Abstract
Soil sampling, collection, and analysis are a costly and labor-intensive activity that cannot cover the entire farmlands; hence, it was conceived to use high-speed open-source platforms like Google Earth Engine in this research to estimate soil characteristics remotely using high-resolution open-source satellite data. The objective of this research was to estimate soil pH from Sentinel-1, Sentinel-2, and Landsat-8 satellite-derived indices; data from Sentinel-1, Sentinel-2, and Landsat-8 satellite missions were used to generate indices and as proxies in a statistical model to estimate soil pH. Step-wise multiple regression (SWMR), artificial neural networks (ANN), and random forest (RF) regression were used to develop predictive models for soil pH, SWMR, ANN, and RF regression models. The SWMR greedy method of variable selection was used to select the appropriate independent variables that were highly correlated with soil pH. Variables that were retained in the SWMR are B2, B11, Brightness index, Salinity index 2, Salinity index 5 of Sentinel-2 data; VH/VV index of Sentinel 1 and TIR1 (thermal infrared band1) Landsat-8 with p-value < 0.05. Among the four statistical models developed, the class-wise RF model performed better than other models with a cumulative correlation coefficient of 0.87 and RMSE of 0.35. The better performance of class-wise RF models can be attributed to different spectral characteristics of different soil pH groups. More than 70% of the soils in Angul and Balangir districts are acidic soils, and therefore, the training of the dataset was affected by that leading to misclassification of neutral and alkaline soils hindering the performance of single class models. Our results showed that the spectral bands and indices can be used as proxies to soil pH with individual classes of acidic, neutral, and alkaline soils. This study has shown the potential in using big data analytics to predict soil pH leading to the accurate mapping of soils and help in decision support.
Similar content being viewed by others
References
Ahmad, M. W., Mourshed, M., & Rezgui, Y. (2017). Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings, 147, 77–89.
Bai, L., Wang, C., Zang, S., Zhang, Y., Hao, Q., & Wu, Y. (2016). Remote sensing of soil alkalinity and salinity in the Wuyu’er-Shuangyang River Basin, Northeast China. Remote Sensing, 8(2), 163.
Banerjee, K., Panda, S., Bandyopadhyay, J., & Jain, M. K. (2014). Forest canopy density mapping using advance geospatial technique. International Journal of Innovative Science, Engineering & Technology, 1(7), 358–363.
Bannari, A., Guédon, A., & El-Ghmari, A. (2016). Mapping slight and moderate saline soils in irrigated agricultural land using advanced land imager sensor (EO-1) data and semi-empirical models. Communications in Soil Science and Plant Analysis, 47(16), 1883–1906.
Breaux, H. J. (1967). On stepwise multiple linear regression. Army Ballistic Research Lab Abredeen Proving Ground MD.
Breitenbach, M., Nielsen, R., & Grudic, G. (2003). Probabilistic Random Forests: Predicting Data Point Specific Misclassification Probabilities. CU-CS-954–03.
Buerge, I. J., Bächli, A., Kasteel, R., Portmann, R., López-Cabeza, R., Schwab, L. F., & Poiger, T. (2019). Behavior of the chiral herbicide imazamox in soils: PH-dependent, enantioselective degradation, formation and degradation of several chiral metabolites. Environmental Science & Technology, 53(10), 5725–5732.
Byrne, J. M., & Yang, M. (2016). Spatial variability of soil magnetic susceptibility, organic carbon and total nitrogen from farmland in northern China. CATENA, 145, 92–98.
Chang, D.-H., & Islam, S. (2000). Estimation of soil physical properties using remote sensing and artificial neural network. Remote Sensing of Environment, 74(3), 534–544.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46.
De Sousa, L. M., Poggio, L., Batjes, N. H., Heuvelink, G. B., Kempen, B., Riberio, E., & Rossiter, D. (2020). SoilGrids 2.0: Producing quality-assessed soil information for the globe. Soil Discussions, 2020, 1–37.
Douaoui, A., Hartani, T., & Lakehal, M. (2006). La salinisation dans la plaine du Bas-Cheliff: acquis et perspectives. Presented at the Economies d’eau en Systèmes IRrigués au Maghreb. Deuxième atelier régional du projet Sirma.
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., et al. (2012). Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120, 25–36.
Eisele, A., Chabrillat, S., Hecker, C., Hewson, R., Lau, I. C., Rogass, C., et al. (2015). Advantages using the thermal infrared (TIR) to detect and quantify semi-arid soil properties. Remote Sensing of Environment, 163, 296–311.
Eli-Chukwu, N. C. (2019). Applications of artificial intelligence in agriculture: A review. Engineering, Technology & Applied Science Research, 9(4), 4377–4383.
Elshorbagy, A., & Parasuraman, K. (2008). On the relevance of using artificial neural networks for estimating soil moisture content. Journal of Hydrology, 362(1–2), 1–18.
Fichter, W. (1984). Reduction of root-mean-square error in faceted space antennas. AIAA Journal, 22(11), 1679–1684.
Forkuor, G., Hounkpatin, O. K., Welp, G., & Thiel, M. (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), e0170478.
Gao, B.-C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266.
Gascon, F., Cadau, E., Colin, O., Hoersch, B., Isola, C., Fernández, B. L., & Martimort, P. (2014). Copernicus Sentinel-2 mission: products, algorithms and Cal/Val (Vol. 9218, p. 92181E). Presented at the Earth observing systems XIX, International Society for Optics and Photonics.
Gopal, P. M., & Bhargavi, R. (2019). A novel approach for efficient crop yield prediction. Computers and Electronics in Agriculture, 165, 104968.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.
Gorelick, N. (2013). Google earth engine (Vol. 15, p. 11997). Presented at the EGU General Assembly Conference Abstracts.
Grishin, I., & Timirgaleeva, R. (2020). Remote sensing: The method of GIS application for monitoring the state of soils (Vol. 175, p. 06009). Presented at the E3S Web of Conferences, EDP Sciences.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., et al. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS One, 12(2), e0169748.
Kah, M., Beulke, S., & Brown, C. D. (2007). Factors influencing degradation of pesticides in soil. Journal of Agricultural and Food Chemistry, 55(11), 4487–4492.
Kartalopoulos, S. V., & Kartakapoulos, S. V. (1997). Understanding neural networks and fuzzy logic: basic concepts and applications. Wiley-IEEE Press.
Khan, N. M., Rastoskuev, V. V., Shalina, E. V., & Sato, Y. (2001). Mapping salt-affected soils using remote sensing indicators-a simple approach with the use of GIS IDRISI.
Lee, W., Sanchez, J., Mylavarapu, R., & Choe, J. (2003). Estimating chemical properties of Florida soils using spectral reflectance. Transactions of the ASAE, 46(5), 1443.
Li, J., & Mocko, M. (2020). Machine learning for a citizen data scientist: an experience with JMP.
Li, S., & Chen, X. (2014). A new bare-soil index for rapid mapping developing areas using landsat 8 data. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(4), 139.
Ließ, M., Glaser, B., & Huwe, B. (2012). Uncertainty in the spatial prediction of soil texture: Comparison of regression tree and Random Forest models. Geoderma, 170, 70–79.
Liu, K., He, Y., Xu, S., Hu, L., Luo, K., Liu, X., et al. (2018). Mechanism of the effect of pH and biochar on the phytotoxicity of the weak acid herbicides imazethapyr and 2, 4-D in soil to rice (Oryza sativa) and estimation by chemical methods. Ecotoxicology and Environmental Safety, 161, 602–609.
Loveland, T. R., & Irons, J. R. (2016). Landsat 8: The plans, the reality, and the legacy. Remote Sensing of Environment, 185, 1–6.
Malley, D. F., Yesmin, L., Wray, D., & Edwards, S. (1999). Application of near-infrared spectroscopy in analysis of soil mineral nutrients. Communications in Soil Science and Plant Analysis, 30(7–8), 999–1012.
McBratney, A. B., Santos, M. M., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1–2), 3–52.
Mekonnen, Y., Namuduri, S., Burton, L., Sarwat, A., & Bhansali, S. (2019). Machine learning techniques in wireless sensor network based precision agriculture. Journal of the Electrochemical Society, 167(3), 037522.
Merry, R., & Janik, L. (2001). Mid infrared spectroscopy for rapid and cheap analysis of soils. Presented at the Proceedings of the 10th Australian agronomy conference, Australian society of agronomy.
Millard, K., & Richardson, M. (2015). On the importance of training data sample selection in random forest image classification: A case study in peatland ecosystem mapping. Remote Sensing, 7(7), 8489–8515.
Minasny, B., McBratney, A., Malone, B., & Wheeler, I. (2013). Digital mapping of soil carbon. Advances in agronomy, 118, 1–47.
Mishra, A. (2007). A review on genesis and taxonomic classification of soils of Orissa. Orissa Review, 63(6), 53–56.
Neina, D. (2019). The role of soil pH in plant nutrition and soil remediation. Applied and Environmental Soil Science, 2019, 1–9.
Ozer, D. J. (1985). Correlation and the coefficient of determination. Psychological Bulletin, 97(2), 307.
Pahlavan-Rad, M. R., & Akbarimoghaddam, A. (2018). Spatial variability of soil texture fractions and pH in a flood plain (case study from eastern Iran). CATENA, 160, 275–281.
Parastatidis, D., Mitraka, Z., Chrysoulakis, N., & Abrams, M. (2017). Online global land surface temperature estimation from Landsat. Remote Sensing, 9(12), 1208.
Potin, P., Bargellini, P., Laur, H., Rosich, B., & Schmuck, S. (2012). Sentinel-1 mission operations concept (pp. 1745–1748). Presented at the 2012 IEEE International Geoscience and Remote Sensing Symposium, IEEE.
Ranjbar, F., & Jalali, M. (2016). The combination of geostatistics and geochemical simulation for the site-specific management of soil salinity and sodicity. Computers and Electronics in Agriculture, 121, 301–312.
Rikimaru, A., Roy, P., & Miyatake, S. (2002). Tropical forest cover density mapping. Tropical Ecology, 43(1), 39–47.
Rodrigo-Comino, J., López-Vicente, M., Kumar, V., Rodríguez-Seijo, A., Valkó, O., Rojas, C., et al. (2020). Soil science challenges in a new era: A transdisciplinary overview of relevant topics. Air, Soil and Water Research, 13, 1178622120977491.
Roelofsen, H. D., van Bodegom, P. M., Kooistra, L., van Amerongen, J. J., & Witte, J.-P.M. (2015). An evaluation of remote sensing derived soil pH and average spring groundwater table for ecological assessments. International Journal of Applied Earth Observation and Geoinformation, 43, 149–159.
Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., et al. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172.
Sall, J., Stephens, M. L., Lehman, A., & Loring, S. (2017). JMP start statistics: A guide to statistics and data analysis using JMP. Sas Institute.
Spadotto, C. A., & Hornsby, A. G. (2003). Organic compounds in the environment: Soil sorption of acidic pesticides: modeling pH effects. Embrapa Meio Ambiente-Artigo em periódico indexado (ALICE).
Taghizadeh-Mehrjardi, R., Nabiollahi, K., & Kerry, R. (2016). Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran. Geoderma, 266, 98–110.
Todd, S. W., & Hoffer, R. M. (1998). Responses of spectral indices to variations in vegetation cover and soil background. Photogrammetric Engineering and Remote Sensing, 64, 915–922.
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E., et al. (2012). GMES Sentinel-1 mission. Remote Sensing of Environment, 120, 9–24.
Tucker, C. J., Elgin, J., Jr., McMurtrey Iii, J., & Fan, C. (1979). Monitoring corn and soybean crop development with hand-held radiometer spectral data. Remote Sensing of Environment, 8(3), 237–248.
Vogelmann, J., Rock, B., & Moss, D. (1993). Red edge spectral measurements from sugar maple leaves. TitleREMOTE SENSING, 14(8), 1563–1575.
von Tucher, S., Hörndl, D., & Schmidhalter, U. (2018). Interaction of soil pH and phosphorus efficacy: Long-term effects of P fertilizer and lime applications on wheat, barley, and sugar beet. Ambio, 47(1), 41–49.
Wang, X.-X., Liu, S., Zhang, S., Li, H., Maimaitiaili, B., Feng, G., & Rengel, Z. (2018). Localized ammonium and phosphorus fertilization can improve cotton lint yield by decreasing rhizosphere soil pH and salinity. Field Crops Research, 217, 75–81.
Wani, S. P., Chander, G., Bhattacharyya, T., & Patil, M. (2016). Soil health mapping and direct benefit: Transfer of fertilizer subsidy, research report IDC-6.
Wilson, H. F., Satchithanantham, S., Moulin, A. P., & Glenn, A. J. (2016). Soil phosphorus spatial variability due to landform, tillage, and input management: A case study of small watersheds in southwestern Manitoba. Geoderma, 280, 14–21.
Yang, R.-M., Zhang, G.-L., Liu, F., Lu, Y.-Y., Yang, F., Yang, F., et al. (2016). Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecological Indicators, 60, 870–878.
Zhang, Y., Sui, B., Shen, H., & Wang, Z. (2018). Estimating temporal changes in soil pH in the black soil region of Northeast China using remote sensing. Computers and Electronics in Agriculture, 154, 204–212.
Acknowledgements
The authors want to acknowledge the grants from the Department of Agriculture, Odisha state to undertake Bhoochetana project by ICRISAT. We are also grateful to all the participating of farmers, departmental staff, NGOs and University students of University of Agriculture and Technology, Odisha.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
All authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Gogumalla, P., Rupavatharam, S., Datta, A. et al. Detecting Soil pH from Open-Source Remote Sensing Data: A Case Study of Angul and Balangir Districts, Odisha State. J Indian Soc Remote Sens 50, 1275–1290 (2022). https://doi.org/10.1007/s12524-022-01524-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-022-01524-9