A Roadmap to subdaily Land Surface Temperature

This manuscript explores the spatio-temporal trade-off in thermal remote sensing and advocates for sub-daily Land Surface Temperature (LST) measurements. We present a novel approach to dynamic temperature measurements based on OroraTech’s growing constellation of cost-efficient cubesats equipped with thermal infrared sensors. With this, we aim to overcome current limitations in spatial and temporal LST coverage and pave the way for a new era of thermal remote sensing capabilities. Emphasizing the significance of high-frequency LST data, we showcase its applications in dynamic land processes, such as irrigation management, soil organic carbon estimation, wildfire risk prediction, and drought monitoring. Furthermore, we outline the design and capabilities of our constellation, focusing on thermal anomaly detection and unprecedented revisit frequencies. Further, we provide an early validation of LST products from FOREST-2, which is our precursor mission for the full constellation of sensors. Finally, we discuss the potential of data fusion techniques to further increase the spatial resolution for certain use cases. Our forthcoming work aims to demonstrate the potential downstream performance of a sub-daily LST data product, providing valuable insights for various scientific and practical applications.