Soil line concept and its use for soil mapping and monitoring (overview)

The soil line concept was proposed in 1977. Since then, it has been widely used for satellite monitoring of vegetation, land cover and soils. A critical review of scientific publications on the use of the soil line concept in remote sensing is carried out. The review is based on the analysis of publications indexed in the scientific databases RISC and Scopus. The search was performed using the terms “soil line” and “soil AND line AND spectral AND reflection” in the titles of articles, in keywords and in abstracts of publications for all available years. The following types of publications were included in the sample: articles in scientific journals, review articles, book chapters, and articles in conference proceedings. A total of 104 articles were analyzed. It was found that the soil line concept is most widely used in the creation of vegetation spectral indices for monitoring vegetation cover. It is also used to assess the state of land cover and in soil monitoring. The leader in the number of publications in this field of knowledge are specialists from China. Together with specialists from the USA and Russia, they have published about half of all publications. By affiliation of the first author, publications of the Chinese Academy of Sciences and the V.V. Dokuchaev Soil Science Institute (Russia) prevail. The concept of soil line is promising for mapping and monitoring of separate groups of soil properties, less often - separate soil properties by the character of their open surface image. For its wider use, additional studies of the influence of individual properties of different soils on their spetcral reflectivity in the visible and NIR spectral ranges are needed. The hypothesis about the possibility of satellite mapping and monitoring of soil types (or other classification divisions) based on the soil line concept requires extensive experimental confirmation.

1. Kir'yanova E.Yu., Savin I.Yu., Liniya pochv kak indikator neodnorodnostei pochvennogo pokrova (Soil line as an indicator of soil cover heterogeneities), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, Vol. 8, No. 4, pp. 310–318.

2. Koroleva P.V., Rukhovich D.I., Rukhovich A.D., Rukhovich D.D., Kulyanitsa A.L., Trubnikov A.V., Kalinina N.V., Simakova M.S. Mestopolozhenie otkrytoi poverkhnosti pochvy i linii pochvy v spektral'nom prostranstve RED-NIR (Location of the exposed soil surface and soil line in the RED-NIR spectral space), Pochvovedenie, 2017, No. 12, pp. 1435–1446.

3. Kulyanitsa A.L., Koroleva P.V., Rukhovich D.I., Rukhovich A.D., Rukhovich D.D., Simakova M.S., Postroenie kart koehffitsientov “a” i “b” linii pochv, rasschitannykh po 34 raznovremennym kadram LANDSAT (Construction of maps of coefficients “a” and “b” of soil lines calculated from 34 multi-temporal LANDSAT frames), Informatsiya i kosmos, 2016, No. 1, pp. 100–114.

4. Savin I.Yu., Deshifrirovaniye pochvennogo pokrova lesostepi Tsentral'no-Chernozemnogo rayona po srednemasshtabnym kosmicheskim snimkam: Dis. … kand. geogr. nauk (Detection of soil patterns of the forest-steppe of the Central Black Earth region based on medium-scale satellite imagery, Cand. geogr. sci. thesis), Moscow, 1990. 300 p.

5. Savin I.Yu., Spatial aspects of applied Soil Science, Dokuchaev Soil Bulletin, 2020, Vol. 101, pp. 5–18, DOI: https://doi.org/10.19047/0136-1694-2020-101-5-18.

6. Ukrainskii P.A., Zemlyakova A.V., Opredelenie parametrov pochvennoi linii dlya avtomatizirovannogo raspoznavaniya otkrytoi poverkhnosti pochvy na kosmicheskikh snimkakh (Determination of soil line parameters for automated recognition of open soil surface on space images), Mezhdunarodnyi zhurnal prikladnykh i fundamental'nykh issledovanii, 2014, No. 9–1, pp. 140–144.

7. Baret F., Jacquemoud S., Hanocq J.F., About the soil line concept in remote-sensing, Adv. Space Res., 1993, Vol. 13, pp. 281–284.

8. Baret F., Guyot G., Potentials and limits of vegetation indexes for LAI and APAR assessment, Remote Sens. Environ., 1991, Vol. 35, pp. 161–173.

9. Baret F., Guyot G., Major D., TSAVI – A Vegetation Index which Minimizes Soil Brightness Effects on LAI and APAR Estimation, In: Proc. of 12 th Canadian Symposium on Remote Sensing and 1989 International Geoscience and Remote Sensing Symposium, IGARSS’89: Vancouver, 1989, pp. 1355–1358.

10. Chen S.-M., Zou S.-Q., Mao Y.-L., Liang W.-X., Ding H., Inversion of Soil Organic Matter Content in Wetland Using Multispectral Data Based on Soil Spectral Reconstruction, Spectroscopy and Spectral Analysis, 2018, Vol. 38(3), pp. 912–917, DOI: https://doi.org/10.3964/j.issn.1000-0593(2018)03-0912-06.

11. Demattê J.A.M., Campos R.C., Alves M.C., Fiorio P.R., Nanni M.R., Visible-NIR reflectance: A new approach on soil evaluation, Geoderma, 2004, Vol. 121(1–2), pp. 95–112.

12. Demattê J.A.M., Fongaro C.T., Rizzo R., Safanelli J.L., Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images, Remote Sensing of Environment, 2018, Vol. 212, pp. 161–175, DOI: https://doi.org/10.1016/j.rse.2018.04.047.

13. Deng L., Mao Z., Li X., ... Duan F., Yan Y., UAV-based multi-spectral remote sensing for precision agriculture: A comparison between different cameras, ISPRS Journal of Photogrammetry and Remote Sensing, 2018, Vol. 146, pp. 124–136.

14. Fox G.A., Sabbagh G.J., Estimation of soil organic matter from red and near-infrared remotely sensed data using a soil line Euclidean distance technique, Soil Sci. Soc. Am. J., 2002, Vol. 66, pp. 1922–1929.

15. Fox G.A., Sabbagh G.J., Searcy S.W., Yang C., An automated soil line identification routine for remotely sensed images, Soil Sci. Soc. Am. J., 2004, Vol. 68, pp. 1326–1331.

16. Fox G.A., Metla R., Soil property analysis using principal components analysis, soil line, and regression models, Soil Sci. Soc. Am. J., 2005, Vol. 69, pp. 1782–1788.

17. Galvao L.S., Vitorello I., Variability of laboratory measured soil lines of soils from southeastern Brazil, Remote Sens. Environ., 1998, Vol. 63, pp. 166–181.

18. Gilabert M.A., González-Piqueras J., García-Haro F.J., Meliá J., A generalized soil-adjusted vegetation index, Remote Sensing of Environment, 2002, Vol. 82(2–3), pp. 303–310.

19. Gitelson A.A., Stark R., Grits U., Rundquist D., Kaufman Y., Derry D., Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction, Int. J. Remote Sens., 2002, Vol. 23, pp. 2537–2562.

20. Huete A.R., Post D.F., Jackson R.D., Soil spectral effects on 4-space vegetation discrimination, Remote Sens. Environ., 1984, Vol. 15, Iss. 2, pp. 155–165, DOI: https://doi.org/10.1016/0034-4257(84)90043-9.

21. Jaishanker R., Thomaskutty A.V., Senthivel T., Sridhar V.N., Soil line transformation based relative radiometric normalization, Int. J. Remote Sens., 2006, Vol. 27, pp. 5103–5108.

22. Jiang H., Wei X., Chen Z., Zhu M., Yao Y., Zhang X., Jia K., Influence of different soil reflectance schemes on the retrieval of vegetation LAI and FVC from PROSAIL in agriculture region, Computers and Electronics in Agriculture, 2023, Vol. 212, art. no. 108165, DOI: https://doi.org/10.1016/j.compag.2023.108165.

23. Koroleva P.V., Rukhovich D.I., Kalinina N.V., Simakova M.S., Kulyanitsa A.L., Rukhovich A.D., Rukhovich D.D., Trubnikov A.V., Characterization of soil types and subtypes in n-dimensional space of multitemporal (empirical) soil line, Eurasian Soil Science, 2018, Vol. 51, No. 9, pp. 1021–1033.

24. Liu H.-J., Meng X.-T., Wang X., Bao Y.-L., Yu Z.-Y., Zhang X.-L., Soil Classification Model Based on the Characteristics of Soil Reflectance Spectrum, Spectroscopy and Spectral Analysis, Vol. 39(8), pp. 2481–2485, DOI: https://doi.org/10.3964/j.issn.1000-0593(2019)08-2481-05.

25. Nakalembe C., Becker-Reshef I., Bonifacio R., Hu G., Humber M.L., Justice C.J., Keniston J., Mwangi K., Rembold F., Shukla S., Urbano F., Whitcraft A.C., Li Y., Zappacosta M., Jarvis I., Sanchez A., A review of satellite-based global agricultural monitoring systems available for Africa, Global Food Security, 2021, Vol. 29, Art. no. 100543, DOI: https://doi.org/10.1016/j.gfs.2021.100543.

26. Prudnikova E.Yu., Savin I.Yu., The possibilities of soil line concept application for the detection of soil properties, In: GlobalSoilMap: Digital Soil Mapping from Country to Globe, Proc. of the GlobalSoilMap 2017 Conference, 2018, pp. 97–102.

27. Prudnikova E., Savin I., Vindeker G., Grubina P., Shishkonakova E., Sharychev D., Influence of Soil Background on Spectral Reflectance of Winter Wheat Crop Canopy, Remote Sensing, 2019, Vol. 11(16), Art. no. 1932, DOI: https://doi.org/10.3390/rs11161932.

28. Qin Q., You L., Zhao Y., Zhao S., Yao Y., Soil line automatic identification algorithm based on two-dimensional feature space, Transactions of the Chinese Society of Agricultural Engineering, 2012, Vol. 28(3), pp. 167–171, DOI: https://doi.org/10.3969/j.issn.1002-6819.2012.03.029.

29. Richardson A.J., Wiegand C., Distinguishing vegetation from soil background information (by gray mapping of Landsat MSS data), Photogramm. Eng. Remote Sens., 1977, Vol. 43, pp. 1541–1552.

30. Shoshany M., Roitberg E., Goldshleger N., Kizel F., Universal quadratic soil spectral reflectance line and its deviation patterns' relationships with chemical and textural properties: A global data base analysis, Remote Sensing of Environment, 2022, Vol. 280, art. no. 113182, DOI: https://doi.org/10.1016/j.rse.2022.113182.

31. “Soil line” A Dictionary of Earth Sciences. Retrieved May 16, 2024 from Encyclopedia.com: URL: https://www.encyclopedia.com/science/dictionaries-thesauruses-pictures-and-press-releases/soil-line.

32. Thoma D., Gupta S., Bauer M., Evaluation of optical remote sensing models for crop residue cover assessment, J. Soil Water Conserv., 2004, Vol. 59, pp. 224–233.

33. Wang X., Wang M., Wang S., Wu Y., Extraction of vegetation information from visible unmanned aerial vehicle images, Transactions of the Chinese Society of Agricultural Engineering, 2015, Vol. 31(5), pp. 152–159.

34. Wu X.-P., Xu H.-Q., Cross-Comparison between GF-2 PMS2 and ZY-3 MUX Sensor Data, Spectroscopy and Spectral Analysis, 2019, Vol. 39(1), pp. 310–318, DOI: https://doi.org/10.3964/j.issn.1000-0593(2019)01-0310-09.