New Contributions of Remote Sensing to Land System Science in the Big Data Era

As a key component of global change, land cover and land use maps have been increasingly important for improved understanding of global environmental change and feedbacks between social and environmental systems. Land cover/use data are fundamental for understanding land system sciences (LSS); however, existing land cover/use products cannot fully support the LSS studies given the missing more specific land use and management information, for example, current efforts focus on biophysical remote sensing that misses key social and economic information that is needed for understanding LSS. With the increasing availability of open access high spatial, temporal and/or spectral remote sensing (RS) data, we are entering an unprecedented era of big data in remote sensing, that opens opportunities and challenges. A multitude of new algorithms and approaches using the power of the time series data analysis has improved the existing efforts from mapping land cover to land use and land management. More specific land use information, such as crop types (e.g., rice paddies, soybean), forest types (e.g., rubber plantation, oil palm), and land management (e.g., irrigation, cropping intensity, rangeland management) have become accessible in the new remote sensing era. This progress provides new and improved knowledge to support current GLP themes and global efforts to implement the 2030 agenda for Sustainable Development and associated goals (SDGs).