Search Results (88)

Using MODIS-Aqua satellite observations, this study analyzes the spatiotemporal distribution characteristics of particulate organic carbon (POC) in China’s marginal seas from 2003 to 2024. The statistical relationships between various marine environmental variables, including sea surface temperature (SST), nutrients, and primary production (PP), and POC concentrations are explored using partial least squares path modeling (PLS-PM). Finally, a box model approach is conducted to assess the POC budget in the study area. The results indicate that the POC concentration in the marginal seas of China generally exhibits a characteristic of being high in spring and low in summer. The highest concentration of POC is observed in the Bohai Sea, followed by the Yellow Sea, and the lowest in the East China Sea, with coastal waters exhibiting higher POC concentrations compared to the central areas. The spatial distribution and seasonal changes in POC are jointly influenced by PP, water mass exchange, resuspended sediments, and terrestrial inputs. Large-scale climate modes show statistical associations with POC concentration in the open waters of China’s marginal seas. PP and respiratory consumption are identified as the predominant input and output fluxes, respectively, in China’s marginal seas. This study enriches the understanding of carbon cycling processes and carbon sink mechanisms in marginal seas. Full article

Coastal upwelling off La Guajira, Colombia, is an atypical system where persistent easterly winds drive upwelling along a zonally oriented coastline. To characterize its seasonal cycle and variability, the ROMS AGRIF hydrodynamic model was implemented under climatological forcing. Three indicators were analyzed: the 25 °C isotherm, the 36.5 isohaline, and sea-level anomalies. The simulations showed that upwelling initiates in December, reaches maximum intensity during February–April, and weakens from September to November. At maturity, vertical velocities up to 8.5 m·day⁻¹ and the shoaling of Subtropical Underwater (T = 22–25 °C; S = 36.5–37.0) dominate the coastal domain, producing widespread surface cooling (23–24 °C) and salinity enhancement. During relaxation, weaker winds and the influence of the Caribbean Coastal Undercurrent displace the upwelled waters to below 80–100 m in depth, with surface temperatures above 27 °C. Model performance against MODIS Aqua SST was high (d > 0.99; RMSE < 1.7 °C), confirming its reliability to reproduce the observed thermal cycle. The multiparametric approach reveals that upwelling persistence depends on both seasonal trade wind forcing and regional circulation. This framework provides a more integrated description of the Guajira upwelling system than previous studies and supports applications in fisheries management, ecosystem monitoring, and maritime operations. Full article

China’s coastal fisheries face challenges to their sustainability due to climate and human-induced pressures on key habitat drivers. This study provides an 18-year (2003–2020) assessment of six key ecological and data-available environmental factors (sea-surface temperature (SST), salinity, transparency, currents (eastward velocity, EV; northward velocity, NV), and net primary productivity (NPP), selected for their ecological relevance and data availability, across the Bohai, Yellow, and East China Seas at a spatial resolution of 0.083°. Non-parametric trend tests and seasonal climatologies were applied using MODIS-Aqua and CMEMS data with a refined quasi-analytical algorithm (QAA-v6). The results show distinct gradients: SST ranging from 9 to 13 °C (Bohai Sea) to >20 °C (East China Sea); transparency ranging from <5 m (turbid coasts) to 29.20 m (offshore). Seasonal peaks occurred for SST (summer: 18.92 °C), transparency (summer: 12.54 m), and primary productivity (spring: 1289 mg/m²). Long-term trends reveal regional SST warming in the northern Yellow Sea (9.78% of the area), but cooling in the central East China Sea. Widespread increases in transparency were observed (65.14% of the area), though productivity declined significantly (27.3%). The drivers showed spatial coupling (e.g., SST–salinity r = 0.95), but the long-term trends were decoupled. This study provides a comprehensive and long-term assessment of multiple key habitat drivers across China’s coastal seas. The results provide an unprecedented empirical baseline and dynamic management tools for China’s changing coastal ecosystems. Full article

Ocean ecosystem services provisioning is driven by phytoplankton, which form the base of the ocean food chain in aquatic ecosystems and play a critical role as the Earth‘s carbon sink. Phytoplankton is highly sensitive to temperature, making it vulnerable to the effects of temperature variations. The aim of this research was to develop and test a workflow analysis to monitor the impact of sea surface temperature (SST) on phytoplankton biomass and primary production by combining field and remote sensing data of Chl-a and net primary production (NPP) (as proxies of phytoplankton biomass). The tropical zone was used as a case study to test the procedure. Firstly, machine learning algorithms were applied to the field data of SST, Chl-a and NPP, showing that the Random Forest was the most effective in capturing the dataset’s patterns. Secondly, the Random Forest algorithm was applied to MODIS SST images to build Chl-a and NPP time series. The time series analysis showed a significant increase in SST which corresponded to a significant negative trend in Chl-a concentrations and NPP variation. The recurrence plot of the time series revealed significant disruptions in Chl-a and NPP evolutions, potentially linked to El Niño–Southern Oscillation (ENSO) events. Therefore, the analysis can help to highlight the effects of temperature variation on Chl-a and NPP, such as the long-term evolution of the trend and short perturbation events. The methodology, starting from local studies, can support broader spatial–temporal-scale studies and provide insights into future scenarios. Full article

Sea surface temperature (SST) is an important physical parameter that plays an important role in the study of various dynamic and thermodynamic processes in the ocean. Common SST retrieval methods are divided into single-channel methods (such as the single window algorithm) and multi-channel methods (such as the split window algorithm). To solve the problem of the low resolution of SST data used in coastal research, this study proposed a split window algorithm by adjusting the two important parameters, atmospheric transmittance and regression coefficients, to estimate SST using remotely sensed GF-5A images with a resolution of 100 m. The results were indirectly validated using MODIS temperature product and directly validated using measured data. The GF-5A image data obtained on 18 July 2024 were compared with MODIS data, giving R² of 0.985 and RMSE of 0.139 K. For the GF-5A image data obtained on 31 December 2024, the indirectly verified R² was 0.996 and the RMSE was 0.116 K. The R² and RMSE values of the direct verification of the accuracy of data from the two GF-5A images and the measured data were 0.999 and 0.613 K, respectively, which are better than the SST retrieval results of Landsat 8 data obtained at the same resolution. This work provides data support for subsequent research on the ecological environment and plant resources in the Bohai Bay. Full article

Particulate organic carbon (POC) plays a crucial role in oceanic climate change. However, existing research is limited by several factors, including the scarcity of long-term data, extensive datasets, and a comprehensive understanding of POC dynamics. This study utilizes monthly average POC remote sensing data from the MODIS/AQUA satellite to analyze the spatiotemporal variations of POC in the East China Sea from 2003 to 2022. Employing correlation analysis, spatial autocorrelation models, and the Geodetector model, we explore responses to key influencing factors such as climatic elements. The results indicate that POC concentrations are higher in the western nearshore areas and lower in the eastern offshore regions of the East China Sea (ECS). Additionally, concentrations are observed to be lower in southern regions compared to northern ones. From 2003 to 2022, POC concentrations exhibited a fluctuating downward trend with an average annual concentration of 121.05 ± 4.57 mg/m³. Seasonally, monthly average POC concentrations ranged from 105.48 mg/m³ to 158.36 mg/m³; notably higher concentrations were recorded during spring while summer showed comparatively lower levels. Specifically, POC concentrations peaked in April before rapidly declining from May to June—reaching a minimum—and then gradually increasing again from June through December. Correlation analysis revealed significant influences on POC levels by particulate inorganic carbon (PIC), sea surface temperature (SST), chlorophyll (Chl), and photosynthetically active radiation (PAR). The Geodetector model further elucidated that these factors vary in their impact: Chl was identified as having the strongest influence (q = 0.84), followed by PIC (q = 0.75) and SST (q = 0.64) as primary influencing factors; PAR was recognized as a secondary factor with q = 0.30. This study provides new insights into marine carbon cycling dynamics within the context of climate change. Full article

The sea surface temperature (SST) is an important parameter reflecting the energy exchange between the ocean and the atmosphere, which has a key impact on climate change, marine ecology and fisheries. However, most of the existing SST fusion methods suffer from poor portability and a lack of consideration of cloudy conditions, which can affect the data accuracy and reliability. To address these problems, this paper proposes an infrared and microwave SST fusion method based on cloudy conditions. The method categorizes the fusion process according to three scenarios—clear sky, completely cloudy, and partially cloudy—adjusting the fusion approach for each condition. In this paper, three representative global datasets from home and abroad are selected, while the South China Sea region, which suffers from extreme weather, is used as a typical study area for validation. By introducing the buoy observation data, the fusion results are evaluated using the metrics of bias, RMSE, URMSE, r and coverage. The experimental results show that the biases of the three fusion results of VIRR-RH, AVHRR-RH and MODIS-RH are −0.611 °C, 0.043 °C and 0.012 °C, respectively. In the South China Sea region under extreme weather conditions, the bias is −0.428 °C, the RMSE is 0.941 °C, the URMSE is 0.424 °C and the coverage rate reaches 25.55%. These results confirm that this method not only produces significant fusion effects but also exhibits strong generalization and adaptability, being unaffected by specific sensors or regions. Full article

This study examined the impact of sea surface temperature (SST) on urban temperature across four cities located in three different countries (United States of America, Japan, and Morocco), all at nearly the same latitude, focusing on the summer season over the period from 2003 to 2020, because previously no one attempted to analyze the impact of SST on land surface temperature (LST). Data were acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) for LST and SST to evaluate the correlation between urban temperature and SST, the trends over time, and the relationship between urban areas and LST. The novelty of this study lies in its being the first to investigate the impact of SST on urban temperature based on a city’s proximity to warm and cold ocean currents. The findings revealed a positive correlation between LST and SST across all cities analyzed in this study (San Francisco, Tangier, Tokyo, and Atlantic City), and in some instances a significant positive relationship was observed at a 95% confidence level, but still the significance is in the range of weak to moderate. Specifically, the study found that during both daytime and nighttime, Tangier exhibited a decreasing trend in LST (99% confidence level) and SST. On the contrary, San Francisco displayed an increasing trend in both LST and SST during the daytime, but at nighttime, while SST continued to rise, LST showed a decreasing trend. Further analysis differentiated cities influenced by warm ocean currents (Tokyo and Atlantic City) from those affected by cold currents (San Francisco and Tangier). In Tokyo, influenced by a warm ocean current, there was a decreasing trend in LST despite increased SST. Conversely, Atlantic City, also influenced by warm ocean currents, showed an increasing trend in both LST and SST during the daytime. At nighttime, both Tokyo and Atlantic City exhibited increasing trends in LST and SST. Additionally, this study explored the correlation between urban areas and LST, finding that cities influenced by warm ocean currents (Tokyo and Atlantic City) showed a positive correlation between urban areas and LST. In contrast, cities influenced by cold ocean currents (San Francisco and Tangier) displayed a negative correlation between urban areas and LST. Overall, this research highlights the complex interplay between SST and urban temperatures, demonstrating how ocean currents and urbanization can influence temperature trends differently in cities at similar latitudes. Full article

Understanding and monitoring nearshore environments is essential, given that these fine-scaled ecosystems are integral to human well-being. While satellites offer an opportunity to gain synchronous and spatially extensive data of coastal areas, off-the-shelf calibrated satellite sea surface temperature (SST) measurements have only been available at coarse resolutions of 1 km or larger. In this study, we develop a novel methodology to create a simple linear equation to calibrate fine-scale Landsat thermal infrared radiation brightness temperatures (calibrated for land sensing) to derive SST at a resolution of 100 m. The constants of this equation are derived from correlations of coincident MODIS SST and Landsat data, which we filter to find optimal pairs. Validation against in situ sensor data at varying distances from the shore in Northern California shows that our SST estimates are more accurate than prior off-the-shelf Landsat data calibrated for land surfaces. These fine-scale SST estimates also demonstrate superior accuracy compared with coincident MODIS SST estimates. The root mean square error for our minimally filtered dataset (n = 557 images) ranges from 0.76 to 1.20 °C with correlation coefficients from r = 0.73 to 0.92, and for our optimal dataset (n = 229 images), the error is from 0.62 to 0.98 °C with correlations from r = 0.83 to 0.92. Potential error sources related to stratification and seasonality are examined and we conclude that Landsat data represent skin temperatures with an error between 0.62 and 0.73 °C. We discuss the utility of our methodology for enhancing coastal monitoring efforts and capturing previously unseen spatial complexity. Testing the calibration methodology on Landsat images before and after the temporal bounds of accurate MODIS SST measurements shows successful calibration with lower errors than the off-the-shelf, land-calibrated Landsat product, extending the applicability of our approach. This new approach for obtaining high-resolution SST data in nearshore waters may be applied to other upwelling regions globally, contributing to improved coastal monitoring, management, and research. Full article

Since 2007, Ulva prolifera (U. prolifera) originating in northern Jiangsu (NJ) has consistently expanded to the southern coast of the Shandong Peninsula. However, the underlying reasons for the 2007 sudden bloom of U. prolifera on a large scale remain unknown. This study uses remote sensing data from MODIS/AQUA spanning the period 2003–2022 to investigate the sea surface temperature (SST) structure changes in the southern Yellow Sea (SYS) over the past 20 years. The results demonstrate the following. (1) Since 2007, the NJ northward current and the Yangtze estuary warm current have exhibited higher temperatures, earlier northward intrusions, and larger influence areas, leading to a faster warming rate in NJ before mid-May. This rapid increase in SST to a level suitable for early U. prolifera growth triggers large-scale blooms. (2) The change in temperature structure is primarily induced by a prolonged and intense La Niña event in 2007–2008. However, since 2016, under stable global climate conditions, the temperature structure of the SYS has returned to the pre-2007 state, corresponding to a decrease in the scale of U. prolifera blooms. Full article

This study scrutinizes interannual (2003–2023) variations in coastal upwelling along the Guangdong Province during summers (June–August) in the northeastern South China Sea (NESCS) by comprehensively applying the moderate-resolution imaging spectroradiometer (MODIS) remote sensing sea surface temperature (SST) and chlorophyll concentration (CHL) data and the model reanalysis product. The results show that SST and upwelling intensity in the sea area have significant (p < 0.05) rising trends in the last 21 years. The CHL shows an upward but insignificant trend, which is affected simultaneously by the rise in SST and the enhancement of upwelling. Further analysis reveals that the interannual variations in upwelling are robustly related to the wind fields’ variations in the coastal region. A clockwise/counter-clockwise anomaly in the wind field centered on the NESCS facilitates alongshore/onshore winds near the Guangdong coast, which can strengthen/weaken coastal upwelling. Based on the correlation between wind field variations and large-scale climate factors, long-term variations in the upwelling intensity can be primarily predicted by the Oceanic Niño Index. Full article

We introduce a novel method that combines satellite data, advanced clustering techniques, machine learning feature extraction, and statistical models to enhance fishery forecasting accuracy. Focusing on jumbo flying squid in the southeast Pacific Ocean near Peru, we utilize MODIS-Aqua and MODIS-Terra satellite data on sea surface temperature (SST) to construct a deep convolutional embedded clustering (DCEC) model and extract the monthly SST features ($F_M)$ based on an optimized number of clusters determined by the Davies–Bouldi index (DBI). We use the extracted $F_M$ to construct a series of Generalized Additive Models (GAM) to forecast the catch per unit effort (CPUE) of jumbo flying squid within a spatial resolution of 0.5° × 0.5°. Our results demonstrate the following findings: (1) The SST feature clusters obtained through the DCEC model could capture the SST monthly variations; (2) The GAM models with $F_M$ outperform the models with the traditional monthly average SST in terms of predictive accuracy; (3) Using both $F_M$ and average SST together can further improve model performance. This study demonstrates the effectiveness of the DCEC combined with DBI in extracting marine environmental features and highlights the ocean environment feature extraction method to enhance the precision and reliability of fishery forecasting models. Full article

The Arctic plays a crucial role in the intricate workings of the global climate system. With the rapid development of information technology, satellite remote sensing technology has emerged as the main method for sea ice surface temperature (IST) observation. To obtain Arctic IST, we used the FengYun-3D Microwave Radiation Imager (FY-3D/MWRI) brightness temperature (T_b) data for IST inversion using multiple linear regressions. Measured data on IST parameters in the Arctic are difficult to obtain. We used the Moderate-Resolution Imaging Spectroradiometer (MODIS) MYD29 IST data as the baseline to obtain the coefficients for the MWRI IST inversion function. The relation between MWRI T_b data and MODIS MYD29 IST product was established and the microwave IST inversion equation was obtained for the months of January to December 2019. Based on the R² results and the IST inversion results, we compared and analyzed the MWRI IST data from the months of January to April, November, and December with the Operation IceBridge KT19 IR Surface Temperature data and the Northern High Latitude Level 3 Sea and Sea Ice Surface Temperature (NHL L3 SST/IST). We found that compared MWRI IST with NHL L3 IST, the correlation coefficients (Corr) > 0.72, mean bias ranged from −1.82 °C to −0.67 °C, and the standard deviation (Std) ranged from 3.61 °C to 4.54 °C; comparing MWRI IST with KT19 IST, the Corr was 0.69, the bias was 0.51 °C, and the Std was 4.34 °C. The obtained error conforms to the precision requirement. From these results, we conclude that the FY-3D/MWRI T_b data are suitable for IST retrieval in the Arctic using multiple linear regressions. Full article

The aim of this study was to develop methods for determining the most significant contrasts in satellite ocean color data arising in the presence of a submesoscale eddy structure, as well as to determine the corresponding depths of the upper layer of the sea where these contrasts are formed. The research was carried out on the example of the chain of submesoscale eddies identified in the Tumen River water transport area in the Japan/East Sea. MODIS Aqua/Terra satellite data of the remotely sensed reflectance (Rrs) and Rrs band ratio at various wavelengths, chlorophyll-a concentration, and, for comparison, sea surface temperature (sst) were analyzed. Additionally, the results of ship surveys in September 2009 were used to study the influence of eddy vertical structure on the obtained remote characteristics. The best characteristic for detecting the studied eddies in satellite ocean color data was the MODIS chlor_a standard product, which is an estimate of chlorophyll-a concentration obtained by a combination of the three-band reflectance difference algorithm (CI) for low concentrations and the band-ratio algorithm (OCx) for high concentrations. At the same time, the weakest contrasts were in sst data due to similar water heating inside and outside the eddies. The best eddy contrast-to-noise ratio according to Rrs spectra is achieved at 547 nm in the spectral region of seawater with maximum transparency and low relative errors of measurements. The Rrs at 678 nm and associated products may be a significant characteristic for eddy detection if there are many phytoplankton in the eddy waters. The maximum depth of the remotely sensed contrast formation of the considered eddy vertical structure was ~6 m, which was significantly less than the maximum spectral penetration depth of solar radiation for remote sensing, which was in the 14–17 m range. The results obtained can be used to determine the characteristics that provide the best contrast for detecting eddy structures in remotely sensed reflectance data and to improve the interpretation of remote spectral ocean color data in the areas of eddies activity. Full article

The first NOAA full-mission reanalysis (RAN1) of the sea surface temperature (SST) from the two Moderate Resolution Imaging Spectroradiometers (MODIS) onboard Terra (24 February 2000–present) and Aqua (4 July 2002–present) was performed. The dataset was produced using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) enterprise SST system from Collection 6.1 brightness temperatures (BTs) in three MODIS thermal emissive bands centered at 3.7, 11, and 12 µm with a spatial resolution of 1 km at nadir. In the initial stages of reprocessing, several instabilities in the MODIS SST time series were observed. In particular, Terra SSTs and corresponding BTs showed three ‘steps’: two on 30 October 2000 and 2 July 2001 (due to changes in the MODIS operating mode) and one on 25 April 2020 (due to a change in its nominal blackbody temperature, BBT, from 290 to 285 K). Additionally, spikes up to several tenths of a kelvin were observed during the quarterly warm-up/cool-down (WUCD) exercises, when the Terra MODIS BBT was varied. Systematic gradual drifts of ~0.025 K/decade were also seen in both Aqua and Terra SSTs over their full missions due to drifting BTs. These calibration instabilities were mitigated by debiasing MODIS BTs using the time series of observed minus modeled (‘O-M’) BTs. The RAN1 dataset was evaluated via comparisons with various in situ SSTs. The data meet the NOAA specifications for accuracy (±0.2 K) and precision (0.6 K), often by a wide margin, in a clear-sky ocean domain of 19–21%. The long-term SST drift is typically less than 0.01 K/decade for all MODIS SSTs, except for the daytime ‘subskin’ SST, for which the drift is ~0.02 K/decade. The MODIS RAN1 dataset is archived at NOAA CoastWatch and updated monthly in a delayed mode with a latency of two months. Additional archival with NASA JPL PO.DAAC is being discussed. Full article