Big Earth Data Journal is calling for papers for a Special Issue on 10th Anniversary Special Issue of Big Earth Data. The journal has published pioneering research that leverages Earth observations, big data analytics, and interdisciplinary collaboration to address global challenges, including climate change, biodiversity loss, sustainable urbanization, disaster monitoring, etc. To celebrate the journal’s 10th anniversary, we invite submissions to a special issue that reflects on the journal’s decade-long impact, showcases cutting-edge advancements in Big Earth Data research, and defines future directions for the field. This special issue will not only honor the progress made but also encourage the community to tackle emerging challenges and seize opportunities in the next era of data-driven Earth science. Welcome to submit!

Dr. Karl Kaiser, professor of marine and coastal environmental science in the College of Marine Sciences and Maritime Studies at Texas A&M University at Galveston is exploring an innovative idea: using satellites to spot microplastics in the ocean. How? By studying how tiny plastic particles change the way light reflects off the water — and how that changes the color we see from space. If this connection works, it could give scientists a powerful new tool to track microplastic pollution across the globe without ever leaving orbit.

Researchers have developed a compact, high-precision gas mapping system by integrating on-chip dual microcombs with a nanomaterial-functionalized fiber sensor array. This hybrid architecture achieves the simultaneous, highly selective detection of 12 distinct gas species with a LOD (limit of detection) of 24.3 parts per billion. By leveraging the specific chemical interactions of nanomaterials driven by precise optical comb lines, the system offers a robust solution for analyzing complex gas mixtures in environmental and industrial settings.

A new study explains the brain mechanisms behind moments when we first recognize a blurry object, a primal ability that enabled our ancestors to avoid threats.  Based on this understanding, the team built an AI model with a human-like perceptual mechanism that learn new tasks with little training.