Towards analysis ready data of optical CubeSat images: Demonstrating a hierarchical normalization framework at a wetland site

Zhan Li,Daniel Scheffler,Nicholas C Coops,Nicholas Leach,Torsten Sachs

doi:10.1016/j.jag.2021.102502

Abstract

Optical images of the Earth at very high spatial resolutions (VHR, typically < 5 m) are seeing rapid growth in volumes over the past 5 years, due in part to the fast-expanding constellations of CubeSats. Special preprocessing of these VHR images is required to ensure their geometric and radiometric consistency for quantitative analyses for a wide range of Earth and environmental sciences and applications. Here we describe a hierarchical normalization framework (HiNF) to achieve and evaluate geometric and radiometric normalization of these VHR images towards producing analysis ready data (ARD) of optical CubeSat images. We demonstrated HiNF at a spatially heterogeneous and temporally dynamic wetland site in northeastern Germany by generating a stack of temporally consistent ~ biweekly 5-m images over 8 years (2013–2020) at visible and near infrared bands (VNIR). The HiNF combined images from rigorously calibrated multispectral sensors onboard large satellites (Landsat-7/8 and Sentinel-2) and less well calibrated sensors onboard RapidEye (SmallSats) and PlanetScope (CubeSats). A two-stage radiometric normalization procedure produced two levels of image normalization and resulted in more normalized images that passed the quality control in time series compared to common one-stage procedures. The outcome of this novel procedure allows for downstream applications to balance between the quality and the quantity of available normalized CubeSat images in a time series. The HiNF provides a new approach to quantitative evaluations of radiometric normalizations using daily MODIS imagery as bridging benchmark data. The quantitative evaluations showed the HiNF resulted in greater normalization efficacy in the visible bands than in the NIR over the predominantly wetland area. The two normalization levels yielded statistically similar efficacy for the NIR band and the widely-used normalized difference vegetation index according to the Chow test (at significance level of 0.05) but less so for the visible bands. The HiNF facilitates generating ARD of optical CubeSat images and assuring their qualities through its demonstrated efficacy and its quantitative evaluation approach. Such ARD-quality time series of VHR images from CubeSats allow for improved analyses and quantitative applications of this new stream of multispectral images at spatial scales that are better related to ground measurements and environmental management in terrestrial ecosystems.

Highlights

CubeSats are low-cost and miniaturized satellites made of commer cial off-the-shelf components (Lee et al, 2020)
We focus on detailing the algorithms used in our demonstration example, other al gorithms of similar types can be fit into the hierarchical normalization framework (HiNF) according to users’ resources and needs
We found that the average residual errors in the alignment between CubeSat and HLS images were reduced from 9.99 m before the Step-1 co-registration to 4.80 m after the coregistration

Summary

Introduction

CubeSats are low-cost and miniaturized satellites made of commer cial off-the-shelf components (Lee et al, 2020). The low costs and miniature sizes allow them to be launched in bulk into low Earth orbits (Puig-Suari et al, 2001), which facilitate the establishment of constel lations comprising large numbers of satellites. Such constellations pro vide multipoint sensing of the earth and enhance observational coverages, enabling Earth observations (EO) at unprecedented spatial and temporal resolutions that are impractical for traditional large sat ellites to achieve (Poghosyan and Golkar, 2017). Some recent studies have demonstrated the opportunities to advance a wide range of earth and environmental sciences and applications via optical CubeSat images at very high resolutions (VHR, typically < 5 m) despite their limited spectral bands and spectral resolutions, such as in hydrology (Cooley et al, 2019; McCabe et al, 2017), ecology (Riihimaki et al, 2019), agriculture (Aragon et al, 2018; Cai et al, 2019), and disaster man agement (Santilli et al, 2018)

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