Least Square Based Iteration Approach for Agricultural Soil Moisture Retrieval Using Multi-Sensor Data
The main objective of this study is to develop a robust soil moisture retrieval approach using multi-sensor remote sensing data. Firstly, the water cloud model was employed to eliminate the vegetation effects on SAR observations over vegetated areas, thus to obtain the bare soil backscatter associat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-03-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2020.1752642 |
| _version_ | 1797661103937814528 |
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| author | Xiang Zhang Xinming Tang Xiaoming Gao Hui Zhao |
| author_facet | Xiang Zhang Xinming Tang Xiaoming Gao Hui Zhao |
| author_sort | Xiang Zhang |
| collection | DOAJ |
| description | The main objective of this study is to develop a robust soil moisture retrieval approach using multi-sensor remote sensing data. Firstly, the water cloud model was employed to eliminate the vegetation effects on SAR observations over vegetated areas, thus to obtain the bare soil backscatter associated with soil moisture. Then, against the underdetermined system for soil moisture retrieval, the advanced integral equation model and calibrated integral equation model were integrated to construct soil moisture retrieval scheme in combination with multi-sensor SAR observations. Through the above processing the influences of vegetation and surface roughness can be minimized in the developed soil moisture retrieval scheme. Finally, the least square based iteration approach was applied to derive soil moisture with multi-sensor SAR observations as inputs. Based on the field measurements and multi-sensor SAR data (C-band and X-band). acquired on June 2015 and October 2015 over Hebei agricultural areas, quantitative evaluation of the developed approach was implemented. The results indicate that accurate soil moisture was obtained by the developed approach over corn covered area and bare agricultural area. In comparison with the results obtained by LUT method, the least square based iteration approach achieved better estimations for soil moisture. |
| first_indexed | 2024-03-11T18:40:27Z |
| format | Article |
| id | doaj.art-2a886649e8a14153895b46b433254c89 |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-11T18:40:27Z |
| publishDate | 2020-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-2a886649e8a14153895b46b433254c892023-10-12T13:36:23ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712020-03-0146211312910.1080/07038992.2020.17526421752642Least Square Based Iteration Approach for Agricultural Soil Moisture Retrieval Using Multi-Sensor DataXiang Zhang0Xinming Tang1Xiaoming Gao2Hui Zhao3Ministry of Natural Resources of P.R. ChinaMinistry of Natural Resources of P.R. ChinaMinistry of Natural Resources of P.R. ChinaNational Geomatics Center of ChinaThe main objective of this study is to develop a robust soil moisture retrieval approach using multi-sensor remote sensing data. Firstly, the water cloud model was employed to eliminate the vegetation effects on SAR observations over vegetated areas, thus to obtain the bare soil backscatter associated with soil moisture. Then, against the underdetermined system for soil moisture retrieval, the advanced integral equation model and calibrated integral equation model were integrated to construct soil moisture retrieval scheme in combination with multi-sensor SAR observations. Through the above processing the influences of vegetation and surface roughness can be minimized in the developed soil moisture retrieval scheme. Finally, the least square based iteration approach was applied to derive soil moisture with multi-sensor SAR observations as inputs. Based on the field measurements and multi-sensor SAR data (C-band and X-band). acquired on June 2015 and October 2015 over Hebei agricultural areas, quantitative evaluation of the developed approach was implemented. The results indicate that accurate soil moisture was obtained by the developed approach over corn covered area and bare agricultural area. In comparison with the results obtained by LUT method, the least square based iteration approach achieved better estimations for soil moisture.http://dx.doi.org/10.1080/07038992.2020.1752642 |
| spellingShingle | Xiang Zhang Xinming Tang Xiaoming Gao Hui Zhao Least Square Based Iteration Approach for Agricultural Soil Moisture Retrieval Using Multi-Sensor Data Canadian Journal of Remote Sensing |
| title | Least Square Based Iteration Approach for Agricultural Soil Moisture Retrieval Using Multi-Sensor Data |
| title_full | Least Square Based Iteration Approach for Agricultural Soil Moisture Retrieval Using Multi-Sensor Data |
| title_fullStr | Least Square Based Iteration Approach for Agricultural Soil Moisture Retrieval Using Multi-Sensor Data |
| title_full_unstemmed | Least Square Based Iteration Approach for Agricultural Soil Moisture Retrieval Using Multi-Sensor Data |
| title_short | Least Square Based Iteration Approach for Agricultural Soil Moisture Retrieval Using Multi-Sensor Data |
| title_sort | least square based iteration approach for agricultural soil moisture retrieval using multi sensor data |
| url | http://dx.doi.org/10.1080/07038992.2020.1752642 |
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