Prediction of Spatial Winter Wheat Yield by Combining Multiscale Time Series of Vegetation and Meteorological Indices
In the context of climate change and the development of sustainable agricultural, crop yield prediction is key to ensuring food security. In this study, long-term vegetation and meteorological indices were obtained from the MOD09A1 product and daily weather data. Three types of time series data were...
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MDPI AG
2025-04-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/5/1114 |
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| author | Hao Xu Hongfei Yin Jia Liu Lei Wang Wenjie Feng Hualu Song Yangyang Fan Kangkang Qi Zhichao Liang WenJie Li Xiaohu Zhang Rongjuan Zhang Shuai Wang |
| author_facet | Hao Xu Hongfei Yin Jia Liu Lei Wang Wenjie Feng Hualu Song Yangyang Fan Kangkang Qi Zhichao Liang WenJie Li Xiaohu Zhang Rongjuan Zhang Shuai Wang |
| author_sort | Hao Xu |
| collection | DOAJ |
| description | In the context of climate change and the development of sustainable agricultural, crop yield prediction is key to ensuring food security. In this study, long-term vegetation and meteorological indices were obtained from the MOD09A1 product and daily weather data. Three types of time series data were constructed by aggregating data from an 8-day period (DP), 9-month period (MP), and six growth periods (GP). And we developed the yield prediction model by using random forest (RF) and long short-term memory (LSTM) networks. Results showed that the average root mean squared error (RMSE) of the RF model in each province was 0.5 Mg/ha lower than that of the LSTM model. Both the RF and LSTM prediction accuracies increased with the later growth stages data. Partial dependence plots showed that the influence degree of DVI on yield was above 2 Mg/ha. When the time length of the feature variables was shortened to MP or GP, the growing degree days (GDD), average minimum temperature (AveTmin), and effective precipitation (EP) showed stronger nonlinear relationships with the statistical yields. |
| format | Article |
| id | doaj-art-05472ac262cb4a14a2a1d10ff88ab04e |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-05472ac262cb4a14a2a1d10ff88ab04e2025-08-20T03:47:48ZengMDPI AGAgronomy2073-43952025-04-01155111410.3390/agronomy15051114Prediction of Spatial Winter Wheat Yield by Combining Multiscale Time Series of Vegetation and Meteorological IndicesHao Xu0Hongfei Yin1Jia Liu2Lei Wang3Wenjie Feng4Hualu Song5Yangyang Fan6Kangkang Qi7Zhichao Liang8WenJie Li9Xiaohu Zhang10Rongjuan Zhang11Shuai Wang12Shandong Academy of Agricultural Sciences, Jinan 250100, ChinaSchool of Finance and Taxation, Shandong University of Finance and Economics, Jinan 250014, ChinaChinese Academy of Agricultural Sciences, Beijing 100081, ChinaShandong Academy of Agricultural Sciences, Jinan 250100, ChinaShandong Academy of Agricultural Sciences, Jinan 250100, ChinaShandong Academy of Agricultural Sciences, Jinan 250100, ChinaShandong Academy of Agricultural Sciences, Jinan 250100, ChinaShandong Academy of Agricultural Sciences, Jinan 250100, ChinaShandong Academy of Agricultural Sciences, Jinan 250100, ChinaQilu Aerospace Information Research Institute, Jinan 100094, ChinaNational Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, ChinaDongying Academy of Agricultural Sciences, Dongying 257091, ChinaShandong Academy of Agricultural Sciences, Jinan 250100, ChinaIn the context of climate change and the development of sustainable agricultural, crop yield prediction is key to ensuring food security. In this study, long-term vegetation and meteorological indices were obtained from the MOD09A1 product and daily weather data. Three types of time series data were constructed by aggregating data from an 8-day period (DP), 9-month period (MP), and six growth periods (GP). And we developed the yield prediction model by using random forest (RF) and long short-term memory (LSTM) networks. Results showed that the average root mean squared error (RMSE) of the RF model in each province was 0.5 Mg/ha lower than that of the LSTM model. Both the RF and LSTM prediction accuracies increased with the later growth stages data. Partial dependence plots showed that the influence degree of DVI on yield was above 2 Mg/ha. When the time length of the feature variables was shortened to MP or GP, the growing degree days (GDD), average minimum temperature (AveTmin), and effective precipitation (EP) showed stronger nonlinear relationships with the statistical yields.https://www.mdpi.com/2073-4395/15/5/1114crop yieldphenologyrandom forestlong short-term memory |
| spellingShingle | Hao Xu Hongfei Yin Jia Liu Lei Wang Wenjie Feng Hualu Song Yangyang Fan Kangkang Qi Zhichao Liang WenJie Li Xiaohu Zhang Rongjuan Zhang Shuai Wang Prediction of Spatial Winter Wheat Yield by Combining Multiscale Time Series of Vegetation and Meteorological Indices Agronomy crop yield phenology random forest long short-term memory |
| title | Prediction of Spatial Winter Wheat Yield by Combining Multiscale Time Series of Vegetation and Meteorological Indices |
| title_full | Prediction of Spatial Winter Wheat Yield by Combining Multiscale Time Series of Vegetation and Meteorological Indices |
| title_fullStr | Prediction of Spatial Winter Wheat Yield by Combining Multiscale Time Series of Vegetation and Meteorological Indices |
| title_full_unstemmed | Prediction of Spatial Winter Wheat Yield by Combining Multiscale Time Series of Vegetation and Meteorological Indices |
| title_short | Prediction of Spatial Winter Wheat Yield by Combining Multiscale Time Series of Vegetation and Meteorological Indices |
| title_sort | prediction of spatial winter wheat yield by combining multiscale time series of vegetation and meteorological indices |
| topic | crop yield phenology random forest long short-term memory |
| url | https://www.mdpi.com/2073-4395/15/5/1114 |
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