Lei Guan’s research while affiliated with Ocean University of China and other places

Coral bleaching events have become more frequent in recent years due to the impact of widespread marine heatwaves. The Coral Reef Watch (CRW) program, part of the National Oceanic and Atmospheric Administration (NOAA), assesses bleaching risk by considering measures of daily coral heat stress (Hotspot, HS) and accumulated heat stress (Degree Heating Week, DHW). However, there is a mismatch between coral bleaching alerts through satellite monitoring and records of coral bleaching in the South China Sea (SCS) and its surrounding seas in the historical database. Through comparison with field records of bleaching events in the SCS, this study examined the optimization of the DHW under a fixed or variable HS threshold, evaluating the accuracy of coral bleaching monitoring through a range of evaluation indices, including the Peirce Skill Score (PSS) and the Area Under the Curve (AUC). Our results show that when the DHW index was calculated based on the current operational HS threshold (1°C), reducing the DHW threshold from 4°C to 1.86°C-weeks significantly improved PSS from 0.17 to 0.66, and AUC from 0.58 to 0.83. Further, by optimizing both HS and DHW, evaluation statistics were further improved, with the PSS increasing to 0.71 and the AUC increasing to 0.85. While both methods could significantly optimize the operational bleaching alert level for the SCS, the results suggest that optimization of both the HS and DHW thresholds is better than optimizing DHW alone. As marine heatwaves become more frequent, accurately predicting when and where coral bleaching is likely to occur will be critical to improving the estimation of regional coral stress due to climate change and for understanding coral reefs’ response to recurrent bleaching events.

The sea ice albedo can amplify global climate change and affect the surface energy in the Arctic. In this paper, the data from Visible and Infra-Red Radiometer (VIRR) onboard Fengyun-3C satellite are applied to derive the Arctic sea ice albedo. Two radiative transfer models, namely, 6S and FluxNet, are used to simulate the reflectance and albedo in the shortwave band. Clear sky sea ice albedo in the Arctic region (60°~90°N) from 2016 to 2019 is derived through the physical process, including data preprocessing, narrowband to broadband conversion, anisotropy correction, and atmospheric correction. The results are compared with aircraft measurements and AVHRR Polar Pathfinder-Extended (APP-x) albedo product and OLCI MPF product. The bias and standard deviation of the difference between VIRR albedo and aircraft measurements are −0.040 and 0.071, respectively. Compared with APP-x product and OLCI MPF product, a good consistency of albedo is shown. And analyzed together with melt pond fraction, an obvious negative relationship can be seen. After processing the 4-year data, an obvious annual trend can be observed. Due to the influence of snow on the ice surface, the average surface albedo of the Arctic in March and April can reach more than 0.8. Starting in May, with the ice and snow melting and melt ponds forming, the albedo drops rapidly to 0.5–0.6. Into August, the melt ponds begin to freeze and the surface albedo increases.

In this study, we develop an improved algorithm for retrieving sea surface temperature (SST) from the Advanced Geosynchronous Radiation Imager (AGRI) on the Fengyun-4A satellite (FY-4A). First, we use a multi-channel Nonlinear SST(NLSST) algorithm that combines data from the 3.7, 8.5, 10.7, and 12 μm channels during the nighttime, while during the daytime it combines data from the three long-wavelength bands centered at 8.5, 10.7, and 12 μm. Second, to minimize the impact of water vapor and obtain more accurate SST, we provide different retrieval coefficients obtained from the in situ SST and the observed brightness temperature for different latitude regions and different time periods using two-thirds of the matchups from January 2019 to December 2021. We validate the retrieved FY-4A/AGRI SST and operational FY-4A/AGRI SST by comparing them with in situ SST using one-third of the matchups from January 2019 to December 2021. Compared with the in situ data, the full-disk retrieved AGRI SST has the bias, median, standard deviation (STD), robust standard deviation (RSD) and root mean square error (RMSE) of 0.01K, 0.03K, 0.59K, 0.52K and 0.59K in daytime, respectively. In nighttime, the bias, median, STD, RSD, and RMSE are 0.02K, 0.05K, 0.63K, 0.55K, and 0.63K, respectively. Our analyses of the results further demonstrate that the improved algorithm significantly improves the accuracy compared to the operational AGRI SST, correcting the large bias in the temporal and spatial scales and effectively accounting for the effect of water vapor and satellite zenith angle.

Sea surface temperature (SST) is one of the most important factors related to the ocean and the climate. In studying the domains of eddies, fronts, and current systems, high-resolution SST data are required. However, the passive microwave radiometer achieves a higher spatial coverage but lower resolution, while the thermal infrared radiometer has a lower spatial coverage but higher resolution. In this paper, in order to improve the performance of the super-resolution SST images derived from microwave SST data, we propose a transformer-based SST reconstruction model comprising the transformer block and the residual block, rather than purely convolutional approaches. The outputs of the transformer model are then compared with those of the other three deep learning super-resolution models, and the transformer model obtains lower root-mean-squared error (RMSE), mean bias (Bias), and robust standard deviation (RSD) values than the other three models, as well as higher entropy and definition, making it the better performing model of all those compared.

Satellite remote sensing data form the basis of ocean observation and applications. China has established a satellite network platform comprising ocean color satellite constellations, ocean dynamic environment satellite constellations, and ocean observation and monitoring satellite constellations. This platform provides consistent and reliable ocean observation data crucial for marine scientific research, economic development, and early warning and forecasting. This paper comprehensively describes the development process and plans for China’s ocean satellites from their inception. It offers detailed technical specifications of ocean satellites and outlines the current applications of ocean water color satellites (HY-1), ocean dynamics and environment satellites (HY-2), and ocean surveillance and monitoring satellites (GF-3) in ocean parameter inversion, target identification and detection, and early warning and forecasting. In the future, to enhance the level of industrialization in ocean remote sensing in China, it is imperative to leverage the diversity and timeliness of ocean remote sensing data. Additionally, emerging technologies such as cloud computing and artificial intelligence should be harnessed, and the application potential of various satellite data resources should be explored.

The Fengyun-4A (FY-4A) satellite is a new-generation geostationary meteorological satellite developed by China. The advanced geosynchronous radiation imager (AGRI), one of the key payloads onboard FY-4A, can monitor sea surface temperature (SST). This paper compares FY-4A/AGRI SST with in situ and Himawari-8/advanced Himawari imager (AHI) SST. The study area spans 30°E–180°E, 60°S–60°N, and the study period is from January 2019 to December 2021. The matching time window of the three data is 30 min, and the space window is 0.1°. The quality control criterion is to select all clear sky and well-distributed matchups within the study period, removing the influence of SST fronts. The results of the difference between FY-4A/AGRI and in situ SST show a bias of −0.12 °C, median of −0.05 °C, standard deviation (STD) of 0.76 °C, robust standard deviation (RSD) of 0.68 °C, and root mean square error (RMSE) of 0.77 °C for daytime and a bias of 0.00 °C, median of 0.05 °C, STD of 0.78 °C, RSD of 0.72 °C, and RMSE of 0.78 °C for nighttime. The results of the difference between FY-4A/AGRI SST and Himawari-8/AHI SST show a bias of 0.04 °C, median of 0.10 °C, STD of 0.78 °C, RSD of 0.70 °C, and RMSE of 0.78 °C for daytime and the bias of 0.30 °C, median of 0.34 °C, STD of 0.81 °C, RSD of 0.76 °C, and RMSE of 0.86 °C for nighttime. The three-way error analysis also indicates a relatively larger error of AGRI SST. Regarding timescale, the bias and STD of FY-4A/AGRI SST show no seasonal correlation, but FY-4A/AGRI SST has a noticeable bias jump in the study period. Regarding spatial scale, FY-4A/AGRI SST shows negative bias at the edge of the AGRI SST coverage in the Pacific region near 160°E longitude and positive bias in high latitudes of the southern hemisphere. The accuracy of FY-4A/AGRI SST depends on the satellite zenith angle and water vapor. Further research on the FY-4A/AGRI SST retrieval algorithm accounting for the variability of water vapor will be conducted.

Due to the application demand, users have higher expectations for the accuracy and resolution of sea surface temperature (SST) products. Recent advances in deep learning show great advantages in exploiting massive ocean datasets, and provides opportunities for investigating regional SST predictions in an efficiency approach. However, for deep learning-based SST prediction to be adopted by users, the output must be accurate. This paper investigates the 7-day SST prediction over the China seas and their adjacent waters at a 0.05° spatial resolution. To improve the prediction’s accuracy, we designed a deep learning model combining the three-dimensional convolution and long short-term memory under multi-input multi-output strategy. The Operational SST and Sea Ice Analysis (OSTIA) SST anomaly was used as training data. To test the model prediction ability, we verified the predicted results with the Sub-seasonal to Seasonal (S2S) prediction data from 2015 to 2019. Validation of the predicted SSTs using the OSTIA test datasets show that the root-mean-square error increases from 0.27°C to 0.53°C during the 1- to 7-day lead time, with predictability decreases from southeast to northwest in the study area. Furthermore, the comparison of predicted SST and S2S data with Argo shows that our model is slightly more accurate, which can achieve -0.08°C bias, with a standard deviation of 0.35°C for a 1-day lead time and -0.07°C bias, with a standard deviation of 0.59°C for a 7-day lead time. The results indicate that the proposed deep learning model is accurate and can be applied in regional daily SST prediction.

In recent years, coral reef ecosystems have been affected by global climate change and human factors, resulting in frequent coral bleaching events. A severe coral bleaching event occurred in the northwest of Hainan Island, South China Sea, in 2020. In this study, we used the CoralTemp sea surface temperature (SST) and Sentinel-2B imagery to detect the coral bleaching event. From 31 May to 3 October, the average SST of the study area was 31.01 °C, which is higher than the local bleaching warning threshold value of 30.33 °C. In the difference images of 26 July and 4 September, a wide range of coral bleaching was found. According to the temporal variation in single band reflectance, the development process of bleaching is consistent with the changes in coral bleaching thermal alerts. The results show that the thermal stress level is an effective parameter for early warning of large-scale coral bleaching. High-resolution difference images can be used to detect the extent of coral bleaching. The combination of the two methods can provide better support for coral protection and research.