# A Python Framework for Crop Yield Estimation Using Sentinel-2 Satellite Data
###### Abstract
Overall, the continuous development and integration of WOFOST with other models and technologies underscore its critical role in advancing crop yield prediction and supporting agricultural decision making [33; 34; 35]. While these models offer high accuracy, they often require detailed field data, extensive parameter calibration, and computational resources, limiting their accessibility for smallholder farmers. Developing a web application for the automated processing of Sentinel-2 data to facilitate systematic crop monitoring and to provide support to smallholder farmers necessitates the integration of advanced technologies and methodologies, as outlined in the referenced studies. The integration of machine learning and artificial intelligence can significantly enhance the ability of smallholder farmers to manage crops by providing expert advice and automatic disease detection, achieving high accuracy in identifying plant diseases and recommending fertilizers [36]. The use of Sentinel-2 data facilitates the precise mapping of cropping systems over multiple years, which is essential for understanding the spatial and temporal distribution of crops in smallholder regions [37]. This workflow, implemented on Google Earth Engine, effectively classifies complex cropping patterns and provides valuable insights into crop type changes. A cloud-based GIS system leveraging Sentinel-2 data to visualize crop type maps and calculate vegetation indices, offering a scalable solution for agricultural monitoring, has been developed [38]. The ADDPro pipeline further enhances efficiency by automating satellite data downloading and processing on AWS, delivering near-real-time vegetation health information while reducing data transfer costs through efficient compression techniques [39]. Open-source remote sensing tools present a cost-effective decision support system for precision agriculture, particularly benefiting resource-constrained settings [40]. Automated workflows for processing high-resolution satellite images have proven invaluable for tracking crop development and storing critical spectro-temporal data for smallholder agriculture [41]. The \"Sentinel-2 for Agriculture\" project advances global agricultural monitoring by developing open-source systems to generate relevant agricultural products, thereby enhancing market transparency and improving food production data [42]. Additionally, integrating multiscale and multitemporal systems, and combining satellite and UAV data, enables improved plant management and fosters better environmental health [43]. Finally, creating a real-time cropland mask using Sentinel-2 data is crucial for timely agricultural monitoring across diverse agro-ecological contexts, ensuring responsive and effective decision making [44]. Collectively, these studies highlight the potential of a web application powered by Sentinel-2 data and advanced processing techniques to deliver actionable insights to smallholder farmers and experts involved in the field, improving crop monitoring and promoting sustainable agricultural practices. This study proposes an alternative methodology that automates Sentinel-2 data processing for crop monitoring through a cost-effective user-friendly web application. Unlike traditional crop growth models, this approach focuses on real-time vegetation index analysis and predictive insights tailored to smallholder farmers. By leveraging open-source tools such as Python and Google Earth Engine in order to eliminate the need for proprietary software and complex data preprocessing, it makes advanced crop monitoring more accessible and scalable. The aim of this research was to investigate the potential for automating the processing of Sentinel-2 satellite data to enhance crop growth monitoring and provide valuable agricultural insights for smallholder farmers. The specific objectives and final outputs of the study include the following:  
1. Analyzing vegetation indices across different phenological stages to generate more detailed and actionable crop growth monitoring insights. 2.