UAV remote sensing inversion is an efficient and accurate method for obtaining information on vegetation coverage, biomass and other parameters. It is widely used on forest, grassland and other terrestrial vegetation. However, it is rarely used on aquatic vegetation, especially in intertidal zones and other complex environments. Additionally, it is mainly used for inversion of coverage, and there have been few studies thus far on biomass assessment. In this paper, we applied multispectral UAV aerial photography data to evaluate the biomass of seaweed in an intertidal zone. During the ebb tide, UAV aerial photography and in situ sampling data were collected in the study area. After optimizing the spectral index and performing a multiple linearity test, the spectral parameters were selected as the input of the evaluation model. Combined with two machine learning algorithms, namely random forest (RF) and gradient boosting decision tree (GBDT), the biomasses of three species of seaweed (Ulva pertusa, Sargassum thunbergii and Sargassum fusiforme) in the intertidal zone were assessed. In addition, the input parameters of the machine learning algorithms were optimized by one-way ANOVA and Pearson’s correlation analysis. We propose a method to assess the biomass of intertidal seaweed based on multispectral UAV data combined with statistics and machine learning. The results show that the two machine learning algorithms have different accuracies in terms of biomass evaluation using multispectral images; the gradient boosting decision tree can evaluate the biomass of seaweed in the intertidal zone more accurately.

In this work, we investigate sea surface temperature (SST) cooling under binary typhoon conditions. We particularly focus on parallel- and cross-type typhoon paths during four typhoon events: Tembin and Bolaven in 2012, and Typhoon Chan-hom and Linfa in 2015. Wave-induced effects were simulated using a third-generation numeric model, WAVEWATCH III (WW3), and were subsequently included in SST simulations using the Stony Brook Parallel Ocean Model (sbPOM). Four wave-induced effects were analyzed: breaking waves, nonbreaking waves, radiation stress, and Stokes drift. Comparison of WW3-simulated significant wave height (SWH) data with measurements from the Jason-2 altimeter showed that the root mean square error (RMSE) was less than 0.6 m with a correlation (COR) of 0.9. When the four typhoon-wave-induced effects were included in sbPOM simulations, the simulated SSTs had an RMSE of 1 °C with a COR of 0.99 as compared to the Argos data. This was better than the RMSE and COR recovered between the measured and simulated SSTs, which were 1.4 °C and 0.96, respectively, when the four terms were not included. In particular, our results show that the effects of Stokes drift, as well as of nonbreaking waves, were an important factor in SST reduction during binary typhoons. The horizontal profile of the sbPOM-simulated SST for parallel-type typhoon paths (Typhoons Tembin and Bolaven) suggested that the observed finger pattern of SST cooling (up to 2 °C) was probably caused by drag from typhoon Tembin. SST was reduced by up to 4 °C for cross-type typhoon paths (Typhoons Chan-hom and Linfa). In general, mixing significantly increased when the four wave-induced effects were included. The vertical profile of SST indicated that disturbance depth increased (up to 100 m) for cross-type typhoon paths because the mixing intensity was greater for cross-type typhoons than for parallel-type typhoons.

The aim of this study was to use unmanned aerial vehicles (UAVs) as a supplement to satellite remote sensing to accurately assess benthic seaweed biomass in intertidal zones, in order to improve inversion accuracy results and investigate the spatial distribution patterns of seaweed. By adopting non-multicollinearity vegetation indices (feature sets) from PlanetScope and Sentinel-2, and using benthic seaweed biomass inverted from multispectral UAV imagery as the label set for satellite pixel biomass values, machine learning methods (Gradient boosting decision tree, GBDT) can effectively improve the accuracy of biomass estimation results for Ulva pertusa and Sargassum thunbergii species (Ulva pertusa, $R_{Sentinel2}^2$ = 0.74, $R_{PlanetScope}^2$ = 0.8; Sargassum thunbergii, $R_{Sentinel2}^2$ = 0.88, $R_{PlanetScope}^2$ = 0.69). The average biomasses of Ulva pertusa and Sargassum thunbergii in the intertidal zone of Gouqi Island are 456.84 g/m² and 2606.60 g/m², respectively, and the total resources are 3.5 × 10⁸ g and 1.4 × 10⁹ g, respectively. In addition, based on the hyperspectral data, it was revealed that a major source of error is the patchy distribution of seaweed.

Acoustic technology is an essential tool for detecting marine biological resources and has been widely used in sound-scattering layer (SSL) research. The North Equatorial Current (NEC) warm pool region of the Central and Western Pacific Ocean has a vast distribution of micronekton and zooplankton; analyzing the SSL characteristics in this region is vital for monitoring the marine environment and studying the marine ecosystem. In this study, we statistically analyzed the spatiotemporal factors of 10–200 m SSL in the NEC of the Central and Western Pacific Ocean using acoustic survey data collected by the “Songhang” research vessel (RV) in 2022, and the influence of environmental factors on the scattering layer distribution was analyzed using the Generalized Additive Model (GAM). The results showed that the SSL in the warm pool area of the NEC is distributed in shallow waters above 100 m. The primary scatterers are micronekton and zooplankton, and this SSL had diel vertical migration behavior. By comparing Akaike’s Information Criterion of different GAMs, the model consisting of six factors, namely, temperature, current velocity, turbidity, solar altitude angle, longitude, and latitude, was remarkable. Each model’s factor effects primarily influence the contribution of the volume-backscatter strength (Sv). The cumulative deviation explanation rate of the Sv was 67.2%, among which the highest explanation rate of solar altitude angle variance was 35.4%, the most critical environmental factor. The results of this study can provide a reference for long-term studies on ecological changes and their effects on micronekton and zooplankton distribution.

Probing the coverage and biomass of seaweed is necessary for achieving the sustainable utilization of nearshore seaweed resources. Remote sensing can realize dynamic monitoring on a large scale and the spectral characteristics of objects are the basis of remote sensing applications. In this paper, we measured the spectral data of six dominant seaweed species in different dry and wet conditions from the intertidal zone of Gouqi Island: Ulva pertusa, Sargassum thunbergii, Chondrus ocellatus, Chondria crassiaulis Harv., Grateloupia filicina C. Ag., and Sargassum fusifarme. The different seaweed spectra were identified and analyzed using a combination of one-way analysis of variance (ANOVA), support vector machines (SVM), and a fusion model comprising extreme gradient boosting (XGBoost) and SVM. In total, 14 common spectral variables were used as input variables, and the input variables were filtered by one-way ANOVA. The samples were divided into a training set (266 samples) and a test set (116 samples) at a ratio of 3:1 for input into the SVM and fusion model. The results showed that when the input variables were the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), V_re, A_be, R_g, L_re, L_g, and L_r and the model parameters were g = 1.30 and c = 2.85, the maximum discrimination rate of the six different wet and dry states of seaweed was 74.99%, and the highest accuracy was 93.94% when distinguishing between the different seaweed phyla (g = 6.85 and c = 2.55). The classification of the fusion model also shows similar results: The overall accuracy is 73.98%, and the mean score of the different seaweed phyla is 97.211%. In this study, the spectral data of intertidal seaweed with different dry and wet states were classified to provide technical support for the monitoring of coastal zones via remote sensing and seaweed resource statistics.

Against the background of global climate change, the warming water temperature of Antarctic surface water (<500 m) is worthy of study. Therefore, the purpose of this study was to investigate short-term variations in the water temperature of Antarctic surface water from January 2000 to December 2016, using offline coupled ocean models, i.e., the WAVEWATCH-III (WW3) model and the Stony Brook Parallel Ocean Model (sbPOM). The validation of the WW3-simulated significant wave height (SWH) against the measurements from the Jason-2 altimeter produced a root mean square error (RMSE) of 0.39 m with a 0.30-m bias. Moreover, the sbPOM-simulated water temperatures were compared with the collocated measurements from Argo buoys, yielding, and RMSE for water temperatures of less than 1.0 °C and a correlation coefficient (COR) of 0.92. The annual variation in the sbPOM-simulated water temperatures of the Antarctic surface water layer showed that the sea surface temperatures (SSTs) of the Pacific Ocean and Atlantic Ocean were greater than the SST of the Indian Ocean. Moreover, the SST rapidly increased to 2 °C in the Pacific Ocean in specific years due to El Nino and La Nina events. The vertical profile of the water temperature showed that the depth of the cool water in the Indian Ocean has decreased to 100 m since 2008, while the depth in the Pacific Ocean and Atlantic Ocean is about 200 m. It was also found that the seasonal variations in the water temperature at depths of 0 m, 50 m, and 100 m in the Pacific Ocean were more sensitive to El Nino events between January and June. However, it was revealed in this work that the increasing intensity of El Nino and La Nina events could reduce water warming in the Pacific Ocean.