Abstract: Deep learning has great potential for environmental monitoring, yet real-world applications often face challenges from large-scale, multimodal, and noisy datasets. We introduce GreenNet, a flexible and open-source framework that makes it easier to build and scale deep learning models for remote sensing and environmental data. GreenNet offers reusable neural network modules, simple data integration tools, and built-in explainability features tailored for geospatial applications. To demonstrate its effectiveness, we apply it to case studies such as deforestation detection, urban heat island mapping, and air quality forecasting. These examples show that GreenNet delivers strong predictive performance while significantly reducing the effort needed to develop models. By connecting domain-specific data processing with modern deep learning techniques, GreenNet aims to make AI more accessible, reproducible, and interpretable for researchers and practitioners in environmental science.