将随机森林与作物模型相结合,提高了冬小麦和油菜的产量预测

M. S. Dhillon, Thorsten Dahms, Carina Kuebert-Flock, Thomas Rummler, J. Arnault, Ingolf Stefan-Dewenter, T. Ullmann
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引用次数: 5

摘要

随着全球卫星产品的可用性和多样性的增加以及新算法的快速发展,快速准确的产量估算仍然是精准农业和粮食安全的目标。然而,提供准确作物产量结果的合适方法的一致性和可靠性仍然需要探索。该研究调查了作物建模和机器学习(ML)的耦合,以提高冬小麦(WW)和油菜(OSR)的产量预测,并为2019年德国巴伐利亚自由州(70,550平方公里)提供了示例。主要目标是找出耦合方法[光利用效率(LUE) +随机森林(RF)]与不使用光利用效率的其他模型提供的结果相比,是否会产生更好和更准确的产量预测。设计了四种不同的射频模型[RF1(输入:归一化植被指数(NDVI)), RF2(输入:气候变量),RF3(输入:NDVI +气候变量),RF4(输入:LUE产生的生物量+气候变量)],以及一个半经验LUE模型,以不同的输入要求来寻找作物监测的最佳预测因子。结果表明,单独利用NDVI(在RF1中)和气候变量(在RF2中)不能作为作物监测最准确、可靠和精确的解决方案;然而,它们的组合使用(在RF3中)产生了更高的精度。值得注意的是,研究表明,与仅依赖LUE的结果相比,将LUE模型变量与RF4模型耦合可以将相对均方根误差(RRMSE)从- 8% (WW)和- 1.6% (OSR)降低,并将r2 (WW和OSR)提高14.3%。此外,该研究通过输入三种不同的空间输入来比较模型的产出:Sentinel-2(S)-MOD13Q1 (10 m)、Landsat (L)-MOD13Q1 (30 m)和MOD13Q1 (MODIS) (250 m). S-MOD13Q1数据相对于L-MOD13Q1 (30 m)和MOD13Q1 (250 m)具有更高的平均r2 [0.80 (WW), 0.69 (OSR)]和更低的RRMSE(%)(9.18, 10.21)。基于卫星的作物生物量、太阳辐射和温度是影响这两种作物产量预测的最重要变量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating random forest and crop modeling improves the crop yield prediction of winter wheat and oil seed rape
The fast and accurate yield estimates with the increasing availability and variety of global satellite products and the rapid development of new algorithms remain a goal for precision agriculture and food security. However, the consistency and reliability of suitable methodologies that provide accurate crop yield outcomes still need to be explored. The study investigates the coupling of crop modeling and machine learning (ML) to improve the yield prediction of winter wheat (WW) and oil seed rape (OSR) and provides examples for the Free State of Bavaria (70,550 km2), Germany, in 2019. The main objectives are to find whether a coupling approach [Light Use Efficiency (LUE) + Random Forest (RF)] would result in better and more accurate yield predictions compared to results provided with other models not using the LUE. Four different RF models [RF1 (input: Normalized Difference Vegetation Index (NDVI)), RF2 (input: climate variables), RF3 (input: NDVI + climate variables), RF4 (input: LUE generated biomass + climate variables)], and one semi-empiric LUE model were designed with different input requirements to find the best predictors of crop monitoring. The results indicate that the individual use of the NDVI (in RF1) and the climate variables (in RF2) could not be the most accurate, reliable, and precise solution for crop monitoring; however, their combined use (in RF3) resulted in higher accuracies. Notably, the study suggested the coupling of the LUE model variables to the RF4 model can reduce the relative root mean square error (RRMSE) from −8% (WW) and −1.6% (OSR) and increase the R 2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. Moreover, the research compares models yield outputs by inputting three different spatial inputs: Sentinel-2(S)-MOD13Q1 (10 m), Landsat (L)-MOD13Q1 (30 m), and MOD13Q1 (MODIS) (250 m). The S-MOD13Q1 data has relatively improved the performance of models with higher mean R 2 [0.80 (WW), 0.69 (OSR)], and lower RRMSE (%) (9.18, 10.21) compared to L-MOD13Q1 (30 m) and MOD13Q1 (250 m). Satellite-based crop biomass, solar radiation, and temperature are found to be the most influential variables in the yield prediction of both crops.
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