An AI-Based Nested Large–Small Model for Passive Microwave Soil Moisture and Land Surface Temperature Retrieval Method
Retrieving soil moisture (SM) and land surface temperature (LST) provides crucial environmental data for smart agriculture, enabling precise irrigation, crop health monitoring, and yield optimization. The rapid advancement of Artificial intelligence (AI) hardware offers new opportunities to overcome...
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MDPI AG
2025-03-01
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| author | Mengjie Liang Kebiao Mao Jiancheng Shi Sayed M. Bateni Fei Meng |
| author_facet | Mengjie Liang Kebiao Mao Jiancheng Shi Sayed M. Bateni Fei Meng |
| author_sort | Mengjie Liang |
| collection | DOAJ |
| description | Retrieving soil moisture (SM) and land surface temperature (LST) provides crucial environmental data for smart agriculture, enabling precise irrigation, crop health monitoring, and yield optimization. The rapid advancement of Artificial intelligence (AI) hardware offers new opportunities to overcome the limitations of traditional geophysical parameter retrieval methods. We propose a nested large–small model method that uses AI techniques for the joint iterative retrieval of passive microwave SM and LST. This method retains the strengths of traditional physical and statistical methods while incorporating spatiotemporal factors influencing surface emissivity for multi-hierarchical classification. The method preserves the physical significance and interpretability of traditional methods while significantly improving the accuracy of passive microwave SM and LST retrieval. With the use of the terrestrial area of China as a case, multi-hierarchical classification was applied to verify the feasibility of the method. Experimental data show a significant improvement in retrieval accuracy after hierarchical classification. In ground-based validation, the ascending and descending orbit SM retrieval models 5 achieved MAEs of 0.026 m<sup>3</sup>/m<sup>3</sup> and 0.030 m<sup>3</sup>/m<sup>3</sup>, respectively, improving by 0.015 m<sup>3</sup>/m<sup>3</sup> and 0.012 m<sup>3</sup>/m<sup>3</sup> over the large model, and 0.032 m<sup>3</sup>/m<sup>3</sup> and 0.028 m<sup>3</sup>/m<sup>3</sup> over AMSR2 SM products. The ascending and descending orbit LST retrieval models 5 achieved MAEs of 1.67 K and 1.72 K, respectively, with improvements of 0.67 K and 0.49 K over the large model, and 0.57 K and 0.56 K over the MODIS LST products. The retrieval model can theoretically be enhanced to the pixel level, potentially maximizing retrieval accuracy, which provides a theoretical and technical basis for the parameter retrieval of AI passive microwave large models. |
| format | Article |
| id | doaj-art-25796b7b271d426ebb0d8697b0c407c6 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-25796b7b271d426ebb0d8697b0c407c62025-08-20T03:08:59ZengMDPI AGRemote Sensing2072-42922025-03-01177119810.3390/rs17071198An AI-Based Nested Large–Small Model for Passive Microwave Soil Moisture and Land Surface Temperature Retrieval MethodMengjie Liang0Kebiao Mao1Jiancheng Shi2Sayed M. Bateni3Fei Meng4School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250100, ChinaInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaNational Space Science Center, Chinese Academy of Sciences, Beijing 100190, ChinaWater Resources Research Center, Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USASchool of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250100, ChinaRetrieving soil moisture (SM) and land surface temperature (LST) provides crucial environmental data for smart agriculture, enabling precise irrigation, crop health monitoring, and yield optimization. The rapid advancement of Artificial intelligence (AI) hardware offers new opportunities to overcome the limitations of traditional geophysical parameter retrieval methods. We propose a nested large–small model method that uses AI techniques for the joint iterative retrieval of passive microwave SM and LST. This method retains the strengths of traditional physical and statistical methods while incorporating spatiotemporal factors influencing surface emissivity for multi-hierarchical classification. The method preserves the physical significance and interpretability of traditional methods while significantly improving the accuracy of passive microwave SM and LST retrieval. With the use of the terrestrial area of China as a case, multi-hierarchical classification was applied to verify the feasibility of the method. Experimental data show a significant improvement in retrieval accuracy after hierarchical classification. In ground-based validation, the ascending and descending orbit SM retrieval models 5 achieved MAEs of 0.026 m<sup>3</sup>/m<sup>3</sup> and 0.030 m<sup>3</sup>/m<sup>3</sup>, respectively, improving by 0.015 m<sup>3</sup>/m<sup>3</sup> and 0.012 m<sup>3</sup>/m<sup>3</sup> over the large model, and 0.032 m<sup>3</sup>/m<sup>3</sup> and 0.028 m<sup>3</sup>/m<sup>3</sup> over AMSR2 SM products. The ascending and descending orbit LST retrieval models 5 achieved MAEs of 1.67 K and 1.72 K, respectively, with improvements of 0.67 K and 0.49 K over the large model, and 0.57 K and 0.56 K over the MODIS LST products. The retrieval model can theoretically be enhanced to the pixel level, potentially maximizing retrieval accuracy, which provides a theoretical and technical basis for the parameter retrieval of AI passive microwave large models.https://www.mdpi.com/2072-4292/17/7/1198passive microwave remote sensingsoil moistureland surface temperaturemulti-hierarchy classificationjoint iterative retrieval |
| spellingShingle | Mengjie Liang Kebiao Mao Jiancheng Shi Sayed M. Bateni Fei Meng An AI-Based Nested Large–Small Model for Passive Microwave Soil Moisture and Land Surface Temperature Retrieval Method Remote Sensing passive microwave remote sensing soil moisture land surface temperature multi-hierarchy classification joint iterative retrieval |
| title | An AI-Based Nested Large–Small Model for Passive Microwave Soil Moisture and Land Surface Temperature Retrieval Method |
| title_full | An AI-Based Nested Large–Small Model for Passive Microwave Soil Moisture and Land Surface Temperature Retrieval Method |
| title_fullStr | An AI-Based Nested Large–Small Model for Passive Microwave Soil Moisture and Land Surface Temperature Retrieval Method |
| title_full_unstemmed | An AI-Based Nested Large–Small Model for Passive Microwave Soil Moisture and Land Surface Temperature Retrieval Method |
| title_short | An AI-Based Nested Large–Small Model for Passive Microwave Soil Moisture and Land Surface Temperature Retrieval Method |
| title_sort | ai based nested large small model for passive microwave soil moisture and land surface temperature retrieval method |
| topic | passive microwave remote sensing soil moisture land surface temperature multi-hierarchy classification joint iterative retrieval |
| url | https://www.mdpi.com/2072-4292/17/7/1198 |
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