Sentinel-2 Proves Superior to MODIS in Predicting Crop Yields

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• Sentinel-2 delivers more accurate crop yield estimates than MODIS, thanks to its finer spatial resolution and vegetation-sensitive spectral bands.
• Research comparing both sensors showed Sentinel-2 consistently achieved lower prediction errors for cotton and maize across different agro-ecological zones.
• Key indicators like the Enhanced Vegetation Index and Leaf Area Index proved especially powerful when captured through Sentinel-2’s higher-quality imagery.
• With MODIS nearing the end of its operational life, Sentinel-2 is emerging as the leading tool for precise, field-level crop monitoring and data-driven farm management.

Precision agriculture has taken a decisive step forward with new research showing that the Sentinel-2 satellite outperforms MODIS in predicting crop yields.

The study, published in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, compared the two systems and found Sentinel-2 delivers more accurate, detailed, and consistent data, a development that could reshape modern farming.

For decades, the Moderate Resolution Imaging Spectrometer (MODIS) has been the standard tool for large-scale crop yield monitoring, valued for its frequent data collection and reliability.

However, as MODIS nears retirement, attention has turned to the newer Sentinel-2 constellation, which offers sharper spatial resolution and advanced spectral bands tuned to vegetation health.

Led by Kennedy Adriko of Forschungszentrum Jülich in Germany, the research team conducted a sensor-to-sensor comparison of MODIS and Sentinel-2 across two agro-ecological zones. Using matched spatiotemporal data and regression models, they measured which system produced more accurate yield estimates.

The results were clear: Sentinel-2 consistently delivered higher precision. For cotton, it achieved an RMSE of 123.52 lb/acre with a correlation value of 0.76, compared to MODIS’s 129.20 and 0.74. For corn, the advantage widened, with Sentinel-2 recording 8.40 Bu/acre and 0.79, outperforming MODIS’s 8.69 and 0.66.

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Adriko explained that Sentinel-2’s finer spatial detail captures subtle variations in crop growth, essential for predicting yields at both regional and field scales.

The study also found key predictive indicators such as the Enhanced Vegetation Index and Leaf Area Index, performed more reliably with Sentinel-2 data.

The implications extend far beyond academic research. For farmers and agribusinesses, more accurate yield forecasting means better decisions about irrigation, fertiliser use, and resource allocation.

It could also spur innovation in agri-tech platforms built around Sentinel-2’s capabilities, offering new tools for precision management.

The transition from MODIS to Sentinel-2 marks more than a technological upgrade; it represents a shift towards smarter, data-driven agriculture that promises higher productivity, sustainability, and profitability.