A Technical Review of Planet Smallsat Data: Practical Considerations for Processing and Using PlanetScope Imagery

Abstract

With the ability to capture daily imagery of Earth at very high spatial resolutions, commercial smallsats are emerging as a key resource for the remote sensing community. Planet (Planet Labs, Inc., San Francisco, CA, USA) operates the largest constellation of Earth imaging smallsats, which have been capturing multispectral imagery for consumer use since 2016. Use of these images is growing in the remote sensing community, but the variation in radiometric and geometric quality compared to traditional platforms (i.e., Landsat, MODIS, etc.) means the images are not always ‘analysis ready’ upon download. Neglecting these variations can impact derived products and analyses. Users also must contend with constantly evolving technology, which improves products but can create discrepancies across sensor generations. This communication provides a technical review of Planet’s PlanetScope smallsat data streams and extant literature to provide practical considerations to the remote sensing community for utilizing these images in remote sensing research. Radiometric and geometric issues for researchers to consider are highlighted alongside a review of processing completed by Planet and innovations being developed by the user community to foster the adoption and use of these images for scientific applications.

Who Speaks for the Forest? Local Knowledge, Participatory Mapping and Collaborative Evaluation for Gis Analysis in the Tropics of Central Bali, Indonesia

“…However, some key aspects of these image assets have been understood as lacking in comparison to traditional satellite datasets. For example, Frazier observes that the geometric and radiometric quality of PS assets does not match 'analysis ready' datasets (Frazier, 2021). As the authors observe, these limitations can be overcome, but they require access to services from PS that are subject to a licensing agreement and are thus of limited use to resource constrained research communities, including within many organizations in emerging economies.…”

Crop Intensity Mapping Using Dynamic Time Warping and Machine Learning from Multi-Temporal PlanetScope Data

“…The time-series dataset can then be employed for various applications; one is to map cropping intensity on agricultural land, which can be used to increase protection of productive agricultural lands from land-use conversion. The problem with PlanetScope is geometric and radiometric inconsistency [19], which can hamper mapping accuracy.…”

“…However, this has not yet been applied to map cropping intensity. In addition, the PlanetScope data suffers from radiometric and geometric inconsistencies due to sensor upgrades in the constellation [19]. Therefore, a study to explore the capability of time-series PlanetScope data for mapping cropping intensity remains necessary.…”

New Generation Hyperspectral Data From DESIS Compared to High Spatial Resolution PlanetScope Data for Crop Type Classification

“…The PlanetScope satellite constellation [20] consists of over 200 cubesats (also known as Doves) with a spatial resolution of 3-4 m at nadir and a swath size of 24.6 km X 16.4 km [28], [46]- [48]. The constellation consists of three generations (Dove Classic, Dove-R, and SuperDoves), each with different sensor characteristics [49]. The Dove Classic cubesats deployed during the demonstration phase were launched from the ISS at 375 km [49].…”