Big Earth Data

Big Earth Data and Artificial Intelligence for Sustainable Farmland

Sustainable farmland management is fundamental to achieving the United Nations Sustainable Development Goals (SDGs), especially SDG 2 “Zero Hunger” and SDG 15 “Life on Land.” The field is undergoing a profound shift from experience-based practices toward data-driven and intelligent decision-making. Big Earth Data, including satellite remote sensing (e.g., Landsat, Sentinel, Gaofen), unmanned aerial vehicle (UAV) imagery, Internet of Things (IoT) sensors, as well as meteorological, soil, crop, and socio-economic data, has become a key driver for agricultural monitoring, resource optimization, and environmental protection. Meanwhile, rapid advances in artificial intelligence (AI), including deep learning, computer vision, time-series analysis, and geospatial artificial intelligence (GeoAI), have enabled efficient processing of massive, multi-source spatiotemporal data, supporting accurate monitoring, prediction, and optimization of farmland ecosystems.

Despite significant progress, challenges remain in data integration and standardization, model interpretability, spatiotemporal scaling, and the translation of innovations into scalable, practical solutions for farmers and policymakers. This Special Issue aims to address these gaps by publishing interdisciplinary research that connects Big Earth Data, AI, and sustainable farmland management. We welcome high-quality contributions that advance knowledge sharing, technological innovation, and global cooperation to enhance farmland resilience, strengthen food security, and promote green and low-carbon agriculture.

Potential Topics related to big earth data (including but not limited to):

     ·         Identification and sustainability comparison of representative global farmland models

·         Intelligent monitoring of farmland soil conditions and cultivated land quality

·         Smart monitoring of crop growth dynamics, productivity, and yield potential

·         Extraction, mapping, and functional evaluation of farmland infrastructure

·         Intelligent monitoring and early warning systems for farmland-related disasters

·         Trade-off and synergy analysis between agricultural production and ecological sustainability

·         Data-driven and AI-enabled farmland monitoring and decision support systems