## 5 Discussion and Conclusions
### Practical Implications, Policy Recommendations and Future Research
(2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Satellite-Based Soybean Yield Forecast: Integrating Machine Learning and Weather Data for Improving Crop Yield Prediction in Southern Brazil. _Agric. For. Meteorol._**2020**, _284_, 107886. [CrossRef]
* [PERSON] et al. (2020) [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning. _Remote Sens._**2020**, _12_, 1357. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. Integrating Satellite and Climate Data to Predict Wheat Yield in Australia Using Machine Learning Approaches. _Agric. For. Meteorol._**2019**, _274_, 144-159. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] [PERSON] Machine Learning Approaches and Sentinel-2 Data in Crop Type Mapping. In _Data Science in Agriculture and Natural Resource Management_; [PERSON], [PERSON] [PERSON], [PERSON], [PERSON], [PERSON]; Studies in Big Data; Springer: Singapore, 2022; pp. 161-180. ISBN 9789811658471. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Satellite-Based Data Fusion Crop Type Classification and Mapping in Rio Grande Do Sul, Brazil. _ISPRS J. Photogramm. Remote Sens._**2021**, _176_, 196-210. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Integrating Multi-Source Data for Rice Yield Prediction across China Using Machine Learning and Deep Learning Approaches. _Agric. For. Meteorol._**2021**, _297_, 108275. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Regional-Scale Monitoring of Cropland Intensity and Productivity with Multi-Source Satellite Image Time Series. _GISci. Remote Sens._**2018**, _55_, 539-567. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa. _Sustainability_**2020**, _12_, 2539. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON] Towards Crop Yield Estimation at a Finer Spatial Resolution Using Machine Learning Methods over Agricultural Regions. _Theor. Appl. Climatol._**2021**, _146_, 1387-1401. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm. _Remote Sens._**2019**, _11_, 535. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Yield Forecasting with Machine Learning and Small Data: What Gains for Grains? _Agric. For. Meteorol._**2021**, _308-309_, 108555. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors. _Remote Sens._**2022**, _14_, 1136. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Mapping Spatial-Temporal Nationwide Soybean Planting Area in Argentina Using Google Earth Engine. _Int. J. Remote Sens._**2022**, _43_, 1724-1748. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] A High-Resolution Cropland Map for the West African Sahel Based on High-Density Training Data, Google Earth Engine, and Locally Optimized Machine Learning. _Remote Sens._**2020**, _12_, 1436. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Operational Framework to Predict Field Level Crop Biomass Using Remote Sensing and Data Driven Models. _Int. J. Appl. Earth Obs. Goinf._**2022**, _108_, 102725. [CrossRef]
* [PERSON] et al.