| Soil moisture estimation by using particle filter assimilated conditional vegetation temperature index |
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| DOI：10.7606/j.issn.1000-7601.2018.03.35 |
| Key Words: conditional vegetation temperature index soil moisture wheat growing period estimation crop growth model particle filter Guanzhong Plain |
| Author Name | Affiliation | | XIE Yi | College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri\|Hazards, Ministry of Agriculture, Beijing 100083, China | | WANG Peng-xin | College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri\|Hazards, Ministry of Agriculture, Beijing 100083, China | | LI Li | College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri\|Hazards, Ministry of Agriculture, Beijing 100083, China | | XUN Lan | College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri\|Hazards, Ministry of Agriculture, Beijing 100083, China | | ZHANG Shu-yu | Shaanxi Provincial Meteorological Bureau, Xi’an， Shaanxi 710014, China |
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| Abstract: |
| In order to exactly obtain the temporal-spatial information of soil moisture (0~20 cm) in main growing period of winter wheat in Guanzhong Plain during 2013 to 2015, in this paper, based on the Landsat-8 remote sensing data, inverted the conditional vegetation temperature index (CVTI), constructed the soil moisture inverting model combined with the linear correlation between CVTI and measured soil moisture. Using the particle filter (PF) algorithm was assimilated the soil moisture based on CVTI inversion and simulation of the CERES-Wheat model, obtained the assimilation of the soil moisture value with daily step length. The precision of the simulated , inverted and assimilated soil moisture values were inspected by the field-measured soil moisture respectively. The results were indicated that: The linear correlation between CVTI and field-measured soil moisture was notable, especially at jointing stage and heading-filling stage of wheat, the relativity was achieved extremely significant (P<0.01). The linear correlation between the assimilated and measured soil moisture (r=0.96, P<0.001) was large estimated than the correlation between the simulated and measured soil moisture (r=0.71, P<0.01) and the correlation between inverted and measured soil moisture (r=0.89, P<0.001). The root mean square errors (RMSEs) and mean relative errors (MREs) of the assimilated soil moisture was reduced 0.025 mm3·mm-3 and 2.70% respectively compared with the simulated soil moisture, and was reduced 0.016 mm3·mm-3 and 4.15% respectively compared with the inverted soil moisture. These results indicated that the assimilation process was improved estimating accuracy of the soil moisture time series. Therefore, based on the CVTI and PF algorithm, the soil moisture content in main growing period of wheat in Guanzhong Plain can be accurately estimated. |
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