-2 MSI. The quantitative models exhibit satisfactory validation accuracy but limited spatial robustness across sensors in practical mapping. This study proposes a new broadband spectral index, the Ratio Soil Index (RSI), applied at 30-meter resolution. Using field synchronized SOC measurements and spectral analysis, we developed broadband indices from MODIS Terra, Landsat OLI, and Sentinel MSI. The RSI showed strong correlations with measured SOC, with coefficients of 0.72, 0.74, and 0.77 for the three sensors. Its spatial patterns were consistent with ground observations within the 95% confidence interval. The findings demonstrate that the RSI, with its concise formulation, reliable mapping performance, and ability to identify the variations of SOC, offers a scalable and reproducible metric for national SOC monitoring under changing agricultural management.
Data availability
The satellite remote sensing data used for spectral index mapping can be accessed via the Google Earth Engine (GEE). And the GEE script is available at: https://code.earthengine.google.com/cfaa76a180574b0b0226783e7e387c76. Additional data related to this paper are available from the corresponding author upon reasonable request.
References
Wiesmeier, M. et al. Soil organic carbon storage as a key function of soils – a review of drivers and indicators at various scales. Geoderma 333, 149–162 (2019).
Rui, L. I. et al. Soil degradation: a global threat to sustainable use of black soils. Pedosphere 35, 264–279 (2025).
Zhao, Jin et al. For the protection of black soils. Nat Food 6(119), 120 (2025).
Schmidt, M. W. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).
Cheng, H. et al. Study of loss or gain of soil organic carbon in Da’an region, Jilin Province in China. J. Geochem. Explor. 112, 272–275 (2012).
Gholizadeh, A. et al. Soil organic carbon and texture retrieving and mapping using proximal, airborne and sentinel-2 spectral imaging. Remote Sens. Environ. 218, 89–103 (2018).
Broeg, T. et al. Using local ensemble models and landsat bare soil composites for large-scale soil organic carbon maps in cropland. Geoderma 444, 116850 (2024).
Zhang, L. et al. A CNN-LSTM model for soil organiccarbon content prediction withlong time series of MODIS-basedphenological variables. Remote Sens. 14, 4441 (2022).
Ben-Dor, E. Quantitative remote sensing of soil properties. Adv. Agron. 75, 173–244 (2002).
Clark, R. N. & Roush, T. L. Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications. J. Geophys. Res.: Solid Earth 89(B7), 6329–6340 (1984).
Stenberg, B. et al. Chapter five – visible and near infrared spectroscopy in soil science. Adv. Agron. 107, 163–215 (2010).
Schwartz, G., Eshel, G. & Ben-Dor, E. Reflectance spectroscopy as a tool for monitoring contaminated soils. Soil Contam. 6790 (2011).
Rossel, R. V. et al. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131, 59–75 (2006).
Chabrillat, S. et al. Imaging spectroscopy for soil mapping and monitoring. Surv. Geophys. 40, 361–399 (2019).
Demattê, J. A. et al. Spectral regionalization of tropical soils in the estimation of soil attributes. Rev. Ciênc. Agron. 47, 589–598 (2016).
Bengera, I. & Norris, K. H. Determination of moisture content in soybeans by direct spectrophotometry. Isr. J. Agric. Res. 18, 124–132 (1968).
Morra, M. J., Hall, M. H. & Freeborn, L. L. Carbon and nitrogen analysis of soil fractions using near-infrared reflectance spectroscopy. Soil Sci. Soc. Am. J. 55, 288–291 (1991).
Mccarty, G. W. et al. Mid-Infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement. Soil Sci. Soc. Am. J. 66, 640–646 (2002).
Brown, D. J. Using a global VNIR soil-spectral library for local soil characterization and landscape modeling in a 2nd-order Uganda watershed. Geoderma 140, 444–453 (2007).
Wang, S. et al. Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: assessing potential of airborne and spaceborne optical soil sensing. Remote Sens. Environ. 271, 112914 (2022).
Rossel, R. A. V. & Behrens, T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158, 46–54 (2010).
Moura-Bueno, J. M. et al. Stratification of a local vis–nir–swir spectral library by homogeneity criteria yields more accurate soil organic carbon predictions. Geoderma 337, 565–581 (2019).
Bellon-Maurel, V. E. & McBratney, A. Near–infrared (NIR) and mid–infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils e critical review and research perspectives. Soil Biol. Biochem. 43, 1398–1410 (2011).
Ward, K. J. et al. A remote sensing adapted approach for soil organic carbon prediction based on the spectrally clustered LUCAS soil database. Geoderma 353, 297–307 (2019).
Rouse, J. W. Monitoring vegetation systems in the great plains with erts. In Third NASA Earth Resources Technology Satellite Symposium 1, 309–3171 (1973).
Jordan, C. F. Derivation of leaf area index from quality of light on the forest floor. Ecology 50, 663–666 (1969).
Richardson, A. J. & Wiegand, C. L. Distinguishing vegetation from soil background information. Photogramm. Eng. Remote Sens. 43, 1541–1552 (1977).
Gitelson, A. A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol. 161, 165–173 (2004).
Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1979).
Sandholt, I., Rasmussen, K. & Andersen, J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sens. Environ. 79, 213–224 (2002).
Haboudane, D. et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 81, 416–426 (2002).
Krishnan, P. et al. Reflectance technique for predicting soil organic matter. Soil Sci. Soc. Am. J. 44, 1282–1285 (1980).
Jin, X. et al. Remote estimation of soil organic matter content in the Sanjiang Plain, Northest China: The optimal band algorithm versus the GRA-ANN model. Agric. For. Meteorol. 218, 250–260 (2016).
Meng, X. et al. A long-term global Mollisols SOC content prediction framework: Integrating prior knowledge, geographical partitioning, and deep learning models with spatio-temporal validation. Remote Sens. Environ. 318, 114592 (2025).
Gholizadeh, A. et al. Soil organic carbon estimation using VNIR–SWIR spectroscopy: the effect of multiple sensors and scanning conditions. Soil Tillage Res. 211, 105017 (2021).
Bai, Z. et al. Estimation of soil organic carbon using vis-NIR Spectral data and spectral feature bands selection in Southern Xinjiang. China. Sens. 22, 6124 (2022).
Miloš, B. & Bensa, A. Prediction of soil organic carbon using VIS-NIR spectroscopy: application to red mediterranean soils from croatia. Eurasian J. Soil Sci. 6, 365–373 (2017).
Liu, L. et al. Quantitative retrieval of organic soil properties from Visible Near-Infrared Shortwave Infrared (Vis-NIR-SWIR) spectroscopy using fractal-based feature extraction. Remote Sens. 8, 1035 (2016).
Ribeiro, S. G. et al. Soil organic carbon content prediction using soil-reflected spectra: a comparison of two regression methods. Remote Sens. 13, 4752 (2021).
Huete, A. R. & Escadafal, R. Assessment of biophysical soil properties through spectral decomposition techniques. Remote Sens. Environ. 35, 149–159 (1991).
Francos, N., Ogen, Y. & Ben-Dor, E. Spectral assessment of organic matter with different composition using reflectance spectroscopy. Remote Sens. 13, 1549 (2021).
Mulder, V. L. et al. The use of remote sensing in soil and terrain mapping – a review. Geoderma 162, 1–19 (2011).
Stevens, A. et al. Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils. Geoderma 144, 395–404 (2007).
Xu, Z. et al. Evaluating the capability of satellite hyperspectral Imager, the ZY1–02D, for topsoil nitrogen content estimation and mapping of farmlands in black soil area. Remote Sens. 14, 1008 (2022).
Liu, Y. et al. Assessment of spatio-temporal variations in vegetation cover in Xinjiang from 1982 to 2013 based on GIMMS-NDVI. Acta Ecol. Sinica 36, 6198–6208 (2016).
DZ/T 0279. 27-2016: Analysis methods for regional geochemical sample-part 27: Determination of organic carbon contents by potassium dichromate volumetric method. http://www.doc88.com/p-7724868306719.html (accessed on 17 January 2024).
Zepp, S. et al. Optimized bare soil compositing for soil organic carbon prediction of topsoil croplands in Bavaria using Landsat. ISPRS J. Photogramm. Remote Sens. 202, 287–302 (2023).
Dou, X. et al. Prediction of soil organic matter using multi-temporal satellite images in the songnen plain. Geoderma 356, 113896 (2019).
Chen, J., Ban, Y. & Li, S. China: open access to earth land–cover map. Nature 514, 434–434 (2014).
Chen, J. et al. Global land cover mapping at 30 m resolution: a POK–based operational approach. ISPRS J. Photogramm. Remote Sens. 103, 7–27 (2015).
You, N. et al. The 10-m crop type maps in Northeast China during 2017–2019. Sci. Data 1, 41 (2021).
Castaldi, F. et al. Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon. Remote Sens. Environ. 179, 54–65 (2016).
De, J. S. Simpls: an alternative approach to partial least squares regression. Chemom. Intell. Lab. Syst. 18, 251–263 (1993).
Inoue, Y. et al. Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sens. Environ. 126, 210–221 (2012).
Acknowledgments
This research was supported by Science and Technology Research Project of the Education Department of Jilin Province (JJKH20230344KJ), The Seventh Batch of Jilin Province Youth Science and Technology Talents Support Program (QT202322), Science and Technology Development Plan Project of Jilin Province, China (YDZJ202401539ZYTS). The soil organic carbon content testing was performed at the Heilongjiang Provincial Research Center of Geological and Mineral Experimental Testing. We thank Quansheng Wang, Jie Cao, Jincheng Lei, Yan Zhang, and Jingjing Zuo from the School of Modern Industry, Jilin Jianzhu University, for their support in data collection and processing.
Funding
Science and Technology Research Project of the Education Department of Jilin Province, JJKH20230344KJ, The Seventh Batch of Jilin Province Youth Science and Technology Talents Support Program, QT202322, Science and Technology Development Plan Project of Jilin Province, China, YDZJ202401539ZYTS.
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Z.X. prepared the conceptualization and methodology of the study, developed the software, conducted the formal analysis and data curation, performed the visualization, wrote the original draft, contributed to the validation, supervised the research, administered the project, and acquired the funding. D.H. contributed to software development, participated in the investigation and data curation, validated the results, performed part of the formal analysis, and reviewed and edited the original draft. N.L. supervised the study, co-administered the project, and contributed to funding acquisition. W.L. edited the original draft and participated in the investigation. T.D. provided resources and assisted in data curation. H.C. and Z.W. participated in the investigation and processing of the data.
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Xu, Z., Hou, D., Lin, N. et al. Satellite remote sensing enables monitoring of soil organic carbon decline in croplands of Jilin China.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-38386-x
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DOI: https://doi.org/10.1038/s41598-026-38386-x
Keywords
- Soil organic carbon (SOC)
- SOC decline
- Broadband spectral index
- Jilin province
- Multi-source remote sensing
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