Out-of-year corn yield prediction at field-scale using Sentinel-2 satellite imagery and machine learning methods

Early Season Forecasting of Corn Yield at Field Level from Multi-Source Satellite Time Series Data

“…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].…”

“…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.…”

“…The forecasting of C ab was proposed as it has been shown to be the best yield predictor derived from Sentinel-2 data [26]. All available information prior to the cut-off date, derived from Sentinel-2 (S2), crop stress indices (CS) from agroclimatic data, and Land Surface Temperature (LST) from Landsat, was considered.…”

“…This may be partly explained by possible differences in crop progress and climate/environmental condition changes from season to season, or from location to location, where these seasonal changes in phenology are primarily influenced by temperature and water regimes [25]. To face such generalization issues affecting modern ML approaches, in the context of yield prediction, a possible solution is to resample remote sensing satellite image data over periods calculated over thermal time, i.e., the number of growing degrees-days accumulated from the sowing date, rather than calendar time [26], with the aim of mitigating possible shifts in crop phenology at a given date, due to different temperature regimes.…”

Enhancing Corn Yield Prediction in Iowa: A Concatenate-Based 2D-CNN-BILSTM Model with Integration of Sentinel-1/2 and SoilGRIDs Data

“…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].…”