Combined remote sensing and multi-criteria analysis of wetland soil potential for rice production: case study of Ogun river basin, Nigeria

References

• Adagunodo, T. A., Bayowa, O. G., Usikalu, M. R., & Ojoawo, A. I. (2019). Radiogenic heat production in the coastal plain sands of ipokia, dahomey basin, Nigeria. MethodsX, 6, 1608–1616. https://doi.org/10.1016/j.mex.2019.07.006
   PubMed Web of Science ®Google Scholar
• Adeleye, N., Osabuohien, E. S., Adeogun, S., Fashola, S., Tasie, O., & Adeyemi, G. (2020). Access to land and food security: analysis of ‘priority crops’ production in Ogun State, Nigeria. In The palgrave handbook of agricultural and rural development in Africa (pp. 291–311). Palgrave Macmillan. doi: 10.1007/978-3-030-41513-6_14.
   Google Scholar
• Adepoju, K. A., & Adelabu, S. A. (2020). Improving accuracy of landsat-8 OLI classification using image composite and multisource data with google earth engine. Remote Sensing Letters, 11(2), 107–116. https://doi.org/10.1080/2150704X.2019.1690792
   Web of Science ®Google Scholar
• Adewara, M. B., Oyewole, A. M., & Adenaiya, O. O. (2019). Mapping spatiotemporal land use land coverDynamics of yewa South LGA of Ogun State for urbanization monitoring. International Journal of Research in Engineering and Science (IJRES), 7(3), 69–79. http://eprints.federalpolyilaro.edu.ng/id/eprint/426.
   Google Scholar
• Ahmed, K. R., Akter, S., Marandi, A., & Schüth, C. (2021). A simple and robust wetland classification approach by using optical indices, unsupervised and supervised machine learning algorithms. Remote Sensing Applications: Society and Environment, 23, 100569. https://doi.org/10.1016/j.rsase.2021.100569
   Google Scholar
• Akinola, R., Pereira, L. M., Mabhaudhi, T., de Bruin, F. M., & Rusch, L. (2020). A review of indigenous food crops in Africa and the implications for more sustainable and healthy food systems. Sustainability, 12(8), 3493–3504. https://doi.org/10.3390/su12083493
   PubMed Web of Science ®Google Scholar
• Akintuyi, A. O., Fasona, M. J., Ayeni, A. O., & Soneye, A. S. (2021). Land use/land cover and climate change interaction in the derived savannah region of Nigeria. Environmental Monitoring and Assessment, 193(12), 1–24. https://doi.org/10.1007/s10661-021-09642-6
   Web of Science ®Google Scholar
• Al-Omran, A. M., Al-Wabel, M. I., El-Maghraby, S. E., Nadeem, M. E., & Al-Sharani, S. (2013). Spatial variability for some properties of the wastewater irrigated soils. Journal of the Saudi Society of Agricultural Sciences, 12(2), 167–175. https://doi.org/10.1016/j.jssas.2012.12.001
   Google Scholar
• Alkhader, A. M. F. (2015). The impact of phosphorus fertilizers on heavy metals content of soils and vegetables grown on selected farms in Jordan. Agrotechnology, 5(1), 1–15. https://doi.org/10.4172/2168-9881.1000137
   Google Scholar
• Almeida, C. R. D., Teodoro, A. C., & Gonçalves, A. (2021). Study of the Urban Heat Island (UHI) using remote sensing data/techniques: A systematic review. Environments, 8(10), 105. https://doi.org/10.3390/environments8100105
   Web of Science ®Google Scholar
• Amin, A., Nasim, W., Fahad, S., Ali, S., Ahmad, S., Rasool, A., Paz, J. O., Hammad, H. M., Sultana, S. R., Mubeen, M., Bakhat, H. F., Ahmad, N., Shah, G. M., Adnan, M., Noor, M., Basir, A., Saud, S., Habib Ur Rahman, M., & Paz, J. O. (2018). Evaluation and analysis of temperature for historical (1996–2015) and projected (2030–2060) climates in Pakistan using SimCLIM climate model: Ensemble application. Atmospheric Research, 213, 422–436. https://doi.org/10.1016/j.atmosres.2018.06.021
   Web of Science ®Google Scholar
• Anderson, J. R. (1976). A land use and land cover classification system for use with remote sensor data (Vol. 964). US Government Printing Office USGS.
   Google Scholar
• Appiah, D. O., Schröder, D., Forkuo, E. K., & Bugri, J. T. (2015). Application of geo-information techniques in land use and land cover change analysis in a peri-urban district of Ghana. ISPRS International Journal of Geo-Information, 4(3), 1265–1289. https://doi.org/10.3390/ijgi4031265
   Web of Science ®Google Scholar
• Arab, S. T., Islam, M., Shamsuzzoha, M., Alam, K. F., Muhsin, N., Noguchi, R., & Ahamed, T. (2022). A review of remote sensing applications in agriculture and forestry to establish big data analytics. Remote Sensing Application, 59: 1–24. doi:10.1007/978-981-19-0213-0_1.
   Google Scholar
• Ayehu, G. T., & Besufekad, S. A. (2015). Land suitability analysis for rice production: A GIS based multi-criteria decision approach. American Journal of Geographic Information System, 4(3), 95–104. doi:10.5923/j.ajgis.20150403.02
   Google Scholar
• Ayeni, A. O., Ogunsesan, A. A., & Adekola, O. A. (2019). Provisioning ecosystem services provided by the hadejia nguru wetlands, Nigeria–current status and future priorities. Scientific African, 5, e00124. https://doi.org/10.1016/j.sciaf.2019.e00124
   Google Scholar
• Ayoade, M. A. (2017). Suitability assessment and mapping of Oyo State, Nigeria, for rice cultivation using GIS. Theoretical and Applied Climatology, 129(3), 1341–1354
   Web of Science ®Google Scholar
• Azabdaftari, A., & Sunarb, F. (2016). Soil salinity mapping using multitemporal Landsat data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 7, 3–9.
   Google Scholar
• Badamfirooz, J., & Mousazadeh, R. (2019). Quantitative assessment of land use/land cover changes on the value of ecosystem services in the coastal landscape of Anzali international wetland. Environmental Monitoring and Assessment, 191(11), 1–13. https://doi.org/10.1007/s10661-019-7802-8
   PubMed Web of Science ®Google Scholar
• Bakx, T. R., Koma, Z., Seijmonsbergen, A. C., & Kissling, W. D. (2019). Use and categorization of light detection and ranging vegetation metrics in avian diversity and species distribution research. Diversity & Distributions, 25(7), 1045–1059. https://doi.org/10.1111/ddi.12915
   Web of Science ®Google Scholar
• Balogun, I., & Ishola, K. (2017). Projection of future changes in land use/landcover using cellular automata/Markov model over Akure city, Nigeria. Journal of Remote Sensing Technology, 5(1), 22–31. https://doi.org/10.18005/JRST0501003
   Google Scholar
• Bank, (1991). Forest sector policy paper. The World Bank. Washington. DC.
   Google Scholar
• Barbier, E. B., Hacker, S. D., Kennedy, C., Koch, E. W., Stier, A.C., & Silliman, B. R. (2017). The value of estuarine and coastal ecosystem services. Ecological Monographs, 81(2), 169–193. https://doi.org/10.1890/10-1510.1
   Google Scholar
• Basavaraja, P. K., Dey, P., Saqeebulla, H. M., & Yogendra, N. D. (2017). Geo-reference-based soil fertility status in Hassan district of Karnataka, India for development of nutrient plan. Indian Journal of Soil Conservation, 45(2), 141–147.
   Google Scholar
• Beeri, O., Phillips, R., Hendrickson, J., Frank, A. B., & Kronberg, S. (2007). Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie. Remote Sensing of Environment, 110(2), 216–225. https://doi.org/10.1016/j.rse.2007.02.027
   Web of Science ®Google Scholar
• Behzadian, M., Otaghsara, S. K., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051–13069. https://doi.org/10.1016/j.eswa.2012.05.056
   Web of Science ®Google Scholar
• Berbel, J., Bournaris, T., Manos, B., Matsatsinis, N., & Viaggi, D. (Eds.). (2018). Multicriteria analysis in agriculture: Current trends and recent applications. Springer International Publishing.
   Google Scholar
• Bevington, A. E., Twilley, R. R., & Sasser, C. E. (2022). Deltaic floodplain wetland vegetation dynamics along the sediment surface elevation gradient and in response to disturbance from river flooding and hurricanes in wax lake. Geomorphology, Vol. 398 (pp. 108011).
   Google Scholar
• Bhat, R. A., Dar, S. A., Dar, D. A., & Dar, G. H. (2018). Municipal solid waste generation and current scenario of its management in India. International Journal of Advance Research in Science and Engineering, 7(2), 419–431.
   Google Scholar
• Bouyoucos, G. J. (1962). Hydrometer method improved for making particle size analyses of soils 1. Agronomy Journal, 54(5), 464–465. https://doi.org/10.2134/agronj1962.00021962005400050028x
   Web of Science ®Google Scholar
• Brady, N. C., & Weil, R. R. (2014). The nature and properties of soil. In (14th New Int. Pearson New International Edition (pp.870-875).
   Google Scholar
• Brejda, J. J., Moorman, T. B., Smith, J. L., Karlen, D. L., Allan, D. L., & Dao, T. H. (2000). Distribution and variability of surface soil properties at a regional scale. Soil Science Society of America Journal, 64(3), 974–982
   Web of Science ®Google Scholar
• Cai, M., Ren, C., Xu, Y., Lau, K. K. L., & Wang, R. (2018). Investigating the relationship between local climate zone and land surface temperature using an improved WUDAPT methodology–A case study of Yangtze River Delta, China. Urban Climate, 24, 485–502. https://doi.org/10.1016/j.uclim.2017.05.010
   Web of Science ®Google Scholar
• Cambardella, C. A., Moorman, T. B., Novak, J. M., Parkin, T. B., Karlen, D. L., Turco, R. F., & Konopka, A. E. (1994). Field‐scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal, 58(5), 1501–1511. https://doi.org/10.2136/sssaj1994.03615995005800050033x
   Web of Science ®Google Scholar
• Change, C. (2016). Agriculture and food security. the state of food and agriculture (FAO Ed.). FAO. http://www.fao.org/state-of-food-agriculture/en/
   Google Scholar
• Chavez, P. S. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment, 24(3), 459–479. https://doi.org/10.1016/0034-4257(88)90019-3
   Web of Science ®Google Scholar
• Cloete, P. C., & Idsardi, E. F. (2013). Consumption of indigenous and traditional food crops: Perceptions and realities from South Africa. Agroecology and Sustainable Food Systems, 37(8), 902–914. doi:10.1080/21683565.2013.805179.
   Web of Science ®Google Scholar
• Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46. https://doi.org/10.1016/0034-4257(91)90048-B
   Web of Science ®Google Scholar
• Dada, S. (2017). Public-private dialogue report on land administration, registration and acquisition in Ogun State. Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ).
   Google Scholar
• Datta, S., Sinha, D., Chaudhary, V., Kar, S., & Singh, A. (2022). Water pollution of wetlands: a global threat to inland, wetland, and aquatic phytodiversity. In Ashok K. R(Ed.,) Handbook of research on monitoring and evaluating the ecological health of wetlands (pp. 27–50). IGI Global.
   Google Scholar
• De Jesus, J. B., & Santana, I. D. M. (2017). Estimation of land surface temperature in caatinga area using Landsat 8 data. Journal of Hyperspectral Remote Sensing, 7(3), 150–157. doi:10.29150/jhrs.v7.3.p150-157.
   Google Scholar
• Dehm, D., Becker, R., & Godre, A. (2019). SUAS based multispectral imagery for monitoring wetland inundation and vegetation. 2019110326. https://doi.org/10.20944/preprints201911.0326.v1
   Google Scholar
• Di Vittorio, C. A., & Georgakakos, A. P. (2018). Land cover classification and wetland inundation mapping using MODIS. Remote Sensing of Environment, 204, 1–17. https://doi.org/10.1016/j.rse.2017.11.001
   Web of Science ®Google Scholar
• Ding, H., & Shi, W. (2013). Land-use/land-cover change and its influence on surface temperature: A case study in Beijing City. International Journal of Remote Sensing, 34(15), 5503–5517. https://doi.org/10.1080/01431161.2013.792966
   Web of Science ®Google Scholar
• Dou, F., Soriano, J., Tabien, R. E., & Chen, K. (2016). Soil texture and cultivar effects on rice (Oryza sativa, L.) grain yield, yield components and water productivity in three water regimes. PloS one, 11(3), e0150549. https://doi.org/10.1371/journal.pone.0150549
   PubMed Web of Science ®Google Scholar
• Duarte, L., Teodoro, A. C., Monteiro, A. T., Cunha, M., & Gonçalves, H. (2018). QPhenoMetrics: An open-source software application to assess vegetation phenology metrics. Computers and Electronics in Agriculture, 148, 82–94. https://doi.org/10.1016/j.compag.2018.03.007
   Web of Science ®Google Scholar
• Eldeiry, A. A., & Garcia, L. A. (2008). Detecting soil salinity in alfalfa fields using spatial modeling and remote sensing. Soil Science Society of America Journal, 72(1), 201–211. https://doi.org/10.2136/sssaj2007.0013
   Web of Science ®Google Scholar
• Environment Protection Authority. (2018). Consumption advice for recreationally caught game—East Gippsland. EPA publication 1672.2. EPA Victoria.
   Google Scholar
• FAO. (2007). Land evaluation, towards a revised framework, FAO Rome.
   Google Scholar
• Faraco, M. A., Uribe-Opazo, M. A., Silva, E. A. A. D., Johann, J. A., & Borssoi, J. A. (2008). Seleção de modelos de variabilidade espacial para elaboração de mapas temáticos de atributos físicos do solo e produtividade da soja. Revista Brasileira de Ciência do Solo, 32, 463–476.
   Google Scholar
• Fathizad, H., Rostami, N., & Faramarzi, M. (2015). Detection and prediction of land cover changes using markov chain model in semi-arid rangeland in western Iran. Environmental Monitoring and Assessment, 187(10), 1–12. https://doi.org/10.1007/s10661-015-4805-y
   PubMed Web of Science ®Google Scholar
• Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Snyder, P. K., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J. H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A., Prentice, I. C., Ramankutty, N., & Snyder, P. K. (2005). Global consequences of land use. science, 309(5734), 570–574. https://doi.org/10.1126/science.1111772
   PubMed Web of Science ®Google Scholar
• Furby, S., Caccetta, P., & Wallace, J. (2010). Salinity monitoring in western Australia using remotely sensed and other spatial data. Journal of Environmental Quality, 39(1), 16–25. https://doi.org/10.2134/jeq2009.0036
   PubMed Web of Science ®Google Scholar
• Gao, T., Yang, X., Jin, C., Y, X., Ma, H., L, L., J, Y., Yu, H., D, Y., Q, Y., Xiao, Z., & Xu, B. (2013). Spatio-temporal variation in vegetation biomass and its relationships with climate factors in the Xilingol grasslands, Northern China. PLoS One, 8(12), e83824. https://doi.org/10.1371/journal.pone.0083824
   PubMed Web of Science ®Google Scholar
• Good, T., & Giordano, P. A. (2019). Methods for constructing a colour composite image. Google Patents.
   Google Scholar
• Guha, S., & Govil, H. (2021a). A long-term monthly analytical study on the relationship of LST with normalized difference spectral indices. European Journal of Remote Sensing, 54(1), 487–512. https://doi.org/10.1080/22797254.2021.1965496
   Web of Science ®Google Scholar
• Guha, S., & Govil, H. (2021b). Annual assessment on the relationship between land surface temperature and six remote sensing indices using landsat data from 1988 to 2019. Geocarto International, 1–20. https://doi.org/10.1080/10106049.2021.1886339
   Web of Science ®Google Scholar
• Guha, S., Govil, H., Taloor, A. K., Gill, N., & Dey, A. (2022). Land surface temperature and spectral indices: A seasonal study of Raipur City. Geodesy and Geodynamics, 13(1), 72–82. https://doi.org/10.1016/j.geog.2021.05.002
   Web of Science ®Google Scholar
• Habibie, M. I., Noguchi, R., Shusuke, M., & Ahamed, T. (2021). Land suitability analysis for maize production in Indonesia using satellite remote sensing and GIS-based multicriteria decision support system. GeoJournal, 86(2), 777–807. https://doi.org/10.1007/s10708-019-10091-5
   Web of Science ®Google Scholar
• Hassan, M. M., & Nazem, M. N. I. (2016). Examination of land use/land cover changes, urban growth dynamics, and environmental sustainability in Chittagong city, Bangladesh. Environment, Development, and Sustainability, 18(3), 697–716. https://doi.org/10.1007/s10668-015-9672-8
   Web of Science ®Google Scholar
• Herbert, E. R., Boon, P., Burgin, A. J., Neubauer, S. C., Franklin, R. B., Ardón, M., Hopfensperger, K. N., Lamers, L. P., & Gell, P. (2015). A global perspective on wetland salinization: Ecological consequences of a growing threat to freshwater wetlands. Ecosphere, 6(10), 1–43. https://doi.org/10.1890/ES14-00534.1
   Web of Science ®Google Scholar
• Hu, K., Wang, S., Li, H., Huang, F., & Li, B. (2014). Spatial scaling effects on variability of soil organic matter and total nitrogen in suburban Beijing. Geoderma, 226-227, 54–63. https://doi.org/10.1016/j.geoderma.2014.03.001
   Web of Science ®Google Scholar
• Huang, S. W., Jin, J. Y., Yang, L. P., & Bai, Y. L. (2006). Spatial variability of soil nutrients and influencing factors in a vegetable production area of Hebei Province in China. Nutrient Cycling in Agroecosystems, 75(1), 201–212. https://doi.org/10.1007/s10705-006-9027-9
   Web of Science ®Google Scholar
• Hussain, S. (2018). Land use/land cover classification by using satellite NDVI tool for sustainable water and climate change in Southern Punjab. COMSATS Univ. Islamabad. MS thesis. https://doi.org/10.13140/RG.2.2.32363.69923.
   Google Scholar
• Hussain, S., Ahmad, A., Wajid, A., Khaliq, T., Hussain, N., Mubeen, M., & Nasim, W. (2020). Irrigation scheduling for cotton cultivation. In Shakeel A, and Mirza H (Eds.,), Cotton production and uses (pp. 59–80). Springer.
   Google Scholar
• Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Ching-Lai. H & Kwangsun. Y (Eds.,),Multiple attribute decision making (pp. 58–191). Springer.
   Google Scholar
• Hyandye, C., & Martz, L. W. (2017). Markovian and cellular automata land-use change predictive model of the usangu catchment. International Journal of Remote Sensing, 38(1), 64–81. https://doi.org/10.1080/01431161.2016.1259675
   Web of Science ®Google Scholar
• Ige, S. O., Ajayi, V. O., Adeyeri, O. E., & Oyekan, K. S. A. (2017). Assessing remotely sensed temperature-humidity index as human comfort indicator relative to land use land cover change in Abuja, Nigeria. Spatial Information Research, 25(4), 523–533. https://doi.org/10.1007/s41324-017-0118-2
   Google Scholar
• Imanudin, M., Armanto, M.E, S., & Susanto, R. H. (2011). Developing seasonal operation water table management in tidal lowland reclamations areas at South Sumatra Indonesia. Journal of Tropical Soils, 16(3), 233–244. https://doi.org/10.5400/jts.2011.16.3.233
   Google Scholar
• Iqbal, F. (2010). Remote sensing and GIS application for site-specific agriculture; agriculture: Agro-informatics for sustainable agriculture. Beau Basin: VDM Verlag Dr. Müller, 1, 3–978.
   Google Scholar
• Isidoro, D., & Grattan, S. R. (2011). Predicting soil salinity in response to different irrigation practices, soil types, and rainfall scenarios. Irrigation Science, 29(3), 197–211. doi:10.1007/s00271-010-0223-7.
   Web of Science ®Google Scholar
• Islam, M., D, S., & Ahmed, R. (2011). Land-use change prediction in Dhaka city using GIS-aided markov chain modeling. Journal of Life Earth Science, 6, 81–89. https://doi.org/10.3329/jles.v6i0.9726
   Google Scholar
• Jaji, M. O., Bamgbose, O., Odukoya, O. O., & Arowolo, T. A. (2007). Water quality assessment of Ogun river, south west Nigeria. Environmental Monitoring and Assessment, 133(1), 473–482. https://doi.org/10.1007/s10661-006-9602-1
   PubMed Web of Science ®Google Scholar
• Jensen, J. R. (2005). Introductory digital image processing: A remote sensing perspective (Third ed.). Pearson Prentice Hall.
   Google Scholar
• Kananke, T. C., Wansapala, J., & Gunaratne, A. (2015). Pb and Cr contaminations of irrigation water, soils, and green leafy vegetables collected from different areas of Colombo district, Sri Lanka. Pakistan Journal of Nutrition, 14(9), 60–593. https://doi.org/10.3923/pjn.2015.593.602
   Google Scholar
• Khanal, S., KC, K., Fulton, J. P., Shearer, S., & Ozkan, E. (2020). Remote sensing in agriculture—accomplishments, limitations, and opportunities. Remote Sensing, 12(22), 3783. https://doi.org/10.3390/rs12223783
   Web of Science ®Google Scholar
• Kumar, A., & Yadav, D. S. (2005). Influence of continuous cropping and fertilization on nutrient availability and productivity of alluvial soil. Journal of the Indian Society of Soil Science, 53(2), 194–198.
   Google Scholar
• Lai, Y. J., Liu, T. Y., & Hwang, C. L. (1994). Topsis for MODM. European Journal of Operational Research, 76(3), 486–500. https://doi.org/10.1016/0377-2217(94)90282-8
   Web of Science ®Google Scholar
• Lake, P. S. (2000). Disturbance, patchiness, and diversity in streams. Journal of the North American Benthological Society, 19(4), 573–592. https://doi.org/10.2307/1468118
   Web of Science ®Google Scholar
• Leena, M. M., Silvia, M. G., Vinitha, K., Moses, J. A., & Anandharamakrishnan, C. (2020). Synergistic potential of nutraceuticals: Mechanisms and prospects for futuristic medicine. Food & Function, 11(11), 9317–9337. https://doi.org/10.1039/D0FO02041A
   PubMed Web of Science ®Google Scholar
• Leinweber, P., & Schulten, H. R. (1998). Nonhydrolyzable organic nitrogen in soil size separates from long‐term agricultural experiments. Soil Science Society of America Journal, 62(2), 383–393. https://doi.org/10.2136/sssaj1998.03615995006200020014x
   Web of Science ®Google Scholar
• Li, W., Dong, R., Fu, H., Wang, J., Yu, L., & Gong, P. (2020). Integrating google earth imagery with landsat data to improve 30-m resolution land cover mapping. Remote Sensing of Environment, 237, 111563. doi:10.1016/j.rse.2019.111563.
   Web of Science ®Google Scholar
• Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2004). Remote Sensing and Image Interpretation (5th ed.). John Wiley & Sons Inc.
   Google Scholar
• Liping, C., Yujun, S., & Saeed, S. (2018). Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PloS one, 13(7), e0200493. https://doi.org/10.1371/journal.pone.0200493
   PubMed Web of Science ®Google Scholar
• Liu, H., & Zhou, Q. (2004). Accuracy analysis of remote sensing change detection by rule-based rationality evaluation with post-classification comparison. International Journal of Remote Sensing, 25(5), 1037–1050. https://doi.org/10.1080/0143116031000150004
   Web of Science ®Google Scholar
• Lobell, D. B., Lesch, S. M., Corwin, D. L., Ulmer, M. G., Anderson, K. A., Potts, D. J., Baltes, M. J., Matos, M. R., & Baltes, M. J. (2010). Regional‐scale assessment of soil salinity in the Red River Valley using multi‐year MODIS EVI and NDVI. Journal of Environmental Quality, 39(1), 35–41. https://doi.org/10.2134/jeq2009.0140
   PubMed Web of Science ®Google Scholar
• Lu, Y., Song, S., Wang, R., Liu, Z., Meng, J., Sweetman, A. J., Wang, T., Ferrier, R. C., Li, H., Luo, W., & Wang, T. (2015). Impacts of soil and water pollution on food safety and health risks in China. Environment International, 77, 5–15. https://doi.org/10.1016/j.envint.2014.12.010
   PubMed Web of Science ®Google Scholar
• Ma, C., Zhang, G. Y., Zhang, X. C., Zhao, Y. J., & Li, H. Y. (2012). Application of Markov model in wetland change dynamics in Tianjin Coastal Area, China. Procedia Environmental Sciences, 13, 252–262.
   Google Scholar
• Ma, X., Zuo, H., Tian, M., Zhang, L., Meng, J., Zhou, X., Liu, Y., Chang, X., & Liu, Y. (2016). Assessment of heavy metals contamination in sediments from three adjacent regions of the Yellow River using metal chemical fractions and multivariate analysis techniques. Chemosphere, 144, 264–272. https://doi.org/10.1016/j.chemosphere.2015.08.026
   PubMed Web of Science ®Google Scholar
• Mallick, J., Talukdar, S., Pal, S., & Rahman, A. (2021). A novel classifier for improving wetland mapping by integrating image fusion techniques and ensemble machine learning classifiers. Ecological Informatics, 65, 101426
   Web of Science ®Google Scholar
• McFeeters, S. K. (1996). The use of normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714
   Web of Science ®Google Scholar
• McLean, E. O. (1982). pH and lime requirements. in: Page ALE, editor. methods of soil analysis. Madison, WI, USA: American Society of Agronomists Inc. natural color image. In Poster session 3, GIS development proceedings, ACRS.
   Google Scholar
• Mitchell, S. A. (2013). The status of wetlands, threats and the predicted effect of global climate change: The situation in Sub-Saharan Africa. Aquatic Sciences, 75(1), 95–112. https://doi.org/10.1007/s00027-012-0259-2
   Web of Science ®Google Scholar
• Mohajane, M., Essahlaoui, A., Oudija, F., Hafyani, M. E., Hmaidi, A. E., Ouali, A. E., Giovanni Randazzo, G., & Teodoro, A. C. (2018). Land use/land cover (LULC) using landsat data series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments, 5(12), 131. https://doi.org/10.3390/environments5120131
   Web of Science ®Google Scholar
• Mohanrajan, S. N., & Loganathan, A. (2022). Novel Vision Transformer–Based Bi-LSTM Model for LU/LC Prediction—Javadi Hills, India. Applied Sciences, 12(13), 6387.
   Google Scholar
• Mzuku, M., Khosla, R., Reich, R., Inman, D., Smith, F., & MacDonald, L. (2005). Spatial variability of measured soil properties across site‐specific management zones. Soil Science Society of America Journal, 69(5), 1572–1579. https://doi.org/10.2136/sssaj2005.0062
   Web of Science ®Google Scholar
• Nkwunonwo, U. C., Odika, P. O., & Onyia, N. I. (2020). A review of the health implications of heavy metals in food chain in Nigeria. The Scientific World Journal, 2020, 1–11. 2020 https://doi.org/10.1155/2020/6594109
   Google Scholar
• Obiora, S. C., Chukwu, A., & Davies, T. C. (2016). Heavy metals and health risk assessment of arable soils and food crops around Pb–Zn mining localities in Enyigba, southeastern Nigeria. Journal of African Earth Sciences, 116, 182–189. https://doi.org/10.1016/j.jafrearsci.2015.12.025
   Web of Science ®Google Scholar
• Ochege, F. U., & Okpala-Okaka, C. (2017). Remote sensing of vegetation cover changes in the humid tropical rainforests of Southeastern Nigeria (1984–2014). Cogent Geoscience, 3(1), 1307566. https://doi.org/10.1080/23312041.2017.1307566
   Web of Science ®Google Scholar
• Ojo, S., Olusina, J. O., & Ngene, B. U. (2022). Mapping of Land use and change detection analysis of yewa south local government of Ogun State, Nigerian. Journal of Geomatics, 16(1): 14-22.
   Google Scholar
• Oke, A. O., Sangodoyin, A. Y., & Omodele, T. (2017). Classification of river water quality in Ogun and Ona River Basins, Nigeria using CCME framework: Implications for sustainable environmental management. Cogent Environmental Science, 3(1), 1295696. https://doi.org/10.1080/23311843.2017.1295696
   Web of Science ®Google Scholar
• Okoye, J., & Oni, K. (2017). Promotion of indigenous food preservation and processing knowledge and the challenge of food security in Africa. Journal of Food Security, 5(3), 75–877. doi:10.12691/jfs-5-3-3.
   Google Scholar
• Olson, D. L. (2004). Comparison of weights in TOPSIS models. Mathematical and Computer Modelling, 40(7–8), 721–727. https://doi.org/10.1016/j.mcm.2004.10.003
   Web of Science ®Google Scholar
• Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1
   Web of Science ®Google Scholar
• Orimoloye, I. R., Kalumba, A. M., Mazinyo, S. P., & Nel, W. (2020). Geospatial analysis of wetland dynamics: Wetland depletion and biodiversity conservation of Isimangaliso Wetland, South Africa. Journal of King Saud University-Science, 32(1), 90–96. https://doi.org/10.1016/j.jksus.2018.03.004
   Web of Science ®Google Scholar
• Oshunsanya, S. O. (2018). Introductory chapter: relevance of soil pH to agriculture, soil pH for nutrient availability and crop performance. IntechOpen. https://www.intechopen.com/books/soil-ph-for-nutrient-availability-and-crop-performance/introductory-chapter-relevance-of-soil-phto-agriculture
   Google Scholar
• Özkan, B., Dengiz, O., & Demirağ Turan, İ. (2019). Site suitability assessment and mapping for rice cultivation using multi-criteria decision analysis based on fuzzy-AHP and TOPSIS approaches under semi humid ecological condition in delta plain. Paddy and Water Environment, 17(4), 665–676. https://doi.org/10.1007/s10333-019-00692-8
   Web of Science ®Google Scholar
• Pal, S., & Akoma, O. C. (2009). Water scarcity in wetland area within kandi block of West Bengal: A hydro-ecological assessment. Ethiopian Journal of Environmental Studies and Management, 2(3):1-12. https://doi.org/10.4314/ejesm.v2i3.48260
   Google Scholar
• Parizi, E., Hosseini, S. M., Ataie-Ashtiani, B., & Simmons, C. T. (2020). Normalized difference vegetation index as the dominant predicting factor of groundwater recharge in phreatic aquifers: Case studies across Iran. Scientific Reports, 10(1), 1–19. https://doi.org/10.1038/s41598-020-74561-4
   PubMed Web of Science ®Google Scholar
• Pontius, R. G., Jr., & Millones, M. (2011). Death to Kappa: The birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407–4429. https://doi.org/10.1080/01431161.2011.552923
   Web of Science ®Google Scholar
• Purnamasari, R. A., Ahamed, T., & Noguchi, R. (2019). Land suitability assessment for cassava production in Indonesia using GIS, remote sensing, and multi-criteria analysis. Asia-Pacific Journal of Regional Science, 3(1), 1–32. https://doi.org/10.1007/s41685-018-0079-z
   Google Scholar
• Radočaj, D., Jurišić, M., & Antonić, O. (2021). Determination of soil C: N suitability zones for organic farming using an unsupervised classification in eastern Croatia. Ecological Indicators, 123, 107382. https://doi.org/10.1016/j.ecolind.2021.107382
   Web of Science ®Google Scholar
• Rao, Y., Xu, Y., Zhang, J., Guo, Y., & Fu, M. (2018). Does subclassified industrial land have a characteristic impact on land surface temperatures? Evidence for and implications of coal and steel processing industries in a Chinese mining city. Ecological indicators, 89, 22–34.
   Web of Science ®Google Scholar
• Rasul, A., Balzter, H., & Smith, C. (2015). Spatial variation of the daytime surface Urban Cool Island during the dry season in Erbil, Iraqi Kurdistan, from landsat 8. Urban Climate, 14, 176–186. https://doi.org/10.1016/j.uclim.2015.09.001
   Web of Science ®Google Scholar
• Raza, S. M. H., Mahmood, S. A., Khan, A. A., & Liesenberg, V. (2018). Delineation of potential sites for rice cultivation through multi-criteria evaluation (MCE) using remote sensing and GIS. International Journal of Plant Production, 12(1), 1–11. https://doi.org/10.1007/s42106-017-0001-z
   Web of Science ®Google Scholar
• Rezaei, S. A., & Gilkes, R. J. (2005). The effects of landscape attributes and plant community on soil chemical properties in rangelands. Geoderma, 125(1–2), 167–176. https://doi.org/10.1016/j.geoderma.2004.07.010
   Web of Science ®Google Scholar
• Robertson, G. P. (2008). In: GS+: geostatistics for the environmental sciences. Gamma Design Software. Plainwell.
   Google Scholar
• Rogers, K., Saintilan, N., & Copeland, C. (2012). Modeling wetland surface elevation dynamics and its application to forecasting the effects of sea-level rise on estuarine wetlands. Ecological Modelling, 244, 148–157. https://doi.org/10.1016/j.ecolmodel.2012.06.014
   Web of Science ®Google Scholar
• Rouse, J. W., Haas, R. H., Scheel, J. A., & Deering, D. W. (1974) ‘Monitoring vegetation systems in the great plains with ERTS.’ Proceedings, 3rd Earth Resource Technology Satellite (ERTS) symposium, vol. 1, p. 48–62.
   Google Scholar
• Saaty, T.L. (1980). The analytic hierarchy process. wetland assessment and restoration potential in the Norwood Young America watershed. Retrieved February 4, 2022 from http:www.gisSmumnedu.research-atsmu/gradpapers.shtml.
   Google Scholar
• Safadoust, A., Doaei, N., Mahboubi, A. A., Mosaddeghi, M. R., Gharabaghi, B., Voroney, P., & Ahrens, B. (2016). Long-term cultivation and landscape position effects on aggregate size and organic carbon fractionation on surface soil properties in the semi-arid region of Iran. Arid Land Research and Management, 30(4), 345–361. https://doi.org/10.1080/15324982.2015.1016244
   Web of Science ®Google Scholar
• Santra, P., Chopra, U. K., & Chakraborty, D. (2008). Spatial variability of soil properties and its application in predicting surface map of hydraulic parameters in an agricultural farm. Current Science. 95(7):937–945. http://www.jstor.org/stable/241031935
   Google Scholar
• Savant, N. K., & De Datta, S. K. (1982). Nitrogen transformations in wetland rice soils. In Advances in agronomy, 35 (pp. 241–302). doi: 10.1016/S0065-2113(08)60327-2. Academic Press.
   Google Scholar
• Schloeder, C. A., Zimmerman, N. E., & Jacobs, M. J. (2001). Comparison of methods for interpolating soil properties using limited data. Soil Science Society of America Journal, 65(2), 470–479. https://doi.org/10.2136/sssaj2001.652470x
   Web of Science ®Google Scholar
• Senjobi, B. A., & Ogunkunle, O. A. (2010). Effect of land use on soil degradation and soil productivity decline on alfisols and ultisols in Ogun State in Southwestern, Nigeria. Agriculturae Conspectus Scientificus, 75(1), 9–19. https://hrcak.srce.hr/53003.
   Google Scholar
• Shaddad, S. M., Buttafuoco, G., Elrys, A., & Castrignanò, A. (2019). Site-specific management of salt-affected soils: A case study from Egypt. Science of the Total Environment, 688, 153–161. https://doi.org/10.1016/j.scitotenv.2019.06.214
   PubMed Web of Science ®Google Scholar
• Shanmugapriya, P., Rathika, S., Ramesh, T., & Janaki, P. (2019). Applications of remote sensing in agriculture-A Review. International Journal of Current Microbiology and Applied Sciences, 8(1), 2270–2283. https://doi.org/10.20546/ijcmas.2019.801.238
   Google Scholar
• Sharma, S., Phartiyal, M., Madhav, S., & Singh, P. (2021). Global wetlands: Categorization, distribution and global scenario. Wetlands Conservation: Current Challenges and Future Strategies, 1–16. https://doi.org/10.1002/9781119692621.ch1.
   Google Scholar
• Skakun, S., Franch, B., Vermote, E., Roger, J. C., Becker-Reshef, I., Justice, C., & Kussul, N. (2017). Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information, and a Gaussian mixture model. Remote Sensing of Environment, 195, 244–258. https://doi.org/10.1016/j.rse.2017.04.026
   Web of Science ®Google Scholar
• Smith, P., House, J. I., Bustamante, M., Sobocká, J., Harper, R., Pan, G., and Pugh, T. A. (2016). Global change pressures on soils from land use and management. Global Change Biology, 22(3), 1008–1028
   PubMed Web of Science ®Google Scholar
• Smyth, A. J., & Montgomery, R. F. (1962). Soils and land use in central Western Nigeria ( pp. 217). Western Nigerian Government Press.
   Google Scholar
• Spurgeon, D. J., Keith, A. M., Schmidt, O., Lammertsma, D. R., & Faber, J. H. (2013). Land-use and land-management change: Relationships with earthworm and fungi communities and soil structural properties. BMC Ecology, 13(1), 1–13. https://doi.org/10.1186/1472-6785-13-46
   PubMedGoogle Scholar
• Staff, S. S. (1999). Soil taxonomy: a basic system of soil classification for making and interpreting soil surveys. Agriculture handbook, 436.
   Google Scholar
• Sys, C., Van Ranst, E., & Debaveye, J. (1991). Land evaluation part I principles in land evaluation and crop production calculations. general administration for development cooperation (GADC). Agricultural Publications.
   Google Scholar
• Tariq, A., & Shu, H. (2020). CA-markov chain analysis of seasonal land surface temperature and land use land cover change using optical multi-temporal satellite data of faisalabad, Pakistan. Remote Sensing, 12(20), 3402. https://doi.org/10.3390/rs12203402.
   Web of Science ®Google Scholar
• Taubenböck, H., Wiesner, M., Felbier, A., Marconcini, M., Esch, T., & Dech, S. (2014). New dimensions of urban landscapes: The spatio-temporal evolution from a polynuclei area to a mega-region based on remote sensing data. Applied Geography, 47, 137–153. https://doi.org/10.1016/j.apgeog.2013.12.002
   Web of Science ®Google Scholar
• Temür, G. (2016). Bazı Çeltik (Oryza sativa L.) Çeşitlerinde silisyumun verim, verim öğeleri ve kaliteye etkilerinin belirlenmesi. Yüksek Lisans Tezi, Ordu Üniversitesi, Fen Bilimleri Enstitüsü.
   Google Scholar
• Tepanosyan, G., Muradyan, V., Hovsepyan, A., Pinigin, G., Medvedev, A., & Asmaryan, S. (2021). Studying spatial-temporal changes and relationship of land cover and surface Urban Heat Island derived through remote sensing in Yerevan, Armenia. Building and Environment, 187, 107390. https://doi.org/10.1016/j.buildenv.2020.107390
   Web of Science ®Google Scholar
• Theobald, D. M., & Hobbs, N. T. (1998). Forecasting rural land-use change: A comparison of regression-and spatial transition-based models. Geographical and Environmental Modelling, 2, 65–82.
   Google Scholar
• Tobore, A., Adedeji, O., Senjobi, B., & Olalere, S. (2021). Geospatial suitability assessment of wetland soils for rice production in federal university of agriculture abeokuta, Ogun State, Nigeria. Discovery Agriculture, 7(17), 1–25.
   Google Scholar
• Tobore, A., Senjobi, B., & Oyerinde, G. (2021). Spatio temporal analysis and simulation pattern of land use and land cover change in odeda peri-urban of Ogun State. Nigeria Jordan Journal of Earth, and Environmental Sciences, 12(4), 326–336.
   Google Scholar
• Tobore, A., Senjobi, B., Ogundiyi, T., & Bamidele, S. (2021b). Geospatial assessment of wetland soils for rice production in Ajibode using geospatial techniques. Open Geosciences, 13(1), 310–320. https://doi.org/10.1515/geo-2020-0227
   Web of Science ®Google Scholar
• Tsu Wei, T., Marthandan, G., Yee‐Loong Chong, A., Ooi, K., & Arumugam, S. (2009). What drives Malaysian m‐commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109(3), 370–388. https://doi.org/10.1108/02635570910939399
   Web of Science ®Google Scholar
• UN Development Programme (2010). Human development report (2010). The real wealth of nations: Pathways to human development. Online available at: http://hdr.undp
   Google Scholar
• USDA-NRCS. (1994). Inherent factors affecting soil phosphorus.
   Google Scholar
• Ustaoglu, E., Sisman, S., & Aydınoglu, A. C. (2021). Determining agricultural suitable land in peri-urban geography using GIS and Multi-Criteria Decision Analysis (MCDA) techniques. Ecological Modelling, 455, 109610. https://doi.org/10.1016/j.ecolmodel.2021.109610
   Web of Science ®Google Scholar
• Verhoeven, J. T., & Setter, T. L. (2010). Agricultural use of wetlands: Opportunities and limitations. Annals of Botany, 105(1), 155–163. https://doi.org/10.1093/aob/mcp172
   PubMed Web of Science ®Google Scholar
• Walkley, A., & Black, I. A. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science, 37(1), 29–38. https://doi.org/10.1097/00010694-193401000-00003
   Google Scholar
• Wang, Z. Q. (1999). Geostatistics applied in ecology. Science Press. 109126. in Chinese.109126. in Chinese.
   Google Scholar
• Wang, S. W., Gebru, B. M., Lamchin, M., Kayastha, R. B., & Lee, W. K. (2020). Land use and land cover change detection and prediction in the Kathmandu district of Nepal using remote sensing and GIS. Sustainability,12(3925): 18–122. doi:10.3390/su12093925.
   Web of Science ®Google Scholar
• Western, A. W., Zhou, S. L., Grayson, R. B., McMahon, T. A., Blöschl, G., & Wilson, D. J. (2004). Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes. Journal of Hydrology, 286(2), 113–134. https://doi.org/10.1016/j.jhydrol.2003.09.014
   Google Scholar
• Wilding, L. P. (1985). Spatial variability: its documentation, accomodation and implication to soil surveys. Soil spatial variability, Las Vegas NV, 30 (pp. 166–194).
   Google Scholar
• Wojcicki, K. J., & Woskowicz-Slezak, B. (2015). Anthropogenic causes of wetland loss and degradation in the lower Kłodnica valley (southern Poland). Environmental and Socio-Economic Studies, 3(4), 20–29. https://doi.org/10.1515/environ-2015-0070
   Google Scholar
• Xiao, J., Shen, Y., Ge, J., Tateishi, R., Tang, C., Liang, Y., & Huang, Z. (2004). Evaluating urban expansion and land-use change in Shijiazhuang, China by using GIS and remote sensing. Landscape and Urban Planning, 75(6), 69–80. https://doi.org/10.1016/j.landurbplan.2004.12.005
   Google Scholar
• Xie, Z., Huete, A., Ma, X., Restrepo-Coupe, N., Devadas, R., Clarke, K., & Lewis, M. (2016). Landsat and GRACE observations of arid wetland dynamics in a dryland river system under multi-decadal hydroclimatic extremes. Journal of Hydrology, 543, 818–831. https://doi.org/10.1016/j.jhydrol.2016.11.001
   Web of Science ®Google Scholar
• Xu, H. (2006). Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179
   Web of Science ®Google Scholar
• Xu, T., Weng, B., Yan, D., Wang, K., Li, X., Bi, W., Li, M., Cheng, X., & Liu, Y. (2019). Wetlands of international importance: Status, threats, and future protection. International Journal of Environmental Research and Public Health, 16(10), 1818. https://doi.org/10.3390/ijerph16101818
   PubMed Web of Science ®Google Scholar
• Zabel, F., Putzenlechner, B., & Mauser, W. (2014). Global agricultural land resources–a high-resolution suitability evaluation and its perspectives until 2100 under climate change conditions. PloS one, 9(9), e107522. https://doi.org/10.1371/journal.pone.0107522
   PubMed Web of Science ®Google Scholar
• Zhao, J., Zhang, H., Zhang, Z., Guo, X., Li, X., & Chen, C. (2015). Spatial and temporal changes in vegetation phenology at middle and high latitudes of the Northern Hemisphere over the past three decades. Remote Sensing, 7(8), 10973–10995. https://doi.org/10.3390/rs70810973
   Web of Science ®Google Scholar
• Zolekar, R. B., & Bhagat, V. S. (2015). Multi-criteria land suitability analysis for agriculture in the hilly zone: remote sensing and GIS approach. Computers and Electronics in Agriculture, 118, 300–321. https://doi.org/10.1016/j.compag.2015.09.016
   Web of Science ®Google Scholar