Development of SST Validation Methodology for Upcoming SSTM/EOS-06 Sensor: A Prelude Experiment on SLSTR/Sentinel-3A

• Published in: IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
• Main Authors: Abhinav, G., Rao, T. D. V. P., Nagamani, P. V., Ramana, M. V., Chauhan, P.
• Format: Article
• Language: English
• Subjects: Archives & records; Blended product; Climate change; Data points; Datasets; EOS-06; Evaluation; In situ measurement; intercomparison; iQuam; Land surface temperature; Measurement; Microwave imagery; Ocean temperature; Oceansat-3; Precipitable water; radiometer; Radiometers; Root-mean-square errors; Satellite broadcasting; Satellites; Sea and Land Surface Temperature Radiometer (SLSTR); Sea measurements; Sea surface; Sea surface temperature; sea surface temperature (SST); Skin; SST monitor (SSTM); Surface temperature; Temperature measurement; Time measurement; validation; Water vapor; Water vapour; Wind speed
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2022.3217062

Radiometers aboard satellites provide an essential sea surface temperature (SST) data archive, which is one of the key indicators of climate change. Since new radiometers are planned to fill the gap in the temporal data coverage, it is important to quantify the retrieval accuracy to utilize the data and seamlessly merge it into an existing SST data archive. In this regard, we validate the SST product from Sea and Land Surface Temperature Radiometer (SLSTR)/Sentinel-3A with in situ measurements to create a reference frame for evaluating the new sensors products. We used the National Oceanic and Atmospheric Administration (NOAA) i Quam SST dataset for validating satellite retrievals, since it is easily obtainable unlike skin-SST measurements. Several criteria have yielded a total of 30 353 high-quality cloud-free collocated data points between SLSTR and i Quam SST datasets for 2017. Detailed evaluation of biases globally under different wind speeds and precipitable water vapor (PWV) conditions indicates the fundamental difference between the satellite and in situ SST, and recommendations are made for how datasets should be handled. SLSTR-retrieved skin-SST agrees with the i Quam subsurface-SST, irrespective of region and time of measurement, within −0.17 ± 0.04 K for all-day, −0.11 ± 0.06 K for daytime, and −0.21 ± 0.05 K for nighttime datasets, with respective root mean square error (RMSE) values of 0.62 ± 0.10, 0.58 ± 0.09, and 0.65 ± 0.13 K. These small differences are partly due to the difference between ocean skin-temperature and subsurface-temperature, measurement uncertainties and sampling mismatch. Comprehensive validation carried out for SLSTR-retrieved SST has direct implications for future validation studies and future blended infrared, microwave, and in situ SST products.