China’s Coffee Map Just Got a Lot Smarter—Here’s Why It Matters
Real talk: A new remote sensing model is rewriting how we track coffee farms in China. Researchers used machine learning and Sentinel-2 satellite data to map Yunnan’s Pu’er region with near-95% accuracy, creating a template that’s lightweight, scalable, and surprisingly precise. This isn’t just about counting trees—it’s about redefining how governments, farmers, and traders monitor sustainability in complex landscapes. The study, published in Frontiers in Remote Sensing, hinges on tracking seasonal changes in vegetation, terrain, and administrative boundaries to distinguish coffee from tea bushes or shrubs. In controlled tests, the model correctly classified land pixels as coffee or non-coffee 95%…
