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The rapid growth of phytoplankton and microalgae has presented considerable environmental and societal challenges to the sustainable development of human society. Given the inherent limitations of satellite-based algal bloom detection techniques that rely on chlorophyll and fluorescence methods, this study proposes a method that employs hyperspectral data to calculate water chromatic indices (WCIs), including hue, saturation (S), dominant wavelength (λd), and integrated apparent visual wavelength (IAVW), to identify algal blooms. A global in situ hyperspectral dataset was constructed, comprising 13,110 entries, of which 9595 were for normal waters and 3515 for algal bloom waters. The findings of our investigation indicate statistically significant discrepancies in chromaticity parameters between normal and algal bloom waters, with a p-value of 0.05. It has been demonstrated that different algal blooms exhibit distinct chromatic characteristics. For algae of the same type, the chromaticity parameters increase exponentially with chlorophyll concentration for hue and λd, while S shows low correlation and IAVW displays a good linear relationship with chlorophyll concentration. The application of this method to the Bohai Sea (coastal) and Taihu Lake (inland water) for the extraction of algal blooms revealed a clear separation in chromaticity parameters between normal and algal bloom waters. Moreover, the method can be applied to satellite data, offering an alternative approach for the detection of algal blooms based on satellite data. The indices can serve as ground truth values for colorimetric indices and provide a benchmark for the validation of satellite chromatic products.

Accurate cloud detection is a crucial initial stage in optical satellite remote sensing. In this study, a daytime cloud mask model is proposed for the Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun 4A (FY-4A) satellite based on a deep learning approach. The model, named “Convolutional and Attention-based Cloud Mask Net (CACM-Net)”, was trained using the 2021 dataset with CALIPSO data as the truth value. Two CACM-Net models were trained based on a satellite zenith angle (SZA) < 70° and >70°, respectively. The study evaluated the National Satellite Meteorological Center (NSMC) cloud mask product and compared it with the method established in this paper. The results indicate that CACM-Net outperforms the NSMC cloud mask product overall. Specifically, in the SZA < 70° subset, CACM-Net enhances accuracy, precision, and F1 score by 4.8%, 7.3%, and 3.6%, respectively, while reducing the false alarm rate (FAR) by approximately 7.3%. In the SZA > 70° section, improvements of 12.2%, 19.5%, and 8% in accuracy, precision, and F1 score, respectively, were observed, with a 19.5% reduction in FAR compared to NSMC. An independent validation dataset for January–June 2023 further validates the performance of CACM-Net. The results show improvements of 3.5%, 2.2%, and 2.8% in accuracy, precision, and F1 scores for SZA < 70° and 7.8%, 11.3%, and 4.8% for SZA > 70°, respectively, along with reductions in FAR. Cross-comparison with other satellite cloud mask products reveals high levels of agreement, with 88.6% and 86.3% matching results with the MODIS and Himawari-9 products, respectively. These results confirm the reliability of the CACM-Net cloud mask model, which can produce stable and high-quality FY-4A AGRI cloud mask results.

Upwelling, which mixes deep and surface waters, significantly enhances the productivity of surface waters and plays a critical role in marine ecosystems, especially in key fishing areas like Zhoushan. This study utilized merged sea surface temperature data and an upwelling edge detection algorithm based on temperature gradients to analyze the characteristics of upwelling in Zhoushan and the Yangtze River Estuary over the past 28 years. The results indicate that upwelling in Zhoushan begins in April, peaks in July, gradually weakens, and disappears by October. The phenomenon is most pronounced during the summer months (June to August), with significant spatial distribution differences in April and September. Notably, high probability values of upwelling centers and core areas are mainly concentrated near Ma’an Island, Zhongjieshan Island, and Taohua Island. In these areas, upwelling remains stable during the summer, forming a unique “footprint” distribution pattern, and these are also the locations of the Zhoushan National Marine Ranch. From April to August, the extent of the upwelling area gradually decreases and stabilizes. These findings emphasize the frequent upwelling activity around Zhoushan and its significant contribution to the formation of local fisheries. Additionally, considering that the formation of natural upwelling in the East China Sea depends on the southern monsoon, the study suggests establishing artificial upwelling systems during periods unfavorable for natural upwelling, based on high probability areas, to enhance fishery yields and support the development of local fisheries.

The traditional method for retrieving cloud optical thickness (COT) is carried out through a Look-Up Table (LUT). Researchers must make a series of idealized assumptions and conduct extensive observations and record features in this scenario, consuming considerable resources. The emergence of deep learning effectively addresses the shortcomings of the traditional approach. In this paper, we first propose a daytime (SOZA < 70°) COT retrieval algorithm based on FY-4A AGRI. We establish and train a Convolutional Neural Network (CNN) model for COT retrieval, CM4CR, with the CALIPSO’s COT product spatially and temporally synchronized as the ground truth. Then, a deep learning method extended from video prediction models is adopted to predict COT values based on the retrieval results obtained from CM4CR. The COT prediction model (CPM) consists of an encoder, a predictor, and a decoder. On this basis, we further incorporated a time embedding module to enhance the model’s ability to learn from irregular time intervals in the input COT sequence. During the training phase, we employed Charbonnier Loss and Edge Loss to enhance the model’s capability to represent COT details. Experiments indicate that our CM4CR outperforms existing COT retrieval methods, with predictions showing better performance across several metrics than other benchmark prediction models. Additionally, this paper also investigates the impact of different lengths of COT input sequences and the time intervals between adjacent frames of COT on prediction performance.

Ulva prolifera (U. prolifera) green tide outbreaks have occurred in the Yellow Sea of China for many years, causing serious losses to marine ecology and the marine economy. The monitoring and tracking of U. prolifera green tide is a crucial aspect of marine ecological disaster prevention and control management. This paper aims to investigate the spatiotemporal distribution changes in U. prolifera green tide in the Yellow Sea throughout its life cycle. A survey of the Yellow Sea from 2008 to 2023 was conducted using multi-source remote sensing images. Long-term monitoring and analysis of U. prolifera revealed the evolution process of the green tide, including the early development, outbreak, decline, and extinction stages, considering time, space, and frequency of occurrence. Additionally, this study examined peak coverage change patterns over the past 16 years and analyzed the influence of environmental factors such as sea surface temperature and sea surface wind field on the development of U. prolifera. The research results serve as a valuable reference for the monitoring, early warning, and scientific prevention and control of U. prolifera green tide in the Yellow Sea, as well as other similar marine disaster areas.

Predicting the mesoscale eddies in the ocean is crucial for advancing our understanding of the ocean and climate systems. Establishing spatio-temporal correlation among input data is a significant challenge in mesoscale eddy prediction tasks, especially for deep learning techniques. In this paper, we first present a deep learning solution based on a video prediction model to capture the spatio-temporal correlation and predict future sea surface height data accurately. To enhance the performance of the model, we introduced a novel metadata embedding module that utilizes neural networks to fuse remote sensing metadata with input data, resulting in increased accuracy. To the best of our knowledge, our model outperforms the state-of-the-art method for predicting sea level anomalies. Consequently, a mesoscale eddy detection algorithm will be applied to the predicted sea surface height data to generate mesoscale eddies in future. The proposed solution achieves competitive results, indicating that the prediction error for the eddy center position is 5.6 km for a 3-day prediction and 13.6 km for a 7-day prediction.

Remote sensing technology plays a crucial role in the rapid and wide-scale monitoring of water quality, which is of great significance for water pollution prevention and control. In this study, the downstream and nearshore areas of the Huaihe River Basin were selected as the study area. By utilizing spectral information from standard solution measurements in the laboratory and in situ water quality data matched with satellite spatiotemporal data, inversion models for total phosphorus (TP) and ammonia nitrogen (NH3-N) water quality parameters were developed. The validation results using field measurements demonstrated that the inversion models performed well, with coefficients of determination (R2) of 0.7302 and 0.8024 and root mean square errors of 0.02614 mg/L and 0.0368 mg/L for total phosphorus and ammonia nitrogen, respectively. By applying the models to Sentinel-2 satellite images from 2022, the temporal and spatial distribution characteristics of total phosphorus and ammonia nitrogen concentrations in the study area were obtained. The ammonia nitrogen concentration ranged from 0.05 to 0.30 mg/L, while the total phosphorus concentration ranged from 0.10 to 0.40 mg/L. Overall, the distribution appeared to be stable. The southern region of the Guan River estuary showed slightly higher water quality parameter concentrations compared to the northern region, while the North Jiangsu Irrigation Main Canal estuary was affected by the dilution of river water, resulting in lower concentrations in the estuarine area.

Total suspended solids (TSS) and chlorophyll-a (Chl-a) are critical water quality parameters. Focusing on the Pearl River Estuary and its coastal waters, this study compared the performance of XGBoost- and BPNN-based algorithms in estimating TSS and Chl-a levels. The XGBoost-based algorithm demonstrated better performance and was then used to estimate TSS and Chl-a in the Pearl River Estuary and coastal waters from 2000 to 2021. According to our results, TSS and Chl-a were relatively high mainly in the northwest and low in the southeast. Furthermore, values were high in spring and summer and low in fall and winter, with high values emerging near the estuary of the Pearl River. In summer, a band zone with high Chl-a was observed from south of Yamen to south of Hong Kong. In terms of trends, TSS and Chl-a concentrations in the area around the Hong Kong–Zhuhai–Macao Bridge tended to decrease from 2000 to 2021. As the construction of the bridge began, changes in water flow caused by the bridge piers and artificial islands were influenced, the change in the rate of TSS in the west area of the bridge was greater than 0, and the TSS in the upstream area of the west side changed from decreasing to increasing trends. Concerning Chl-a concentrations, the change in the rate in the downstream area of the west side of the bridge was greater than 0. The study may provide a helpful example for similar estuarine and coastal waters in other coastal areas.

Offshore wind farms (OWFs), built extensively in recent years, induce changes in the surrounding water environment. The changes in the suspended sediment concentration (SSC) and chlorophyll-a concentration (Chl-aC) induced by an OWF in the Yangtze River Estuary were analyzed based on Chinese Gaofen (GF) satellite data. The results show the following: (1) The flow near the wind turbines makes the bottom water surge, driving the sediment to “re-suspend” and be lost, deepening the scour pit around the bottom of the wind turbines, which is known as “self-digging”. The interaction between the pillar of a wind turbine and tidal currents makes hydrodynamic factors more complicated. Blocking by wind turbines promoting the scour of the bottom seabed of the OWF results in speeding up the circulation rate of sediment loss and “re-suspension”, which contributes to the change in the SSC and Chl-aC. This kind of change in sediment transport in estuarine areas due to human construction affects the balance of the ecological environment. Long-term sediment loss around wind turbines also influences the safety of wind turbines. (2) The SSC and Chl-aC are mainly in the range of 200–600 mg/L and 3–7 μg/L, respectively, in the OWF area, higher than the values obtained in surrounding waters. The SSC and Chl-aC downstream of the OWF are higher than those upstream, with differences of 100–300 mg/L and 0.5–2 μg/L. High SSC and Chl-aC “tails” appear downstream of wind turbines, consistent with the direction of local tidal currents, with lengths in the range of 2–4 km. In addition, the water environment in the vicinity of a wind turbine array, with a roughly 2–5 km scope (within 4 km during flooding and around 2.5 km during ebbing approximately) downstream of the wind turbine array, is impacted by the OWF. (3) In order to solve the problem of “self-digging” induced by OWFs, it is suggested that the distance between two wind turbines should be controlled within 2–3.5 km in the main flow direction, promising that the second row of wind turbines will be placed on the suspended sediment deposition belt induced by the first row. In this way, the problems of ecosystem imbalance and tidal current structure change caused by sediment loss because of local scouring can be reduced. Furthermore, mutual compensation between wind turbines can solve the “self-digging” problem to a certain extent and ensure the safety of OWFs.

Numerous studies (hereafter GA: general approach studies) have been made to classify aerosols into desert dust (DD), biomass-burning (BB), clean continental (CC), and clean maritime (CM) types using only aerosol optical depth (AOD) and Ångström exponent (AE). However, AOD represents the amount of aerosol suspended in the atmospheric column while the AE is a qualitative indicator of the size distribution of the aerosol estimated using AOD measurements at different wavelengths. Therefore, these two parameters do not provide sufficient information to unambiguously classify aerosols into these four types. Evaluation of the performance of GA classification applied to AErosol Robotic NETwork (AERONET) data, at sites for situations with known aerosol types, provides many examples where the GA method does not provide correct results. For example, a thin layer of haze was classified as BB and DD outside the crop burning and dusty seasons respectively, a thick layer of haze was classified as BB, and aerosols from known crop residue burning events were classified as DD, CC, and CM by the GA method. The results also show that the classification varies with the season, for example, the same range of AOD and AE were observed during a dust event in the spring (20th March 2012) and a smog event in the autumn (2nd November 2017). The results suggest that only AOD and AE cannot precisely classify the exact nature (i.e., DD, BB, CC, and CM) of aerosol types without incorporating more optical and physical properties. An alternative approach, AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA), is proposed to provide aerosol amount and size information using AOD and AE, respectively, from the Terra-MODIS (MODerate resolution Imaging Spectroradiometer) Collection 6.1 Level 2 combined Dark Target and Deep Blue (DTB) product and AERONET Version 3 Level 2.0 data. Although AEROSA is also based on AOD and AE, it does not claim the nature of aerosol types, instead providing information on aerosol amount and size. The purpose is to introduce AEROSA for those researchers who are interested in the generic classification of aerosols based on AOD and AE, without claiming the exact aerosol types such as DD, BB, CC, and CM. AEROSA not only provides 9 generic aerosol classes for all observations but can also accommodate variations in location and season, which GA aerosol types do not.

Polar-orbiting satellites have been widely used for detecting sea fog because of their wide coverage and high spatial and spectral resolution. FengYun-3D (FY-3D) is a Chinese satellite that provides global sea fog observation. From January 2021 to October 2022, the backscatter and virtual file manager products from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) were used to label samples of different atmospheric conditions in FY-3D images, including clear sky, sea fog, low stratus, fog below low stratus, mid–high-level clouds, and fog below the mid–high-level clouds. A 13-dimensional feature matrix was constructed after extracting and analyzing the spectral and texture features of these samples. In order to detect daytime sea fog using a 13-dimensional feature matrix and CALIPSO sample labels, four supervised classification models were developed, including Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Neural Network. The accuracy of each model was evaluated and compared using a 10-fold cross-validation procedure. The study found that the SVM, KNN, and Neural Network performed equally well in identifying low stratus, with 85% to 86% probability of detection (POD). As well as identifying the basic components of sea fog, the SVM model demonstrated the highest POD (93.8%), while the KNN had the lowest POD (92.4%). The study concludes that the SVM, KNN, and Neural Network can effectively distinguish sea fog from low stratus. The models, however, were less effective at detecting sub-cloud fog, with only 11.6% POD for fog below low stratus, and 57.4% POD for fog below mid–high-level clouds. In light of this, future research should focus on improving sub-cloud fog detection by considering cloud layers.

Accurate and rapid monitoring of green macroalgae blooms (GMB; also known as green tide) provide valuable information in decision making for mitigating its environmental impacts and economic losses. Plenty of spectral-based algorithms mainly rely on Rayleigh-corrected or atmospherically-corrected reflectance. However, this inherently requires additional information beyond satellite observations for atmospheric correction. This work developed a new method for detecting GMB just by using satellite top-of-atmosphere reflectance (Rtoa) without any atmospheric correction scheme based on Tasseled-cap-like transformation (TCT-like). Here we conducted a case study on Chinese four-band HJ-CCD sensor for illustrating the reliability and sensitivity of this method for detecting GMB of Ulva prolifera. The performance of the method was validated statistically via cross-sensor and cross-index comparisons with existing algorithms from atmospherically-corrected reflectance. Results showed that the Rtoa-based method can effectively detect GMB with high accuracy, and achieved high robustness against observing conditions such as various background water conditions, thin cloud cover and sun-glint. In addition to the findings presented here, this study may provide a promising way to detect massive macroalgae blooms of other species using optical satellite data.

A novel remote sensing algorithm was developed based on Landsat 8 Operational Land Imager (OLI) data to separately recognize concurrent Sargassum and Ulva prolifera in the South Yellow Sea. This algorithm has three main steps: 1) classification of macroalgae-containing pixels from normal seawater pixels by means of a mature floating algae index (FAI) approach; 2) first-round separate recognition of the Sargassum and Ulva prolifera targets using a newly developed “Sargassum and Ulva prolifera Index I (SUI-I)” method; and 3) further fine identification of the algae by applying another new index (SUI-II) to the above output. The validation of our developed algorithm generated high and satisfactory predictive accuracies. The present study concludes that the Landsat 8 OLI data have great potential for detecting and distinguishing the mixed growth of Sargassum and Ulva prolifera, which will help in closely monitoring macroalgae blooms in oceanic waters.

Shanghai has gained much attention in terms of air quality research owing to its importance to economic capital and its huge population. This study utilizes ground-based remote sensing instrument observations, namely by Multiple AXis Differential Optical Absorption Spectroscopy (MAX-DOAS), and in situ measurements from the national air quality monitoring platform for various atmospheric trace gases including Nitrogen dioxide (NO2), Sulfur dioxide (SO2), Ozone (O3), Formaldehyde (HCHO), and Particulate Matter (PM; PM10: diameter ≤ 10 µm, and PM2.5: diameter ≤ 2.5 µm) over Shanghai from June 2020 to May 2021. The results depict definite diurnal patterns and strong seasonality in HCHO, NO2, and SO2 concentrations with maximum concentrations during winter for NO2 and SO2 and in summer for HCHO. The impact of meteorology and biogenic emissions on pollutant concentrations was also studied. HCHO emissions are positively correlated with temperature, relative humidity, and the enhanced vegetation index (EVI), while both NO2 and SO2 depicted a negative correlation to all these parameters. The results from diurnal to seasonal cycles consistently suggest the mainly anthropogenic origin of NO2 and SO2, while the secondary formation from the photo-oxidation of volatile organic compounds (VOCs) and substantial contribution of biogenic emissions for HCHO. Further, the sensitivity of O3 formation to its precursor species (NOx and VOCs) was also determined by employing HCHO and NO2 as tracers. The sensitivity analysis depicted that O3 formation in Shanghai is predominantly VOC-limited except for summer, where a significant percentage of O3 formation lies in the transition regime. It is worth mentioning that seasonal variation of O3 is also categorized by maxima in summer. The interdependence of criteria pollutants (O3, SO2, NO2, and PM) was studied by employing the Pearson’s correlation coefficient, and the results suggested complex interdependence among the pollutant species in different seasons. Lastly, potential source contribution function (PSCF) analysis was performed to have an understanding of the contribution of different source areas towards atmospheric pollution. PSCF analysis indicated a strong contribution of local sources on Shanghai’s air quality compared to regional sources. This study will help policymakers and stakeholders understand the complex interactions among the atmospheric pollutants and provide a baseline for designing effective control strategies to combat air pollution in Shanghai.

As a second-order expression of the phytoplankton absorption coefficient (aph(λ)), the specific absorption coefficient of phytoplankton (aph*(λ)) is an essential optical parameter that refines the aph(λ) normalized by phytoplankton pigment concentration. Based on the in-situ samples collected in the Bohai Sea, Yellow Sea, and the East China Sea, this study presents a combined approach to determine the specific absorption coefficients of micro- (am*(λ)), nano- (an*(λ)), and picophytoplankton (ap*(λ)). Together with am*(λ) estimated by the two-component assumption model, our method effectively extracted an*(λ) and ap*(λ) through the least square method. Independent in-situ validation datasets tested the performances of the proposed am*(λ), an*(λ), and ap*(λ) by modeling aph(λ), and generated encouraging and acceptable predictive errors. The derived mean absolute percentage errors ranged from approximately 35%–55% for several typical wavebands (namely, 412, 443, 490, 555, 660, and 680 nm). Validation by using satellite-ground synchronization samples also produced comparative predictive errors. The spatial distribution of aph(λ) and the absorption of micro-, nano-, and picophytoplankton were mapped through applying the proposed specific absorption coefficients to Geostationary Ocean Color Imager (GOCI) images. This showed a spatial rule that pico- and nanophytoplankton dominate the total absorption, rather than microphytoplankton. The annual Moderate Resolution Imaging Spectroradiometer (MODIS) aph(443) product from 2002 to 2019 was used to assess the transferability of those specific absorption coefficients and showed a good performance. The accurate acquirement of aph(λ) can provide basical datasets for further environment research, such as estimating primary ocean productivity.

The SEMARA approach, an integration of the Simplified and Robust Surface Reflectance Estimation (SREM) and Simplified Aerosol Retrieval Algorithm (SARA) methods, was used to retrieve aerosol optical depth (AOD) at 550 nm from a Landsat 8 Operational Land Imager (OLI) at 30 m spatial resolution, a Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 m resolution, and a Visible Infrared Imaging Radiometer Suite (VIIRS) at 750 m resolution over bright urban surfaces in Beijing. The SEMARA approach coupled (1) the SREM method that is used to estimate the surface reflectance, which does not require information about water vapor, ozone, and aerosol, and (2) the SARA algorithm, which uses the surface reflectance estimated by SREM and AOD measurements obtained from the Aerosol Robotic NETwork (AERONET) site (or other high-quality AOD) as the input to estimate AOD without prior information on the aerosol optical and microphysical properties usually obtained from a look-up table constructed from long-term AERONET data. In the present study, AOD measurements were obtained from the Beijing AERONET site. The SEMARA AOD retrievals were validated against AOD measurements obtained from two other AERONET sites located at urban locations in Beijing, i.e., Beijing_RADI and Beijing_CAMS, over bright surfaces. The accuracy and uncertainties/errors in the AOD retrievals were assessed using Pearson’s correlation coefficient (r), root mean squared error (RMSE), relative mean bias (RMB), and expected error (EE = ± 0.05 ± 20%). EE is the envelope encompassing both absolute and relative errors and contains 68% (±1σ) of the good quality retrievals based on global validation. Here, the EE of the MODIS Dark Target algorithm at 3 km resolution is used to report the good quality SEMARA AOD retrievals. The validation results show that AOD from SEMARA correlates well with AERONET AOD measurements with high correlation coefficients (r) of 0.988, 0.980, and 0.981; small RMSE of 0.08, 0.09, and 0.08; and small RMB of 4.33%, 1.28%, and −0.54%. High percentages of retrievals, i.e., 85.71%, 91.53%, and 90.16%, were within the EE for Landsat 8 OLI, MODIS, and VIIRS, respectively. The results suggest that the SEMARA approach is capable of retrieving AOD over urban areas with high accuracy and small errors using high to medium spatial resolution satellite remote sensing data. This approach can be used for aerosol monitoring over bright urban surfaces such as in Beijing, which is frequently affected by severe dust storms and haze pollution, to evaluate their effects on public health.

Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are important atmospheric trace gases for determining air quality, human health, climate change, and ecological conditions both regionally and globally. In this study, the Ozone Monitoring Instrument (OMI), total column nitrogen dioxide (NO2), and sulfur dioxide (SO2) were used from 2005 to 2020 to identify pollution hotspots and potential source areas responsible for air pollution in Jiangsu Province. The study investigated the spatiotemporal distribution and variability of NO2 and SO2, the SO2/NO2 ratio, and their trends, and potential source contribution function (PSCF) analysis was performed to identify potential source areas. The spatial distributions showed higher values (>0.60 DU) of annual mean NO2 and SO2 for most cities of Jiangsu Province except for Yancheng City (2 and SO2 in winter, followed by spring, autumn, and summer. Coal-fire-based room heating and stable meteorological conditions during the cold season may cause higher NO2 and SO2 in winter. Notably, the occurrence frequency of NO2 and SO2 of >1.2 was highest in winter, which varied between 9.14~32.46% for NO2 and 7.84~21.67% for SO2, indicating a high level of pollution across Jiangsu Province. The high SO2/NO2 ratio (>0.60) indicated that industry is the dominant source, with significant annual and seasonal variations. Trends in NO2 and SO2 were calculated for 2005–2020, 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan (FYP)), 2011–2015 (during the 12th FYP), and 2013–2017 (the Action Plan of Air Pollution Prevention and Control (APPC-AC)). Annually, decreasing trends in NO2 were more prominent during the 12th FYP period (2011–2015: −0.024~−0.052 DU/year) than in the APPC-AC period (2013–2017: −0.007~−0.043 DU/year) and 2005–2020 (−0.002 to −0.012 DU/year). However, no prevention and control policies for NO2 were included during the 11th FYP period (2006–2010), resulting in an increasing trend in NO2 (0.015 to 0.031) observed throughout the study area. Furthermore, the implementation of China’s strict air pollution control policies caused a larger decrease in SO2 (per year) during the 12th FYP period (−0.002~−0.075 DU/year) than in the 11th FYP period (−0.014~−0.071 DU/year), the APPC-AC period (−0.007~−0.043 DU/year), and 2005–2020 (−0.015~−0.032 DU/year). PSCF analysis indicated that the air quality of Jiangsu Province is mainly influenced by local pollution sources.

Aerosol optical depth (AOD) is an important atmospheric parameter for climate change assessment, human health, and for total ecological situation studies both regionally and globally. This study used 21-year (2000–2020) high-resolution (1 km) Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm-based AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra and Aqua satellites. MAIAC AOD was evaluated against Aerosol Robotic Network (AERONET) data across three sites (Xuzhou-CUMT, NUIST, and Taihu) located in Jiangsu Province. The study also investigated the spatiotemporal distributions and variations in AOD, with associated trends, and measured the impact of meteorology on AOD in the 13 cities of Jiangsu Province. The evaluation results demonstrated a high correlation (r = 0.867~0.929) between MAIAC AOD and AERONET data, with lower root mean squared error (RMSE = 0.130~0.287) and mean absolute error (MAE = 0.091~0.198). In addition, the spatial distribution of AOD was higher (>0.60) in most cities except the southeast of Nantong City (AOD < 0.4). Seasonally, higher AOD was seen in summer (>0.70) than in spring, autumn, and winter, whereas monthly AOD peaked in June (>0.9) and had a minimum in December (2), and primary aerosols. China’s strict air pollution control policies and control of vehicular emissions helped to decrease AOD from 2010 to 2019, enhancing air quality throughout the study area. A notably similar pattern was observed for AOD and meteorological parameters (LST: land surface temperature, WV: water vapor, and P: precipitation), signifying that meteorology plays a role in terms of increasing and decreasing AOD.

Carbon dioxide (CO2) is the most important greenhouse gas and several satellites have been launched to monitor the atmospheric CO2 at regional and global scales. Evaluation of the measurements obtained from these satellites against accurate and precise instruments is crucial. In this work, aircraft measurements of CO2 were carried out over Qinhuangdao, China (39.9354°N, 119.6005°E), on 14, 16, and 19 March 2019 to validate the Greenhous gases Observing SATellite (GOSAT) and the Orbiting Carbon Observatory 2 (OCO-2) CO2 retrievals. The airborne in situ instruments were mounted on a research aircraft and the measurements were carried out between the altitudes of ~0.5 and 8.0 km to obtain the vertical profiles of CO2. The profiles captured a decrease in CO2 concentration from the surface to maximum altitude. Moreover, the vertical profiles from GEOS-Chem and the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker were also compared with in situ and satellite datasets. The satellite and the model datasets captured the vertical structure of CO2 when compared with in situ measurements, which showed good agreement among the datasets. The dry-air column-averaged CO2 mole fractions (XCO2) retrieved from OCO-2 and GOSAT showed biases of 1.33 ppm (0.32%) and −1.70 ppm (−0.41%), respectively, relative to the XCO2 derived from in situ measurements.

This study investigates spatiotemporal changes in air pollution (particulate as well as gases) during the COVID-19 lockdown period over major cities of Bangladesh. The study investigated the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites, PM2.5 and PM10 from Copernicus Atmosphere Monitoring Service (CAMS), and NO2 and O3 from TROPOMI-5P, from March to June 2019–2020. Additionally, aerosol subtypes from the Cloud-Aerosol Lidar and Infrared Pathfinder (CALIPSO) were used to explore the aerosol types. The strict lockdown (26 March–30 May 2020) led to a significant reduction in AOD (up to 47%) in all major cities, while the partial lockdown (June 2020) led to increased and decreased AOD over the study area. Significant reductions in PM2.5 (37–77%) and PM10 (33–70%) were also observed throughout the country during the strict lockdown and partial lockdown. The NO2 levels decreased by 3–25% in March 2020 in the cities of Rajshahi, Chattogram, Sylhet, Khulna, Barisal, and Mymensingh, in April by 3–43% in Dhaka, Chattogram, Khulna, Barisal, Bhola, and Mymensingh, and May by 12–42% in Rajshahi, Sylhet, Mymensingh, and Rangpur. During the partial lockdown in June, NO2 decreased (9–35%) in Dhaka, Chattogram, Sylhet, Khulna, Barisal, and Rangpur compared to 2019. On the other hand, increases were observed in ozone (O3) levels, with an average increase of 3–12% throughout the country during the strict lockdown and only a slight reduction of 1–3% in O3 during the partial lockdown. In terms of aerosol types, CALIPSO observed high levels of polluted dust followed by dust, smoke, polluted continental, and clean marine-type aerosols over the country in 2019, but all types were decreased during the lockdown. The study concludes that the strict lockdown measures were able to significantly improve air quality conditions over Bangladesh due to the shutdown of industries, vehicles, and movement of people.

Knowledge of the distribution and variation of water turbidity directly represent important information related to the marine ecology and multiple biogeochemical processes, including sediment transport and resuspension and heat transfer in the upper water layer. In this study, a neural network (NN) approach was applied to derive the water turbidity using the geostationary ocean color imager (GOCI) data in turbid estuaries of the Yellow River and the Yangtze River. The results showed a good agreement between the GOCI-derived turbidity and in situ measured data with a determination coefficient (R2) of 0.84, root mean squared error (RMSE) of 58.8 nephelometric turbidity unit (NTU), mean absolute error of 25.1 NTU, and mean relative error of 34.4%, showing a better performance than existing empirical algorithms. The hourly spatial distributions of water turbidity in April 2018 suggested that high turbidity regions were distributed in the Yellow River estuary, Yangtze River estuary, Hangzhou Bay, and coastal waters of Zhejiang Province. Furthermore, the relationship between water turbidity and tide were estimated. A defined turbid zone was defined to evaluate the diurnal variations of turbidity, which has subtle changes at different times. Our results showed an inverse relationship between turbidity and tide over six selected stations, i.e., when the value of turbidity is high, then the corresponding tidal height is usually low, and vice versa. The combined effects of tidal height and tidal currents could explain the phenomena, and other factors such as winds also contribute to the turbidity distributions.

Accurate knowledge of the carbon budget on global and regional scales is critically important to design mitigation strategies aimed at stabilizing the atmospheric carbon dioxide (CO2) emissions. For a better understanding of CO2 variation trends over Asia, in this study, the column-averaged CO2 dry air mole fraction (XCO2) derived from the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker (CT) was compared with that of Greenhouse Gases Observing Satellite (GOSAT) from September 2009 to August 2019 and with Orbiting Carbon Observatory 2 (OCO-2) from September 2014 until August 2019. Moreover, monthly averaged time-series and seasonal climatology comparisons were also performed separately over the five regions of Asia; i.e., Central Asia, East Asia, South Asia, Southeast Asia, and Western Asia. The results show that XCO2 from GOSAT is higher than the XCO2 simulated by CT by an amount of 0.61 ppm, whereas, OCO-2 XCO2 is lower than CT by 0.31 ppm on average, over Asia. The mean spatial correlations of 0.93 and 0.89 and average Root Mean Square Deviations (RMSDs) of 2.61 and 2.16 ppm were found between the CT and GOSAT, and CT and OCO-2, respectively, implying the existence of a good agreement between the CT and the other two satellites datasets. The spatial distribution of the datasets shows that the larger uncertainties exist over the southwest part of China. Over Asia, NOAA CT shows a good agreement with GOSAT and OCO-2 in terms of spatial distribution, monthly averaged time series, and seasonal climatology with small biases. These results suggest that CO2 can be used from either of the datasets to understand its role in the carbon budget, climate change, and air quality at regional to global scales.

Over time, the initial algorithms to derive atmospheric density from accelerometers have been significantly enhanced. In this study, we discussed one of the accurate accelerometers—the Earth’s Magnetic Field and Environment Explorers, more commonly known as the Swarm satellites. Swarm satellite–C level 2 (measurements from the Swam accelerometers) density, solar index (F10.7), and geomagnetic index (Kp) data have been used for a year (mid 2014–2015), and the different types of temporal (the diurnal, multi–day, solar–rotational, semi–annual, and annual) atmospheric density variations have been investigated using the statistical approaches of correlation coefficient and wavelet transform. The result shows the density varies due to the recurrent geomagnetic force at multi–day, solar irradiance during the day, appearance and disappearance of the Sun’s active region, Sun–Earth distance, large scale circulation, and the formation of an aurora. Additionally, a correlation coefficient was used to observe whether F10.7 or Kp contributes strongly or weakly to annual density, and the result found a strong (medium) correlation with F10.7 (Kp). Accurate density measurement can help to reduce the model’s bias correction, and monitoring the physical mechanisms for the density variations can lead to improvements in the atmospheric density models.

The vertically distributed aerosol optical properties are investigated over Pakistan utilizing the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Level 2 products from 2007 to 2014. For a better understanding of the spatiotemporal characteristics of vertical aerosol layers, the interannual and seasonal variations of nine selected aerosol parameters such as the AOD of the lowest aerosol layer (AODL), the base height of the lowest aerosol layer (HL), the top height of the highest aerosol layer (HH), the volume depolarization ratio of the lowest aerosol layer (DRL), the color ratio of the lowest aerosol layer (CRL), total AOD of all the aerosol layers (AODT), the number of aerosol feature layers (N), the thickness of the lowest aerosol layer (TL), the AOD proportion for the lowest aerosol layer (PAODL) for both day and night times are analyzed. The results show AODT increased slightly from 2007 to 2014 over Pakistan, and relatively high AODT exists over the Punjab and Sindh (southern region), which might be owing to the high level of economic development, frequent dust storms, and profound agricultural activities (anthropogenic emissions). AODT increases from north to south. The reason may be that the southern region is rapidly urbanized and is near the desert. The northern region is dominated by agricultural land, and cities are usually semi-urbanized. The highest AODT appears in summer compared to the other seasons, and during daytime compared to nighttime. The HL and HH vary significantly, owing to the topography of Pakistan. The N is relatively large over Punjab and Sindh compared to the other regions, which might be caused by relatively stronger atmospheric convections. The spatial distribution of the TL showed an inverse relationship with the topography as lower values are observed over elevated regions such as Gilgit-Baltistan and Jammu-Kashmir. The value of the PAODL indicates that 77% of the total aerosols are mainly concentrated in the lowest layer of the atmosphere over Pakistan. The higher values of DRL and CRL indicate non-spherical and large particles over Balochistan and Sindh, which might be related to the proximity to the desert. This study provides very useful information about vertically distributed aerosol optical properties which could help researchers and policymakers to regulate and mitigate air pollution issues of Pakistan.

The unprecedented slowdown in China during the COVID-19 period of November 2019 to April 2020 should have reduced pollution in smog-laden cities. However, moderate resolution imaging spectrometer (MODIS) satellite retrievals of aerosol optical depth (AOD) show a marked increase in aerosols over the Beijing–Tianjin–Hebei (BHT) region and most of Northeast and Central China, compared with the previous winter. Fine particulate (PM2.5) data from ground monitoring stations show an increase of 19.5% in Beijing during January and February 2020, and no reduction for Tianjin. In March and April 2020, a different spatial pattern emerges, with very high AOD levels observed over 50% of the Chinese mainland, and including peripheral regions in the northwest and southwest. At the same time, ozone monitoring instrument (OMI) satellite-derived NO2 concentrations fell drastically across China. The increase in PM2.5 while NO2 decreased in BTH and across China is likely due to enhanced production of secondary particulates. These are formed when reductions in NOx result in increased ozone formation, thus increasing the oxidizing capacity of the atmosphere. Support for this explanation is provided by ground level air quality data showing increased volume of fine mode aerosols throughout February and March 2020, and increased levels of PM2.5, relative humidity (RH), and ozone during haze episodes in the COVID-19 lockdown period. Backward trajectories show the origin of air masses affecting industrial centers of North and East China to be local. Other contributors to increased atmospheric particulates may include inflated industrial production in peripheral regions to compensate loss in the main population and industrial centers, and low wind speeds. Satellite monitoring of the extraordinary atmospheric conditions resulting from the COVID-19 shutdown could enhance understanding of smog formation and attempts to control it.

The optical and physical characteristics of the aerosol vertical layers over Northeastern China (NEC) are investigated using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Level 2 layer products from 2007 to 2014. To better examine the spatial and temporal variations in the characteristics of aerosols over NEC, the region is divided into three parts (Heilongjiang province, Jilin province, and Liaoning province) to analyze the inter-annual and seasonal variations of nine selected aerosol parameters in each part during night and day times. The results reveal that the values of aerosol optical depth (AOD) increase year by year, over the whole NEC, being relatively high over the Liaoning (LN) province; this might be induced by higher levels of economic development and agricultural activity. The highest AOD values appear in summer, which is plausibly related to the temperate monsoon climate in NEC. Higher AOD values exist during the daytime than at night; this is intuitively the result of higher daytime anthropogenic activities. The base altitude of the lowest aerosol layer (BAL) and the top altitude of the highest aerosol layer (TAH) varied significantly due to the topography of NEC. The number of aerosol layers (N) is relatively large over LN, which might be caused by a relatively stronger atmospheric convection over this landscape. The thickness of the lowest aerosol layer (TLL) bore little relationship with the topography of NEC. The AOD proportion of the lowest aerosol layer (PAODL) is high (0.70 to 0.85 for the entire NEC), indicating that aerosols are mainly concentrated in the lowest layer of the atmosphere. The volume depolarization ratio of the lowest aerosol layer (VDRL) is large during spring and winter due to the presence of dust aerosols. The color ratio of the lowest aerosol layer (CRL) is large during the day due to relatively more human activities taking place than at night. Moreover, there is a significantly positive linear correlation between N and TAH, and a negative logarithm correlation between N and PAODL over NEC. The results of this study could provide researchers and the government departments with detailed and certain optical and physical information about aerosol layers over NEC, to help in the treatment of air pollution over NEC.

The emergence of hyperspectral optical satellite sensors for ocean observation provides potential for more detailed information from aquatic ecosystems. The German hyperspectral satellite mission EnMAP (enmap.org) currently in the production phase is supported by a project to explore the capability of using EnMAP data and other future hyperspectral data from space. One task is to identify phytoplankton taxonomic groups. To fulfill this objective, on the basis of laboratory-measured absorption coefficients of phytoplankton cultures (aph(λ)) and corresponding simulated remote sensing reflectance spectra (Rrs(λ)), we examined the performance of spectral fourth-derivative analysis and clustering techniques to differentiate six taxonomic groups. We compared different sources of input data, namely aph(λ), Rrs(λ), and the absorption of water compounds obtained from inversion of the Rrs(λ)) spectra using a quasi-analytical algorithm (QAA). Rrs(λ) was tested as it can be directly obtained from hyperspectral sensors. The last one was tested as expected influences of the spectral features of pure water absorption on Rrs(λ) could be avoided after subtracting it from the inverted total absorption. Results showed that derivative analysis of measured aph(λ) spectra performed best with only a few misclassified cultures. Based on Rrs(λ) spectra, the accuracy of this differentiation decreased but the performance was partly restored if wavelengths of strong water absorption were excluded and chlorophyll concentrations were higher than 1 mg∙m−3. When based on QAA-inverted absorption spectra, the differentiation was less precise due to loss of information at longer wavelengths. This analysis showed that, compared to inverted absorption spectra from restricted inversion models, hyperspectral Rrs(λ) is potentially suitable input data for the differentiation of phytoplankton taxonomic groups in prospective EnMAP applications, though still a challenge at low algal concentrations.

In urban areas, fine particulate matter (PM2.5) associated with local vehicle emissions can cause respiratory and cardiorespiratory disease and increased mortality rates, but less so in rural areas. However, Hong Kong may be a special case, since the whole territory often suffers from regional haze from nearby mainland China, as well as local sources. Therefore, to understand which areas of Hong Kong may be affected by damaging levels of fine particulates, PM2.5 data were obtained from March 2005 to February 2009 for urban, suburban, and rural air quality monitoring stations; namely Central (city area, commercial area, and urban populated area), Tsuen Wan (city area, commercial area, urban populated, and residential area), Tung Chung (suburban and residential area), Yuen Long (urban and residential area), and Tap Mun (remote rural area). To evaluate the relative contributions of regional and local pollution sources, the study aimed to test the influence of weather conditions on PM2.5 concentrations. Thus, meteorological parameters including temperature, relative humidity, wind speed, and wind directions were obtained from the Hong Kong Observatory. The results showed that Hong Kong’s air quality is mainly affected by regional aerosol emissions, either transported from the land or ocean, as similar patterns of variations in PM2.5 concentrations were observed over urban, suburban, and rural areas of Hong Kong. Only slightly higher PM2.5 concentrations were observed over urban sites, such as Central, compared to suburban and rural sites, which could be attributed to local automobile emissions. Results showed that meteorological parameters have the potential to explain 80% of the variability in daily mean PM2.5 concentrations—at Yuen Long, 77% at Tung Chung, 72% at Central, 71% at Tsuen Wan, and 67% at Tap Mun, during the spring to summer part of the year. The results provide not only a better understanding of the impact of regional long-distance transport of air pollutants on Hong Kong’s air quality but also a reference for future regional-scale collaboration on air quality management.

Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysical parameters in multi-sensor remote sensing. Most state-of-the-art SR estimation methods, such as the vector version of the Second Simulation of the Satellite Signal in the Solar Spectrum (6SV) radiative transfer (RT) model, depend on accurate information on aerosol and atmospheric gases. In this study, a Simplified and Robust Surface Reflectance Estimation Method (SREM) based on the equations from 6SV RT model, without integrating information of aerosol particles and atmospheric gasses, is proposed and tested using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper plus (ETM+), and Landsat 8 Operational Land Imager (OLI) data from 2000 to 2018. For evaluation purposes, (i) the SREM SR retrievals are validated against in situ SR measurements collected by Analytical Spectral Devices (ASD) from the South Dakota State University (SDSU) site, USA; (ii) cross-comparison between the SREM and Landsat spectral SR products, i.e., Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) and Landsat 8 Surface Reflectance Code (LaSRC), are conducted over 11 urban (2013−2018), 13 vegetated (2013−2018), and 11 desert/arid (2000 to 2018) sites located over different climatic zones at a global scale; (iii) the performance of the SREM spectral SR retrievals for low to high aerosol loadings is evaluated; (iv) spatio-temporal cross-comparison is conducted for six Landsat paths/rows located in Asia, Africa, Europe, and the United States of America from 2013 to 2018 to consider a large variety of land surfaces and atmospheric conditions; (v) cross-comparison is also performed for the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Soil Adjusted Vegetation Index (SAVI) calculated from both the SREM and Landsat SR data; (vi) the SREM is also applied to the Sentinel-2A and Moderate Resolution Imaging Spectrometer (MODIS) data to explore its applicability; and (vii) errors in the SR retrievals are reported using the mean bias error (MBE), root mean squared deviation (RMSD), and mean systematic error (MSE). Results depict significant and strong positive Pearson’s correlation (r), small MBE, RMSD, and MSE for each spectral band against in situ ASD data and Landsat (LEDAPS and LaSRC) SR products. Consistency in SREM performance against Sentinel-2A (r = 0.994, MBE = −0.009, and RMSD = 0.014) and MODIS (r = 0.925, MBE = 0.007, and RMSD = 0.014) data suggests that SREM can be applied to other multispectral satellites data. Overall, the findings demonstrate the potential and promise of SREM for use over diverse surfaces and under varying atmospheric conditions using multi-sensor data on a global scale.

A cross-sectional area (CSA) is a key element in the optical properties of suspended particles. The seasonal evolution of CSA has great potential for use in mapping total suspended particles, and such mapping plays an important role in water quality monitoring. In this study, the spatiotemporal variations in CSA in the Bohai Sea and the Yellow Sea were studied using temperature, salinity and chlorophyll-a data collected by four cruises. The CSA field data covered a wide range of spatiotemporal variabilities in the Bohai Sea and the Yellow Sea. The results revealed that the largest CSA (>2 m−1) was found in the coastal area, while the CSA (≤1 m−1) on the outer shelf was much smaller. Large values of CSA (>15 m−1) were observed in winter, whereas the smallest values of CSA (0~2 m−1) were observed in summer. These results suggest that vertical mixing and ocean stratification might be important physical mechanisms that influences the CSA seasonal distribution in the surface layer. The results also showed that phytoplankton played an important role in the CSA, with an R2 value of 0.601. The seasonal patterns of CSA documented in this study provide a fundamental theory for research on optical properties, mapping transparency, and photosynthetically active radiation.

Knowledge about the spatiotemporal distribution of sea surface salinity (SSS) provides valuable and important information for understanding various marine biogeochemical processes and ecosystems, especially for those coastal waters significantly affected by human activities. Remote-sensing techniques have been used to monitor salinity in the open ocean with their advantages of wide-area surveys and real-time monitoring. However, potential challenges remain when using satellite data with coarse spatiotemporal resolutions, leading to a loss of valuable information. In the current study, based on the local dataset collected over the southern Yellow Sea (SYS), a region-customized algorithm was developed to estimate SSS by using the remote sensing reflectance. The model evaluations indicated that our algorithm yielded good SSS estimation, with a root-mean-square error (RMSE) of 0.29 psu and a mean absolute percentage error (MAPE) of 0.75%. Satellite-derived SSS results compared well with those derived from in situ observations, further suggesting the good performance of our developed algorithm for the study regions. We applied this algorithm to Geostationary Ocean Color Imager (GOCI) data for the month of August from 2011 to 2018 in the SYS, and produced the spatial distribution patterns of the SSS for August of each year. The SSS values were high in offshore waters and lower in coastal waters, especially in the Yangtze River estuary. The negative correlation between the monthly Changjiang River discharge (CRD) and SSS (R = −0.71, p < 0.001) near the Yangtze River estuary was observed, suggesting that the SSS distribution in the Yangtze River estuary was potentially influenced by the CRD. In offshore waters, the correlation between SSS and CRD was weak (R < 0.2), suggesting that the riverine discharge’s effect might be weak.

In this study, Terra-MODIS (Moderate Resolution Imaging Spectroradiometer) Collections 6 and 6.1 (C6 & C6.1) aerosol optical depth (AOD) retrievals with the recommended high-quality flag (QF = 3) were retrieved from Dark-Target (DT), Deep-Blue (DB) and merged DT and DB (DTB) level⁻2 AOD products for verification against Aerosol Robotic Network (AERONET) Version 3 Level 2.0 AOD data obtained from 2004⁻2014 for three sites located in the Beijing-Tianjin-Hebei (BTH) region. These are: Beijing, located over mixed bright urban surfaces, XiangHe located over suburban surfaces, and Xinglong located over hilly and vegetated surfaces. The AOD retrievals were also validated over different land-cover types defined by static monthly NDVI (Normalized Difference Vegetation Index) values obtained from the Terra-MODIS level-3 product (MOD13A3). These include non-vegetated surfaces (NVS, NDVI < 0.2), partially vegetated surfaces (PVS, 0.2 ≤ NDVI ≤ 0.3), moderately vegetated surfaces (MVS, 0.3 < NDVI < 0.5) and densely vegetated surfaces (DVS, NDVI ≥ 0.5). Results show that the DT, DB, and DTB-collocated retrievals achieve a high correlation coefficient of ~ 0.90⁻0.97, 0.89⁻0.95, and 0.86⁻0.95, respectively, with AERONET AOD. The DT C6 and C6.1 collocated retrievals were comparable at XiangHe and Xinglong, whereas at Beijing, the percentage of collocated retrievals within the expected error (↔EE) increased from 21.4% to 35.5%, the root mean square error (RMSE) decreased from 0.37 to 0.24, and the relative percent mean error (RPME) decreased from 49% to 27%. These results suggest significant relative improvement in the DT C6.1 product. The percentage of DB-collocated AOD retrievals ↔EE was greater than 70% at Beijing and Xinglong, whereas less than 66% was observed at XiangHe. Similar to DT AOD, DTB AOD retrievals performed well at XiangHe and Xinglong compared with Beijing. Regionally, DB C6 and C6.1-collocated retrievals performed better than DT and DTB in terms of good quality retrievals and relatively small errors. For diverse vegetated surfaces, DT-collocated retrievals reported small errors and good quality retrievals only for NVS and DVS, whereas larger errors were reported for PVS. MVS. DB contains good quality AOD retrievals over PVS, MVS, and DVS compared with NVS. DTB C6.1 collocated retrievals were better than C6 over NVS, PVS, and DVS. C6.1 is substantially improved overall, compared with C6 at local and regional scales, and over diverse vegetated surfaces.

In this study, MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) level-2 Dark Target (DT) Aerosol Optical Depth (AOD) products at 3 km (DT3K) and 10 km (DT10K) spatial resolutions were validated over the China seas and the eastern Indian Ocean against Maritime Aerosol Network (MAN) Level 1.5 AOD measurements collected through 13 cruises from 2010 to 2014. For this, DT3K and DT10K AOD observations were obtained from four Scientific Data Sets (SDS), i.e., “Effective_Optical_Depth_Average_Ocean” (EODAOAOD), “Effective_Optical_Depth_Best_Ocean” (EODBOAOD), “Image_Optical_Depth_Land_And_Ocean” (IODLAOAOD) and “Optical_Depth_Land_And_Ocean” (ODLAOAOD). The MAN AOD measurements were filtered within (i) ±2 h, (ii) ±4 h, (iii) ±6 h, and (iv) ±12 h of MODIS overpass time. Results showed that the DT10K and DT3K performed equally over the China seas and the eastern Indian Ocean in terms of retrievals quality and agreement with the MAN AOD measurements, whereas the DT3K has less coincident observations than the DT10K. For seasonal analysis, larger underestimation in the DT algorithm was observed in autumn followed by spring, whereas retrievals were well correlated with the MAN AOD data in summer. Overall, this study found that ODLAOAOD observations for the DT3K and DT10K were much better than EODAOAOD, EODBOAOD and IODLAOAOD in terms of high correlation and a large percentage of the AOD retrievals within the Expected Error (EE = +(0.04 + 10%), −(0.02 + 10%)).

In Moderate Resolution Imaging Spectroradiometer (MODIS) Collection (C6) aerosol products, the Dark Target (DT) and Deep Blue (DB) algorithms provide aerosol optical depth (AOD) observations at 3 km (DT3K) and 10 km (DT10K), and at 10 km resolution (DB10K), respectively. In this study, the DB10K is resampled to 3 km grid (DB3K) using the nearest neighbor interpolation technique and merged with DT3K to generate a new DT and DB merged aerosol product (DTB3K) on a 3 km grid using Simplified Merge Scheme (SMS). The goal is to supplement DB10K with high-resolution information over dense vegetation regions where DT3K is susceptible to error. SMS is defined as “an average of the DT3K and DB3K AOD retrievals or the available one with the highest quality flag”. The DT3K and DTB3K AOD retrievals are validated from 2008 to 2012 against cloud-screened and quality-assured AOD from 19 AERONET sites located in Europe. Results show that the percentage of DTB3K retrievals within the expected error (EE = ± (0.05 + 20%)) and data counts are increased by 40% and 11%, respectively, and the root mean square error and the mean bias are decreased by 26% and 54%, respectively, compared to the DT3K retrievals. These results suggest that the DTB3K product is a robust improvement over DT3K alone, and can be used operationally for air quality and climate-related studies as a high-resolution supplement to the current MODIS product suite.

In this study, the MODerate resolution Imaging Spectroradiometer (MODIS) Collections 6 and 6.1 merged Dark Target (DT) and Deep Blue (DB) aerosol products (DTBC6 and DTBC6.1) at 0.55 µm were validated from 2004–2014 against Aerosol Robotic Network (AERONET) Version 2 Level 2.0 AOD obtained from 68 global sites located over diverse vegetated surfaces. These surfaces were categorized by static values of monthly Normalized Difference Vegetation Index (NDVI) observations obtained for the same time period from the MODIS level-3 monthly NDVI product (MOD13A3), i.e., partially/non–vegetated (NDVIP ≤ 0.3), moderately–vegetated (0.3 < NDVIM ≤ 0.5) and densely–vegetated (NDVID > 0.5) surfaces. The DTBC6 and DTBC6.1 AOD products are accomplished by the NDVI criteria: (i) use the DT AOD retrievals for NDVI > 0.3, (ii) use the DB AOD retrievals for NDVI < 0.2, and (iii) use an average of the DT and DB AOD retrievals or the available one with highest quality assurance flag (DT: QAF = 3; DB: QAF ≥ 2) for 0.2 ≤ NDVI ≤ 0.3. For comparison purpose, the DTBSMS AOD retrievals were included which were accomplished using the Simplified Merge Scheme, i.e., use an average of the DTC6.1 and DBC6.1 AOD retrievals or the available one for all the NDVI values. For NDVIP surfaces, results showed that the DTBC6 and DTBC6.1 AOD retrievals performed poorly over North and South America in terms of the agreement with AERONET AOD, and over Asian region in terms of retrievals quality as the small percentage of AOD retrievals were within the expected error (EE = ± (0.05 + 0.15 × AOD). For NDVIM surfaces, retrieval errors and poor quality in DTBC6 and DTBC6.1 were observed for Asian, North American and South American sites, whereas good performance, was observed for European and African sites. For NDVID surfaces, DTBC6 does not perform well over the Asian and North American sites, although it contains retrievals only from the DT algorithm which was developed for dark surfaces. Overall, the performance of the DTBC6.1 AOD retrievals was significantly improved compared to the DTBC6, but still more improvements are required over NDVIP, NDVIM and NDVID surfaces of Asia, NDVIM and NDVID surfaces of North America, and NDVIM surfaces of South America. The performance of the DTBSMS retrievals was better than the DTBC6 and DTBC6.1 retrievals with 11–13% (31%) greater number of coincident observations, 6–9% (14–22%) greater percentage of retrievals within the EE, and 30–100% (46–100%) smaller relative mean bias compared to the DTBC6.1 (DTBC6) at a global scale.

Knowledge of the chlorophyll-a dynamics and their long-term changes is important for assessing marine ecosystems, especially for coastal waters. In this study, the spatial and temporal variability of sea surface chlorophyll-a concentration (Chl-a) in the Bohai Sea were investigated using 13-year (2000–2012) satellite-derived products from MODIS and SeaWiFS observations. Based on linear regression analysis, the results showed that the entire Bohai Sea experienced an increase in Chl-a on a long-term scale, with the largest increase in the central Bohai Sea and the smallest increase in the Bohai strait. Distinct seasonal patterns of Chl-a existed in different sub-regions of the Bohai Sea. A long-lasting Chl-a peak was observed from May to September in coastal waters (Liaodong bay, Qinhuangdao coast, and Bohai bay) and the central Bohai Sea, whereas Laizhou bay had relatively low Chl-a in early summer. In the Bohai strait, two pronounced Chl-a peaks occurred in March and September, but the lowest Chl-a was in summer. This pattern was quite different from those in other regions of the Bohai Sea. The water column condition (stratified or mixed) was likely an important physical factor that affects the seasonal pattern of Chl-a in the Bohai Sea. Meanwhile, increased human activity (e.g., river discharge) played a significant role in changing the Chl-a distribution in both coastal waters and the central Bohai Sea, especially in summer. The increasing trend of Chl-a in the Bohai Sea might be attributed to the increase in nutrient contents from riverine inputs. The Chl-a dynamics documented in this study provide basic knowledge for the future exploration of marine biogeochemical processes and ecosystem evolution in the Bohai Sea.

Particle size distribution (PSD) is an important parameter that is relevant to many aspects of marine ecosystems, such as phytoplankton functional types, optical absorption and scattering from particulates, sediment fluxes, and carbon export. However, only a handful of studies have documented the PSD variability in different regions. Here, we investigate the PSD properties and variability in two shallow and semi-enclosed seas (the Bohai Sea (BS) and Yellow Sea (YS)), using in situ laser diffraction measurements (LISST-100X Type C) and other measurements at 79 stations in November 2013. The results show large variability in particle concentrations (in both volume and number concentrations), with volume concentrations varying by 57-fold. The median particle diameter (Dv50) from each of the water samples also covers a large range (22.4–307.0 μm) and has an irregular statistical distribution, indicating complexity in the PSD. The PSD slopes (2.7–4.5), estimated from a power-law model, cover nearly the entire range reported previously for natural waters. Small mineral particles (with large PSD slopes) are characteristic of near-shore waters prone to sediment resuspension by winds and tides, while large biological particles (with small PSD slopes) dominate the total suspended particulates for waters away from the coast. For the BS and YS, this study provides the first report on the properties and spatial variability of the PSD, which may influence the optical properties of the ocean surface and remote sensing algorithms that are based on estimations of particle concentrations and sizes.

Suspended particles in waters play an important role in determination of optical properties and ocean color remote sensing. To link suspended particles to their optical properties and thereby remote sensing reflectance (Rrs(λ)), cross-sectional area is a key factor. Till now, there is still a lack of methodologies for derivation of the particle cross-sectional area concentration (AC) from satellite measurements, which consequently limits potential applications of AC. In this study, we investigated the relationship between AC and Rrs(λ) based on field measurements in the Bohai Sea (BS) and Yellow Sea (YS). Our analysis confirmed the strong dependence of Rrs(λ) on AC and that such dependence is stronger than on mass concentration. Subsequently, a remote sensing algorithm that uses the slope of Rrs(λ) between 490 and 555 nm was developed for retrieval of AC from satellite measurements of the Geostationary Ocean Color Imager (GOCI). In situ evaluations show that the algorithm displays good performance for deriving AC and is robust to uncertainties in Rrs(λ). When the algorithm was applied to satellite data, it performed well, with a coefficient of determination of 0.700, a root mean squared error of 2.126 m−1 and a mean absolute percentage error of 40.7%, and it yielded generally reasonable spatial and temporal distributions of AC in the BS and YS. The satellite-derived AC using our algorithm may offer useful information for modeling the inherent optical properties of suspended particles, deriving the water transparency, estimating the particle composition and possibly improving particle mass concentration estimations in future.