RUDN Journal of Agronomy and Animal IndustriesRUDN Journal of Agronomy and Animal IndustriesВестник Российского университета дружбы народов. Серия: Агрономия и животноводство2312-797X2312-7988Peoples’ Friendship University of Russia named after Patrice Lumumba1942910.22363/2312-797X-2018-13-4-317-335Land managementЗемлеустройство и кадастрыResearch ArticleDIGITAL SOIL MAPPING FOR SMART AGRICULTURE: THE SOLIM METHOD AND SOFTWARE PLATFORMSЦИФРОВОЕ КАРТИРОВАНИЕ ПОЧВ ДЛЯ ИННОВАЦИОННОГО СЕЛЬСКОГО ХОЗЯЙСТВА: SOLIM МЕТОД И ПЛАТФОРМЫ ПРОГРАММНОГО ОБЕСПЕЧЕНИЯZhuA-Xing XZhuA XDepartment of Geography, University of Wisconsin-Madisonazhu@wisc.eduQinCheng-Zhi ZQinC ZState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciencesazhu@wisc.eduLiangPengLiangPState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciencesazhu@wisc.eduDuFeiDuFDepartment of Geography, University of Wisconsin-Madison.azhu@wisc.eduNanjing Normal UniversityInstitute of Geographic Sciences and Natural Resources ResearchUniversity of Wisconsin-Madison15122018134VOL 13, NO4 (2018)ТОМ 13, №4 (2018)31733528122018Copyright ©; 2018, Zhu A.X., Qin C.Z., Liang P., Du F.Copyright ©; 2018, Zhu A.X., Qin C.Z., Liang P., Du F.2018Zhu A.X., Qin C.Z., Liang P., Du F.Zhu A.X., Qin C.Z., Liang P., Du F.http://creativecommons.org/licenses/by/4.0https://agrojournal.rudn.ru/agronomy/article/view/19429The key challenges faced by many of the existing digital soil mapping (DSM) techniques are the rigid requirements on the size of soil samples to extract the relationships needed and on the stationarity of the extracted relationships. These requirements limit the application of these DSM techniques. This paper provides an overview of the SoLIM approach and an introduction to the operation of SoLIM through the software platforms available. SoLIM is based on the Third Law of Geography, which calls for the comparison of similarity in geographic (environmental) configuration of a prototype and an unsampled location and then use this similarity to predict the value of a soil property at a given location. DSM under SoLIM approach removes requirements on the sample size and the stationarity assumption. In addition, the uncertainty computed based on the similarities can be used to improve the efficiency of error reduction efforts. The SoLIM approach has been implemented in two platforms: SoLIM Solutions and CyberSoLIM. The theoretical foundation and the availability of software platforms under SoLIM make DSM possible and convenient over large and complex geographic regions.digital soil mappingDSMSoLIMFirst Law of GeographySecond Law of GeographyThird Law of Geographyspatial predictionDSMSoLIMцифровое почвенное картированиеПервый закон географииВторой закон географииТретий закон географиипространственное прогнозирование1.McBratney AB, Santos MM, Minasny B. On digital soil mapping. Geoderma. 2003; 117(1-2): 3-52. Available from: doi: 10.1016/S0016-7061(03)00223-4.2.Zhu AX, Lu G, Liu J, Qin CZ, Zhou C. Spatial prediction based on Third Law of Geography Annals of GIS. 2018. Available from: doi: 10.1080/19475683.2018.1534890.3.Tobler WR. A computer movie simulating urban growth in the Detroit region, Economic geography. 1970; 46(sup1):234-240.4.Goodchild MF. The validity and usefulness of laws in geographic information science and geography. Annals of the Association of American Geographers. 2004; 94(2):300-303. Available from: doi: 10.1111/j.1467-8306.2004.09402008.x.5.Goovaerts P. Geostatistics for natural resources evaluation. New York: Oxford University Press; 1997.6.Fotheringham AS, Brunsdon C, Charlton ME. Geographically Weighted Regression - the Analysis of Spatially Varying Relationships. Chichester: John Willey & Sons; 2002.7.Zhu AX. A similarity model for representing soil spatial information. Geoderma. 1997; 77(2-4): 217-242. Available from: doi: 10.1016/S0016-7061(97)00023-28.Zhu AX, Hudson B, Burt JE, Lubich K, Simonson D. Soil mapping using GIS, expert knowledge and fuzzy logic. Soil Science Society of America Journal. 2001;65(5):1463-1472. Available from: doi: 10.2136/sssaj2001.6551463x.9.Qi F, Zhu AX, Harrower M, Burt JE. Fuzzy soil mapping based on prototype category theory. Geoderma. 2006; 136(3-4):774-787. Available from: doi: 10.1016/j.geoderma.2006.06.001.10.Zhu AX, Band LE, Vertessy R, Dutton B. Derivation of soil properties using a Soil-Land Inference Model (SoLIM). Soil Science Society of America Journal. 1997; 61(2):523-533. Available from: doi: 10.2136/sssaj1997.03615995006100020022x11.Zhu AX, Qi F, Moore A, Burt JE. Prediction of soil properties using fuzzy membership. Geoderma. 2010; 158(3-4):199-206. Available from: doi: 10.1016/j.geoderma.2010.05.001.12.Zhu AX, Liu J, Du F, Zhang SJ, Qin CZ, Burt JE, Scholten T. Predictive soil mapping with limited sample data. European Journal of Soil Science. 2015; 66(3):535-547. Available from: doi: 10.1111/ejss.12244.13.Zhu AX. Measuring uncertainty in class assignment for natural resource maps using a similarity model. Photogrammetric Engineering and Remote Sensing. 1997; 63(10):1195-1202.14.Zhang SJ, Zhu AX, Liu J, Yang L, Qin CZ, An YM. An heuristic uncertainty directed field sampling design for digital soil mapping. Geoderma. 2016; 267:123-136. Available from: doi: 10.1016/j.geoderma.2015.12.009.15.Li Y, Zhu AX, Shi Z, Liu J, Du F. Supplemental sampling for digital soil mapping based on prediction uncertainty from both the feature domain and the spatial domain. Geoderma. 2016; 284:73-84. Available from: doi: 10.1016/j.geoderma.2016.08.01316.Zhu AX, Mackay DS. Effects of spatial detail of soil information on watershed modeling. Journal of Hydrology. 2001; 248(1-4):54-77. Available from: doi: 10.1016/S0022-1694(01)00390-0.17.Quinn T, Zhu AX, Burt JE. Effects of detail soil spatial information on watershed modeling across different model scales. International Journal of Applied Earth Observation and Geoinformation. 2005; 7(4):324-338. Available from: doi: 10.1016/j.jag.2005.06.009.18.Zhu AX. A personal construct-based knowledge acquisition process for natural resource mapping. International Journal of Geographic Information Science. 1999; 13(2):119-141. Available from: doi: 10.1080/136588199241382.19.Qi F, Zhu AX. Knowledge discovery from soil maps using inductive learning. International Journal of Geographical information Science. 2003; 17(8):771-795. Available from: doi: 10.1080/13658810310001596049.20.Cheng W, Zhu AX, Qin CZ, Qi F. Updating conventional soil maps by mining soil-environment relationships from individual soil polygons. To be published in Journal of Integrative Agriculture. [Preprint] 2018.21.Qin CZ, Zhu AX, Shi X, Li BL, Pei T, Zhou CH. The quantification of spatial gradation of slope positions. Geomorphology. 2009; 110(3-4):152-161. Available from: doi: 10.1016/j.geomorph.2009.04.003.22.Zhu AX, Liu F, Li BL, Pei T, Qin CZ, Liu GH, Wang YJ, Chen YN, Ma XW, Qi F, Zhou CH. Differentiation of soil conditions over low relief areas using feedback dynamic patterns. Soil Science Society of America Journal. 2010; 74(3):861-869. Available from: doi: 10.2136/sssaj2008.0411.23.Liu F, Geng X, Zhu AX, Fraser W, Waddell A. Soil texture mapping over low relief areas using land surface feedback dynamic patterns extracted from MODIS. Geoderma. 2012; 171:44-52. Available from: doi: 10.1016/j.geoderma.2011.05.00724.Guo S, Zhu AX, Meng L, Burt JE, Du F, Liu J, Zhang G. Unification of soil feedback patterns under different evaporation conditions to improve soil differentiation over flat area. International Journal of Applied Earth Observation and Geoinformation. 2016; 49:126-137. Available from: doi: 10.1016/j.jag.2016.02.002.25.Zeng C, Zhu AX, Liu F, Yang L, Rossiter DG, Liu J, Wang D. The impact of rainfall magnitude on the performance of digital soil mapping over low-relief areas using a land surface dynamic feedback method. Ecological Indicators. 2017; 72:297-309. Available from: doi: 10.1016/j.ecolind.2016.08.023.26.Jiang J, Zhu AX, Qin CZ, Zhu T, Liu J, Du F, Liu J, Zhang G, An Y. CyberSoLIM: a cyber platform for digital soil mapping. Geoderma. 2016; 263:234-243. Available from: doi: 10.1016/j.geoderma.2015.04.018.27.Zhu AX, An YM, Qin CZ, Yang L. Spatial footprint for digital soil mapping based on the Third Law of Geography. To be published in Annals of GIS. [Preprint] 2018.28.Liu J. Integration of samples from multiple sources for predictive mapping over large areas. [Dissertation] University of Wisconsin - Madison, Madison, WI, USA; 2017.29.Liu J, Zhu AX, Rossiter D, Du F, Burt JE. Reliability estimation of individual sample points in individual predictive soil mapping [Preprint] 2018.30.Du F. Knowledge Integration in Geospatial Predictive Mapping. [Dissertation] University of Wisconsin - Madison, Madison, WI, USA; 2017.31.Du F, Zhu AX, Liu J, Yang L. Predictive mapping with small field sample data using semi- supervised machine learning. To be published in Transactions in GIS. [Preprint] 2018.32.Wang DS, Liu JZ, Zhu AX, Wang S, Zeng C, Ma T. Automatic extraction and structuration of soil-environment relationship information from soil survey reports. To be published in Journal of Integrative Agriculture. [Preprint] 2018.