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The Doukkala plains one of the largest irrigated areas in Morocco with a very important agricultural potential. With the integration of new technologies in agriculture, the plain has been subjected to intensive agriculture which has negative impacts on soil quality especially the soil organic matter loss. Therefore, the objective of this study is to combine remote sensing and modelling for monitoring of organic matter content. The obtained results showed that all the examined models showed satisfactory results in the prediction of organic matter with a coefficient of determination R2 ranging from 0.58 to 0.71 and the Root Mean Square Error (RMSE) varied 0.25 and 0.26%. Based on the findings, we can infer that this approach is both efficient and valid for modelling and mapping soil organic matter and may moreover be applied for other areas with same characteristics.
Słowa kluczowe
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Tom
Strony
188--197
Opis fizyczny
Bibliogr 44 poz., rys., tab.
Twórcy
autor
- Laboratory of Geosciences and Environmental Techniques, Department of Earth Sciences, Faculty of Sciences, Chouaïb Doukkali University, BP 20, 24000 El Jadida, Morocco
autor
- Laboratory of Geosciences and Environmental Techniques, Department of Earth Sciences, Faculty of Sciences, Chouaïb Doukkali University, BP 20, 24000 El Jadida, Morocco
autor
- Department of Geology, Laboratory of Geoengineering and Environment, Research Group "Water Sciences and Environment Engineering", Faculty of Sciences, Moulay Ismail University, Zitoune, Meknes BP 11201, Morocco
autor
- Laboratory of Geosciences and Environmental Techniques, Department of Earth Sciences, Faculty of Sciences, Chouaïb Doukkali University, BP 20, 24000 El Jadida, Morocco
autor
- Department of Geology, Laboratory of Geoengineering and Environment, Research Group "Water Sciences and Environment Engineering", Faculty of Sciences, Moulay Ismail University, Zitoune, Meknes BP 11201, Morocco
Bibliografia
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- 39. Xie, R., Xiao, H. 2018. Application of Remote Sensing in the Estimation of Soil Organic Matter Content. Chemical Engineering Transactions, 66, 469–474.
- 40. Ye, Z., Sheng, Z., Liu, X., Ma, Y., Wang, R., Ding, S., Wang, Q. 2021. Using Machine Learning Algorithms Based on GF-6 and Google Earth Engine to Predict and Map the Spatial Distribution of Soil Organic Matter Content. Sustainability, 13(24), 14055.
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- 42. Yu, Q., Yao, T., Lu, H., Feng, W., Xue, Y., Liu, B. 2021. Improving estimation of soil organic matter content by combining Landsat 8 OLI images and environmental data: A case study in the river valley of the southern Qinghai-Tibet Plateau. Computers and Electronics in Agriculture, 185, 106144.
- 43. Zhai, M. 2019. Inversion of organic matter content in wetland soil based on Landsat 8 remote sensing image. Journal of Visual Communication and Image Representation, 64, 102645.
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Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-25fef858-224a-4007-b865-47a5af26259c