Chlorophyll fluorescence is an important indicator of the physiological state of phytoplankton in water bodies. The new generation of ocean color satellite remote sensors usually sets fluorescence bands to detect the phytoplankton fluorescence line height (FLH). Yet, the Geostationary Ocean Color Imager (GOCI) offers no FLH products so far, and the FLH results calculated using the fluorescence band (680 nm) and the two baseline bands (660 and 745 nm) have numerous negative values and are quite different from the FLH products of other satellite ocean color sensors. To address this problem, we established an FLH retrieval algorithm suitable for GOCI. We simulated the spectral datasets of different water types using the radiative transfer model HydroLight and established the band conversion relationship from 680 to 685 nm based on the simulated datasets. The remote sensing reflectance after band conversion was applied to the FLH product inversion, significantly reducing the number of negative FLH values and appreciably improving data availability for GOCI FLH products (from 14.78% to 66.73% on average). The new FLH product has a good correlation with the field-measured data (R² = 0.73), and the relative error was 6.95%. Moreover, after band conversion, the FLH products retrieved by GOCI are in good agreement with the FLH products of MODIS, and fusion products can be further produced to improve the spatiotemporal resolution of the data. In addition, the radiative transfer simulation datasets also revealed that changes in solar zenith angle have little effect on FLH inversion. The hourly GOCI-derived FLH has good spatiotemporal continuity and can clearly reflect the diurnal variation of FLH. It can provide a stable FLH algorithm for further recovery of time-series GOCI FLH products and research on diurnal changes in FLH.

Accurate estimation of particulate organic carbon (POC) export efficiency in the euphotic layer is essential to understand the efficiency of the ocean’s biological carbon pump, but field measurements are difficult to conduct and data are sparse. In this study, we investigated the relationship between POC sinking export efficiency and ocean net primary production (NPP) in the euphotic layer of the northern South China Sea (NSCS), with the help of high spatiotemporal coverage satellite-derived NPP. Annual mean POC export efficiency in euphotic zone is 34% for the shelf areas and 24% for the basin of the NSCS in the context of satellite-derived 16-day-composited NPP. Similar to what is generally observed in the global ocean, the POC export efficiency on the shelf areas appears to be strengthened with the increase of NPP. However, in the basin areas, the opposite relationship is observed. That is, the POC export efficiency significantly decreases with the increase of NPP. Seasonal decoupling between NPP and POC export, phytoplankton size structure, grazing by zooplankton, and dissolved organic carbon export might account for the observed negative relationship between the POC export efficiency and NPP in the euphotic layer of basin region. System comparison between shelf and basin would be helpful to promote understanding of the regulation mechanism of POC export in the tropical marginal seas.

Knowledge of particulate organic carbon export flux (EP) is the key to understanding the marine biological carbon pump. In this study, a satellite remote sensing algorithm based on the food‐web model established by Siegel et al. (2014; https://doi.org/10.1002/2013GB004743) was used to estimate EP in the northern South China Sea (NSCS), which consists of direct sinking flux of large phytoplankton and associated aggregates by gravity and flux of zooplankton feces by grazing. This is the first time that fine spatiotemporal variation of satellite‐derived EP in the NSCS is unveiled. Compared with the results on 1° spatial scale products, the model results based on the satellite products with 1/3° and 1/12° resolutions showed better consistency with the observations. Validation with in situ EP showed that the model exhibited a good performance in the NSCS basin, but the predicted EP in the shelf regions were smaller compared with measurements. The satellite‐derived annual‐mean EP in the shelf areas was 8.47 and 5.56 mmol C·m⁻²·d⁻¹ in the NSCS basin, on the 1/3° spatial scale, with relative differences from observations were about −50% and −15% for the shelf and basin, respectively. The flux of feces from zooplankton grazing, especially that from microzooplankton grazing, might account for the major fraction of the EP in the NSCS. Accurate products of ocean net primary production and empirical coefficients in the food web (e.g., export efficiency of zooplankton grazing and specific mortality rate of nongrazing phytoplankton) suitable for the NSCS, are essential for performance improvement of the satellite‐based EP model.

The bottom depth of coastal benthic habitats plays a vital role in the coastal ecological environment and navigation. In optically shallow waters (OSWs), seafloor reflectance has an impact on the remotely sensed data, and thus, water depth can be retrieved from the remote sensing reflectance ($R_{rs}\left(\lambda \right)$) values provided by satellite imagery. Empirical methods for depth estimation are mainly limited by field measurements coverage. In addition, owing to the diverse range of water bio-optical properties in coastal regions, the high-precision models that could be applied to all OSWs are insufficient. In this study, we developed a novel bottom-depth retrieval method based on Hydrolight simulated datasets, in which $R_{rs}\left(\lambda \right)$ were generated from radiative transfer theory instead of actual satellite data. Additionally, this method takes into consideration the variable conditions of water depth, chlorophyll concentrations, and bottom reflectance. The bottom depth can be derived from $R_{rs}\left(\lambda \right)$ using a data-driven machine learning method based on the random forest (RF) model. The determination coefficient (R²) was greater than 0.98, and the root mean squared error ($RMSE$) was less than 0.4 m for the training and validation datasets. This model shows promise for use in different coastal regions while also broadening the applications that utilize satellite data. Specifically, we derived the bottom depth in three areas in the South China Sea, i.e., the coastal regions of Wenchang city, Xincun Bay, and Huaguang Reef, based on Sentinel-2 imagery. The derived depths were validated by the bathymetric data acquired by spaceborne photon-counting lidar ICESat-2, which was able to penetrate clean shallow waters for sufficient bottom detection. The predicted bottom depth showed good agreement with the true depth, and large-scale mapping compensated for the limitations resulting from along-track ICESat-2 data. Under a variety of circumstances, this general-purpose depth retrieval model can be effectively applied to high spatial resolution imagery (such as that from Sentinel-2) for bottom depth mapping in optically shallow waters.

Based on characteristics analysis about remote sensing reflectance, the Secchi Disk Depth (SDD) in the Qiandao Lake was predicted from the Landsat8/OLI data, and its changing rates on a pixel-by-pixel scale were obtained from satellite remote sensing for the first time. Using 114 matchups data pairs during 2013–2019, the SDD satellite algorithms suitable for the Qiandao Lake were obtained through both the linear regression and machine learning (Support Vector Machine) methods, with remote sensing reflectance (Rrs) at different OLI bands and the ratio of Rrs (Band3) to Rrs (Band2) as model input parameters. Compared with field observations, the mean absolute relative difference and root mean squared error of satellite-derived SDD were within 20% and 1.3 m, respectively. Satellite-derived results revealed that SDD in the Qiandao Lake was high in boreal spring and winter, and reached the lowest in boreal summer, with the annual mean value of about 5 m. Spatially, high SDD was mainly concentrated in the southeast lake area (up to 13 m) close to the dam. The edge and runoff area of the lake were less transparent, with an SDD of less than 4 m. In the past decade (2013–2020), 5.32% of Qiandao Lake witnessed significant (p < 0.05) transparency change: 4.42% raised with a rate of about 0.11 m/year and 0.9% varied with a rate of about −0.09 m/year. Besides, the findings presented here suggested that heavy rainfall would have a continuous impact on the Qiandao Lake SDD. Our research could promote the applications of land observation satellites (such as the Landsat series) in water environment monitoring in inland reservoirs.

Small island countries in the South Pacific are ecologically fragile areas, vulnerable to climate change, and the long-term ecological changes in the sea and land have an important impact on their sustainable development. This study takes Fiji, a typical small island country in the South Pacific, as an example, to analyze the change and connection of marine and terrestrial ecosystem environments based on 30 years of multi-source, satellite, remote-sensing data. From 1991 to 2019, according to the change in forest area in Fiji, three stages were delineated: first was a period of stability, then a decrease, and then a recovery in recent years. From 1991 to 2002, Fiji’s vegetation accounted for 73% of the total area; sea environment surrounding the islands, such as sea level height and sea surface temperature, were relatively low, with high water transparency. From 2002 to 2014, with the development of forestry and tourism, vegetation decreased by 6.89% and bare land increased, which changes the runoff erosion in the drainage basin; correspondingly, the chlorophyll a concentration in three major estuaries was found to be slightly increased with low water transparency. Meanwhile, coupled with the rising sea temperature, the area of Fiji’s coral reefs shrank significantly, with 51.13% of the total loss of coral reefs occurring in the Vanua Levu, where bare land and runoff were more distributed in its drainage basin. From 2014 to 2019, Fiji’s vegetation and coral reef areas recovered from the former stage; affected by short-term climate oscillations such as El Niño-Southern Oscillation (ENSO), the sea surface temperature showed a significant abnormal drop and the water transparency decreased. In the past 30 years (1993–2018), the sea level rise rate around Fiji reached 4 mm/year, and the temperature increased by 0.3 °C, which threatens the coastal ecosystem environment, including coral reefs and mangrove; inappropriate land-use change would worsen the situation in these ecologically fragile areas.