Feasibility of PlanetScope SuperDove constellation for water quality monitoring of inland and coastal waters

Planet’s SuperDove (SD) sensors offer eight bands (seven visible, one near infrared (NIR)) at 3 m spatial and near-daily temporal resolution. The yellow (610 nm) and red-edge (705 nm) bands are valuable for retrieving water quality (WQ) parameters, supporting applications such as harmful algal bloom (HAB) and post-disaster monitoring. To enable scientific use, we assess signal-to-noise ratios (SNRs), with the highest (248:1) at 443 nm and the lowest (8:1) at 865 nm, and other visible bands ranging from 26:1–98:1. We cross-calibrated SD with Sentinel-2 Multi-Spectral Imager (MSI) using near-simultaneous observations over aquatic environments by comparing top-of-atmosphere (TOA; <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:msub><mml:mi>ρ</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mtext> </mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:math> ) reflectance across five shared visible bands (443, 490, 565, 665, and 705 nm), and derived calibration coefficients through linear regression. Before calibration, SD-MSI median <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mrow><mml:msub><mml:mi>ρ</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mtext> </mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:math> differences ranged from ∼0.7–13%, with highest differences at 705 nm. After applying the calibration, these differences reduced to −0.07% to −2.2%, including improvements at 665 nm (from ∼8% to −2.2%) and 705 nm (from ∼13% to −0.1%). Differences in atmospherically corrected remote sensing reflectance ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m3"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi>r</mml:mi><mml:mi>s</mml:mi><mml:mtext> </mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:math> ) also decreased from 16%–95% to 8%–72% post-calibration, with 565 nm showing the lowest (∼8%) and 705 nm the highest (∼72%) residual difference. Remaining <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m4"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi>r</mml:mi><mml:mi>s</mml:mi><mml:mtext> </mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:math> discrepancies are attributed in part to SD’s inter-sensor differences and uncertainties in atmospheric correction. We qualitatively compared chlorophyll-a (Chl a ) and Secchi-disk depth ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m5"><mml:mrow><mml:msub><mml:mi>Z</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mi>d</mml:mi><mml:mtext> </mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:math> ) WQ products from SD and MSI, including a time-series analysis focused on the Dixie Fire and subsequent algal bloom in Lake Almanor (Sept–December 2021). The products captured expected trends, highlighting SD’s potential for WQ monitoring, while elevated uncertainties in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m6"><mml:mrow><mml:msub><mml:mi>ρ</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mtext> </mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m7"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi>r</mml:mi><mml:mi>s</mml:mi><mml:mtext> </mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:math> suggest the need for improved calibration stability and atmospheric correction.