Quantifying Error Introduced by Spatiotemporal Mismatches in Satellite and In-Situ Data Acquisition in Ocean Color Algorithm Development.
Citation:
Cronin-Golomb, O., K. Meyers, W. Salls, AND B. Schaeffer. Quantifying Error Introduced by Spatiotemporal Mismatches in Satellite and In-Situ Data Acquisition in Ocean Color Algorithm Development. ASLO 2024, Madison, WI, June 02 - 07, 2024.
Impact/Purpose:
Satellite data and field data are often not collected at the same time, resulting in a source of error in ocean color algorithm development that is frequently cited in the literature, but is poorly constrained. In this study, we will model particle movement under a range of wind speed and direction conditions and compare the results to the pixel size of Planet, Sentinel 2, and Sentinel 3 satellite imagery. The results of this simulation will clarify a source of disconnect between field and algorithm-derived measurements of water quality, improving developer communication and stakeholder interpretation. This will better inform water quality management decisions that impact environmental well-being, and, consequently, human health and economic stability. These results will be useful to anyone using field data to develop and model ocean color remote sensing algorithms and to anyone interpreting those algorithm outputs. These could be academics, local, state, and federal governmental researchers and policy makers, industry professionals, and the general public. Therefore, the implications of this study are far-reaching, highly impactful, and have long-term relevance.
Description:
In-situ measurements of optically active indicators of water quality are used to develop and validate spatiotemporally robust and cost-effective satellite remote sensing algorithms. Outputs from water quality algorithms routinely inform policymaker decisions that impact environmental well-being, and, consequently, human health and economic stability. Therefore, understanding sources of error in these algorithms and communicating them to managers is crucial for their proper interpretation. Currently, algorithm error is largely attributed to the assumptions inherent in the algorithms themselves. However, the error introduced by temporal mismatch between field and satellite data acquisition is often cited but poorly constrained. In the time between field and satellite data collection, the in-situ surface water parcel that represented the optical conditions of a location at time of sample collection could have traveled outside the bounds of the associated satellite pixel. Here, we assess the possible contributions of this spatiotemporal error by modeling the movement of a surface water parcel over six hours under a variety of wind speeds and directions. We compare the distance and direction traveled to the pixel size of Planet, Sentinel 2, and Sentinel 3 imagery, generating an error estimate for high to coarse resolution imagery under a range of potential sampling conditions. The results of this simulation will clarify a source of disconnect between in-situ and algorithm-derived measurements of water quality, improving stakeholder understanding, communication, and engagement.
