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Long-Term Hindcasts of Wheat Yield in Fields Using Remotely Sensed Phenology, Climate Data and Machine Learning
Remote Sensing ( IF 5 ) Pub Date : 2021-06-22 , DOI: 10.3390/rs13132435
Fiona H. Evans , Jianxiu Shen

Satellite remote sensing offers a cost-effective means of generating long-term hindcasts of yield that can be used to understand how yield varies in time and space. This study investigated the use of remotely sensed phenology, climate data and machine learning for estimating yield at a resolution suitable for optimising crop management in fields. We used spatially weighted growth curve estimation to identify the timing of phenological events from sequences of Landsat NDVI and derive phenological and seasonal climate metrics. Using data from a 17,000 ha study area, we investigated the relationships between the metrics and yield over 17 years from 2003 to 2019. We compared six statistical and machine learning models for estimating yield: multiple linear regression, mixed effects models, generalised additive models, random forests, support vector regression using radial basis functions and deep learning neural networks. We used a 50-50 train-test split on paddock-years where 50% of paddock-year combinations were randomly selected and used to train each model and the remaining 50% of paddock-years were used to assess the model accuracy. Using only phenological metrics, accuracy was highest using a linear mixed model with a random effect that allowed the relationship between integrated NDVI and yield to vary by year (R2 = 0.67, MAE = 0.25 t ha1, RMSE = 0.33 t ha1, NRMSE = 0.25). We quantified the improvements in accuracy when seasonal climate metrics were also used as predictors. We identified two optimal models using the combined phenological and seasonal climate metrics: support vector regression and deep learning models (R2 = 0.68, MAE = 0.25 t ha1, RMSE = 0.32 t ha1, NRMSE = 0.25). While the linear mixed model using only phenological metrics performed similarly to the nonlinear models that are also seasonal climate metrics, the nonlinear models can be more easily generalised to estimate yield in years for which training data are unavailable. We conclude that long-term hindcasts of wheat yield in fields, at 30 m spatial resolution, can be produced using remotely sensed phenology from Landsat NDVI, climate data and machine learning.

中文翻译:

使用遥感物候、气候数据和机器学习对田间小麦产量进行长期后报

卫星遥感提供了一种生成长期产量后报的具有成本效益的方法,可用于了解产量如何随时间和空间变化。本研究调查了使用遥感物候学、气候数据和机器学习以适合优化田间作物管理的分辨率估算产量。我们使用空间加权增长曲线估计从 Landsat NDVI 序列中确定物候事件的时间,并推导出物候和季节性气候指标。使用来自 17,000 公顷研究区域的数据,我们调查了 2003 年至 2019 年 17 年间指标与产量之间的关系。我们比较了六种统计和机器学习模型来估算产量:多元线性回归、混合效应模型、广义加性模型、随机森林,使用径向基函数和深度学习神经网络支持向量回归。我们对围场年使用了 50-50 次训练测试分组,其中随机选择了 50% 的围场年组合并用于训练每个模型,其余 50% 的围场年用于评估模型准确性。仅使用物候指标,使用具有随机效应的线性混合模型的准确度最高,该模型允许综合 NDVI 和产量之间的关系随年份变化(R2 = 0.67, MAE = 0.25 t ha 1 , RMSE = 0.33 t ha 1 , NRMSE = 0.25)。当季节性气候指标也被用作预测指标时,我们量化了准确性的提高。我们使用组合物候和季节性气候指标确定了两个最佳模型:支持向量回归和深度学习模型(R 2 = 0.68,MAE = 0.25 t ha 1,RMSE = 0.32 t ha 1, NRMSE = 0.25)。虽然仅使用物候指标的线性混合模型与也是季节性气候指标的非线性模型的表现类似,但非线性模型可以更容易地推广以估计无法获得训练数据的年份的产量。我们得出的结论是,可以使用来自 Landsat NDVI、气候数据和机器学习的遥感物候学,以 30 m 的空间分辨率生成田间小麦产量的长期后报。
更新日期:2021-06-22
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