## 4 Systematic Literature Review
### Main Findings
#### 4.1.2 Crop Yield Prediction  
Crop yield prediction is fundamental for farmers and decision makers to control yield losses and ensure food security.  
Rice represents one of the essential worldwide crops. Rice yield prediction in China is studied in [66], using climate variables and vegetation indices as predictors over the GEE platform. LASSO (least absolute shrinkage and selection operator), RF and LSTM (long short-term memory networks) methods yielded the best results for prediction periods of two- to one-month leading time. Yield prediction has been applied to other crop types. In [67], RF and K-means were explored to predict cotton and winter wheat in Central Asia, using vegetation indices derived from MODIS and Landsat imagery with high accuracy levels. In [69], the SVM, KNN (k-nearest neighbor regression) and GPR methods revealed the best performance of eight ML methods explored for crop yield prediction in China at the county and regional levels. Climate variables and vegetation indices derived from MODIS imagery were used as predictors, taking the interannual variability of planting area as a constraint. This study used spatial resolution as an essential prediction performance factor. However, climate data achieved significantly better predictive performance than satellite data. In [13], seven methods from the machine learning and deep learning categories were used along with vegetation indices (MODIS) for yield prediction of rice, corn and soybeans in South Korea and the USA. SVMs presented the best prediction performance. Yield prediction of barley, soft wheat and durum wheat in Algeria was presented in [71].  
Vegetation indices and climate data obtained from MODIS and CHIRPS/ECMWF (Climate Hazards Group InfraRed Precipitation with Station data/ European Centre for Medium-Range Weather Forecasts) datasets provided predictors for SVR (support vector regression), LASSO and MLP (multi-layer perceptron) methods. It was observed that the performance of ML models is much less affected when focusing on low-yield years, while one-Hot Encoded features appear to increase the overall accuracy.  
In [72], yield prediction of wheat in Australia resorts to hydro-climatic predictors derived from MERRA-2 (modern-era retrospective analysis) datasets. Feature selection using grey wolf, ant colony, atom search and particle swarm methods significantly increased the KRR (kernel ridge regression) method's prediction performance. In [79], climate data across the growing season provided additional information necessary for yield prediction compared to remote sensing data. Remote sensing data increased the prediction performance when covering the sowing to maturity periods. Additionally, some biotic factors (pathogens and insects) influencing crop growth reflected in leaf characteristics were detected from satellite images. In another study [78], the atmospheric prediction data significantly improved the wheat yield prediction performance provided by climate and vegetation indices from CRU (Climatic Research Unit) and MODIS datasets up to four months before the harvest.  
Prediction of field biomass in China from vegetation indices, evapotranspiration, radar and net primary production variables, derived from Sentinel-1, Sentinel-2, FAO and WaPOR (water productivity through open-access remotely sensed data platform) datasets is studied in [75], using several ML regression methods. Results showed that cumulative vegetation indices have higher predictive power than standard vegetation indices. Additionally, the prediction accuracies of machine-learning models were not consistent as these increased or decreased unexpectedly as the lead time increased.  
Soil organic carbon estimation in Bavaria is addressed in [80] using spectral bands and vegetation indices from Landsat. Data for organic carbon estimation is scarce since images only respect periods when soil is not covered with vegetation. Thus, composite techniques of multitemporal satellite Landsat images were explored for prediction using the RF method.