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High-Accuracy Remote Sensing Identification of Winter Wheat Based on Feature Selection and Cross-Temporal Fusion in Shandong Province, China.

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Title: High-Accuracy Remote Sensing Identification of Winter Wheat Based on Feature Selection and Cross-Temporal Fusion in Shandong Province, China.
Authors: Wang, Xu1 (AUTHOR), Sun, Heyan2 (AUTHOR), Wang, Yu2,3 (AUTHOR), Sui, Long3,4 (AUTHOR), Chen, Hongyan1 (AUTHOR) chenhy@sdau.edu.cn, Liu, Peng2,4 (AUTHOR)
Source: Remote Sensing. Jun2026, Vol. 18 Issue 12, p1927. 26p.
Subjects: Winter wheat, Feature selection, Machine learning, Multisensor data fusion, Remote sensing, Provinces
Geographic Terms: Shandong Sheng (China), China
Abstract: Highlights: What are the main findings? Growth-stage ranking and key spectral variable selection effectively reduced the original 64-dimensional feature space to 16 informative variables. The cross-temporal fusion model achieved high winter wheat identification accuracy (OA = 0.9658, UA = 0.9609) using only 10 fused features. What are the implications of the main findings? Cross-temporal feature fusion provides a more efficient alternative to direct stacking of multi-temporal remote sensing features. The proposed framework supports reliable 10 m regional-scale winter wheat mapping with strong consistency with statistical data. Accurate crop distribution mapping using multi-temporal remote sensing has become increasingly important for agricultural monitoring and management. However, existing methods often rely on the direct stacking of multi-temporal features, which leads to feature redundancy and reduced model efficiency. To address this issue, this study proposes a winter wheat identification framework that integrates growth-stage ranking, key spectral variable selection, and cross-temporal feature fusion. Taking Shandong Province as the study area, eight typical growth stages during the 2023–2024 winter wheat growing season were analyzed using Sentinel-2 imagery. Random Forest models were first constructed for each growth stage to evaluate discriminative ability. Then, spectral variable contributions were quantified using permutation importance, and key spectral variables were selected under correlation constraints. The progressive accumulation model (PAM) was then developed according to the ranking of discriminative ability across different growth stages, while the cross-temporal fusion model (CTFM) was constructed by extracting inter-stage mean values (mean) and inter-stage differences (diff) of key variables. The results show that the feature space was reduced from 64 dimensions (8 stages × 8 variables) to 16 key variables, substantially improving feature representation efficiency. Among the eight growth stages, the jointing, overwintering, heading, and grain-filling stages exhibited relatively strong discriminative ability. In the cross-temporal experiments, CTFM M6, which integrates information from the top six growth stages ranked by discriminative ability, achieved an overall accuracy (OA) of 0.9658 and a user's accuracy (UA) of 0.9609 using only 10 fused features, providing the best balance between identification accuracy and feature dimensionality. Based on this model, a 10 m resolution winter wheat distribution map of Shandong Province was generated, and the estimated planting area showed high consistency with statistical yearbook data. These results demonstrate that the proposed strategy can effectively reduce feature dimensionality while maintaining high identification accuracy, providing an efficient and scalable approach for regional-scale remote sensing mapping of winter wheat. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: High-Accuracy Remote Sensing Identification of Winter Wheat Based on Feature Selection and Cross-Temporal Fusion in Shandong Province, China.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p1927. 26p.
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  Data: <searchLink fieldCode="DE" term="%22Winter+wheat%22">Winter wheat</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Provinces%22">Provinces</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Shandong+Sheng+%28China%29%22">Shandong Sheng (China)</searchLink><br /><searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? Growth-stage ranking and key spectral variable selection effectively reduced the original 64-dimensional feature space to 16 informative variables. The cross-temporal fusion model achieved high winter wheat identification accuracy (OA = 0.9658, UA = 0.9609) using only 10 fused features. What are the implications of the main findings? Cross-temporal feature fusion provides a more efficient alternative to direct stacking of multi-temporal remote sensing features. The proposed framework supports reliable 10 m regional-scale winter wheat mapping with strong consistency with statistical data. Accurate crop distribution mapping using multi-temporal remote sensing has become increasingly important for agricultural monitoring and management. However, existing methods often rely on the direct stacking of multi-temporal features, which leads to feature redundancy and reduced model efficiency. To address this issue, this study proposes a winter wheat identification framework that integrates growth-stage ranking, key spectral variable selection, and cross-temporal feature fusion. Taking Shandong Province as the study area, eight typical growth stages during the 2023–2024 winter wheat growing season were analyzed using Sentinel-2 imagery. Random Forest models were first constructed for each growth stage to evaluate discriminative ability. Then, spectral variable contributions were quantified using permutation importance, and key spectral variables were selected under correlation constraints. The progressive accumulation model (PAM) was then developed according to the ranking of discriminative ability across different growth stages, while the cross-temporal fusion model (CTFM) was constructed by extracting inter-stage mean values (mean) and inter-stage differences (diff) of key variables. The results show that the feature space was reduced from 64 dimensions (8 stages × 8 variables) to 16 key variables, substantially improving feature representation efficiency. Among the eight growth stages, the jointing, overwintering, heading, and grain-filling stages exhibited relatively strong discriminative ability. In the cross-temporal experiments, CTFM M6, which integrates information from the top six growth stages ranked by discriminative ability, achieved an overall accuracy (OA) of 0.9658 and a user's accuracy (UA) of 0.9609 using only 10 fused features, providing the best balance between identification accuracy and feature dimensionality. Based on this model, a 10 m resolution winter wheat distribution map of Shandong Province was generated, and the estimated planting area showed high consistency with statistical yearbook data. These results demonstrate that the proposed strategy can effectively reduce feature dimensionality while maintaining high identification accuracy, providing an efficient and scalable approach for regional-scale remote sensing mapping of winter wheat. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18121927
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 26
        StartPage: 1927
    Subjects:
      – SubjectFull: Winter wheat
        Type: general
      – SubjectFull: Feature selection
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Multisensor data fusion
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Provinces
        Type: general
      – SubjectFull: Shandong Sheng (China)
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: High-Accuracy Remote Sensing Identification of Winter Wheat Based on Feature Selection and Cross-Temporal Fusion in Shandong Province, China.
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            NameFull: Wang, Xu
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              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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