Abstract
The increase in global population, rapid urbanization, and continuous soil degradation have disrupted the balance between food demand and supply. Precision agriculture (PA) is an effective solution for overcoming this challenge. The first step in the PA was to determine the physicochemical attributes of the soil. This research aimed to predict soil chemical properties using Geographic Information Systems (GIS), Remote Sensing (RS), and artificial neural networks (ANN). This research explored soil chemical properties using classical statistics, Geostatistics, ANN, and Spatial Interpolation (SI). Multiple Linear Regression (MLR), Ordinary Least Square Regression (OLS), Group Method of Data Handling (GMDH) Neural Networks, Inverse Distance Weighting (IDW), and Kriging Interpolation were used to model and predict soil chemical properties. These modeling techniques accurately predicted soil organic matter (SOM) and pH. This study compared and evaluated models based on Landsat-8 and Sentinel-2 to predict soil chemical properties. Among the soil chemical properties, SOM was significantly modeled using MLR, with an accuracy of 0.54 Sentinel and 0.39 Landsat. Landsat band-TIRS2 and Sentinel band-9 significantly defined the SOM. The exploratory regression technique predicts SOM by incorporating more variables, such as Landsat bands 10 and 11 and Sentinel bands 1, 2, 5, 9, and 10. OLS regression predicted SOM with an accuracy of 0.55 and 0.54 for Sentinel and Landsat, respectively. Using GMDH Neural Network, a more accurate model was developed incorporating Sentinel bands 10, 9, and 8 and Landsat bands 5, 10, and 11, with the best accuracy of 0.68%.
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References
AgungSetianto AS, Tamia Triandini T (2013) Comparison of Kriging and inverse distance weighted (IDW) interpolation methods in lineament extraction and analysis. J SE Asian Appl Geol 5:21–29
Ahmad S, Kalra A, Stephen H (2010) Estimating soil moisture using remote sensing data: A machine learning approach. Adv Water Resour 33:69–80. https://doi.org/10.1016/j.advwatres.2009.10.008
Ali M, Iqbal IM, Shabbir A, Khan ZUD, Khan MTA (2020) Ethnomedicinal studies on aquatic plants of tehsil Shakargarh, Punjab, Pakistan. Journal of Medicinal Plants 8:15–19
Al-Quraishi AMF, Sadiq HA, Messina JP (2019) Characterization and modeling surface soil physicochemical properties using landsat images: a case study in the iraqi kurdistan region. Int Arch Photogramm, Remote Sens Spat Inform Sci. https://doi.org/10.5194/isprs-archives-XLII-2-W16-21-2019
Ardakani A, Kordnaeij A (2019) Soil compaction parameters prediction using GMDH-type neural network and genetic algorithm. Eur J Environ Civ Eng 23:449–462. https://doi.org/10.1080/19648189.2017.1304269
Aslam B, Ismail S, Ali I (2020) A GIS-based DRASTIC model for assessing aquifer susceptibility of Safdarabad Tehsil, Sheikhupura District, Punjab Province, Pakistan. Model Earth Syst Environ 6:995–1005. https://doi.org/10.1007/s40808-020-00735-8
Azadi S, Karimi-Jashni A (2016) Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province. Iran Waste Manag 48:14–23. https://doi.org/10.1016/j.wasman.2015.09.034
Behrens T, Förster H, Scholten T, Steinrücken U, Spies ED, Goldschmitt M (2005) Digital soil mapping using artificial neural networks. J Plant Nutr Soil Sci 168:21–33. https://doi.org/10.1002/jpln.200421414
Belief E (2018) GIS based spatial modeling to mapping and estimation relative risk of different diseases using inverse distance weighting (IDW) interpolation algorithm and evidential belief function (EBF) (Case study: Minor Part of Kirkuk City, Iraq). Int J Eng Technol 7:185–191
Beuselinck L, Govers G, Poesen J, Degraer G, Froyen L (1998) Grain-size analysis by laser diffractometry: comparison with the sieve-pipette method. CATENA 32:193–208. https://doi.org/10.1016/S0341-8162(98)00051-4
Bhunia GS, Kumar Shit P, Pourghasemi HR (2019) Soil C carbon mapping using remote sensing techniques and multivariate regression model. Geocarto Int 34:215–226. https://doi.org/10.1080/10106049.2017.1381179
Birch EAN, Bag GS, Squire GR (2011) How agro-ecological research helps to address food security issues under new IPM and pesticide reduction policies for global crop production systems. J Exp Bot 62:3251–3261. https://doi.org/10.1093/jxb/err064env.2020.138244
Borujeni IE, Mohammadi J, Salehi MH, Toomanian N, Poch RM (2010) Assessing geopedological soil mapping approach by statistical and geostatistical methods: A case study in the Borujen region, Central Iran. CATENA 82:1–14. https://doi.org/10.1016/j.catena.2010.03.006
Castillo S, Alarcón P, Beato M, Emilio Guerrero J, José Martínez J, Pérez C, Taguas E V (2015) Get immersed in the soil sciences: the first community of avatars in the EGUassembly 2015. In EGU General Assembly Conference Abstracts 15104
Coulibaly K, Sako A (2024) Geostatistical modelling of physicochemical properties of soils impacted by artisanal gold mining and farming: implications for soil environmental quality assessment. Sustain Environ 10(1):2333631. https://doi.org/10.1080/27658511.2024.2333631
Davis E, Wang C, Dow K (2019) Comparing Sentinel-2 MSI and Landsat 8 OLI in soil salinity detection: A case study of agricultural lands in coastal North Carolina. Int J Remote Sens 40:6134–6153. https://doi.org/10.1080/01431161.2019.1587205
Fabiani S, Vanino S, Napoli R, Zajíček A, Duffková R, Evangelou E, Nino P (2020) Assessment of the economic and environmental sustainability of Variable Rate Technology (VRT) application in different wheat intensive European agricultural areas. A Water energy food nexus approach. Environ Sci Policy 114:366–376
Farifteh J, Farshad A, George RJ (2006) Assessing salt-affected soils using remote sensing, solute modelling, and geophysics. Geoderma 130:191–206. https://doi.org/10.1016/j.geoderma.2005.02.003
Forkuor G, Hounkpatin OK, 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 12:0170478. https://doi.org/10.1371/journal.pone.0170478
Gebbers R, Adamchuk VI (2010) Precision agriculture and food security. Science 327:828–831. https://doi.org/10.1126/science.1183899
Gorji T, Yildirim A, Hamzehpour N, Tanik A, Sertel E (2020) Soil salinity analysis of Urmia Lake Basin using Landsat-8 OLI and Sentinel-2A based spectral indices and electrical conductivity measurements. Ecol Ind 112:106173. https://doi.org/10.1016/j.ecolind.2020.106173
Grunwald S, Vasques GM, Rivero RG (2015) Fusion of soil and remote sensing data to model soil properties. Adv Agron 131:1–109. https://doi.org/10.1016/bs.agron.2014.12.004
Hadjimitsis DG, Papadavid G, Agapiou A, Themistocleous K, Hadjimitsis MG, Retalis A, Clayton CRI (2010) Atmospheric correction for satellite remotely sensed data intended for agricultural applications: impact on vegetation indices. Nat Hazard 10:89–95. https://doi.org/10.5194/nhess-10-89-2010,2010
Haider S, Ashraf E, Javed MA (2016) Assessment of Factors Causing Lack of Interest among Small-Scale Farmers in Agriculture System in Tehsil Shakargarh, Pakistan. PSM Biological Research 1:53–57
Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press. https://doi.org/10.1016/j.envsci.2020.08.019
Jiang B, Xu W, Zhang D, Nie F, Sun Q (2022) Contrasting multiple deterministic interpolation responses to different spatial scale in prediction soil organic carbon: A case study in Mollisols regions. Ecol Ind 134:108472. https://doi.org/10.1016/j.ecolind.2021.108472
Jiaxin REN, Wenna YANG, Zhongyi LI, Yanyan HU (2019) Influence of matrix effect on determination accuracy of potassium content in soil and plant samples by flame photometer. Acta Agriculturae Zhejiangensis 31:955. https://doi.org/10.3969/j.issn.1004-1524.2019.06.13
Kalota D (2017) Exploring relation of land surface temperature with selected variables using geographically weighted regression and ordinary least square methods in Manipur State, India. Geocarto Int 32:1105–1119. https://doi.org/10.1080/10106049.2016.1195883
Khaledian Y, Miller BA (2020) Selecting appropriate machine learning methods for digital soil mapping. Appl Math Model 81:401–418. https://doi.org/10.1016/j.apm.2019.12.016
Khanal S, Fulton J, Shearer S (2017) An overview of current and potential applications of thermal remote sensing in precision agriculture. Comput Electron Agric 139:22–32. https://doi.org/10.1016/j.compag.2017.05.001
Khanna A, Kaur S (2019) Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture. Comput Electron Agric 157:218–231. https://doi.org/10.1016/j.compag.2018.12.039
King C, Baghdadi N, Lecomte V, Cerdan O (2005) The application of remote-sensing data to monitoring and modelling of soil erosion. CATENA 62:79–93. https://doi.org/10.1016/j.catena.2005.05.007
Kordnaeij A, Kalantary F, Kordtabar B, Mola-Abasi H (2015) Prediction of recompression index using GMDH-type neural network based on geotechnical soil properties. Soils Found 55:1335–1345. https://doi.org/10.1016/j.sandf.2015.10.001
Krishna KR (2012) Soil fertility and crop production. Science Publishers
Lerch RN, Kitchen NR, Kremer RJ, Donald WW, Alberts EE, Sadler EJ, Ghidey F (2005) Development of a conservation-oriented precision agriculture system: Water and soil quality assessment. J Soil Water Conserv 60:411–421
Letey J (1958) Relationship between soil physical properties and crop production. In: Advances in Soil Science (eds). Springer, pp 277–294
Lin C, Zhu AX, Wang Z, Wang X, Ma R (2020) The refined spatiotemporal representation of soil organic matter based on remote images fusion of Sentinel-2 and Sentinel-3. Int J Appl Earth Obs Geoinf 89:102094
López-Granados F, Jurado-Expósito M, Peña-Barragán JM, García-Torres L (2005) Using geostatistical and remote sensing approaches for mapping soil properties. Eur J Agron 23:279–289. https://doi.org/10.1016/j.eja.2004.12.003
Mahara G, Wang C, Yang K, Chen S, Guo J, Gao Q, Guo X (2016) The association between environmental factors and scarlet fever incidence in Beijing region: using GIS and spatial regression models. Int J Environ Res Public Health 13:1083. https://doi.org/10.3390/ijerph13111083
Mansouri I, Bengora D, Houichi L (2023) Enhancing reservoir dam’s management in Algeria: a comparative study of interpolation methods for topo-bathymetric surveys. Model Earth Syst Environ 1–13. https://doi.org/10.1007/s40808-023-01816-0
Mirchooli F, Kiani-Harchegani M, Darvishan AK, Falahatkar S, Sadeghi SH (2020) Spatial distribution dependency of soil organic carbon content to important environmental variables. Ecol Ind 116:106473. https://doi.org/10.1016/j.ecolind.2020.106473
Mitas L, Mitasova H (1999) Spatial interpolation. In: Longley PA, Goodchild MF, Maguire DJ Rhind DW (eds) Geographical information systems: principles, techniques, management and applications. New York, Wiley, pp 481±92
Mueller TG, Pusuluri NB, Mathias KK, Cornelius PL, Barnhisel RI, Shearer SA (2004) Map quality for ordinary kriging and inverse distance weighted interpolation. Soil Sci Soc Am J 68:2042–2047. https://doi.org/10.2136/sssaj2004.2042
Najafzadeh M, Barani GA, Kermani MRH (2013) GMDH based back propagation algorithm to predict abutment scour in cohesive soils. Ocean Eng 59:100–106. https://doi.org/10.1016/j.oceaneng.2012.12.006
Nawar S, Buddenbaum H, Hill J (2015) Digital mapping of soil properties using multivariate statistical analysis and ASTER data in an arid region. Remote Sensing 7:1181–1205. https://doi.org/10.3390/rs70201181
Panday D, Maharjan B, Chalise D, Shrestha RK, Twanabasu B (2018) Digital soil mapping in the Bara district of Nepal using kriging tool in ArcGIS. PLoS ONE 13:e0206350. https://doi.org/10.1371/journal.pone.0206350
Perpiña CC, Lavalle C, Baranzelli C, Mubareka S (2015) Modelling the spatial allocation of second-generation feedstock (lignocellulosic crops) in Europe. Int J Geogr Inf Sci 29(10):1807–1825. https://doi.org/10.1080/13658816.2015.1051486
Peech M (1965) Hydrogen-ion activity. Methods Soil Anal: Part 2 Chem Microbiol Proper 9:914–926
Pellegrini E, Rovere N, Zaninotti S, Franco I, De Nobili M, Contin M (2021) Artificial neural network (ANN) modelling for the estimation of soil microbial biomass in vineyard soils. Biol Fertil Soils 57:145–151. https://doi.org/10.1007/s00374-020-01498-1
Peng J, Biswas A, Jiang Q, Zhao R, Hu J, Hu SZ (2019) Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China. Geoderma 337:1309–1319. https://doi.org/10.1016/j.geoderma.2018.08.006
Potopová V, Trnka M, Hamouz P, Soukup J, Castraveț T (2020) Statistical modelling of drought-related yield losses using soil moisture-vegetation remote sensing and multiscalar indices in the south-eastern Europe. Agric Water Manag 236:106168. https://doi.org/10.1016/j.agwat.2020.106168
Preissler H, Loercher G (1995) Extraction of soil properties from laboratory and imaging spectrometry data. In: 1995 International Geoscience and Remote Sensing Symposium, IGARSS'95. Quantitative Remote Sensing for Science and Applications 1968–1970. IEEE. https://doi.org/10.1109/IGARSS.1995.524081
Qiao P, Lei M, Yang S, Yang J, Guo G, Zhou X (2018) Comparing ordinary Kriging and inverse distance weighting for soil as pollution in Beijing. Environ Sci Pollut Res 25:15597–15608. https://doi.org/10.1007/s11356-018-1552-y
Robinson TP, Metternicht G (2006) Testing the performance of spatial interpolation techniques for mapping soil properties. Comput Electron Agric 50:97–108. https://doi.org/10.1016/j.compag.2005.07.003
Sadeghi M, Jones SB, Philpot WD (2015) A linear physically based model for remote sensing of soil moisture using short wave infrared bands. Remote Sens Environ 164:66–76. https://doi.org/10.1016/j.rse.2015.04.007
Schimmelpfennig D, Ebel R (2011) On the doorstep of the information age: recent adoption of precision agriculture. Economic Information Bulletin 80, U.S. Deptartment of Agriculture, Economic Research Service, Washington, DC. Available online at http://purl.fdlp.gov/GPO/gpo15242
Schumacher BA (2002) Methods for the determination of total organic carbon in soils and sediments. United States Environmental Protection Agency, pp 18
Sheng J, Yu P, Zhang H, Wang Z (2021) Spatial variability of soil Cd content based on IDW and RBF in Fujiang River, Mianyang, China. J Soils Sediments 21:419–429. https://doi.org/10.1007/s11368-020-02758-1
Shit PK, Bhunia GS, Maiti R (2016) Spatial analysis of soil properties using GIS based geostatistics models. Model Earth Syst Environ 2:1–6. https://doi.org/10.1007/s40808-016-0160-4
Silva SHG, Poggere GC, Menezes MDD, Carvalho GS, Guilherme LRG, Curi N (2016) Proximal sensing and digital terrain models applied to digital soil mapping and modeling of Brazilian Latosols (Oxisols). Remote Sens 8:614. https://doi.org/10.3390/rs8080614
Silvero NEQ, Demattê JAM, Amorim MTA, dos Santos NV, Rizzo R, Safanelli JL, Bonfatti BR (2021) Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison. Remote Sens Environ 252:112117. https://doi.org/10.1016/j.rse.2020.112117
Simmonds SE, Kinlan BP, White C, Paradis GL, Warner RR, Zacherl DC (2014) Geospatial statistics strengthen the ability of natural geochemical tags to estimate range-wide population connectivity in marine species. Mar Ecol Prog Ser 508:33–51. https://doi.org/10.3354/meps10871
Sims JT (2000) Soil test phosphorus: Mehlich-3 P, In: Pierzynski GM (ed) Methods of Phosphorus Analysis for Soils, Sediments, Residuals and Waters, Southern Cooperative Series Bull, No. 396, Kansas State University, Manhttan, pp 17–19
Srisomkiew S, Kawahigashi M, Limtong P (2021) Digital mapping of soil chemical properties with limited data in the Thung Kula Ronghai region. Thail Geoderma 389:114942. https://doi.org/10.1016/j.geoderma.2021.114942
Tariq S R, Rashid N (2013) Multivariate analysis of metal levels in paddy soil, rice plants, and rice grains: a case study from Shakargarh, Pakistan. J Chem 2013. https://doi.org/10.1155/2013/539251
Tokekar P, Vander Hook J, Mulla D, Isler V (2016) Sensor planning for a symbiotic UAV and UGV system for precision agriculture. IEEE Trans Rob 32:1498–1511. https://doi.org/10.1109/TRO.2016.2603528
Uygur V, Irvem A, Karanlik S, Akis R (2010) Mapping of total nitrogen, available phosphorous and potassium in Amik Plain, Turkey. Environ Earth Sci 59:1129–1138. https://doi.org/10.1007/s12665-009-0103-8
Vågen TG, Winowiecki LA, Tondoh JE, Desta LT, Gumbricht T (2016) Mapping of soil properties and land degradation risk in Africa using MODIS reflectance. Geoderma 263:216–225. https://doi.org/10.1016/j.geoderma.2015.06.023
Vaudour E, Gomez C, Fouad Y, Lagacherie P (2019) Sentinel-2 image capacities to predict common topsoil properties of temperate and Mediterranean agroecosystems. Remote Sens Environ 223:21–33. https://doi.org/10.1016/j.rse.2019.01.006
Vaysse K, Arrouays D, McKenzie NJ, Coste S, Lagacherie P (2014) Estimation of GlobalSoilMap.net grids cells from legacy soil data at the regional scale in Southern France. In: Arrouays D et al (eds) GlobalSoilMap: basis of the global spatial soil information system. CRC Press, pp 133–138
Wang B, Waters C, Orgill S, Gray J, Cowie A, Clark A, Li Liu D (2018) High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia. Sci Total Environ 630:367–378. https://doi.org/10.1016/j.scitotenv.2018.02.204
Wang J, Ding J, Yu D, Ma X, Zhang Z, Ge X, Guo Y (2019) Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China. Geoderma 353:172–187. https://doi.org/10.1016/j.geoderma.2019.06.040
Watson DE (1992) Contouring: A Guide to the Analysis and Display of Spatial Data. Tarrytown, NY
Wen B, Aydin A, Duzgoren-Aydin NS (2002) A comparative study of particle size analyses by sieve-hydrometer and laser diffraction methods. Geotech Test J 25:434–442
Zhang ZF (2011) Soil water retention and relative permeability for conditions from oven-dry to full saturation. Vadose Zone J 10:1299–1308. https://doi.org/10.2136/vzj2011.0019
Zhang Y, Guo L, Chen Y, Shi T, Luo M, Ju Q, Wang S (2019) Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province. China Remote Sensing 11:1683. https://doi.org/10.3390/rs11141683
Zhou T, Geng Y, Chen J, Pan J, Haase D, Lausch A (2020) High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms. Sci Total Environ 729:138244. https://doi.org/10.1016/j.scitotenv.2020.138244
Acknowledgements
This research was collectively assisted by the Department of Geography, Islamia College, Peshawar, School of Civil and Environmental Engineering, National University of Science and Technology, Pakistan, and Petroleum and Mining Engineering Department, Faculty of Engineering, Tishk International University, Erbil, Kurdistan Region, Iraq.
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Abbasi, N.T., Zarin, R., Raziq, A. et al. Remote sensing, artificial neural networks, and spatial interpolation methods for modelling soil chemical characteristics. Model. Earth Syst. Environ. 10, 5063–5078 (2024). https://doi.org/10.1007/s40808-024-02050-y
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DOI: https://doi.org/10.1007/s40808-024-02050-y