CL-LGFM: Early-Season Winter Wheat Mapping by Integrating Sentinel-2 NDVI and GPM Precipitation Data—A Case Study in the Chaohu Basin, China.
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| Title: | CL-LGFM: Early-Season Winter Wheat Mapping by Integrating Sentinel-2 NDVI and GPM Precipitation Data—A Case Study in the Chaohu Basin, China. |
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| Authors: | Su, Ning1 (AUTHOR), Li, Peng2 (AUTHOR) pli@iim.ac.cn, Yang, Huiliang3 (AUTHOR), Lin, Fei1,2 (AUTHOR), Hu, Yimin2 (AUTHOR), Xu, Taosheng2,3 (AUTHOR) |
| Source: | Remote Sensing. Sep2026, Vol. 18 Issue 17, p2860. 24p. |
| Subjects: | Winter wheat, Normalized difference vegetation index, Multisensor data fusion, Growing season, Remote sensing, Agricultural remote sensing, Deep learning |
| Geographic Terms: | China |
| Abstract: | Highlights: What are the main findings? A CNN-LSTM framework with lag-aware dynamic gated fusion (CL-LGFM) was proposed to integrate Sentinel-2 NDVI time series and GPM precipitation data for early-season winter wheat mapping. The proposed method achieved reliable winter wheat mapping (OA ≥ 0.90, Kappa ≥ 0.80) on 26 January, at least 10 days earlier than traditional NDVI-based approaches such as SVM, RF, DTW and TCN. What are the implications of the main findings? Incorporating antecedent precipitation and modeling its delayed response in vegetation dynamics provides complementary environmental information, enabling earlier and more reliable winter wheat mapping under weak spectral conditions. The framework provides an effective approach for early-season winter wheat mapping and shows potential for broader regional and multi-crop applications. Early-season winter wheat mapping is crucial for agricultural management and food security, but reliable identification remains challenging under weak spectral conditions during early growth stages. To address this challenge, this study developed a CNN–LSTM with a lag-aware gated fusion model (CL-LGFM) for winter wheat mapping in the Chaohu Basin, China, using a reconstructed 5-day Sentinel-2 NDVI time series and precipitation data from the Global Precipitation Measurement (GPM) mission. The model employs a dual-branch architecture to jointly learn vegetation and precipitation features and introduces a lag-aware dynamic gated fusion module to capture the delayed response of vegetation to precipitation and enhance multi-source feature fusion. The results show that the proposed method achieved reliable early-season winter wheat mapping (OA ≥ 0.90, Kappa ≥ 0.80) on 26 January, at least 10 days earlier than traditional methods, including SVM, RF, DTW, and TCN, using the same reconstructed 5-day NDVI time series. Optimal performance uses a 7 × 7 patch size and 30-day precipitation window. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A CNN-LSTM framework with lag-aware dynamic gated fusion (CL-LGFM) was proposed to integrate Sentinel-2 NDVI time series and GPM precipitation data for early-season winter wheat mapping. The proposed method achieved reliable winter wheat mapping (OA ≥ 0.90, Kappa ≥ 0.80) on 26 January, at least 10 days earlier than traditional NDVI-based approaches such as SVM, RF, DTW and TCN. What are the implications of the main findings? Incorporating antecedent precipitation and modeling its delayed response in vegetation dynamics provides complementary environmental information, enabling earlier and more reliable winter wheat mapping under weak spectral conditions. The framework provides an effective approach for early-season winter wheat mapping and shows potential for broader regional and multi-crop applications. Early-season winter wheat mapping is crucial for agricultural management and food security, but reliable identification remains challenging under weak spectral conditions during early growth stages. To address this challenge, this study developed a CNN–LSTM with a lag-aware gated fusion model (CL-LGFM) for winter wheat mapping in the Chaohu Basin, China, using a reconstructed 5-day Sentinel-2 NDVI time series and precipitation data from the Global Precipitation Measurement (GPM) mission. The model employs a dual-branch architecture to jointly learn vegetation and precipitation features and introduces a lag-aware dynamic gated fusion module to capture the delayed response of vegetation to precipitation and enhance multi-source feature fusion. The results show that the proposed method achieved reliable early-season winter wheat mapping (OA ≥ 0.90, Kappa ≥ 0.80) on 26 January, at least 10 days earlier than traditional methods, including SVM, RF, DTW, and TCN, using the same reconstructed 5-day NDVI time series. Optimal performance uses a 7 × 7 patch size and 30-day precipitation window. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18172860 |
