Soybean Yield Estimation Using Improved Deep Learning Models With Integrated Multisource and Multitemporal Remote Sensing Data
Accurate soybean yield estimation is critically imperative for modern agricultural systems amid escalating global food security pressures, yet conventional methodologies are constrained for large-scale high-frequency monitoring. To address this, an innovative deep learning framework, TransBiHGRU-PSO...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11079987/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849340822645899264 |
|---|---|
| author | Jian Li Junrui Kang Ji Qi Jian Lu Hongkun Fu Baoqi Liu Xinglei Lin Jiawei Zhao Hengxu Guan Jing Chang Zhihan Liu |
| author_facet | Jian Li Junrui Kang Ji Qi Jian Lu Hongkun Fu Baoqi Liu Xinglei Lin Jiawei Zhao Hengxu Guan Jing Chang Zhihan Liu |
| author_sort | Jian Li |
| collection | DOAJ |
| description | Accurate soybean yield estimation is critically imperative for modern agricultural systems amid escalating global food security pressures, yet conventional methodologies are constrained for large-scale high-frequency monitoring. To address this, an innovative deep learning framework, TransBiHGRU-PSO, is proposed for precise large-scale soybean yield estimation via effective fusion of multisource multitemporal remote sensing data, emphasizing robust and accurate estimation even with anomalous yield data. This framework synergistically integrates an optimized bidirectional hierarchical gated recurrent unit (BiHGRU), a Transformer encoder, and a novel Greenness and Water Content Composite Index, with critical parameters optimized by particle swarm optimization (PSO). County-level yield data from 12 U.S. states were used, supplemented by multitemporal remote sensing datasets (MODIS surface reflectance, vegetation indices, and environmental variables). Empirical analyses showed that TransBiHGRU-PSO demonstrated improved estimation capability and generalizability compared to multiple benchmark models. Notably, with anomalous yield data retained, the model achieved solid test set performance [coefficient of determination (<italic>R</italic><sup>2</sup>) of 0.71 and root-mean-square error (RMSE) of 4.2812 bushels/acre]. Compared to the best traditional machine learning model (support vector regression), <italic>R</italic><sup>2</sup> increased by 52.96% and RMSE decreased by 26.05%, and relative to the best deep learning baseline model (long short-term memory), <italic>R</italic><sup>2</sup> and RMSE improved by 7.04% and 7.04%, respectively. Furthermore, validation of interannual stability (2008–2018, anomalies retained) revealed a mean <italic>R</italic><sup>2</sup> of 0.70 and a mean RMSE of 4.4701 bushels/acre, affirming its consistency under complex real-world conditions. This TransBiHGRU-PSO algorithmic framework, combined with multisource and multitemporal remote sensing data, offers a valuable exploration for large-scale soybean yield estimation. |
| format | Article |
| id | doaj-art-e7dabdfd5563435d897b1cf182b1d14a |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-e7dabdfd5563435d897b1cf182b1d14a2025-08-20T03:43:47ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118184501847710.1109/JSTARS.2025.358891711079987Soybean Yield Estimation Using Improved Deep Learning Models With Integrated Multisource and Multitemporal Remote Sensing DataJian Li0https://orcid.org/0009-0008-7749-0286Junrui Kang1https://orcid.org/0009-0008-8603-0492Ji Qi2Jian Lu3Hongkun Fu4Baoqi Liu5Xinglei Lin6Jiawei Zhao7Hengxu Guan8Jing Chang9https://orcid.org/0000-0003-4825-6382Zhihan Liu10College of Information Technology, Jilin Agricultural University, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaCollege of Engineering and Technology, Jilin Agricultural University, Changchun, ChinaCollege of Agronomy, Jilin Agricultural University, Changchun, ChinaCollege of Agronomy, Jilin Agricultural University, Changchun, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaAccurate soybean yield estimation is critically imperative for modern agricultural systems amid escalating global food security pressures, yet conventional methodologies are constrained for large-scale high-frequency monitoring. To address this, an innovative deep learning framework, TransBiHGRU-PSO, is proposed for precise large-scale soybean yield estimation via effective fusion of multisource multitemporal remote sensing data, emphasizing robust and accurate estimation even with anomalous yield data. This framework synergistically integrates an optimized bidirectional hierarchical gated recurrent unit (BiHGRU), a Transformer encoder, and a novel Greenness and Water Content Composite Index, with critical parameters optimized by particle swarm optimization (PSO). County-level yield data from 12 U.S. states were used, supplemented by multitemporal remote sensing datasets (MODIS surface reflectance, vegetation indices, and environmental variables). Empirical analyses showed that TransBiHGRU-PSO demonstrated improved estimation capability and generalizability compared to multiple benchmark models. Notably, with anomalous yield data retained, the model achieved solid test set performance [coefficient of determination (<italic>R</italic><sup>2</sup>) of 0.71 and root-mean-square error (RMSE) of 4.2812 bushels/acre]. Compared to the best traditional machine learning model (support vector regression), <italic>R</italic><sup>2</sup> increased by 52.96% and RMSE decreased by 26.05%, and relative to the best deep learning baseline model (long short-term memory), <italic>R</italic><sup>2</sup> and RMSE improved by 7.04% and 7.04%, respectively. Furthermore, validation of interannual stability (2008–2018, anomalies retained) revealed a mean <italic>R</italic><sup>2</sup> of 0.70 and a mean RMSE of 4.4701 bushels/acre, affirming its consistency under complex real-world conditions. This TransBiHGRU-PSO algorithmic framework, combined with multisource and multitemporal remote sensing data, offers a valuable exploration for large-scale soybean yield estimation.https://ieeexplore.ieee.org/document/11079987/Deep learning (DL)greenness and water content composite index (GWCCI)multisource remote sensing datayield estimation |
| spellingShingle | Jian Li Junrui Kang Ji Qi Jian Lu Hongkun Fu Baoqi Liu Xinglei Lin Jiawei Zhao Hengxu Guan Jing Chang Zhihan Liu Soybean Yield Estimation Using Improved Deep Learning Models With Integrated Multisource and Multitemporal Remote Sensing Data IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning (DL) greenness and water content composite index (GWCCI) multisource remote sensing data yield estimation |
| title | Soybean Yield Estimation Using Improved Deep Learning Models With Integrated Multisource and Multitemporal Remote Sensing Data |
| title_full | Soybean Yield Estimation Using Improved Deep Learning Models With Integrated Multisource and Multitemporal Remote Sensing Data |
| title_fullStr | Soybean Yield Estimation Using Improved Deep Learning Models With Integrated Multisource and Multitemporal Remote Sensing Data |
| title_full_unstemmed | Soybean Yield Estimation Using Improved Deep Learning Models With Integrated Multisource and Multitemporal Remote Sensing Data |
| title_short | Soybean Yield Estimation Using Improved Deep Learning Models With Integrated Multisource and Multitemporal Remote Sensing Data |
| title_sort | soybean yield estimation using improved deep learning models with integrated multisource and multitemporal remote sensing data |
| topic | Deep learning (DL) greenness and water content composite index (GWCCI) multisource remote sensing data yield estimation |
| url | https://ieeexplore.ieee.org/document/11079987/ |
| work_keys_str_mv | AT jianli soybeanyieldestimationusingimproveddeeplearningmodelswithintegratedmultisourceandmultitemporalremotesensingdata AT junruikang soybeanyieldestimationusingimproveddeeplearningmodelswithintegratedmultisourceandmultitemporalremotesensingdata AT jiqi soybeanyieldestimationusingimproveddeeplearningmodelswithintegratedmultisourceandmultitemporalremotesensingdata AT jianlu soybeanyieldestimationusingimproveddeeplearningmodelswithintegratedmultisourceandmultitemporalremotesensingdata AT hongkunfu soybeanyieldestimationusingimproveddeeplearningmodelswithintegratedmultisourceandmultitemporalremotesensingdata AT baoqiliu soybeanyieldestimationusingimproveddeeplearningmodelswithintegratedmultisourceandmultitemporalremotesensingdata AT xingleilin soybeanyieldestimationusingimproveddeeplearningmodelswithintegratedmultisourceandmultitemporalremotesensingdata AT jiaweizhao soybeanyieldestimationusingimproveddeeplearningmodelswithintegratedmultisourceandmultitemporalremotesensingdata AT hengxuguan soybeanyieldestimationusingimproveddeeplearningmodelswithintegratedmultisourceandmultitemporalremotesensingdata AT jingchang soybeanyieldestimationusingimproveddeeplearningmodelswithintegratedmultisourceandmultitemporalremotesensingdata AT zhihanliu soybeanyieldestimationusingimproveddeeplearningmodelswithintegratedmultisourceandmultitemporalremotesensingdata |