| MODIS drought monitoring index combined with PSO_RBFNN for soil moisture retrieval at the seedling stage of flax in Gansu Province |
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| DOI：10.7606/j.issn.1000-7601.2025.01.28 |
| Key Words: remote sensing soil moisture drought index MODIS RBFNN PSO |
| Author Name | Affiliation | | YANG Hui | College of Information Science and Technology，Gansu Agricultural University, Lanzhou, Gansu 730070, China | | LI Yue | College of Information Science and Technology，Gansu Agricultural University, Lanzhou, Gansu 730070, China | | WU Ling | College of Information Science and Technology，Gansu Agricultural University, Lanzhou, Gansu 730070, China | | WU Bing | College of Information Science and Technology，Gansu Agricultural University, Lanzhou, Gansu 730070, China | | GAO Yuhong | College of Information Science and Technology，Gansu Agricultural University, Lanzhou, Gansu 730070, China | | YAN Bin | College of Information Science and Technology，Gansu Agricultural University, Lanzhou, Gansu 730070, China | | ZHOU Hui | College of Information Science and Technology，Gansu Agricultural University, Lanzhou, Gansu 730070, China | | TANG Jie | College of Information Science and Technology，Gansu Agricultural University, Lanzhou, Gansu 730070, China | | ZHAO Yongwei | College of Information Science and Technology，Gansu Agricultural University, Lanzhou, Gansu 730070, China |
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| Abstract: |
| Given the limitations of a single remote sensing drought monitoring index in fully capturing the dynamic changes in soil moisture during crop growth, this study focuses on field soil relative moisture during the flax seedling stage in Gansu Province. The selected remote sensing drought monitoring indices include crop form and greenness, canopy temperature, and crop canopy water content. Utilizing the MODIS drought monitoring index and measured soil relative humidity data, combined with a radial basis function neural network (PSO_RBFNN) optimized by particle swarm optimization, an inverse model of soil relative humidity in farmland was constructed. The accuracy of inversion results of different models—BP_NN, RBFNN, PSO_RBFNN artificial neural network, and logistic regression (LR)—was verified and compared using measured soil relative humidity data. The results indicated that the combination of the MODIS drought monitoring index and PSO_RBFNN outperformed the other three models in retrieving the soil relative humidity at the flax seedling stage in Gansu Province. The average inversion accuracy for soil relative humidity at 10 cm and 20 cm depths was 89.91% and 91.71%, respectively. Compared with the RBFNN, LR, and BP_NN models, the average accuracy improved by 8.69, 4.94, 4.76 percentage points in 10 cm soil depth and 6.91, 6.86, 9.32 percentage points in 20 cm depths, respectively. Regression analysis showed minimal deviation relative to the 1∶1 slash line, with correlation coefficients for soil relative humidity at 10 cm and 20 cm depths reaching 0.68 and 0.74, respectively. This study highlights the effectiveness of the PSO_RBFNN model and offers a valuable reference for remote sensing monitoring and the inversion of regional farmland soil moisture. |
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