Abstract
Constellations of CubeSats are emerging as a novel observational resource with the potential to overcome the spatiotemporal constraints of conventional single-sensor satellite missions. With a constellation of more than 170 active CubeSats, Planet has realized daily global imaging in the RGB and near-infrared (NIR) at ~3 m resolution. While superior in terms of spatiotemporal resolution, the radiometric quality is not equivalent to that of larger conventional satellites. Variations in orbital configuration and sensor-specific spectral response functions represent an additional limitation. Here, we exploit a Cubesat Enabled Spatio-Temporal Enhancement Method (CESTEM) to optimize the utility and quality of very high-resolution CubeSat imaging. CESTEM represents a multipurpose data-driven scheme for radiometric normalization, phenology reconstruction, and spatiotemporal enhancement of biophysical properties via synergistic use of CubeSat, Landsat 8, and MODIS observations. Phenological reconstruction, based on original CubeSat Normalized Difference Vegetation Index (NDVI) data derived from top of atmosphere or surface reflectances, is shown to be susceptible to large uncertainties. In comparison, a CESTEM-corrected NDVI time series is able to clearly resolve several consecutive multicut alfalfa growing seasons over a six-month period, in addition to providing precise timing of key phenological transitions. CESTEM adopts a random forest machine-learning approach for producing Landsat-consistent leaf area index (LAI) at the CubeSat scale with a relative mean absolute difference on the order of 4–6%. The CubeSat-based LAI estimates highlight the spatial resolution advantage and capability to provide temporally consistent and time-critical insights into within-field vegetation dynamics, the rate of vegetation green-up, and the timing of harvesting events that are otherwise missed by 8- to 16-day Landsat imagery.
Highlights
Satellite remote sensing offers unparalleled opportunities for land surface monitoring and characterization over space and time domains, with far-reaching socioeconomic benefits [1,2,3]
Visual inspection of the scenes demonstrated the utility of this automated approach for retaining only clear-sky scenes in Cubesat Enabled Spatio-Temporal Enhancement Method (CESTEM) processing
This study demonstrated the capacity of Planet’s constellation of CubeSats, equipped with basic optical imagers (i.e., RGB + NIR), to monitor vegetation dynamics at high spatial (~3 m) and temporal (~daily) resolution
Summary
Satellite remote sensing offers unparalleled opportunities for land surface monitoring and characterization over space and time domains, with far-reaching socioeconomic benefits [1,2,3]. Space-borne observing capacity has evolved significantly in terms of spectral, spatial, and temporal resolution since the launch of the first Land Remote Sensing Satellite (Landsat) in 1972 [12,13]. The inevitable trade-off between resolvable spatial detail and frequency of observations [14] continues to affect the capacity to rapidly detect evolving vegetation dynamics at spatial scales fine enough for effective resource management. These spatiotemporal restrictions have been partly overcome via multisensor data fusion algorithms designed to synergistically exploit sensor-specific attributes, such as the high spatial resolution (i.e., 30 m) of Landsat and high revisit frequency (i.e., daily) of MODIS [15]. The conditional requirement of a sufficient number of homogeneous pixel samples at the coarse scale may not be met over heterogeneous landscapes associated with a relatively small characteristic field size [16]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
