The increasing climate variability and production risks faced by perennial cash crops, such as coffee, drives the need for improved climate risk management strategies. Understanding crop growth stages and predicting yield under variable conditions, particularly for perennial crops in data sparse regions of developing countries, are critical for developing tailored solutions. This study aims to investigate the use of remote sensing data during key phenological stages to predict coffee yield and its potential use for climate risk management solutions, particularly index-based insurance (IBI). To explore the complex causal relationships among climate, vegetation variables during key growth stage and yield, the study utilized an integrated dataset, including daily Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (VIs), climate variables, together with farm-based phenology and yield data from 558 coffee farms across the Central Highlands, Vietnam. The study demonstrates that Normalized Difference Vegetation Index (NDVI) strongly indicates flowering anomalies, a crucial stage for coffee yield formation. However, NDVI and other VIs have limited power in directly predicting coffee yield. While reflecting plant health, phenology-based VIs offer weak explanatory power for final yield, and therefore do not enhance climate-based coffee yield predictions. The findings suggest that coffee yield is better predicted by climatic variables, such as rainfall and temperature, than by VIs alone. However, the study finds that NDVI can provide early information on plant conditions from the previous year, capturing cumulative effects of environmental and management factors, and resource availability that influence yields in subsequent seasons. These findings have significant implications for research and practical applications. Future research should focus on developing crop-specific indices and exploring high-resolution remote sensing data with data fusion techniques to overcome spatial limitations. Integrating multi-source datasets using advanced machine learning techniques can enhance the accuracy and reliability of yield prediction models and support the development of scalable and cost-effective IBI products. This study contributes to filling a critical gap in the literature and offering pathways to enhance coffee yield monitoring and prediction, ultimately contributing to better climate risk management solutions for coffee farmers in developing regions.