Zhang Guanjin, Roslan Siti Nur Aliaa Binti, Shafri Helmi Zulhaidi Mohd, Zhao Yanxi, Wang Ci, Quan Ling
Department of Civil Engineering, Faculty of Engineering, University Putra Malaysia, 43400, Serdang, Selangor, Malaysia.
College of Resource and Environment, Anhui Science and Technology University, Chuzhou, 233100, China.
Sci Rep. 2024 Jul 13;14(1):16212. doi: 10.1038/s41598-024-67109-3.
To obtain seasonable and precise crop yield information with fine resolution is very important for ensuring the food security. However, the quantity and quality of available images and the selection of prediction variables often limit the performance of yield prediction. In our study, the synthesized images of Landsat and MODIS were used to provide remote sensing (RS) variables, which can fill the missing values of Landsat images well and cover the study area completely. The deep learning (DL) was used to combine different vegetation index (VI) with climate data to build wheat yield prediction model in Hebei Province (HB). The results showed that kernel NDVI (kNDVI) and near-infrared reflectance (NIRv) slightly outperform normalized difference vegetation index (NDVI) in yield prediction. And the regression algorithm had a more prominent effect on yield prediction, while the yield prediction model using Long Short-Term Memory (LSTM) outperformed the yield prediction model using Light Gradient Boosting Machine (LGBM). The model combining LSTM algorithm and NIRv had the best prediction effect and relatively stable performance in single year. The optimal model was then used to generate 30 m resolution wheat yield maps in the past 20 years, with higher overall accuracy. In addition, we can define the optimum prediction time at April, which can consider simultaneously the performance and lead time. In general, we expect that this prediction model can provide important information to understand and ensure food security.
获取具有高分辨率的及时且精确的作物产量信息对于确保粮食安全非常重要。然而，可用图像的数量和质量以及预测变量的选择常常限制了产量预测的性能。在我们的研究中，利用陆地卫星（Landsat）和中分辨率成像光谱仪（MODIS）的合成图像来提供遥感（RS）变量，其能够很好地填补陆地卫星图像的缺失值并完全覆盖研究区域。利用深度学习（DL）将不同的植被指数（VI）与气候数据相结合，构建河北省（HB）的小麦产量预测模型。结果表明，核归一化植被指数（kNDVI）和近红外反射率（NIRv）在产量预测方面略优于归一化差异植被指数（NDVI）。并且回归算法在产量预测方面具有更显著的效果，而使用长短期记忆网络（LSTM）的产量预测模型优于使用轻梯度提升机（LGBM）的产量预测模型。结合LSTM算法和NIRv的模型在单年份具有最佳的预测效果和相对稳定的性能。然后利用最优模型生成过去20年30米分辨率的小麦产量图，总体精度较高。此外，我们可以将最佳预测时间定义在4月，其能够同时兼顾性能和提前期。总体而言，我们期望这个预测模型能够为理解和确保粮食安全提供重要信息。
