Maize Classification in Arid Regions via Spatiotemporal Feature Optimization and Multi-Source Remote Sensing Integration
This study addresses the challenges of redundant crop identification features and low computational efficiency in complex agricultural environments, particularly in arid regions. Focusing on the Hexi region of Gansu Province, we utilized the Google Earth Engine (GEE) to integrate Sentinel-2 optical...
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MDPI AG
2025-07-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/7/1667 |
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| author | Guang Yang Jun Wang Zhengyuan Qi |
| author_facet | Guang Yang Jun Wang Zhengyuan Qi |
| author_sort | Guang Yang |
| collection | DOAJ |
| description | This study addresses the challenges of redundant crop identification features and low computational efficiency in complex agricultural environments, particularly in arid regions. Focusing on the Hexi region of Gansu Province, we utilized the Google Earth Engine (GEE) to integrate Sentinel-2 optical imagery (10 bands) and Sentinel-1 radar data (VV/VH polarization), constructing a 96-feature set that comprises spectral, vegetation index, red-edge, and texture variables. The recursive feature elimination random forest (RF-RFE) algorithm was employed for feature selection and model optimization. Key findings include: (1) Variables driven by spatiotemporal differentiation were effectively selected, with red-edge bands (B5–B7) during the grain-filling stage in August accounting for 56.7% of the top 30 features, which were closely correlated with canopy chlorophyll content (<i>p</i> < 0.01). (2) A breakthrough in lightweight modeling was achieved, reducing the number of features by 69%, enhancing computational efficiency by 62.5% (from 8 h to 3 h), and decreasing memory usage by 66.7% (from 12 GB to 4 GB), while maintaining classification accuracy (PA: 97.69%, UA: 97.20%, Kappa: 0.89). (3) Multi-source data fusion improved accuracy by 11.54% compared to optical-only schemes, demonstrating the compensatory role of radar in arid, cloudy regions. This study offers an interpretable and transferable lightweight framework for precision crop monitoring in arid zones. |
| format | Article |
| id | doaj-art-e4e2089ca09542718b85df259135be2f |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-e4e2089ca09542718b85df259135be2f2025-08-20T03:36:10ZengMDPI AGAgronomy2073-43952025-07-01157166710.3390/agronomy15071667Maize Classification in Arid Regions via Spatiotemporal Feature Optimization and Multi-Source Remote Sensing IntegrationGuang Yang0Jun Wang1Zhengyuan Qi2College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, ChinaCollege of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, ChinaCollege of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, ChinaThis study addresses the challenges of redundant crop identification features and low computational efficiency in complex agricultural environments, particularly in arid regions. Focusing on the Hexi region of Gansu Province, we utilized the Google Earth Engine (GEE) to integrate Sentinel-2 optical imagery (10 bands) and Sentinel-1 radar data (VV/VH polarization), constructing a 96-feature set that comprises spectral, vegetation index, red-edge, and texture variables. The recursive feature elimination random forest (RF-RFE) algorithm was employed for feature selection and model optimization. Key findings include: (1) Variables driven by spatiotemporal differentiation were effectively selected, with red-edge bands (B5–B7) during the grain-filling stage in August accounting for 56.7% of the top 30 features, which were closely correlated with canopy chlorophyll content (<i>p</i> < 0.01). (2) A breakthrough in lightweight modeling was achieved, reducing the number of features by 69%, enhancing computational efficiency by 62.5% (from 8 h to 3 h), and decreasing memory usage by 66.7% (from 12 GB to 4 GB), while maintaining classification accuracy (PA: 97.69%, UA: 97.20%, Kappa: 0.89). (3) Multi-source data fusion improved accuracy by 11.54% compared to optical-only schemes, demonstrating the compensatory role of radar in arid, cloudy regions. This study offers an interpretable and transferable lightweight framework for precision crop monitoring in arid zones.https://www.mdpi.com/2073-4395/15/7/1667GEEmaizefeature optimizationrandom forestrecursive feature elimination (RF-RFE) |
| spellingShingle | Guang Yang Jun Wang Zhengyuan Qi Maize Classification in Arid Regions via Spatiotemporal Feature Optimization and Multi-Source Remote Sensing Integration Agronomy GEE maize feature optimization random forest recursive feature elimination (RF-RFE) |
| title | Maize Classification in Arid Regions via Spatiotemporal Feature Optimization and Multi-Source Remote Sensing Integration |
| title_full | Maize Classification in Arid Regions via Spatiotemporal Feature Optimization and Multi-Source Remote Sensing Integration |
| title_fullStr | Maize Classification in Arid Regions via Spatiotemporal Feature Optimization and Multi-Source Remote Sensing Integration |
| title_full_unstemmed | Maize Classification in Arid Regions via Spatiotemporal Feature Optimization and Multi-Source Remote Sensing Integration |
| title_short | Maize Classification in Arid Regions via Spatiotemporal Feature Optimization and Multi-Source Remote Sensing Integration |
| title_sort | maize classification in arid regions via spatiotemporal feature optimization and multi source remote sensing integration |
| topic | GEE maize feature optimization random forest recursive feature elimination (RF-RFE) |
| url | https://www.mdpi.com/2073-4395/15/7/1667 |
| work_keys_str_mv | AT guangyang maizeclassificationinaridregionsviaspatiotemporalfeatureoptimizationandmultisourceremotesensingintegration AT junwang maizeclassificationinaridregionsviaspatiotemporalfeatureoptimizationandmultisourceremotesensingintegration AT zhengyuanqi maizeclassificationinaridregionsviaspatiotemporalfeatureoptimizationandmultisourceremotesensingintegration |