Application of the artificial intelligence approach and remotely sensed imagery for soil moisture evaluation
The current research attempts to present a modeling framework for determining soil moisture conditions by using remotely sensed imagery products. In this way, identifying various pixels with similar patterns from satellite images could be a reliable method to have an appropriate view over the soil m...
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| Format: | Article |
| Language: | English |
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IWA Publishing
2022-05-01
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| Series: | Hydrology Research |
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| Online Access: | http://hr.iwaponline.com/content/53/5/684 |
| _version_ | 1811221520113991680 |
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| author | Vahid Nourani |
| author_facet | Vahid Nourani |
| author_sort | Vahid Nourani |
| collection | DOAJ |
| description | The current research attempts to present a modeling framework for determining soil moisture conditions by using remotely sensed imagery products. In this way, identifying various pixels with similar patterns from satellite images could be a reliable method to have an appropriate view over the soil moisture condition of a particular region. In this context, an artificial intelligence-based self-organizing map (SOM) method is employed to classify homogenous pixels over Phoenix, which is located in the south of Arizona, utilizing parameters extracted from satellite images. The central pixels of clusters are selected as the cluster indicator, with one from each cluster. Then, feed-forward neural networks (FFNNs) consisting of three layers of input, hidden, and output are trained by employing the extracted satellite images time series of the central pixels of the clusters. Finally, the soil moisture conditions of the representative pixels of the clusters are simulated by the trained models. The results reveal the suitability of SOM-based clustering to identify the specific points by which soil moisture can represent the soil moisture condition over the related regions. The proposed methodology and obtained results can be further used to provide a cost-effective method to determine the soil moisture condition of the region by reducing the costs of monitoring. HIGHLIGHTS
An SOM is used to cluster homogenous pixels.;
The soil moisture conditions of the representative pixel for each cluster are simulated by using an ANN.;
The results reveal the suitability of the SOM clustering method to identify the specific points by which the soil moisture can represent the soil moisture condition.; |
| first_indexed | 2024-04-12T08:01:53Z |
| format | Article |
| id | doaj.art-35a27151d7244729b1c20469fbd1ccb5 |
| institution | Directory Open Access Journal |
| issn | 1998-9563 2224-7955 |
| language | English |
| last_indexed | 2024-04-12T08:01:53Z |
| publishDate | 2022-05-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Hydrology Research |
| spelling | doaj.art-35a27151d7244729b1c20469fbd1ccb52022-12-22T03:41:17ZengIWA PublishingHydrology Research1998-95632224-79552022-05-0153568469910.2166/nh.2022.111111Application of the artificial intelligence approach and remotely sensed imagery for soil moisture evaluationVahid Nourani0 Center of Excellence in Hydroinformatics and Faculty of Civil Engineering, University of Tabriz, 29 Bahman Avenue, Tabriz 5166616421, Iran The current research attempts to present a modeling framework for determining soil moisture conditions by using remotely sensed imagery products. In this way, identifying various pixels with similar patterns from satellite images could be a reliable method to have an appropriate view over the soil moisture condition of a particular region. In this context, an artificial intelligence-based self-organizing map (SOM) method is employed to classify homogenous pixels over Phoenix, which is located in the south of Arizona, utilizing parameters extracted from satellite images. The central pixels of clusters are selected as the cluster indicator, with one from each cluster. Then, feed-forward neural networks (FFNNs) consisting of three layers of input, hidden, and output are trained by employing the extracted satellite images time series of the central pixels of the clusters. Finally, the soil moisture conditions of the representative pixels of the clusters are simulated by the trained models. The results reveal the suitability of SOM-based clustering to identify the specific points by which soil moisture can represent the soil moisture condition over the related regions. The proposed methodology and obtained results can be further used to provide a cost-effective method to determine the soil moisture condition of the region by reducing the costs of monitoring. HIGHLIGHTS An SOM is used to cluster homogenous pixels.; The soil moisture conditions of the representative pixel for each cluster are simulated by using an ANN.; The results reveal the suitability of the SOM clustering method to identify the specific points by which the soil moisture can represent the soil moisture condition.;http://hr.iwaponline.com/content/53/5/684artificial intelligencephoenixremote sensingsoil moisturespatiotemporal modeling |
| spellingShingle | Vahid Nourani Application of the artificial intelligence approach and remotely sensed imagery for soil moisture evaluation Hydrology Research artificial intelligence phoenix remote sensing soil moisture spatiotemporal modeling |
| title | Application of the artificial intelligence approach and remotely sensed imagery for soil moisture evaluation |
| title_full | Application of the artificial intelligence approach and remotely sensed imagery for soil moisture evaluation |
| title_fullStr | Application of the artificial intelligence approach and remotely sensed imagery for soil moisture evaluation |
| title_full_unstemmed | Application of the artificial intelligence approach and remotely sensed imagery for soil moisture evaluation |
| title_short | Application of the artificial intelligence approach and remotely sensed imagery for soil moisture evaluation |
| title_sort | application of the artificial intelligence approach and remotely sensed imagery for soil moisture evaluation |
| topic | artificial intelligence phoenix remote sensing soil moisture spatiotemporal modeling |
| url | http://hr.iwaponline.com/content/53/5/684 |
| work_keys_str_mv | AT vahidnourani applicationoftheartificialintelligenceapproachandremotelysensedimageryforsoilmoistureevaluation |