| Retrieval of soil moisture at critical period of water demand of winter wheat in Jiangsu Province using MODIS drought index and RBFNN |
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| DOI：10.7606/j.issn.1000-7601.2022.06.27 |
| Key Words: remote sensing drought indices MODIS RBFNN |
| Author Name | Affiliation | | LI Jing | The Meteorological Observatory of Nanjing, Nanjing, Jiangsu 210019, China | | REN Yifang | angsu Climate Center, Nanjing, Jiangsu 210000, China | | DAI Zhujun | The Meteorological Observatory of Nanjing, Nanjing, Jiangsu 210019, China
Key Laboratory of Transportation Meteorology, China Meteorological Administration, Nanjing, Jiangsu 210041, China | | JIN Qiong | The Meteorological Observatory of Nanjing, Nanjing, Jiangsu 210019, China | | SHEN Cheng | The Meteorological Observatory of Nanjing, Nanjing, Jiangsu 210019 | | ZHANG Lei | National Meteorological Center, Beijing 100081, China |
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| Abstract: |
| The use of remote sensing indices to retrieve soil moisture has become one of the most important means for monitoring drought. However, a single remote sensing drought index has certain limitations in retrieving soil moisture. In this study, 5 suitable MODIS remote sensing drought monitoring indices were selected. Combined with Radial Basis Function Neural Network (RBFNN), the soil relative moisture in the critical water demand period of winter wheat in Jiangsu Province in 2018 was retrieved synergistically. The research showed that the soil moisture model retrieved by RBFNN collaborative remote sensing index had better inversion effect than a single remote sensing index. The correlation coefficients with the measured soil relative humidity at different depths of 10 cm and 20 cm reach 0.5161 and 0.4307, respectively, which integrated the remote sensing information of multiple channels to reflect the change of local soil moisture. Meanwhile, the relative moisture distribution map of winter wheat at the depth of 10 cm retrieved by using RBFNN in May 2017 of Jiangsu Province was close to the observed soil moisture results, which indicates that the inversion model was effective. The research results improve the inversion accuracy of soil moisture and provide a certain service reference for local soil moisture inversion. |
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