| Construction and application of ELM-ESTARFM remote sensing inversion model for soil moisture in desert steppe area |
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| DOI：10.7606/j.issn.1000-7601.2024.03.25 |
| Key Words: soil moisture environmental factors extreme learning machine random forest |
| Author Name | Affiliation | | WANG Huan | College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia 010018, China | | LI Ruiping | College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia 010018, China | | WANG Fuqiang | College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia 010018, China | | ZHAO Jianwei | College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia 010018, China | | MIAO Cunli | Natural Resources Bureau of Keshiketengqi, Chifeng, Inner Mongolia 025350, China | | JI Xiaojing | Inner Mongolia Autonomous Region Center for Surveying, Mapping and Geoinformation, Hohhot, Inner Mongolia 010018, China |
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| Abstract: |
| The high spatiotemporal resolution and high\|precision assessment of soil moisture are of great significance for drought monitoring. To explore the optimal model of remote sensing inversion for soil moisture in the desert steppe region of Inner Mongolia, China, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was carried out based on Landsat and MODIS data. Combining with multi\|factor environmental factors including underlying surface factors, topographic factors, meteorological factors, and vegetation factors,the soil moisture content inversion model was constructed by extreme learning machine (ELM) and random forest (RF) methods. Comparing with the soil moisture content inversion model constructed by Landsat (without fusion), the optimal soil moisture content inversion model was selected. The soil moisture content distribution characteristics of different land use types in the study area were analyzed. The results showed that the normalized vegetation index was the most important predictor of soil moisture content and environmental factors (R2=0.85, 0.82, 0.79 at soil depth of 0~10, 10~20, 20~30 cm), followed by precipitation (R2=0.73, 0.68, 0.71), elevation (R2=0.71, 0.70, 0.71), water index (R2=0.69, 0.69, 0.68), and normalized salinity index (R2=0.68, 0.67、0.65). Compared with the model without spatiotemporal fusion, the accuracy of the model constructed by ESTARFM spatiotemporal fusion was improved, and the R2, RMSE, and MAE of the ELM model were 0.89, 6.58%, and 3.93%, respectively, and the R2, RMSE, and MAE of the RF model were 0.78, 7.25%, and 4.95%, respectively. The MAE was 0.75, 7.37%, and 5.24%, respectively, and the R2, RMSE, and MAE of the RF model were 0.71, 7.48%, and 5.30%, respectively, indicating that the ELM model had a better inversion effect on soil moisture content than the RF model, and ELM-ESTARFM was the optimal model for soil moisture content inversion. On this basis, the improved ELM-ESTARFM remote sensing inversion model was used to monitor the soil moisture content in the whole area of Wushenqi, and it was found that the soil moisture content in the north and northwest of the study area was high, and the soil moisture content in the southern area was low. For different soil depths, the soil moisture content was 18.92%, 19.34%, and 21.84% in the cultivated land area at 0~10, 10~20, and 20~30 cm soil depths, 11.80%, 11.87%, 12.40% in woodland, 10.97%, 11.02% and 12.22% in grassland, and 5.07%, 5.35%, and 5.67% in sandy land, respectively. |
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