 Low-resolution satellite imagery has been widely used for crop monitoring and yield forecasting for decades. It is cost-effective and provides a large geographic coverage, making it a popular choice for both national and regional scales. Qualitative and quantitative approaches can be differentiated, ranging from using low-resolution satellite imagery as the primary predictor of final crop yield to complex crop growth models, which incorporate remote sensing-derived indicators. Additionally, vegetation performance anomalies detected through low-resolution images continue to be essential components of early warning and drought monitoring systems at the regional level. As new sensors become available, they offer higher resolutions and thus reduce the limitations caused by mixed pixel nature of low-resolution images. Furthermore, the continuity of existing systems is necessary for maintaining long-term datasets required by most yield prediction methods. This article was authored by Oscar Rojas, Igor Savon, Clement Datsberger, and others. We are article.tv, links in the description below.