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基于多源遥感和机器学习的玉米最佳估产时段研究

The study on the optimal estimation time period for maize yield prediction based on the multi-source remote sensing and machine learning

  • 摘要: 准确及时预测作物产量能够更好地预测市场粮食价格波动、指导农业种植规划和保障国家粮食安全。作物产量形成具有非线性的时空特征,作物生长的动态变化与产量之间关系复杂,导致在单一时段难以准确预测作物产量,因此确定合适的估产时段显得尤为重要。该研究利用2013—2020年河南省玉米生长季的中分辨率成像光谱仪(MODIS)数据、气象数据与产量统计数据,将玉米生长时段划分了7种时间段,结合高斯过程回归(GPR)、轻量级梯度提升机(LightGBM)等5种机器学习算法不同时间段的玉米产量预测,探寻河南省玉米产量预测最佳估产时段。结果表明:1)河南省玉米产量预测的最佳估产时段为7月4日至9月14日(J5-J14)。2)最佳预测模型为高斯过程回归,预测模型的R2为0.68、RMSE为647.55 kg/hm2、MAPE为9.39%。3)分析年度产量预测空间误差分布特征,表明极端气候是影响作物产量预测精度的关键因素之一。该研究通过选择合适的估产时段可以利用最少的数据来获取最精准的产量预测值,为玉米产量预测提供重要参考方法。

     

    Abstract: Accurate and timely prediction of crop yields is critical for anticipating market price fluctuations, guiding agricultural planning, and ensuring national food security. Given the nonlinear spatiotemporal characteristics of yield formation and the complex interactions between crop growth dynamics and final yields, it is difficult to accurately predict crop yields within a single time period. Therefore, determining an appropriate yield estimation period is of paramount importance. This study utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological data, and yield statistics from the maize growing season in Henan Province from 2013 to 2020 to divide the maize growth period into seven period scenarios. Five machine learning algorithms, including the Gaussian Process Regression (GPR), the Light Gradient Boosting Machine (LightGBM), were used to predict maize yield in different period scenarios, aiming to explore the optimal yield estimation period for maize yield prediction in Henan Province. Results show that: 1) July 4 to September 14 (J5-J14) constituted the optimal estimation period; 2) The best prediction models are the GPR, with an R2 of 0.68, the RMSE of 647.55 kg/hm2, and the MAPE of 9.39%. and 3) The analysis of spatial error distribution characteristics of annual yield prediction further revealed that extreme climate was one of the key factors affecting the accuracy of crop yield prediction. This study provides an important reference method for maize yield prediction by selecting the appropriate yield estimation period to obtain the most accurate yield prediction value with the least amount of data.

     

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