Soil moisture retrieval over agricultural fields through integration of synthetic aperture radar and optical images
The soil roughness and two-way vegetation transmissivity parameters affect the radar backscattered signal; therefore, the results of the soil moisture retrieval from Synthetic Aperture Radar (SAR) images. In this paper, the Improved Water Cloud Model was extended to specify the effects of the above...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-11-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2021.1974276 |
| _version_ | 1797679042152890368 |
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| author | Milad Mardan Salman Ahmadi |
| author_facet | Milad Mardan Salman Ahmadi |
| author_sort | Milad Mardan |
| collection | DOAJ |
| description | The soil roughness and two-way vegetation transmissivity parameters affect the radar backscattered signal; therefore, the results of the soil moisture retrieval from Synthetic Aperture Radar (SAR) images. In this paper, the Improved Water Cloud Model was extended to specify the effects of the above parameters. The surface roughness parameter values were calculated for each ground measurement site using an artificial neural network. This parameter was then used in the proposed models to rectify the effects of surface roughness on soil moisture estimation. Also, the Normalized Difference Water Index, derived from Landsat 5 imagery, was used to account for the impact of the vegetation canopy on the SAR backscatter. The accuracy of the proposed model was assessed using AIRSAR C-, L-, and P-band data, ALOS PALSAR L-band and two reference data were collected during NASA’s SMEX03 and SMAPVEX08 campaigns. The experimental results indicated that the proposed models had improved the RMSE and R values with respect to those in the IWCM in all bands and polarizations, with the highest values concerning the L band and VV polarization. In this band, the value of RMSE decreased by 0.002 (6.25%), and R increased by 0.054 (9.45%). |
| first_indexed | 2024-03-11T23:08:41Z |
| format | Article |
| id | doaj.art-b1cad82af515402094187dac776d8670 |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:08:41Z |
| publishDate | 2021-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-b1cad82af515402094187dac776d86702023-09-21T12:43:07ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262021-11-015881276129910.1080/15481603.2021.19742761974276Soil moisture retrieval over agricultural fields through integration of synthetic aperture radar and optical imagesMilad Mardan0Salman Ahmadi1University of KurdistanUniversity of KurdistanThe soil roughness and two-way vegetation transmissivity parameters affect the radar backscattered signal; therefore, the results of the soil moisture retrieval from Synthetic Aperture Radar (SAR) images. In this paper, the Improved Water Cloud Model was extended to specify the effects of the above parameters. The surface roughness parameter values were calculated for each ground measurement site using an artificial neural network. This parameter was then used in the proposed models to rectify the effects of surface roughness on soil moisture estimation. Also, the Normalized Difference Water Index, derived from Landsat 5 imagery, was used to account for the impact of the vegetation canopy on the SAR backscatter. The accuracy of the proposed model was assessed using AIRSAR C-, L-, and P-band data, ALOS PALSAR L-band and two reference data were collected during NASA’s SMEX03 and SMAPVEX08 campaigns. The experimental results indicated that the proposed models had improved the RMSE and R values with respect to those in the IWCM in all bands and polarizations, with the highest values concerning the L band and VV polarization. In this band, the value of RMSE decreased by 0.002 (6.25%), and R increased by 0.054 (9.45%).http://dx.doi.org/10.1080/15481603.2021.1974276soil moisturesoil surface roughnesswater cloud modelradar and optical imageryartificial neural network |
| spellingShingle | Milad Mardan Salman Ahmadi Soil moisture retrieval over agricultural fields through integration of synthetic aperture radar and optical images GIScience & Remote Sensing soil moisture soil surface roughness water cloud model radar and optical imagery artificial neural network |
| title | Soil moisture retrieval over agricultural fields through integration of synthetic aperture radar and optical images |
| title_full | Soil moisture retrieval over agricultural fields through integration of synthetic aperture radar and optical images |
| title_fullStr | Soil moisture retrieval over agricultural fields through integration of synthetic aperture radar and optical images |
| title_full_unstemmed | Soil moisture retrieval over agricultural fields through integration of synthetic aperture radar and optical images |
| title_short | Soil moisture retrieval over agricultural fields through integration of synthetic aperture radar and optical images |
| title_sort | soil moisture retrieval over agricultural fields through integration of synthetic aperture radar and optical images |
| topic | soil moisture soil surface roughness water cloud model radar and optical imagery artificial neural network |
| url | http://dx.doi.org/10.1080/15481603.2021.1974276 |
| work_keys_str_mv | AT miladmardan soilmoistureretrievaloveragriculturalfieldsthroughintegrationofsyntheticapertureradarandopticalimages AT salmanahmadi soilmoistureretrievaloveragriculturalfieldsthroughintegrationofsyntheticapertureradarandopticalimages |