Estimating soil organic carbon levels in cultivated soils from satellite image using parametric and data-driven methods

References

• Abatzoglou, J. T., D. J. McEvoy, and K. T. Redmond. 2017. “The West Wide Drought Tracker: Drought Monitoring at Fine Spatial Scales.” Bulletin of the American Meteorological Society 98 (9): 1815–1820. doi:10.1175/BAMS-D-16-0193.1.
   Web of Science ®Google Scholar
• Aggarwal, C. C. 2018. Neural Networks and Deep Learning. Vol. 10. Springer.
   Google Scholar
• Alipour, T., M. R. Sarajian, and A. Esmaeily. 2003. “Land Surface Temperature Estimation from a Thermal Band of Landsat Sensor, Case Study: Alashtar City.” The International Achieves of the Photogrammetry, Remote Sensing and Spatial Information Sciences 38 (4): 1–6.
   Google Scholar
• Angelopoulou, T., N. Tziolas, A. Balafoutis, G. Zalidis, and D. Bochtis. 2019. “Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review.” Remote Sensing 11 (6): 676. doi:10.3390/rs11060676.
   Web of Science ®Google Scholar
• Baret, F., S. Jacquemoud, and J. F. Hanocq. 1993. “The Soil Line Concept in Remote Sensing.” Remote Sensing Reviews 7 (1): 65–82. doi:10.1080/02757259309532166.
   Google Scholar
• Behrens, T., K. Schmidt, R. A. MacMillan, and R. A. Viscarra Rossel. 2018. “Multi-Scale Digital Soil Mapping with Deep Learning.” Scientific Reports 8 (1): 15244. doi:10.1038/s41598-018-33516-6.
   PubMed Web of Science ®Google Scholar
• Belgiu, M. and L. Drăguţ. 2016. “Random Forest in Remote Sensing: A Review of Applications and Future Directions.” ISPRS Journal of Photogrammetry and Remote Sensing 114: 24–31. doi:10.1016/j.isprsjprs.2016.01.011.
   Web of Science ®Google Scholar
• Ben-Dor, E., J. R. Irons, and G. F. Epema. 1999. ”Soil Reflectante”. In Manual of Remote Sensing: Remote Sensing for Earth Science, edited by A. N. Rencz, R. A. Ryerson, Vol. 3, 111–187. Hoboken, NJ, USA:John Wiley & Sons lnc. ISBN
   Google Scholar
• Blackmer, A. M. and S. White. 1998. “Using Precision Farming Technologies to Improve Management of Soil and Fertilizer Nitrogen.” Australian Journal of Agricultural Research 49 (3): 555–564. doi:10.1071/A97073.
   Google Scholar
• Breiman, L., J. H. Friedman, R. A. Olshen, and C. J. Stone. 1984. Classification and Regression Trees. Routledge.
   Google Scholar
• Casa, R., F. Castaldi, S. Pascucci, B. Basso, and S. Pignatti. 2013. “Geophysical and Hyperspectral Data Fusion Techniques for In-Field Estimation of Soil Properties.” Vadose Zone Journal 12 (4): vzj2012.0201. doi:http://dx.doi.org/10.2136/vzj2012.0201.
   Web of Science ®Google Scholar
• Castaldi, F., A. Palombo, F. Santini, S. Pascucci, S. Pignatti, and R. Casa. 2016. “Evaluation of the Potential of the Current and Forthcoming Multispectral and Hyperspectral Imagers to Estimate Soil Texture and Organic Carbon.” Remote Sensing of Environment 179: 54–65. doi:10.1016/j.rse.2016.03.025.
   Web of Science ®Google Scholar
• Castaldi, F., A. Hueni, S. Chabrillat, K. Ward, G. Buttafuoco, B. Bomans, and B. van Wesemael. 2019. “Evaluating the Capability of the Sentinel 2 Data for Soil Organic Carbon Prediction in Croplands.” ISPRS Journal of Photogrammetry and Remote Sensing 147: 267–282. doi:10.1016/j.isprsjprs.2018.11.026.
   Web of Science ®Google Scholar
• Chen, D., N. Chang, J. Xiao, Q. Zhou, and W. Wu. 2019. “Mapping Dynamics of Soil Organic Matter in Croplands with MODIS Data and Machine Learning Algorithms.” The Science of the Total Environment 669: 844–855. doi:10.1016/j.scitotenv.2019.03.151.
   PubMed Web of Science ®Google Scholar
• Cressie, N. A. 1993. Statistics for Spatial Data. New York: John Willy and Sons, Inc.
   Google Scholar
• Croft, H., N. J. Kuhn, and K. Anderson. 2012. “On the Use of Remote Sensing Techniques for Monitoring Spatio-Temporal Soil Organic Carbon Dynamics in Agricultural Systems.” Catena 94: 64–74. doi:10.1016/j.catena.2012.01.001.
   Web of Science ®Google Scholar
• Forkuor, G., O. K. L. Hounkpatin, G. Welp, M. Thiel, and D. Hui. 2017. “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 12 (1): 1–21. doi:10.1371/journal.pone.0170478.
   Web of Science ®Google Scholar
• Fox, G. A. and R. Metla. 2005. “Soil Property Analysis Using Principal Components Analysis, Soil Line, and Regression Models.” Soil Science Society of America Journal 69 (6): 1782–1788. doi:10.2136/sssaj2004.0362.
   Web of Science ®Google Scholar
• Gholizadeh, A., D. Žižala, M. Saberioon, and L. Borůvka. 2018. “Soil Organic Carbon and Texture Retrieving and Mapping Using Proximal, Airborne and Sentinel-2 Spectral Imaging.” Remote Sensing of Environment 218: 89–103. doi:10.1016/j.rse.2018.09.015.
   Web of Science ®Google Scholar
• Gilabert, M. A., J. González-Piqueras, F. J. Garcıa-Haro, and J. Meliá. 2002. “A Generalized Soil-Adjusted Vegetation Index.” Remote Sensing of Environment 82 (2–3): 303–310. doi:10.1016/S0034-4257(02)00048-2.
   Web of Science ®Google Scholar
• Goldstein, H. 2011. Multilevel Statistical Models. John Wiley & Sons.
   Google Scholar
• Gomez, C., R. Oltra-Carrio, S. Bacha, P. Lagacherie, and X. Briottet. 2015. “Evaluating the Sensitivity of Clay Content Prediction to Atmospheric Effects and Degradation of Image Spatial Resolution Using Hyperspectral VNIR/SWIR Imagery.” Remote Sensing of Environment 164: 1–15. doi:10.1016/j.rse.2015.02.019.
   Web of Science ®Google Scholar
• Gomez, C., K. Adeline, S. Bacha, B. Driessen, N. Gorretta, P. Lagacherie, J. M. Roger, and X. Briottet. 2018. “Sensitivity of Clay Content Prediction to Spectral Configuration of VNIR/SWIR Imaging Data, from Multispectral to Hyperspectral Scenarios.” Remote Sensing of Environment 204: 18–30. doi:10.1016/j.rse.2017.10.047.
   Web of Science ®Google Scholar
• Grinand, C., D. Arrouays, B. Laroche, and M. P. Martin. 2008. “Extrapolating Regional Soil Landscapes from an Existing Soil Map: Sampling Intensity, Validation Procedures, and Integration of Spatial Context.” Geoderma 143 (1–2): 180–190. doi:10.1016/j.geoderma.2007.11.004.
   Web of Science ®Google Scholar
• Hengl, T. 2006. “Finding the Right Pixel Size.” Computers & Geosciences 32 (9): 1283–1298. doi:10.1016/j.cageo.2005.11.008.
   Web of Science ®Google Scholar
• Irons, J. R., J. L. Dwyer, and J. A. Barsi. 2012. “The Next Landsat Satellite: The Landsat Data Continuity Mission.” Remote Sensing of Environment 122: 11–21. doi:10.1016/j.rse.2011.08.026.
   Web of Science ®Google Scholar
• Ismail, M. and R. K. Yacoub. 2012. “Digital Soil Map Using the Capability of New Technology in Sugar Beet Area, Nubariya, Egypt.” The Egyptian Journal of Remote Sensing and Space Science 15 (2): 113–124. doi:10.1016/j.ejrs.2012.08.001.
   Google Scholar
• James, G., D. Witten, T. Hastie, and R. Tibshirani. 2013. An Introduction to Statistical Learning, 18. Vol. 112. New York: springer.
   Google Scholar
• Jeong, G., H. Oeverdieck, S. J. Park, B. Huwe, and M. Ließ. 2017. “Spatial Soil Nutrients Prediction Using Three Supervised Learning Methods for Assessment of Land Potentials in Complex Terrain.” Catena 154: 73–84. doi:10.1016/j.catena.2017.02.006.
   Web of Science ®Google Scholar
• Koroleva, P. V., D. I. Rukhovich, A. D. Rukhovich, D. D. Rukhovich, A. L. Kulyanitsa, A. V. Trubnikov, and M. S. Simakova. 2017. “Location of Bare Soil Surface and Soil Line on the RED–NIR Spectral Plane.” Eurasian Soil Science 50 (12): 1375–1385. doi:10.1134/S1064229317100040.
   Web of Science ®Google Scholar
• Ladoni, M., H. A. Bahrami, S. K. Alavipanah, and A. A. Norouzi. 2010. “Estimating Soil Organic Carbon from Soil Reflectance: A Review.” Precision Agriculture 11 (1): 82–99. doi:10.1007/s11119-009-9123-3.
   Web of Science ®Google Scholar
• Liu, Z., M. A. Shao, and Y. Wang. 2011. “Effect of Environmental Factors on Regional Soil Organic Carbon Stocks Across the Loess Plateau Region, China.” Agriculture, Ecosystems & Environment 142 (3–4): 184–194. doi:10.1016/j.agee.2011.05.002.
   Web of Science ®Google Scholar
• McBratney, A. B., M. M. Santos, and B. Minasny. 2003. “On Digital Soil Mapping.” Geoderma 117 (1–2): 3–52. doi:10.1016/S0016-7061(03)00223-4.
   Web of Science ®Google Scholar
• Nocita, M., A. Stevens, C. Noon, and B. J. G. van Wesemael. 2013. “Prediction of Soil Organic Carbon for Different Levels of Soil Moisture Using Vis-NIR Spectroscopy Geoderma .” 199: 37–42.
   Web of Science ®Google Scholar
• Nussbaum, M., L. Ettlin, A. Çöltekin, B. Suter, and M. Egli. 2011. “The Relevance of Scale in Soil Maps.” Bulletin BGS 32: 63–70.
   Google Scholar
• Ose, K., T. Corpetti, and L. Demagistri. 2016. ”Multispectral Satellite Image Processing.” In Optical Remote Sensing of Land Surface: Techniques and Methods, edited by N. Baghdadi, M. Zribi N. Baghdadi, M. Zribi, 58–124. Oxford: Elsevier Inc. 10.1016/B978-1-.
   Google Scholar
• Pimstein, A., G. Notesco, and E. Ben-Dor. 2011. “Performance of Three Identical Spectrometers in Retrieving Soil Reflectance Under Laboratory Conditions.” Soil Science Society of America Journal 75 (2): 746–759. doi:10.2136/sssaj2010.0174.
   Web of Science ®Google Scholar
• Pribyl, D. W. 2010. “A Critical Review of the Conventional SOC to SOM Conversion Factor.” Geoderma 156 (3–4): 75–83. doi:10.1016/j.geoderma.2010.02.003.
   Web of Science ®Google Scholar
• R Core Team, & & 2018. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
   Google Scholar
• RCC-ACIS (2018). Rcc-Acis. Retrieved October 18, 2018, from http://www.rcc-acis.org/index.html.
   Google Scholar
• Richardson, A. J. and C. L. Wiegand. 1977. “Distinguishing Vegetation from Soil Background Information.” Photogrammetric Engineering and Remote Sensing 43 (12): 1541–1552.
   Web of Science ®Google Scholar
• Sahwan, W., B. Lucke, M. Kappas, and R. Baumler. 2018. “Assessing the Spatial Variability of Soil Surface Colors in Northern Jordan Using Satellite Data from Landsat-8 and Sentinel-2.” European Journal of Remote Sensing 51 (1): 850–862. doi:https://doi.org/10.1080/22797254.2018.1502624.
   Web of Science ®Google Scholar
• SDSU. 2006. Soil Testing Procedures in Use at the South Dakota State Soil Testing and Plant Analysis Laboratory. Brookings, South Dakota.: SDSU.
   Google Scholar
• Silvero, N. E., J. A. Demattê, J. de Souza Vieira, F. A. de Oliveira Mello, M. T. A. Amorim, R. R. Poppiel, and B. R. Bonfatti. 2021. “Soil Property Maps with Satellite Images at Multiple Scales and Its Impact on Management and Classification.” Geoderma 397: 115089. doi:10.1016/j.geoderma.2021.115089.
   Web of Science ®Google Scholar
• Stal, U. 1981. Land Resource Regions and Major Land Resource Areas of the United States.
   Google Scholar
• Stenberg, B., R. A. Viscarra Rossel, A. M. Mouazen, and J. Wetterlind. 2010. ”Visible and Near Infrared Spectroscopy in Soil Science”. In Advances in Agronomy, edited by P. M. Bertsch, R. L. Phillips, K. M. Scow, L. P. Wilding, Vol. 107, 163–215. San Diego, CA, USA:Elsevier.
   Google Scholar
• USDA-NRCS. 2018. “Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture”. Web Soil Survey. Accessed 10 12 2018. https://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx
   Google Scholar
• Viscarra Rossel, R. A., A. Chappell, P. De Caritat, and N. J. McKenzie. 2011. “On the Soil Information Content of Visible–near Infrared Reflectance Spectra.” European Journal of Soil Science 62 (3): 442–453. doi:10.1111/j.1365-2389.2011.01372.x.
   Web of Science ®Google Scholar
• Wang, X., Y. Zhang, P. M. Atkinson, and H. Yao. 2020. “Predicting Soil Organic Carbon Content in Spain by Combining Landsat TM and ALOS PALSAR Images.” International Journal of Applied Earth Observation and Geoinformation 92: 102182. doi:10.1016/j.jag.2020.102182.
   Web of Science ®Google Scholar
• Wollenhaupt, N. C. and R. P. Wolkowski. 1994. “Grid Soil Sampling.” Better Crops 78 (4): 6–9.
   Google Scholar
• Xie, B., J. Ding, X. Ge, X. Li, L. Han, and Z. Wang. 2022. “Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms.” Sensors 22 (7): 2685. doi:10.3390/s22072685.
   PubMed Web of Science ®Google Scholar
• Zanter, K. 2016. “Landsat 8 (L8) Data Users Handbook.” Landsat Science Official Website 33.
   Google Scholar
• Žížala, D., R. Minařík, and T. Zádorová. 2019. “Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions.” Remote Sensing 11 (24): 2947. doi:10.3390/rs11242947.
   Web of Science ®Google Scholar