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The mechanism of wetland distribution (WD) has been well studied, but further research is needed on the mechanism of wetland change (WC). This study developed a model of the impact of changes in human activity (HA) and natural environment factors on WC from an asynchronous–spatiotemporal perspective, integrating remote sensing technologies and partial least squares–structural equation modeling (PLS–SEM). In the model, HA was reflected by economic and population data. The natural environment was reflected by the fundamental natural environment (FNE), which was mainly based on terrain, and the non-stable natural environment (NNE), which was mainly based on hydrological and temperature conditions. The model met the accuracy requirements in the Pearl River Delta (PRD). The results showed that there were differences in the response of WD and WC to driving factors from 1980 to 2020 in the PRD. FNE had a negative impact on WD, however, FNE changes (FNEC) had a positive impact on WC (mainly wetlands decrease). HA could affect NNE and subsequently WD, but NNE changes (NNEC) only began to affect WC after 2010. HA had a negative impact on WD and WC from 1980 to 2010, but both negative and positive impacts existed after 2010. By coupling areas of HA changes (HAC) with wetland decrease, it was found that HA should be restricted in the southeast of Foshan (areas where HA increase led to wetland decrease) to protect wetlands; The junction between Zhaoqing and Foshan (areas where HA decrease lead to wetland decrease) requires investment in improving the natural environment. The model proposed in this study can be applied to other areas with severe wetland degradation from HA and natural conditions, to assist in local wetland restoration and management.

Particulate organic carbon (POC) plays a vital role in carbon cycling in aquatic ecosystems. A knowledge of POC source dynamics is important for a comprehensive understanding of many biogeochmeical processes in inland and coastal waters. Yet, determining POC sources from remote sensing have not been fully explored. Using bio-optical datasets from 10 cruises in Taihu Lake, Poyang Lake, Daya Bay and the Pearl River Estuary, an optical classification scheme was developed to detect the dominant source of POC from remote sensing, taking into consideration the absorbing feature of phytoplankton pigment in the near-infrared waveband. Following the approach of POC source identification, the study waters were distinguished into two classes, the algae-based origin and detritus-dominance source. POC sources were thus mapped with 2016–2019 VIIRS imagery data, which exhibited significant temporal variability and spatial heterogeneity for the four study waters across different eutrophic states. It was observed that Poyang Lake was mostly terrigenous in all the study years and phytoplankton-related source was dominated in Daya Bay. Taihu Lake showed heterogeneous conditions varying with time and space, whereas most water areas was contributed by the terrestrial origin. Specially, the Pearl River Estuary presented a typical pattern of POC sources dynamics, with an exogenous source in the north-west waters, while endogenous in the south-east open areas. By implication, the methodology of POC source determination in the present study, will be conveniently applied to the common water color satellites and effectively identify the POC origins in time and space in future, which allows for a better understanding the carbon cycling and biogeochemical processes in inland and coastal waters with different eutrophication conditions.

Taking the coral communities of the Xuwen coral reef in 2020 as the research object, we analyzed the species composition, diversity, and interspecific Spearman correlation of the scleractinian coral communities, investigated the features and spatial distribution of the scleractinian coral community, and discussed the correlation between the community composition and environmental factors to identify the affecting factors and their sources. These results showed that (1) compared with the survey in 2004, the coverage of corals in 2020 had significantly decreased, while the dominant genera were still Goniopora and Porites. The coral morphology was massive, and the diversity of the coral community (Shannon–Wiener index, H’) was 2.87. The distribution of coral was uneven. The competition among some dominant species of coral was intense. (2) The mass coral bleaching event in the NSCS in August 2020 did not cause severe coral death in the short term on the Xuwen coral reef. (3) The growth of the coral community in 2020 might be greatly affected by high suspended solids and nutrient levels, which were related to the current, mariculture, and coastal erosion. (4) Anthropogenic activities such as coastal aquaculture and fishing were the major factors leading to the reduction in coral coverage on Xuwen coral reef in the past 10 years.

Ocean color sensors, typically installed on polar-orbiting satellites, have been used to monitor oceanic processes for last three decades. However, their temporal resolution is not considered to be adequate for monitoring highly dynamic oceanic processes, especially when considering data gaps due to cloud contamination. The Advanced Himawari Imager (AHI) onboard the Himawari-8, a geostationary satellite operated by the Japan Meteorological Agency (JMA), acquires imagery every 10 min at 500 m to 2000 m spatial resolution. The AHI sensor with three visible, one near-infrared (NIR), and two shortwave-infrared (SWIR) bands displays good potential in monitoring oceanic processes at high temporal resolution. This study investigated and identified an appropriate atmospheric correction method for AHI data; developed a model for Total Suspended Solids (TSS) concentrations estimation using hyperspectral data and in-situ measurements of TSS; validated the model; and assessed its potential to capture diurnal changes using AHI imagery. Two image-based atmospheric correction methods, the NIR-SWIR method and the SWIR method were tested for correcting the AHI data. Then, the new model was applied to the atmospherically corrected AHI data to map TSS and its diurnal changes in the Pearl River Estuary (PRE) and neighboring coastal areas. The results indicated that the SWIR method outperformed the NIR-SWIR method, when compared to in-situ water-leaving reflectance data. The results showed a good agreement between the AHI-derived TSS and in-situ measured data with a coefficient of determination (R²) of 0.85, mean absolute error (MAE) of 3.1 mg/L, a root mean square error (RMSE) of 3.9 mg/L, and average percentage difference (APD) of 30% (TSS range 1–40 mg/L). Moreover, the diurnal variation in the turbidity front, using the Normalized Suspended Material Index (NSMI), showed the capability of AHI data to track diurnal variation in turbidity fronts, due to high TSS concentrations at high temporal frequency. The present study indicates that AHI data with high image capturing frequency can be used to map surface TSS concentrations. These TSS measurements at high frequency are not only important for monitoring the sensitive coastal areas but also for scientific understanding of the spatial and temporal variation of TSS.

Amino acids are the material basis of almost all life activities. An improved understanding of the source, state, and cycle of amino acids is essential for determining the energy flow and material circulation of marine ecosystems. In the present study, an in situ rapid detection method of ultraviolet (UV; 266 nm) laser-induced fluorescence (LIF) technology was used to detect three natural, aromatic amino acids in the seawater. The laser-induced fluorescence peaks of aromatic amino acids tryptophan, tyrosine, and phenylalanine were located at 350 nm, 300 nm, and 280 nm, respectively. High, linear correlations between the concentrations of the aromatic amino acids and the fluorescence peak heights were observed, and the lowest detectable concentrations of tryptophan, tyrosine, and phenylalanine were 4.70 × 10−9 mol/L, 2.76 × 10−8 mol/L, and 6.05 × 10−7 mol/L, respectively, which allowed us to quantify their concentrations by using laser-induced fluorescence. This paper not only provides a practical method for the detection of aromatic amino acids in seawater, but a new means to further understand the biogeochemical processes of carbon cycles in the deep sea.

Optical approaches were developed to examine the relationship between the optically active and the optically inactive fractions of dissolved organic carbon in inland waters. A multiple linear regression model was developed on the basis of an extensive dataset from Taihu Lake, and validated employing data from another large shallow eutrophic lake (Chaohu Lake) in southern China. The model was used to estimate the concentration of dissolved organic matter ( n = 191) using the absorption characteristics of its chromophoric fraction with a high correlation coefficient ( R ^2 = 0.62) and a low root mean squared error (RMSE = 9.67%). This intra-ecosystem validity allows us to improve our understanding of carbon dynamics using optical remote sensing approaches for these optically complex lakes, where multiple sources and sinks of dissolved organic matter were present.