Abstract
Soil sampling is critical to obtaining reliable input for farm field-level digital soil mapping (DSM). Sample size and location are the key issues for soil sampling. However, sample size is often restricted by available budgets. In this case, recognizing the key sample locations is necessary. Existing methods have optimized the sample locations in a global manner without considering the impacts of local heterogeneity of soil. In this paper, a novel sampling approach based on the local heterogeneity of soil with a limited sample size (40 samples in this research) was developed. First, the local heterogeneity of soil was inferred. Second, the sub-regions were divided based on the level of local soil heterogeneity and the corresponding sample numbers were determined. Finally, the key sample locations were determined based on the fuzzy memberships. To validate the proposed method, it was compared with stratified random sampling, k-means sampling and conditional Latin hypercube sampling. The ordinary kriging method was applied to map five soil properties, including soil organic matter, pH, total nitrogen, available phosphorus and available potassium. The comparative experiments showed that the proposed method has better robustness in satisfying good mapping accuracy for multi-soil properties at the farm field level compared with the competing sampling methods, as indicated by the relatively lower and more stable mean bias error (MBE) and root mean square error (RMSE) values. It can be concluded that the consideration of local heterogeneity of soil is helpful to recognize the key sample locations for limited sample sizes.
Similar content being viewed by others
References
An, Y. M., Yang, L., Zhu, A. X., Qin, C. Z., & Shi, J. J. (2018). Identification of representative samples from existing samples for digital soil mapping. Geoderma, 311, 109–119. doi: https://doi.org/10.1016/j.geoderma.2017.03.014
Bazzi, C. L., Schenatto, K., Upadhyaya, S., Rojo, F., Kizer, E., & Ko-Madden, C. (2019). Optimal placement of proximal sensors for precision irrigation in tree crops. Precision Agriculture, 20(4), 663–674. doi: https://doi.org/10.1007/s11119-018-9604-3
Bezdek, J. C., Ehrlich, R., & Full, W. (1984). Fcm - the Fuzzy C-Means Clustering-Algorithm. Computers & Geosciences, 10(2–3), 191–203. Doi https://doi.org/10.1016/0098-3004(84)90020-7
Biswas, A., & Zhang, Y. K. (2018). Sampling Designs for Validating Digital Soil Maps: A Review. Pedosphere, 28(1), 1–15. doi: https://doi.org/10.1016/S1002-0160(18)60001-3
Brus, D. J. (2019). Sampling for digital soil mapping: A tutorial supported by R scripts. Geoderma, 338, 464–480. doi: https://doi.org/10.1016/j.geoderma.2018.07.036
Brus, D. J., & Heuvelink, G. B. M. (2007). Optimization of sample patterns for universal kriging of environmental variables. Geoderma, 138(1–2), 86–95. doi: https://doi.org/10.1016/j.geoderma.2006.10.016
Carney, R. M. (2011). ArcOSAUR: ArcGIS Operations for Surface Analysis Using Rasters. Integrative and Comparative Biology, 51, E171–E171
Chaplot, V., Lorentz, S., Podwojewski, P., & Jewitt, G. (2010). Digital mapping of A-horizon thickness using the correlation between various soil properties and soil apparent electrical resistivity. Geoderma, 157(3–4), 154–164. doi: https://doi.org/10.1016/j.geoderma.2010.04.006
Cheng, Z. Q., Meng, J. H., & Wang, Y. M. (2016). Improving Spring Maize Yield Estimation at Field Scale by Assimilating Time-Series HJ-1 CCD Data into the WOFOST Model Using a New Method with Fast Algorithms. Remote Sensing, 8(4), doi: https://doi.org/10.3390/rs8040303
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., et al. (2015). System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geoscientific Model Development, 8(7), 1991–2007. doi: https://doi.org/10.5194/gmd-8-1991-2015
Debaene, G., Niedzwiecki, J., Pecio, A., & Zurek, A. (2014). Effect of the number of calibration samples on the prediction of several soil properties at the farm-scale. Geoderma, 214, 114–125. doi: https://doi.org/10.1016/j.geoderma.2013.09.022
Gao, B. B., Pan, Y. C., Chen, Z. Y., Wu, F., Ren, X. H., & Hu, M. G. (2016). A Spatial Conditioned Latin Hypercube Sampling Method for Mapping Using Ancillary Data. Transactions in GIS, 20(5), 735–754. doi: https://doi.org/10.1111/tgis.12176
Ghotbi, A. R., & Taciroglu, E. (2021). Structural seismic damage and loss assessments using a multi-conditioning ground motion selection approach based on an efficient sampling technique. Bulletin of Earthquake Engineering, 19(3), 1271–1287. doi: https://doi.org/10.1007/s10518-020-01016-6
Gok, G., & Gurbuz, O. A. (2020). Application of geostatistics for grid and random sampling schemes for a grassland in Nigde, Turkey. Environmental Monitoring and Assessment, 192(5), doi: https://doi.org/10.1007/s10661-020-08281-7
Lesch, S. M. (2005). Sensor-directed response surface sampling designs for characterizing spatial variation in soil properties. Computers and Electronics in Agriculture, 46(1–3), 153–179. doi: https://doi.org/10.1016/j.compag.2004.11.004
Ma, T. W., Brus, D. J., Zhu, A. X., Zhang, L., & Scholten, T. (2020). Comparison of conditioned Latin hypercube and feature space coverage sampling for predicting soil classes using simulation from soil maps. Geoderma, 370, doi: https://doi.org/10.1016/j.geoderma.2020.114366
Malone, B. P., Minasny, B., & Brungard, C. (2019). Some methods to improve the utility of conditioned Latin hypercube sampling. Peerj, 7, doi: https://doi.org/10.7717/peerj.6451
Mehnatkesh, A., Ayoubi, S., Jalalian, A., & Sahrawat, K. L. (2013). Relationships between soil depth and terrain attributes in a semi arid hilly region in western Iran. Journal of Mountain Science, 10(1), 163–172. doi: https://doi.org/10.1007/s11629-013-2427-9
Miller, B. A., Koszinski, S., Wehrhan, M., & Sommer, M. (2015). Impact of multi-scale predictor selection for modeling soil properties. Geoderma, 239, 97–106. doi: https://doi.org/10.1016/j.geoderma.2014.09.018
Oliver, M. A., & Webster, R. (1990). Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information Systems, 4(3), 313–332. doi: https://doi.org/10.1080/02693799008941549
Pusch, M., Samuel-Rosa, A., Oliveira, A. L. G., Magalhães, P. S. G., & do Amaral, L. R. (2022). Improving soil property maps for precision agriculture in the presence of outliers using covariates. Precision Agriculture. doi: https://doi.org/10.1007/s11119-022-09898-z
Qin, C. Z., Zhu, A. X., Qiu, W. L., Lu, Y. J., Li, B. L., & Pei, T. (2012). Mapping soil organic matter in small low-relief catchments using fuzzy slope position information. Geoderma, 171, 64–74. doi: https://doi.org/10.1016/j.geoderma.2011.06.006
Rehman, S. (1998). Solar radiation over Saudi Arabia and comparisons with empirical models. Energy, 23(12), 1077–1082. doi: https://doi.org/10.1016/S0360-5442(98)00057-7
Royle, J. A., & Nychka, D. (1998). An algorithm for the construction of spatial coverage designs with implementation in SPLUS. Computers & Geosciences, 24(5), 479–488. doi: https://doi.org/10.1016/S0098-3004(98)00020-X
Samuel-Rosa, A., Heuvelink, G. B. M., Vasques, G. M., & Anjos, L. H. C. (2015). Do more detailed environmental covariates deliver more accurate soil maps? Geoderma, 243, 214–227. doi: https://doi.org/10.1016/j.geoderma.2014.12.017
Su, N., Xu, T. S., Song, L. T., Wang, R. J., & Wei, Y. Y. (2015). Variable rate fertilization system with adjustable active feed-roll length. International Journal of Agricultural and Biological Engineering, 8(4), 19–26. doi: https://doi.org/10.3965/j.ijabe.20150804.1644
Sun, X. L., Wang, H. L., Zhao, Y. G., Zhang, C. S., & Zhang, G. L. (2017). Digital soil mapping based on wavelet decomposed components of environmental covariates. Geoderma, 303, 118–132. doi: https://doi.org/10.1016/j.geoderma.2017.05.017
Vasat, R., Boruvka, L., & Jaksik, O. (2012). Number of sampling points influences the parameters of soil properties spatial distribution and kriged maps.Digital Soil Assessments and Beyond,251–256
Wadoux, A., & Brus, D. J. (2021). How to compare sampling designs for mapping? European Journal of Soil Science, 72(1), 35–46. doi: https://doi.org/10.1111/ejss.12962
Walvoort, D. J. J., Brus, D. J., & de Gruijter, J. J. (2010). An R package for spatial coverage sampling and random sampling from compact geographical strata by k-means. Computers & Geosciences, 36(10), 1261–1267. doi: https://doi.org/10.1016/j.cageo.2010.04.005
Wang, J. F., Jiang, C. S., Hu, M. G., Cao, Z. D., Guo, Y. S., Li, L. F., et al. (2013). Design-based spatial sampling: Theory and implementation. Environmental Modelling & Software, 40, 280–288. doi: https://doi.org/10.1016/j.envsoft.2012.09.015
Wang, J. F., Li, X. H., Christakos, G., Liao, Y. L., Zhang, T., Gu, X., et al. (2010). Geographical Detectors-Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China. International Journal of Geographical Information Science, 24(1), 107–127. doi: https://doi.org/10.1080/13658810802443457
Wang, J. F., Stein, A., Gao, B. B., & Ge, Y. (2012a). A review of spatial sampling. Spatial Statistics, 2, 1–14. doi: 10.1016/j.spasta.2012a.08.001
Wang, J. H., Ge, Y., Heuvelink, G. B. M., & Zhou, C. H. (2014). Spatial Sampling Design for Estimating Regional GPP With Spatial Heterogeneities. IEEE Geoscience and Remote Sensing Letters, 11(2), 539–543. doi: https://doi.org/10.1109/Lgrs.2013.2274453
Wang, J. H., Ge, Y., Heuvelink, G. B. M., Zhou, C. H., & Brus, D. (2012b). Effect of the sampling design of ground control points on the geometric correction of remotely sensed imagery. International Journal of Applied Earth Observation and Geoinformation, 18, 91–100. doi: 10.1016/j.jag.2012b.01.001
Webster, R., Welham, S. J., Potts, J. M., & Oliver, M. A. (2006). Estimating the spatial scales of regionalized variables by nested sampling, hierarchical analysis of variance and residual maximum likelihood. Computers & Geosciences, 32(9), 1320–1333. doi: https://doi.org/10.1016/j.cageo.2005.12.002
Yang, L., Brus, D. J., Zhu, A. X., Li, X. M., & Shi, J. J. (2018). Accounting for access costs in validation of soil maps: A comparison of design-based sampling strategies. Geoderma, 315, 160–169. doi: https://doi.org/10.1016/j.geoderma.2017.11.028
Yang, L., Li, X. M., Shi, J. J., Shen, F. X., Qi, F., Gao, B. B., et al. (2020). Evaluation of conditioned Latin hypercube sampling for soil mapping based on a machine learning method. Geoderma, 369, doi: https://doi.org/10.1016/j.geoderma.2020.114337
Yang, L., Zhu, A. X., Qi, F., Qin, C. Z., Li, B. L., & Pei, T. (2013). An integrative hierarchical stepwise sampling strategy for spatial sampling and its application in digital soil mapping. International Journal of Geographical Information Science, 27(1), 1–23. doi: https://doi.org/10.1080/13658816.2012.658053
Zhang, G. L., Liu, F., & Song, X. D. (2017). Recent progress and future prospect of digital soil mapping: A review. Journal of Integrative Agriculture, 16(12), 2871–2885. doi: https://doi.org/10.1016/S2095-3119(17)61762-3
Zhu, A., Yang, L., Fan, N., Zeng, C., & Zhang, G. (2018). The review and outlook of digital soil mapping. Progress in Geography, 37(1), 66–78
Zhu, A. X., Yang, L., Li, B. L., Qin, C. Z., English, E., Burt, J. E., et al. (2008). Purposive Sampling for Digital Soil Mapping for Areas with Limited Data. Digital Soil Mapping with Limited Data, 233–. doi: https://doi.org/10.1007/978-1-4020-8592-5_20
Acknowledgements
This work was supported by the Key Scientific Research Projects of Colleges and Universities in Henan Province (project number: 22A170021) and the National Natural Science Foundation of China (project number: 41801085). We thank the Sheltala Farm and the Aerospace Information Research Institute, Chinese Academy of Sciences for supplying the study data. The authors would like to thank the reviewers and editors for their valuable advice and comments.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wang, Y., Qi, Q., Bao, Z. et al. A novel sampling design considering the local heterogeneity of soil for farm field-level mapping with multiple soil properties. Precision Agric 24, 1–22 (2023). https://doi.org/10.1007/s11119-022-09926-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11119-022-09926-y