# A Python Framework for Crop Yield Estimation Using Sentinel-2 Satellite Data
###### Abstract  
Remote sensing technologies are essential for monitoring crop development and improving agricultural management. This study investigates the automation of Sentinel-2 satellite data processing to enhance wheat growth monitoring and provide actionable insights for smallholder farmers. The objectives include (i) analyzing vegetation indices across phenological stages to refine crop growth monitoring and (ii) developing a cost-effective user-friendly web application for automated Sentinel-2 data processing. The methodology introduces the \"Area Under the Curve\" (AUC) of vegetation indices as an independent variable for yield forecasting. Among the indices examined (NDVI, EVI, GNDVI, LAI, and a newly developed RE-PAP), GNDVI and LAI emerged as the most reliable predictors of wheat yield. The findings highlight the importance of the Tillering to the Grain Filling stage in predictive modeling. The developed web application, integrating Python with Google Earth Engine, enables real-time automated crop monitoring, optimizing resource allocation, and supporting precision agriculture. While the approach demonstrates strong predictive capabilities, further research is needed to improve its generalizability. Expanding the dataset across diverse regions and incorporating machine learning and Natural Language Processing (NLP) could enhance automation, usability, and predictive accuracy. crop yield estimation; automated Sentinel-2 data processing; Python framework; Google Earth Engine +
Footnote †: journal: _arXiv preprint arXiv:1207.03301_  
0000-0002-0001-9999-999  
Sentinel-2 has revealed that these data sources can complement each other in predicting crop biomass and yield, with PRISMA's narrowbands offering slightly better performance in some cases [3]. Moreover, the fusion of Sentinel-1 and Sentinel-2 data with environmental factors has been effective in constructing dense NDVI time series, crucial for continuous crop monitoring despite challenges like cloudy weather conditions [4]. The combination of PlanetScope and Sentinel-2 imagery with environmental data has also improved wheat yield estimation, demonstrating the benefits of integrating multiple data sources for enhanced accuracy [5]. However, challenges remain, such as the need for more robust methods to ensure the reliability of satellite-derived crop information and the integration of additional biophysical variables to fully explain yield variability [6; 7]. Overall, the synergy between satellite data and advanced analytical techniques holds great potential for improving agricultural management and food security, though further research is needed to address existing limitations and optimize methodologies [8; 9]. Furthermore, the integration of advanced machine learning techniques with satellite data not only enhances yield predictions, but also facilitates the real-time monitoring of crop health and stress factors. For instance, recent studies have demonstrated that combining Sentinel-1's radar backscatter data with optical imagery from Sentinel-2 can significantly improve the estimation of crop biomass under varying weather conditions, providing a more resilient approach to agricultural assessments [10]. Additionally, incorporating environmental variables such as soil moisture and temperature into these models has been used to capture the complexities of yield variability caused by climatic fluctuations, ultimately leading to more accurate forecasting methods [11]. As researchers continue to refine these methodologies, there is an opportunity to develop comprehensive frameworks that leverage both remote sensing technologies and on-the-ground data collection, creating a holistic view of agricultural productivity that is essential for addressing food security challenges globally. Vegetation indices (VIs) play a crucial role in crop monitoring and yield prediction, leveraging remote sensing technologies to provide valuable insights into crop health and productivity.