Solving key observational issues in tracking fine-scale changes of our planet from space
Our Earth has experienced rapid environmental changes tightly tied to anthropogenic activities. Satellite remote sensing offers a quantitative means to monitor such changes but is often limited to coarse spatial or temporal resolutions. Only very recently, with the arrival of Planet's Dove satellites, a constellation of CubeSats made of 190+ satellite sensors to produce daily and global coverage at a 3-meter resolution, have we had the opportunity for fine-scale Earth's surface monitoring.
However, several issues remain with CubeSat observations that further hinder its broader applications: 1) Frequent clouds and cloud shadows often contaminate the satellite signal, 2) CubeSat observations source from 190+ satellite sensors with varying sun angles, causing data inconsistency issues across different sensors, and 3) accurate biophysical interpretation of satellite signal remains lacking.
Dr. Jin Wu and Dr. Jing Wang from the Global Ecology and Remote Sensing (GEARS) Lab at the School of Biological Sciences, The University of Hong Kong (HKU), conducted research to address these issues by developing novel observational methods that provide better accuracy on tracking fine-scale changes from space.
For example, the team has recently developed an automatic cloud and cloud shadow screening method for CubeSats, which leverages the spatial and temporal information of satellite reflectance bands, and has been demonstrated to enable cloud and shadow screening with the highest accuracy and least sensitivity to land cover type. The research outcome thus advances the monitoring of atmospheric cloud covers, while improving the data quality assessments for land-surface monitoring and biophysical extraction. This research has recently been published in scientific journalRemote Sensing of Environment(RSE).
The team has put much effort in recent years into improving the processing and interpretation of CubeSats. For example, to improve its data consistency over space and time, the team developed a rigorous method to cross-calibrate CubeSats to the same level as a more stable single-sensor satellite—Moderate Resolution Imaging Spectroradiometer (MODIS), that has been rigorously calibrated with sun-sensor geometry issues and has demonstrated consistently high data quality. In order to perform a direct and accurate biophysical interpretation from space, the team proposed a spectral unmixing approach that effectively classified the forest canopy into leafy vs leafless phenophases, from which it would enable fine-scale accurate phenology monitoring of tropical forests. Similarly, by integrating proximate drone surveys with CubeSats, the team demonstrated the feasibility to monitor plant phenology at the tree-crown scale.
"Our research has made significant observational advances to make full use of new-generation satellite data, and ultimately facilitate the monitoring of Earth's environmental changes, especially for those rapid and fine-scale changes," said Dr. Jing Wang, the leading author of the two journal papers published inRSE.
"There have been a series of papers inRSEon similar topics. Our work is not another one, but a new attempt to explore the possibility to enable satellite techniques for crown-scale phenology monitoring, which thus represents the cutting-edge research frontier and also opens a world of possibilities for individual-based ecology studies using satellite techniques," added Dr. Jin Wu, Principal Investigator of Global Ecology and Remote Sensing (GEARS) Lab at HKU.
With these advances, the GEARS lab is aiming to leverage CubeSats and other geospatial technologies to facilitate the relevant research fields, which include but are not limited to ecological scaling principles, biodiversity research, forest growth, health, and management practices, climate change impact assessments and mitigation strategies, and ultimately the nature-based solutions to reaching carbon neutral goals.
Jing Wang et al, Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest, Remote Sensing of Environment (2020). DOI: 10.1016/j.rse.2020.111865
Shengbiao Wu et al, Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations, ISPRS Journal of Photogrammetry and Remote Sensing (2020). DOI: 10.1016/j.isprsjprs.2020.10.017