2023
DOI: 10.1016/j.compag.2023.107807
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Out-of-year corn yield prediction at field-scale using Sentinel-2 satellite imagery and machine learning methods

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Cited by 28 publications
(16 citation statements)
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“…The estimation of Leaf Area Index (LAI) and Leaf Chlorophyll Content (C ab ) was facilitated using an Artificial Neural Network (ANN) pre-trained on the raw Sentinel-2 bands accessible within the Sentinel Application Platform (SNAP) [29]. These biophysical parameters are of particular interest due to their ability to enhance the signal-to-noise ratio compared to raw bands and/or vegetation indices when predicting maize yield for a new season using Sentinel-2 data [26].…”
Section: Biophysical Parametersmentioning
confidence: 99%
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“…The estimation of Leaf Area Index (LAI) and Leaf Chlorophyll Content (C ab ) was facilitated using an Artificial Neural Network (ANN) pre-trained on the raw Sentinel-2 bands accessible within the Sentinel Application Platform (SNAP) [29]. These biophysical parameters are of particular interest due to their ability to enhance the signal-to-noise ratio compared to raw bands and/or vegetation indices when predicting maize yield for a new season using Sentinel-2 data [26].…”
Section: Biophysical Parametersmentioning
confidence: 99%
“…The time series data was aggregated at intervals corresponding to the accumulation of 120 GDDs, as suggested in [26]. Figure 4 displays the distribution of elapsed days and accumulated Growing Degree Days (GDDs) since planting.…”
Section: Temporal Resamplingmentioning
confidence: 99%
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“…For example, Ma et al suggested using the Bayesian neural network to estimate corn yield using MODIS images, GLDAS dataset, PRISM dataset, and SSURGO at the county level in the United States between 2005 and 2019 [8]. Desloires et al introduced a stack of machine learning techniques, namely RF, SVR, XG-Boost, and MLP, to predict corn yield based on Sentinel-2 images captured at field scale in Iowa and Nebraska from 2017 to 2021 [9]. Khaki et al proposed the Deep-Corn network for enhancing crop yield at the field scale by counting corn kernels, which used a shortened VGG-16 for feature extraction at different scales [10].…”
Section: Introductionmentioning
confidence: 99%