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An Integrated Approach of Field, Weather, and Satellite Data for Monitoring Maize Phenology

Scientific reports(2021)SCI 3区

Department of Agronomy

Cited 10|Views13
Abstract
Efficient, more accurate reporting of maize (Zea mays L.) phenology, crop condition, and progress is crucial for agronomists and policy makers. Integration of satellite imagery with machine learning models has shown great potential to improve crop classification and facilitate in-season phenological reports. However, crop phenology classification precision must be substantially improved to transform data into actionable management decisions for farmers and agronomists. An integrated approach utilizing ground truth field data for maize crop phenology (2013–2018 seasons), satellite imagery (Landsat 8), and weather data was explored with the following objectives: (i) model training and validation—identify the best combination of spectral bands, vegetation indices (VIs), weather parameters, geolocation, and ground truth data, resulting in a model with the highest accuracy across years at each season segment (step one) and (ii) model testing—post-selection model performance evaluation for each phenology class with unseen data (hold-out cross-validation) (step two). The best model performance for classifying maize phenology was documented when VIs (NDVI, EVI, GCVI, NDWI, GVMI) and vapor pressure deficit (VPD) were used as input variables. This study supports the integration of field ground truth, satellite imagery, and weather data to classify maize crop phenology, thereby facilitating foundational decision making and agricultural interventions for the different members of the agricultural chain.
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Data processing,Machine learning,Science,Humanities and Social Sciences,multidisciplinary
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要点】:本研究提出了一种整合地面实况、卫星图像和气象数据的方法,以提高玉米生育期分类的精确度,为农学家和政策制定者提供有效的作物管理与决策支持。

方法】:研究采用机器学习模型,结合地面实况数据(2013-2018年玉米作物生育期)、Landsat 8卫星图像和气象数据,通过分析不同光谱波段、植被指数、气象参数、地理位置和地面实况数据的组合,以确定最高准确度的模型。

实验】:通过保留交叉验证的方式测试模型性能,最终确定使用植被指数(NDVI、EVI、GCVI、NDWI、GVMI)和蒸气压差(VPD)作为输入变量的模型在玉米生育期分类上表现最佳,具体数据集未在摘要中提及。