The Sea Surface Temperature (SST) is one of the key factors affecting ocean climate change. Hence, Sea Surface Temperature Prediction (SSTP) is of great significance to the study of navigation and meteorology. However, SST data is well-known to suffer from high levels of redundant information, which makes it very difficult to realize accurate predictions, for instance when using time-series regression. This paper constructs a simple yet effective SSTP model, dubbed DSL (given its origination from methods known as DTW, SVM and LSPSO). DSL is based on time-series similarity measure, multiple pattern learning and parameter optimization. It consists of three parts: (1) using Dynamic Time Warping (DTW) to mine the similarities in historical SST series; (2) training a Support Vector Machine (SVM) using the top-k similar patterns, deriving a robust SSTP model that offers a 5-day prediction window based on multiple SST input sequences; and (3) developing an improved Particle Swarm Optimization (PSO) method, dubbed LSPSO, which uses a local search strategy to achieve the combined requirement of prediction accuracy and efficiency. Our method strives for optimal model parameters (pattern length and interval step) and is suited for long-term series, leading to significant improvements in SST trend predictions. Our experimental validation shows a 16.7% reduction in prediction error, at a 76% gain in operating efficiency. We also achieve a significant improvement in prediction accuracy of non-stationary SST time series, compared to DTW, SVM, DS (i.e., DTW + SVM), and a recent deep learning method dubbed Long-Short Term Memory (LSTM).

Aerosol is an essential parameter for assessing the atmospheric environmental quality, and accurate monitoring of the aerosol optical depth (AOD) is of great significance in climate research and environmental protection. Based on Landsat 8 Operational Land Imager (OLI) images and MODIS09A1 surface reflectance products under clear skies with limited cloud cover, we retrieved the AODs in Nanjing City from 2017 to 2018 using the combined Dark Target (DT) and Deep Blue (DB) methods. The retrieval accuracy was validated by in-situ CE-318 measurements and MOD04_3K aerosol products. Furthermore, we analyzed the spatiotemporal distribution of the AODs and discussed a case of high AOD distribution. The results showed that: (1) Validated by CE-318 and MOD04_3K data, the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) of the retrieved AODs were 0.874 and 0.802, 0.134 and 0.188, and 0.099 and 0.138, respectively. Hence, the combined DT and DB algorithms used in this study exhibited a higher performance than the MOD04_3K-obtained aerosol products. (2) Under static and stable meteorological conditions, the average annual AOD in Nanjing was 0.47. At the spatial scale, the AODs showed relatively high values in the north and west, low in the south, and the lowest in the center. At the seasonal scale, the AODs were highest in the summer, followed by spring, winter, and autumn. Moreover, changes were significantly higher in the summer than in the other three seasons, with little differences among spring, autumn, and winter. (3) Based on the spatial and seasonal characteristics of the AOD distribution in Nanjing, a case of high AOD distribution caused by a large area of external pollution and local meteorological conditions was discussed, indicating that it could provide extra details of the AOD distribution to analyze air pollution sources using fine spatial resolution like in the Landsat 8 OLI.

The validation of sea surface temperature (SST) retrieved from the new sensor Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) Satellite is essential for the interpretation, use, and improvement of the new generation SST product. In this study, the magnitude and characteristics of uncertainties in S-NPP VIIRS SST produced by the Naval Oceanographic Office (NAVO) are investigated. The NAVO S-NPP VIIRS SST and eight types of quality-controlled in situ SST from the National Oceanic and Atmospheric Administration in situ Quality Monitor (iQuam) are condensed into a Taylor diagram. Considering these comparisons and their spatial coverage, the NAVO S-NPP VIIRS SST is then validated using collocated drifters measured SST via a three-way error analysis which also includes SST derived from Moderate Resolution Imaging Spectro-radiometer (MODIS) onboard AQUA. The analysis shows that the NAVO S-NPP VIIRS SST is of high accuracy, which lies between the drifters measured SST and AQUA MODIS SST. The histogram of NAVO S-NPP VIIRS SST root-mean-square error (RMSE) shows normality in the range of 0–0.6 °C with a median of ~0.31 °C. Global distribution of NAVO VIIRS SST shows pronounced warm biases up to 0.5 °C in the Southern Hemisphere at high latitudes with respect to the drifters measured SST, while near-zero biases are observed in AQUA MODIS. It means that these biases may be caused by the NAVO S-NPP VIIRS SST retrieval algorithm rather than the nature of the SST. The reasons and correction for this bias need to be further studied.

The atmospheric correction of satellite observations is crucial for both land and ocean remote sensing. However, the optimal approach for each area is different due to the large spectra difference in the ground reflectance between land and ocean. A unified atmospheric correction (UAC) approach based on a look-up table (LUT) of in situ measurements is developed to remove this difference. The LUT is used to select one spectrum as the in situ ground reflectance needed to obtain the initial aerosol reflectance, which in turn is used for determining the two closest aerosol models. The aerosol reflectance, obtained from these aerosol models, is then used to deduce the estimated ground reflectance. This UAC model is then used to process the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data, and its performance is validated with a large number of in situ measurements. The mean bias of the land reflectance for this model is 6.59% with a root mean square error (RMSE) of 19.61%. The mean bias and RMSE of the water-leaving reflectance are 7.59% and 17.10% validated by the in situ measurements using the above-water method, while they are 13.60% and 22.53% using the in-water method. The UAC model provides a useful tool for correcting the satellite-received reflectance without separately having to deal with land and ocean pixels. Further, it can seamlessly expand the satellite ocean color data for terrestrial use and improve quantitative remote sensing over land.

Understanding aerosols optical properties over the oceans is vital for enhancing our knowledge of aerosol effects on climate and pollutant transport between continents. In this study, the characteristics of aerosol optical thickness (AOT) at 500 nm (${\tau }_{500\mathrm{nm}}$), Ångström exponent for the wavelength pair 440–870 nm (α) and volume size distribution (VSD), are presented and analyzed over the East China seas based on the observations at four AERONET sites during 1999–2019. The main results are: (1) the mean ${\tau }_{500\mathrm{nm}}$ (α) value ranged from 0.31 to 0.36 (1.17–1.31); (2) the distribution of ${\tau }_{500\mathrm{nm}}$ (α) is similar to a log-normal distribution with a right-skewed long tail larger than 0.5 (closer to the normal distribution); (3) large AOT (${\tau }_{500\mathrm{nm}}>0.6$) was frequently observed in summer (June and July) and spring (March to May), followed by autumn and winter; (4) all aerosol types were observed, and urban/industrial aerosols and mixed types were dominant throughout the period. The atmospheric column aerosol was characterized by a bimodal lognormal size distribution with a fine mode at effective radius, Reff = 0.16 ± 0.01 μm, and coarse mode at Reff = 2.05 ± 0.1 μm.