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This review will serve as an entry point for international researchers who would like to tap into the vast scientific potential of Chinese-language literature on oceanic fronts. We focused on observational physical oceanography studies of marine fronts. A thorough bibliographic search netted 95 papers published in 1982–2023, with a sharp increase in the total number of papers from 2006–2010 to 2011–2015, when this number almost tripled. This trend continued unabated through the early 2020s. The sharp increase in Chinese-language publications preceded by several years a rapid increase in English-language publications in the same field. Regionally, the overwhelming majority of papers is focused on the China Seas, particularly the East China Seas and northern South China Sea. Elsewhere, a number of papers were dedicated to the Southern Ocean and North Atlantic. Thematically, papers on remote sensing of ocean fronts dominate the literature, with special attention to the development of front-detection algorithms that can be applied to satellite data on sea surface temperature, salinity, chlorophyll, and sea level anomaly. Numerous papers on marine fronts in the China Seas present important results that have to be considered by international researchers. Overall, this review emphasizes the significant contributions made by Chinese oceanographers, particularly to studies of the frontal oceanography of the China Seas. [ABSTRACT FROM AUTHOR]

The papers in this special section examine the use of remote sensing technology to promote environmental sustainability in Asia-Pacific regions. Worldwide urbanization and deforestation are the two main interconnected ways that human activities are continually changing and reshaping the earth's surface. How earth observation and remote sensing technologies can contribute to improve the knowledge of the productivity and sustainability of natural and human ecosystems is an important theme in the global change community. In China, for instance, rapid economic growth and urbanization over the past three decades have resulted in dramatic changes in land use and land cover and have led to severe environmental consequences, which have made China's sustainable development a grand challenge. In the meantime, during the past few decades, environmental changes in the Asian–Pacific region have posed significant challenges to the scientific community. Therefore, the global problem of how earth observation and remote sensing technologies may be applied to assessing, monitoring, modeling, and simulating ecosystems, environments, and resources at various spatial and temporal scales translates into peculiar and very urgent questions and applications in this colossal and dynamic geographical region. [ABSTRACT FROM AUTHOR]

Oceanographic parameters, such as sea surface temperature, surface chlorophyll-a concentration, sea surface ice concentration, sea surface height, etc., are listed as Essential Climate Variables. Therefore, there is a crucial need for persistent and accurate measurements on a global scale. While in situ methods tend to be accurate and continuous, these qualities are difficult to scale spatially, leaving a significant portion of Earth's oceans and seas unmonitored. To tackle this, various remote sensing techniques have been developed. One of the more prominent ways to measure the aforementioned parameters is via satellite spacecraft-mounted remote sensors. This way, spatial coverage is considerably increased while retaining significant accuracy and resolution. Unfortunately, due to the nature of electromagnetic signals, the atmosphere itself and its content (such as clouds, rain, etc.) frequently obstruct the signals, preventing the satellite-mounted sensors from measuring, resulting in gaps—missing data—in satellite recordings. One way to deal with these gaps is via various reconstruction methods developed through the past two decades. However, there seems to be a lack of review papers on reconstruction methods for satellite-derived oceanographic variables. To rectify the lack, this paper surveyed more than 130 articles dealing with the issue of data reconstruction. Articles were chosen according to two criteria: (a) the article has to feature satellite-derived oceanographic data (b) gaps in satellite data have to be reconstructed. As an additional result of the survey, a novel categorising system based on the type of input data and the usage of time series in reconstruction efforts is proposed. [ABSTRACT FROM AUTHOR]

Advanced Earth observation technologies provide a tool for the study of ocean dynamics either in basins or in oceans. In a comparison of when and where, how ocean dynamics evolves in space and time is still a challenge. In view of an evolutionary scale, this paper proposes a novel approach to explore the evolutionary structures of ocean dynamics with time series of a raster dataset. This method, called PoEXES, includes three key steps. Firstly, a cluster-based algorithm is enhanced by process semantics to obtain marine snapshot objects. Secondly, the discriminant rule is formulated on the basis of successive marine snapshot objects' spatiotemporal topologies to identify marine sequence objects and marine linked objects. Thirdly, a sequence-linked object-based algorithm (SLOA) is used for marine sequence objects and linked objects to obtain their evolutionary structures and to achieve four evolutionary relationships, i.e., development, merging, splitting, and a splitting–merging relationship. Using the evolutionary relationships and their occurring orders in a lifespan of ocean dynamics, this paper reformulates five types of evolutionary structures, which consist of origination nodes, linked nodes, sequence nodes and dissipation nodes. The evolutionary-scale-based dynamic structure ensures the optimum evolutionary relationships of ocean dynamics as much as possible, which provides a new way to design a spatiotemporal analysis model for dealing with geographical dynamics. To demonstrate the effectiveness and the advantages of PoEXES, a real 40-year dataset of satellite-derived sea surface temperatures is used to explore the evolutionary structure in global oceans; the new findings may help to better understand global climate change. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST), a critical parameter of the global ocean–atmosphere system, is an essential element in the study and in the application of marine science. Satellite–infrared observations currently represent the only available method for continuous, large-scale observation of SST. Although passive microwave observations are not blocked by clouds, allowing for data collection in all weather conditions, this technological tool is characterized by low spatial resolution. Conversely, infrared observations offer high resolution but are susceptible to cloud obscuration. Accordingly, a technique that effectively fuses microwave and infrared satellite observations into a high-resolution SST field with global coverage close to the actual distribution is of practical significance. This paper describes fusing MODIS infrared remote sensing and AMSR-2 microwave remote sensing SST data with an optimal interpolation (OI) approach to produce a high-resolution SST data. The study chose the coastal Kuroshio region of China to establish an appropriate scale for examining the spatial structure of SST and attaining a more realistic picture of SST observations and impacts. The included discussion of the sources of error in the fusion process provides a reference for improving the accuracy of fused marine remote sensing data. The study also compared the fused SST results and the current international mainstream multi-temporal resolution of the three using the OI algorithm. We compared the fusion product with ARGO data with and without typhoon impact to explore and practice the OI in SST fusion when evaluating the accuracy of different data in the case of external disturbance being present. The research results have great significance for improving regional SST forecast accuracy while ensuring the applicability of various approaches to fusing SST data by incorporating the influence of typhoons in the offshore region of the East China Sea (ECS). Implications for the future development of SST fusion data are also included in the discussion. [ABSTRACT FROM AUTHOR]

In a previous paper, we introduced (i) a specific hyperspectral mixing model for the sea bottom, based on a detailed physical analysis that includes the adjacency effect, and (ii) an associated unmixing method that is supervised (i.e., not blind) in the sense that it requires a prior estimation of various parameters of the mixing model, which is constraining. We here proceed much further, by first analytically showing that the above model can be seen as a specific member of the general class of mixing models involving spectral variability. Therefore, we then process such data with the IP-NMF unsupervised (i.e., blind) unmixing method that we proposed in previous works to handle spectral variability. Such variability especially occurs when the sea depth significantly varies over the considered scene. We show that IP-NMF then yields significantly better pure spectra estimates than a classical method from the literature that was not designed to handle such variability. We present test results obtained with realistic synthetic data. These tests address several reference water depths, up to 7.5 m, and clear or standard water. For instance, they show that when the reference depth is set to 7.5 m and the water is clear, the proposed approach is able to distinguish various classes of pure materials when the water depth varies up to ± 0.2 m around this reference depth, over all pixels of the analyzed scene or over a "subscene": the overall scene may first be segmented, to obtain smaller depths variations over each subscene. The proposed approach is therefore effective and can be used as a building block in performing the subpixel classification of the sea bottom for shallow water. [ABSTRACT FROM AUTHOR]

Blooms of various types of seaweeds have been reported worldwide, with recent expansions in surface waters. While most of the expansions have been attributed to eutrophication due mainly to human activities, any potential role of climate change is unclear. Here we show that, in the East China Sea and Yellow Sea, increased biomass of Sargassum horneri (S. horneri, brown seaweed) from 2000 to 2021 appears to be caused primarily by ocean warming, as S. horneri prefers a certain temperature range to grow. In contrast, while increases of Ulva prolifera (U. prolifera, green seaweed) in the same regions might also be related to ocean warming, during the same period, human activities such as coastal aquaculture or seaweed mitigation may muddle such effects. With the projected ocean warming in the next decades, we hypothesize that S. horneri blooms may occur earlier during the year and may continue to expand in the future. Plain Language Summary: Seaweed macroalgae blooms have been reported around the world in the last two decades, yet the exact reasons are often unclear. This paper attempts to solve this puzzle for the Sargassum and Ulva seaweeds in the East China Sea and Yellow Sea using long‐term observations and laboratory‐based seaweed physiology measurements. The paper shows not only how seaweeds have expanded in the two marginal seas in the past four decades, but also how a warming ocean (due to climate variability) and nutrient enrichment (due mainly to human activities) controlled such expansions. Among 63 large marine ecosystems (LMEs) in the world, the East China Sea (where seaweeds expanded) is one of the three LMEs that experienced "super‐fast" warming. Given the IPCC‐projected seawater temperature rise of 1.6°C by 2050, will these seaweeds continue to expand? Key Points: Multi‐sensor satellite data show significant and near‐synchronized expansions of Sargassum horneri and Ulva prolifera in the past 38 yearsOcean warming appears to be a main factor behind the recent expansions of S. horneri because of its physiological requirementOcean warming may also play a role in U. prolifera expansions, but the effect appears confounded by human mitigation efforts [ABSTRACT FROM AUTHOR]

Remotely sensed sea-surface temperature (SST) retrievals with a significant positive bias during the occurrence of upwelling phenomena along the southeastern coast of Brazil were reported in our companion paper. As a result, this paper proposes an automated bias correction algorithm to improve the MODIS long-wave multichannel SST (MCSST) retrievals during the abovementioned conditions in this region. In this paper, MODIS daytime SST data (SSTMODIS) and differences between brightness temperatures in MODIS channels 31 and 32 (BT31 − BT32) are analyzed simultaneously with hourly wind surface conditions, in situ SST at 0.3 and 10 m in depth (SSTbuoy03 and SSTbuoy10), and sensible and latent heat fluxes from the Cabo Frio buoy data (at 23° S, 42° W) during 2014. The obtained results show that some upwelling events present air temperature ($T_{\mathrm {air}}$) greater than SSTbuoy03 and low-atmospheric water vapor content. A simultaneous occurrence of these factors during upwelling conditions may lead to a warm-skin layer effect and may cause BT31 to be greater than SSTbuoy03 and BT31 − BT32 to be small (−0.18 °C ± 0.22 °C), affecting the MCSST performance. The proposed bias correction algorithm uses a least-squares curve between SSTbuoy03 and SSTMODIS retrievals when BT31 − BT32 ≤ 0.5 °C (i.e., dry atmospheric conditions). The bias correction algorithm has significantly improved the SSTMODIS bias (RMSE) from 1.43 °C to −0.2 °C (1.60 °C to 0.58 °C) when applied to 22 cloud-free pixels of MODIS during January–March of 2015. [ABSTRACT FROM AUTHOR]

Eddies are structures of recognized physical and biogeochemical relevance. Because of their small space and time scales, submesoscale eddy detection is a challenge, and detection methods for these structures are scarce. In this study, we detect submesoscale eddies (SME) by means of a method implemented on satellite chlorophyll-a (CSAT) and sea surface temperature (SST) data from Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The performance of this method is shown through the characterization of the shape and size of the detected eddies. The trajectory and characteristics of one particular eddy could be observed for 6 days over the Argentine continental shelf. The eddy reached the highest CSAT concentration (1.52 mg m−3, decreasing to less than 0.8 mg m−3 in about 3.5 km), and the lowest SST on the same day it developed its largest size. All SME detected within the study period are in agreement with cyclonic eddies and have a radius smaller than the baroclinic Rossby radius of deformation. This paper presents a workflow for the automated recognition of SME that could be applied to other regions with similar characteristics, or different ones with previous changes in the parameters. [ABSTRACT FROM AUTHOR]

This paper describes a new model of the seawater dielectric constant as a function of salinity and temperature at L-band. The model function is developed by fitting the accurate measurement data made at 1.413 GHz to a third-order polynomial. The purpose of this study is to provide an accurate model for earth-observing satellites to retrieve seawater salinities from remote sensing data. In this paper, the development of the model function is introduced along with an analysis of the goodness of fit. The model function is then compared with the model functions of Klein-Swift and Meissner-Wentz. Finally, the comparison is made between the retrieved salinity from the satellite data with the in situ data measured by Argo floats. [ABSTRACT FROM PUBLISHER]

The observation of the sea through remote sensing technologies plays a fundamentalan role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence involves, among other things, variations in the density of nutrients, which, in turn, influence the biological parameters of the habitat. Indeed, there is a connection between the biogeochemical and physical processes that occur within a biological system and the variations observed in its faunal populations. This paper concerns the proposal of an automatic classification system, namely the Mesoscale Events Classifier, dedicated to the recognition of marine mesoscale events. The proposed system is devoted to the study of these phenomena through the analysis of sea surface temperature images captured by satellite missions, such as EUMETSAT's Metop and NASA's Earth Observing System programmes. The classification of these images is obtained through (i) a preprocessing stage with the goal to provide a simultaneous representation of the spatial and temporal properties of the data and enhance the salient features of the sought phenomena, (ii) the extraction of temporal and spatial characteristics from the data and, finally, (iii) the application of a set of rules to discriminate between different observed scenarios. The results presented in this work were obtained by applying the proposed approach to images acquired in the southwestern region of the Iberian peninsula. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) fronts are important for fisheries and marine ecology, as well as upper-ocean dynamics, weather forecasting, and climate monitoring. In this paper, we propose a new approach to detect SST fronts from RADARSAT-2 ScanSAR images, based on the correlation of SAR-derived wind speeds using the gray level cooccurrence matrix (GLCM) approach. Due to the large differences between the correlation of wind speeds for SST fronts compared to other areas, SST fronts can be detected by the threshold method. To eliminate small-scale features (or noise), the 30 km scale is used as the length threshold for the detection of the SST fronts. The proposed method is effective when wind speeds are between 3 and 13 m s−1. The overall accuracy of our method is about 93.6%, which is sufficient for operational applications. [ABSTRACT FROM AUTHOR]

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed. [ABSTRACT FROM AUTHOR]

The retrieval of land surface temperature (LST) from remote sensing techniques has been studied and validated during the past 40 years, leading to important improvements. Accurate LST values are currently obtained through measurements using medium resolution thermal infrared (TIR) sensors. However, the most recent review reports demonstrated that the future TIR LST products need to obtain reliable temperature values at a high spatial resolution (100 m or higher) to study temperature variations between different elements in a heterogeneous kilometric area. The launch of high-resolution TIR sensors in the near future requires studies of the temporal evolution and spatial heterogeneities of the elements in a mixed region. The present study analyzes the LST in a sub-kilometric highly heterogeneous area, combining the use of LST products from high-resolution TIR orbiting sensors with the LST maps created from a TIR camera onboard an unmanned aerial vehicle (UAV). The aim is to estimate the LST variability in a heterogeneous area containing different surfaces (roads, buildings, and grass), observed from different TIR sensors at different spatial resolutions, covering from the meter to the kilometer scales. Several results showed that variations in the LST up to 18 °C were identified with the UAV-TIR camera, and significant differences were also present in the LST products obtained from simultaneous overpasses of high-resolution satellite TIR sensors. A second objective of the study, due to the availability of the high-resolution LST fields, was to explore the thermal advection between different elements and determine if it correlates with the surface energy budget in the same area, thus indicating that this process is of importance for heterogeneous terrains at these scales. This paper also highlights the relevance of the UAV-TIR camera flight for future studies since it is not commonly used in TIR remote sensing but has substantial potential advantages. [ABSTRACT FROM AUTHOR]

The Shantou-Taiwan shoal fishing ground in southeastern China supports a significant population of pelagic fish, which play a key role in the marine ecosystem. An acoustic survey was carried out using a digital scientific echosounder in June 2019. In this paper, the spatial distribution of pelagic fish is analyzed based on acoustic data using geostatistical analysis tools. Meanwhile, the relationship between fish density from acoustic data and sea surface environment factors were evaluated by using generalized additive models (GAMs) based on the satellite-based oceanographic data of sea surface temperature, sea surface chlorophyll-a concentration, sea surface height and sea surface wind. The results showed the following: (1) Fish density and acoustic biomass have strong spatial correlation; the optimal model for acoustic biomass is exponential and the optimal model for fish density is gaussian; based on optimal model, spatial interpolation analysis of fish density and acoustic biomass was performed using the ordinary kriging method, and the higher values of density and acoustic biomass were located in the central and eastern parts of the study area. The total fish density and acoustic biomass is 2.56 × 1010 ind. and 1908.99 m2/m, respectively. (2) In vertical distribution, fish gradually move to the middle and lower layers of water during daytime, and gather in the middle and upper layers of water at night. (3) The variance explanation rate of GAM was 88.2% which indicates that the model has an excellent fitting degree, and the results of GAM showed that longitude, sea surface temperature (SST), sea surface wind (SSW), and sea surface height (SSH) had significant effects on fish density. Results of this study were meaningful for understanding the distribution of fishery resources, and as a guide for fish management in the Shantou offshore water. [ABSTRACT FROM AUTHOR]

The Bayesian hierarchical model has the outstanding capacity to combine prior knowledge with observations, as well as to generate rich spatiotemporal patterns. However, this advanced method has limited use in remotely sensed lattice data applications due to the large computational burden and unreasonable statistical inferences based on statistical units with a fixed scale. This paper presents a multiscale spatial homogeneous subdivision method and develops a Bayesian space–time hierarchical model (BSTHM) for remotely sensed lattice data. This can solve the above-mentioned limitations by constructing multiscale spatial homogeneous units with good statistical properties. The quantitative criteria for subdivision are provided. The outcome of the BSTHM is not only more reasonable in theory but also much easier to interpret; meanwhile, the computational efficiency is also considerably improved. This novel approach and its merits are illustrated by a case study that examines the spatiotemporal variation of PM2.5 pollution in Asia from 2000 to 2014 using remotely sensed data describing PM2.5 annual mean concentrations. Overall spatial pattern, common time trend, and local variation trend were decomposed and quantificationally estimated from an intricate spatiotemporal process. [ABSTRACT FROM AUTHOR]

This paper analyzes the large positive bias of sea surface temperature (SST) retrievals of selected remotely sensed algorithms recorded during the simultaneous occurrence of upwelling and atmospheric subsidence along the coastal waters of Rio de Janeiro, Brazil. The optimal estimator (OE) for retrieving SST and the multichannel (MCSST) and nonlinear (NLSST) estimators are compared using Advanced Very High Resolution Radiometer-3 data. The in situ SST (SSTbuoy) data set used to validate the remotely sensed SST retrievals was collected from five moored buoys (four in the open sea and one in coastal waters). The principal results of this paper are as follows. First, the sensitivity analyses show that OE is quite susceptible to the first-guess SST rather than to the humidity profiles. Second, the comparison between the SSTOE and 365 cloud-free SSTbuoy measurements in open sea waters presents an root mean squared error (RMSE), bias, and standard deviation (STD) with the intervals of [0.5, 0.6], [−0.51, 0.13], and [0.27, 0.48], respectively. Third, the MCSST, NLSST, and OE SST produce a positive bias that can reach 5 K during simultaneous upwelling and atmospheric subsidence in coastal waters. Such unexpected errors are due to low SST values and water vapor compression in the lower layer of the atmosphere related to a temperature inversion. Fourth, an alternative approach using SSTbuoy obtained on the previous day as a first guess instead of the climatological SST significantly improves the errors (SSTOE–SSTbuoy) by reducing RMSE, bias, and STD by 58% (from 3.30 to 1.39 K), 73% (from 3.00 to 0.80 K), and 19% (from 1.38 to 1.12 K), respectively. [ABSTRACT FROM PUBLISHER]

This paper presents a framework for using remote sensing imagery and image processing techniques to analyze the interactions among tropical depressions (TDs) and typhoons. The purpose is to properly understand and predict this phenomenon, in order to reduce their influence on the regions where they pass by. Improvements on the understanding of the interactions among TDs and typhoons are obtained and presented. They carry the potential to improve forecast of severe weather system. In this paper, we analyzed the physical responses of TDs on two typhoons Tembin and Bolaven (2012), including TD's appearance, development, interaction, and mergence. According to the Central Weather Bureau of Taiwan, Tembin and Bolaven were formed about 1550 km apart. In general, the Fujiwhara effect happens when two storms are within a distance of 1400 km. Does the TD have impacts on the typhoons, and could we also call this impact as Fujiwhara effect? The 3-D profiles of the weather systems are reconstructed using the multifunctional transport satellite (MTSAT) infrared cloud images by the image reconstruction technique (IRT). The 3-D profiles of typhoons were implemented for investigating the in-depth distribution of the cloud top from a surface cloud image. The main parameters, which have been analyzed in this paper, are distance, height difference, rotation, and sizes of the two TDs and the two typhoons. The results showed that two TDs occurred between typhoons Tembin and Bolaven, which interacted with each other through upward convection and attraction between two typhoons and the TDs, respectively. The mergence of the two TDs caused the typhoons to start rotating and generate strengthened power. The findings of the TD effects on two typhoons represent a new aspect of the interaction between TD and typhoon. [ABSTRACT FROM PUBLISHER]

The radiometric stability of selected thermal emissive bands (TEBs) of the Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites is examined via a direct intersensor comparison with the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The analysis adapts a “nadir-only” refinement of the simultaneous nadir overpasses (SNOs) analysis to evaluate radiometric deviations in MODIS Collection 6.0 and 6.1 data releases, the latter having been implemented with a version of crosstalk effect mitigation for Terra MODIS bands 27–30. The comparison time series result, with an average precision of 0.4%, shows radiometric deviation in Terra MODIS band 29 ($8.55~\mu \text{m}$) up to 6%, or 3 K in brightness temperature (BT), in Collection 6.0, and correspondingly −1.5%, or −0.7 K in BT, in Collection 6.1, in particular significantly worsened by the safe mode event of February 2016. This paper includes the inter-TEB comparisons of MODIS bands B31 and B32 with SNPP VIIRS as two representative cases of nonideal spectral match demonstrating various key features and new information as revealed under the “nadir-only” framework, and also describes a procedure using both radiance-to-radiance comparison and BT-difference time series to estimate the BT-difference uncertainty specifications as well as the radiance-to-radiance time series baselines that are shifted away from unity. The finding for Terra MODIS band 29 highlights the continual nontrivial impact of crosstalk effect on numerous TEBs of MODIS Collection 6.1 not fully mitigated, which can further impact cloud masks, sea surface temperatures, and other product retrievals. [ABSTRACT FROM AUTHOR]

The multifractal fusion method has proved to be an effective algorithm to mitigate the noise of the sea surface salinity (SSS) of Soil Moisture Ocean Salinity (SMOS) mission. However, the traditional nonparametric weight function used in this method is unable to fully capture the dynamic evolution of the oceanic environment. Considering the multiscale, nonuniform, anisotropic, and flow-dependent nature of the ocean, a prototype with the so-called flexible circle (FLC) weight function or flexible ellipse (FLE) weight function with a set of predefined parameters is proposed in this paper. The improved weight functions could draw dynamic information from the sea surface temperature, Rossby radius of deformation, and surface geostrophic flow to improve the quality of the remotely sensed SSS. The validation against the in situ data indicates that the improved weight functions perform better than the traditional one with a reduced root-mean-square (RMS) and standard deviation (STD) of the differences with respect to EN 4.2.0 profiles (from 0.50 and 0.46 to 0.42 and 0.38 for FLC and 0.39 and 0.36 for FLE in the global ocean). In particular, the FLE scheme could highlight the variation of the strong currents without affecting the computational efficiency. Furthermore, this paper discusses the influences of the error distribution on the fusion results and underlines the importance of error-based adaptions for further improvements. [ABSTRACT FROM AUTHOR]

The distribution of chlorophyll-a (Chl-a) in the Yangtze River Mouth area was analyzed using a new Chl-a inversion model (PMS-C) based on the relationship between in situ Chl-a and GF-4 PMS band combinations. Combining GF-4 PMS with HY-C CZI, this paper revealed that: (1) Chl-a concentration in Yangtze River Mouth was in the range of 2–6 µg/L, being higher in the west than in the east. The high Chl-a area was mainly distributed near the mouth of the Yangtze River and the Chl-a value was in the range of 3.7 µg/L to 5.9 µg/L. Chl-a concentration is higher in spring and summer than in autumn and winter, with a Chl-a concentration difference of 1–2 µg/L. Chl-a downstream of islands and bridges increased by 0.5–1.7 µg/L compared with upstream. (2) Short-term (within 3 h) changes of Chl-a concentration were effectively detected. In summer and autumn, Chl-a obtained at 13:30 in the noon was generally lower than Chl-a obtained at around 10:30 in the morning and it decreased by nearly 0.1–4 μg/L within three hours on the same day. In winter, the concentration of Chl-a decreased in the range of 0–1.9 μg/L. Generally, within three hours, Chl-a in the downstream of the island decreased significantly from 5 μg/L to about 3.8 μg/L, and Chl-a downstream of piers decreased from 3.7 μg/L to about 3 μg/L. (3) Environmental factors including seawater temperature, illumination, and nutrients, as well as dynamic factors such as wind and tidal current can induce Chl-a change in the Yangtze River Mouth. Short-term change of Chl-a concentration is closely related to the specific hydrodynamic conditions, nutrients, and lighting conditions. [ABSTRACT FROM AUTHOR]

The accumulated remote sensing data of altimeters and scatterometers have provided new opportunities for ocean state forecasting and have improved our knowledge of ocean–atmosphere exchanges. Studies on multivariate, multistep, spatiotemporal sequence forecasts of sea level anomalies (SLA) for different modalities, however, remain problematic. In this paper, we present a novel hybrid and multivariate deep neural network, named HMnet3, which can be used for SLA forecasting in the South China Sea (SCS). First, a spatiotemporal sequence forecasting network is trained by an improved convolutional long short-term memory (ConvLSTM) network using a channelwise attention mechanism and multivariate data from 1993 to 2015. Then a time series forecasting network is trained by an improved long short-term memory (LSTM) network, which is realized by ensemble empirical mode decomposition (EEMD). Finally, the two networks are combined by a successive correction method to produce SLA forecasts for lead times of up to 15 days, with a special focus on the open sea and coastal regions of the SCS. During the testing period of 2016–18, the performance of HMnet3 with sea surface temperature anomaly (SSTA), wind speed anomaly (SPDA), and SLA data is much better than those of state-of-the-art dynamic and statistical (ConvLSTM, persistence, and climatology) forecast models. Stricter testbeds for trial simulation experiments with real-time datasets are investigated, where the eddy classification metrics of HMnet3 are favorable for all properties, especially for those of small-scale eddies. [ABSTRACT FROM AUTHOR]

In recent years, rapid urbanization and population growth have led to an overload of waste in big cities of Vietnam. Waste decomposition is always associated with the effects of heat generation as a result of the continuous aerobic and anaerobic processes, resulting in landfill surface temperature is significantly higher than the surrounding area. This paper presents the results of assessment of land surface temperature distribution in two largest landfills in Vietnam, including Nam Son landfill (Hanoi city, northern Vietnam) and Da Phuoc landfill (Ho Chi Minh city, southern Vietnam). Landsat 8 satellite image data is used to calculate land surface temperatures based on the split-window method. The results showed that the landfill surface temperature was much higher than the surrounding area, even compared to the areas characterized by impervious surfaces. The difference between the landfill surface temperature and temperature in areas covered by vegetation was about 20°C. The results obtained in this study provide input information to develop monitoring models of pollution at landfills. [ABSTRACT FROM AUTHOR]

This paper presents the results of integrated space studies of the environmental emergency near the Kamchatka Peninsula which caused the mass death of hydrobionts in autumn 2020. This research is based on the processing and analysis of long-term series of optical multispectral and radar satellite images of the sea surface (~22 000 scenes), including data on the sea surface temperature (from 1981 to 2020) and the concentration of chlorophyll a (from 2000 to 2020). It is revealed that there were strong positive temperature anomalies in the study area in July–September 2020 (the deviations from the climatic norm reached 6°C), which led to a significant change in the biogenic regime and an abnormal increase in the concentration of chlorophyll a (by almost 3.5 times) in late September–early October 2020. This was the main natural factor that led to the development of harmful algal blooms (red tide), which caused the death of hydrobionts both in the Avacha Gulf and in the coastal waters of the entire Kamchatka Peninsula. Based on the results of an analysis of satellite data, it is shown that the possible anthropogenic contribution and contribution of other natural factors to the environmental disaster is insignificant. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate the near-surface air temperature datasets from the ERA-Interim (ERAI), Japanese 55-Year Reanalysis, Modern-Era Retrospective Analysis for Research and Applications Version 2, NCEP1, and NCEP2 reanalysis projects. Reanalysis data were first compared to observations from weather stations located on wheat areas of the United States and Ukraine, and then evaluated in the context of a winter wheat yield forecast model. Results from the comparison with weather station data showed that all datasets performed well (r2 > 0.95) and that more modern reanalysis, such as ERAI, had lower errors [root-mean-square difference (RMSD) ∼0.9 °C than the older, lower resolution datasets, such as NCEP1 (RMSD ∼2.4 °C). We also analyze the impact of using surface air temperature data from different reanalysis products on the estimations made by a winter wheat yield forecast model. The forecast model uses information of the accumulated growing degree day (GDD) during the growing season to estimate the peak normalized difference vegetation index signal. When the temperature data from the different reanalysis projects were used in the yield model to compute the accumulated GDD and forecast the winter wheat yield, the results showed smaller variations between obtained values, with differences in yield forecast error of around 2% in the most extreme case. These results suggest that the impact of temperature discrepancies between datasets in the yield forecast model get diminished as the values are accumulated through the growing season. [ABSTRACT FROM AUTHOR]

In this paper, a time series of 33 TerraSAR-X copolarized synthetic aperture radar (SAR) imagery collected in Stripmap mode over the Gulf of Mexico in a wide range of incidence angles and sea-state condition is exploited, together with a theoretical framework based on the X-Bragg scattering model, to analyze the effects of noise, angle of incidence, (AOI) and wind speed on the standard deviation of the copolarized phase difference ($\sigma _{\phi _{c}}$) evaluated over sea surface with and without oil slicks. This large data set represents an unprecedented opportunity to analyze, for the first time, the influence of both SAR acquisition and surface parameters on the broadening of the copolarized phase difference probability density function (pdf), $p_{\phi _{c}}(\phi _{c})$. Experimental results show that the X-Bragg scattering model, here adopted to predict the sea surface $p_{\phi _{c}}(\phi _{c})$ , gives an understanding of the increasing trend of $\sigma _{\phi _{c}}$ with respect to AOI. It is shown that the noise significantly contributes to broaden $p_{\phi _{c}}(\phi _{c})$ over both slick-free and slick-covered sea surface, while the effects of low-to-moderate wind regimes are negligible. In addition, $\sigma _{\phi _{c}}$ exhibits a larger sensitivity to the scene variability, if compared to single-polarization intensity channels, over both slick-free and oil-covered sea surface. This sensitivity is more pronounced at lower AOIs due to the higher noise equivalent sigma zero (NESZ) that affects larger AOIs. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive procedure to improve the wind geophysical model function (GMF) for the Global Navigation Satellite System Reflectometry (GNSS-R) instrument onboard the TechDemoSat-1 satellite. The observable used to define the GMF is extracted from the measured delay-Doppler maps (DDMs) by correcting for the nongeophysical effects within the measurements. Besides the instrument and the geometric effects as provided in the bistatic radar equation, a calibration term that accounts for the uncalibrated receiver antenna gain and the unknown transmitter antenna gain is proposed to optimize the calculation of GNSS-R observables. Such calibration term is presented as a function of observing elevation and azimuth angles and is shown to remarkably reduce the measurement uncertainties. First, an empirical wind-only GMF is developed using the collocated Advanced Scatterometer (ASCAT) winds and European Centre for Medium-Range Weather Forecasts (ECMWF) model wind output. This empirical GMF agrees well with the model output. Then, the sensitivity of the observable to waves is analyzed using the collocated ECMWF wave parameters. The results show that it is difficult to include mean square slope (MSS) in the development of an empirical GMF, since the difference between ECMWF MSS and the MSS sensed by GNSS-R varies with incidence angle and wind speed. However, it is relevant to take significant wave height ($H_{s}$) in account, particularly for low wind conditions. Consequently, a wind/ $H_{s}$ approach is proposed for improved wind retrievals. [ABSTRACT FROM AUTHOR]

The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC), part of the larger experiment known as Elucidating the Role of Clouds-Circulation Coupling in Climate (EUREC 4 A), was held in the western Atlantic during the period 17 January–11 February 2020. This paper describes observations made during ATOMIC by the US National Oceanic and Atmospheric Administration's (NOAA) Lockheed WP-3D Orion research aircraft based on the island of Barbados. The aircraft obtained 95 h of observations over 11 flights, many of which were coordinated with the NOAA research ship R/V Ronald H. Brown and autonomous platforms deployed from the ship. Each flight contained a mixture of sampling strategies including high-altitude circles with frequent dropsonde deployment to characterize the large-scale environment, slow descents and ascents to measure the distribution of water vapor and its isotopic composition, stacked legs aimed at sampling the microphysical and thermodynamic state of the boundary layer, and offset straight flight legs for observing clouds and the ocean surface with remote sensing instruments and the thermal structure of the ocean with in situ sensors dropped from the plane. The characteristics of the in situ observations, expendable devices, and remote sensing instrumentation are described, as is the processing used in deriving estimates of physical quantities. Data archived at the National Center for Environmental Information include flight-level data such as aircraft navigation and basic thermodynamic information (, 10.25921/7jf5-wv54); high-accuracy measurements of water vapor concentration from an isotope analyzer (, 10.25921/c5yx-7w29); in situ observations of aerosol, cloud, and precipitation size distributions (, 10.25921/vwvq-5015); profiles of seawater temperature made with Airborne eXpendable BathyThermographs (AXBTs; , 10.25921/pe39-sx75); radar reflectivity, Doppler velocity, and spectrum width from a nadir-looking W-band radar (, 10.25921/n1hc-dc30); estimates of cloud presence, the cloud-top location, and the cloud-top radar reflectivity and temperature, along with estimates of 10 m wind speed obtained from remote sensing instruments operating in the microwave and thermal infrared spectral regions (, 10.25921/x9q5-9745); and ocean surface wave characteristics from a Wide Swath Radar Altimeter (, 10.25921/qm06-qx04). Data are provided as netCDF files following Climate and Forecast conventions. [ABSTRACT FROM AUTHOR]

This paper considers methods to classify and discriminate the multidimensional cyclostationary climatological time series. The methods take into account both the seasonal mean cycles and random behavior in the series, simultaneously considering means and all component-by-component autocovariances. This improves classical Hotelling T^2 statistics that classify through mean changes only, and constant-mean speech methods that classify exclusively through sample autocovariance differences. Here, two series are compared by assuming that both follow the same time series model; from this, a test statistic representing a distance between the two series is developed from linear prediction theory. This construction generates a level-\alpha test statistic for Gaussian data that can be used to assess how different the two series are. The derived distances can be used in a clustering algorithm, e.g., to group series with similar behavior. Such information is useful to eliminate or merge the climate stations whose data are redundant to another station or to optimally locate a collection of stations. The techniques are first tested on simulated series with known structures, and then applied to 11 two-dimensional series in the National Oceanic and Atmospheric Administration’s data buoy catalog. Natural clusters emerge which are geographically realistic. Specifically, the methods were able to perfectly group stations in the Gulf of Mexico, the Carolina Coast, the Pacific Ocean, and offshore New England. [ABSTRACT FROM PUBLISHER]

This paper incorporates remote sensing imagery and image processing techniques to analyze typhoons’ evolution into supertyphoons through interactions with cold fronts in the Western Pacific Ocean. The purpose is to enhance understanding and predictability of the tracks and profiles of supertyphoons. Evolutions of typhoons Haiyan (2013) and Hagupit (2014) into supertyphoons were studied. The 3-D profiles of weather systems were reconstructed using multifunctional transport satellite IR cloud images. When interactions between typhoon and cold front happened, enhancements of typhoons were observed causing typhoons Haiyan and Hagupit to strengthen power and evolve into supertyphoons. The origins of typhoons Haiyan and Hagupit are closely located at 152°50E/05°12N and 151°30E/04°19N, respectively. Both typhoons went through The Philippine Sea in the zone of 112°52E–146°11E. The lowest values of central pressures of typhoons Haiyan and Hagupit occurred along their paths at the positions 128°41E/10°16N and 131°06E/11°00N, respectively. Their distances from cold front and average speed of their movement were dominating parameters for the formation of winter supertyphoons. The supertyphoon Haiyan showed higher intensity than the supertyphoon Hagupit. Each of the two typhoons roughly consisted of two paths with the first path moving westerly and the second path northerly. The two typhoons evolved into supertyphoons at the turning points of the two paths. The first paths were about 2330 and 2050 km for typhoons Haiyan and Hagupit, respectively. [ABSTRACT FROM PUBLISHER]

Surface upward longwave radiation (SULR) is an important component of the surface energy balance and is closely related to land surface temperature and emissivity. The estimation of SULR plays an important role in the study of surface energy circulation and climate change. State-of-the-art methods to estimate SULR, including the physical method and the hybrid method, are conducted without considering directional thermal radiation (DTR), which may induce a large error in the estimation, particularly over sparsely vegetated surfaces. In this paper, we modified the physical temperature–emissivity algorithm by combining a directional emissivity model (FRA97) and a kernel-driven DTR model to estimate the SULR of vegetated surfaces while considering the thermal directionality of the land surface. The most suitable kernel-driven model and an angle combination of the DTR were selected from six kernel-driven models and five angular combinations. The sensitivity of the proposed algorithm to the input parameters was also analyzed. The proposed algorithm was then validated with the Wide-angle infrared Dual-mode line/area Array Scanner (WiDAS) data set and longwave radiation data of automatic meteorological stations from the Heihe Watershed Allied Telemetry Experimental Research experiment. The results showed that the five-angle combination with large-angle intervals performs the best. When the leaf area index (LAI) is less than 1.2, the RossThick-LiSparseR model performs the best; when LAI is larger than 1.2, the RossThick-LiDenseR model is the most accurate. The SULR is not sensitive to surface downward longwave radiation and LAI, is slightly sensitive to leaf and soil emissivity at certain LAIs, and is highly sensitive to DTR, which may greatly affect the accuracy of the estimated SULR. The root-mean-square error (RMSE) and the mean bias error (MBE) of the SULR estimated using the WiDAS data and the proposed algorithm are 5.618 and −1.642 W/m2, respectively, thereby improving the estimation accuracy by as much as 7.479 and 10.511 W/m2 at most in terms of RMSE and MBE, respectively, compared with the results calculated without considering the DTR. [ABSTRACT FROM PUBLISHER]

More than two-thirds of the surface of the Earth's surface is the ocean. These areas are not only the space in which the species live, but also the provider of resources. Only from the perspective of marine Fishery, marine Fishery not only provide an important source of food for humans, but also provide a large number of jobs for the society. When using the traditional method to study the ocean, if it is not supplemented by a relatively macroscopic angle, it is easy to be partial, and the ocean remote sensing technology can realize the long-term, large-scale and high-precision simultaneous monitoring of ocean information. Therefore, more and more applications have been applied in research on fishery distribution and fishery environment monitoring. This paper will use marine remote sensing technology to systematically analyze the fishing of marine Fishery, which will be of guiding significance for the actual operation of fishermen in the future. [ABSTRACT FROM AUTHOR]

The extraction of marine environment distinctions is of utmost importance to the development of marine economy and environmental conservation. Traditional extraction method has some defects such as less observation data, large time span and vulnerability to vile weather on the ocean. The eCongnition remote sensing technology features fast classification, high precision and rises superior to the bad weather of the ocean. This paper attempts to apply the eCongnition remote sensing technology to the extraction of marine environment features in order to seek out its changing laws. The findings show that the eCongnition remote sensing technology can analyze the spectral, spatial and textural identities of the ocean. By extracting the characteristics of offshore fishery resources and ocean surface wind speed information, it is found that the marine fishery has a direct bearing on the marine environment factors, i.e. the surface temperature and chlorophyll-a concentration. The optimal ocean surface temperature is 29.58C, and the optimal chlorophyll-a concentration is 0.4 mg/m3; the greater the distance from the sea level, the higher the wind speed on the ocean surface. Experimentally, this study provides the clues to the application of eCongnition remote sensing technology in marine feature extraction. [ABSTRACT FROM AUTHOR]

This paper addresses the joint analysis of multisource and multiresolution remote sensing data for the interpolation of high-resolution sea surface geophysical fields. As case-study application, we consider the interpolation of sea surface temperature (SST) fields. We propose a novel statistical model that combines two key features: an exemplar-based prior and statistical priors. The exemplar-based prior, referred to as a nonlocal prior, exploits similarities between local patches (small field regions) to interpolate missing data areas from previously observed exemplars. This nonlocal prior also sets an explicit conditioning between the multisensor data. Two complementary statistical priors, namely a prior on the spatial covariance and a prior on the marginal distribution of the high-resolution details, are considered as sea surface geophysical fields that are expected to depict specific spectral and marginal features in relation to the underlying turbulent ocean dynamics. We report the experiments on both synthetic and real SST data. These experiments demonstrate the contributions of the proposed combination of nonlocal and statistical priors to interpolate visually consistent and geophysically sound SST fields from multisource satellite data. We further discuss the key features and parameterizations of this model as well as its relevance with respect to classical interpolation techniques. [ABSTRACT FROM PUBLISHER]

In this paper, the retrieval of sea surface current velocity from vertically polarized (V-pol) X-band marine radar data is presented. Three different methods, including the iterative least square approach, the normalized scalar product method, and the polar current shell algorithm, that have been used for horizontally polarized data are employed here. A comprehensive comparison of the performance of the three methods is conducted using the radar images collected by the V-pol radar deployed on the Forschungsplattformen in Nord- und Ostsee No. 3 (FINO3) offshore research platform and the acoustic Doppler current profiler (ADCP) data in the North Sea. The results indicate that all three methods are capable of providing reliable current speed and direction measurements from V-pol data, with similar performance. Based on the experimental data for which the current magnitude is less than 0.5 m/s, the radar-derived results agree best with the ADCP data at a depth of 6–8 m, with the root mean square difference for current velocity $x$- and $y$-components being 7.2–8.9 cm/s. The correlation coefficients between the radar-derived and ADCP-measured current velocity components are as high as 0.87–0.93. [ABSTRACT FROM AUTHOR]

This paper deals with the method developed for rain flagging over oceans using radar backscatter and noise-equivalent brightness temperature (TB) measured by a Ku-band pencil-beam scatterometer launched onboard the Oceansat-2 (OS2) satellite. The OS2 scatterometer (OSCAT) measures radar backscatter and TB by its two pencil-beam antennas at local incidence angles of ∼49° (inner) and ∼57° (outer) in horizontal and vertical polarizations, respectively. These measurements are affected by rain and heavy clouds, in addition to the ocean surface winds, thus affecting retrieval of wind vectors. The characteristics of TB at OSCAT specifications have been studied based on radiative transfer simulations. The horizontally polarized TB is observed to be sensitive to both wind speed and rain, whereas the vertically polarized TB (TBV), typically at 57°, is found to be insensitive to ocean surface wind speed but is sensitive to rain, indicating its typical utility for rain flagging. As the retrievals of wind vectors are affected by rain, then so are the associated cost functions and their characteristics under rain-free and rainy situations. A combined use of TBV and retrieval cost functions has been made for rain flagging needed in wind directional-ambiguity removal, as well as tagging the retrieval quality. A data-driven dynamical method has been developed to specifically utilize TBV for supporting rain detection in view of the slow temporal drift found in the OSCAT TB data. Rain flags have been generated from OSCAT 25-km grid L2A (Version 1.4) data for the period from November 2009 to August 2010, and the results of comparison with Tropical Rainfall Measuring Mission Precipitation Radar 2A25 data are presented. [ABSTRACT FROM PUBLISHER]

This paper utilizes an electromagnetic inverse-scattering algorithm to quantitatively reconstruct the vertical temperature and salinity profiles of snow-covered sea ice from time-series monostatic polarimetric normalized radar cross-section (NRCS) data. The reconstructed profile at a given time step is utilized to provide a priori information for the profile reconstruction at the subsequent time step. This successive use of a priori information in the inversion algorithm results in achieving high reconstruction accuracy over the time period of interest. This inversion scheme is evaluated against the experimental data collected from snow-covered sea ice grown in an Arctic ocean mesocosm facility. It will be shown that the time evolution of the temperature, salinity, and density profiles of an artificially grown snow-covered sea ice can be quantitatively reconstructed using single-frequency time-series radar cross-section data assuming that these profiles are initially known with sufficient accuracy. [ABSTRACT FROM PUBLISHER]

The Caribbean Sea and adjacent land areas are highly sensitive to the projected impacts of global climate change. The countries bordering the Caribbean Sea depend heavily on coastal and marine assets as a major source of livelihood support. Rising sea surface temperatures (SSTs) are known to be associated with coral bleaching, ocean acidification, and other phenomena that threaten livelihoods in the region. The paucity of SST systematic observations in both the Caribbean Sea and adjoining Western Atlantic waters is a limiting factor in the projection of future climate change impacts on the region's marine resources. Remote sensing of SST by satellites began only within the last three decades and although the data collected so far might be insufficient to provide conclusive definitions of long-term SST variations in the Caribbean waters, these data along with the output from climate model simulations provide a useful basis for gaining further insights into plausible SST futures under IPCC SRES scenarios. In this paper, we examine the recent SST records from the NESDIS AVHRR satellite data and NOAA Optimum Interpolation (OI) sea surface temperature V2 and provide a comparative analysis of projected SST changes for the Caribbean Sea up to the end of the twenty-first century, under the SRES A2 and B2 scenarios' simulations of the sea surface skin temperatures (SSsT) using the Hadley Centre's regional model, PRECIS. The implications of these projected SST changes for bleaching of coral reefs, one of the region's most valuable marine resource, and for rainfall are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the Aquarius scatterometer calibration, starting with the instrument calibration. We examine the stability of Aquarius as quantified using the loop-back power and estimated receiver gain to shown Aquarius has been extremely stable to order 0.1 dB since mission start. We show the temperatures of scatterometer components not contained in the loop-back path have been controlled precisely to 0.5°C to minimize any temperature-dependent losses. Combined, these results show Aquarius produces accurate $\sigma_0$ over the mission lifetime. In the next section, we discuss the stability as quantified using external models and again show stability to order 0.1 dB in very good agreement with instrument-only methods. Then, we discuss the methods used to absolutely calibrate Aquarius $\sigma_0$ with respect to previous L-band radar systems. We show that Aquarius is relatively calibrated to order 0.1 dB for copolarization channels and better than 0.2 dB for cross-polarization channels. Finally, we discuss the calibration of the Aquarius wind speed product. We compare the Aquarius wind speed with radiometer wind speed products, other radar scatterometers, and numerical weather products. We show that the Aquarius wind speed product is on par with previous scatterometers in data quality. [ABSTRACT FROM PUBLISHER]

In this study, we investigate the feasibility of using historical remote sensing data to predict the future three-dimensional subsurface ocean temperature structure. We also compare the performance differences between predictive models and real-time reconstruction models. Specifically, we propose a multi-scale residual spatiotemporal window ocean (MSWO) model based on a spatiotemporal attention mechanism, to predict changes in the subsurface ocean temperature structure over the next six months using satellite remote sensing data from the past 24 months. Our results indicate that predictions made using historical remote sensing data closely approximate those made using historical in situ data. This finding suggests that satellite remote sensing data can be used to predict future ocean structures without relying on valuable in situ measurements. Compared to future predictive models, real-time three-dimensional structure reconstruction models can learn more accurate inversion features from real-time satellite remote sensing data. This work provides a new perspective for the application of artificial intelligence in oceanography for ocean structure reconstruction. [ABSTRACT FROM AUTHOR]

The infrared (IR) satellite remote sensing of sea surface skin temperature (SSTskin) is challenging in the northern high-latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SSTskin retrievals is questionable. Several Saildrone uncrewed surface vehicles were deployed at the Pacific side of the Arctic in 2019, and two of them, SD-1036 and SD-1037, were equipped with a pair of IR pyrometers on the deck, whose measurements have been shown to be useful in the derivation of SSTskin with sufficient accuracy for scientific applications, providing an opportunity to validate satellite SSTskin retrievals. This study aims to assess the accuracy of MODIS-retrieved SSTskin from both Aqua and Terra satellites by comparisons with collocated Saildrone-derived SSTskin data. The mean difference in SSTskin from the SD-1036 and SD-1037 measurements is ~0.4 K, largely resulting from differences in the atmospheric conditions experienced by the two Saildrones. The performance of MODIS on Aqua and Terra in retrieving SSTskin is comparable. Negative brightness temperature (BT) differences between 11 μm and 12 μm channels are identified as being physically based, but are removed from the analyses as they present anomalous conditions for which the atmospheric correction algorithm is not suited. Overall, the MODIS SSTskin retrievals show negative mean biases, −0.234 K for Aqua and −0.295 K for Terra. The variations in the retrieval inaccuracies show an association with diurnal warming events in the upper ocean from long periods of sunlight in the Arctic. Also contributing to inaccuracies in the retrieval is the surface emissivity effect in BT differences characterized by the Emissivity-introduced BT difference (EΔBT) index. This study demonstrates the characteristics of MODIS-retrieved SSTskin in the Arctic, at least at the Pacific side, and underscores that more in situ SSTskin data at high latitudes are needed for further error identification and algorithm development of IR SSTskin. [ABSTRACT FROM AUTHOR]

Introduction: Sound waves are refracted along the direction of their propagation owing to spatial and temporal fluctuations in the speed of sound in seawater. Errors are compounded when sound speed profiles (SSPs) with low precision are used to detect and locate distant underwater targets because an accurate SSP is critical for the identification of underwater objects based on acoustic data. Only sparse historical spatiotemporal data on the SSP of the South China Sea are available owing to political issues, its complex atmospheric system, and the unique topography of its seabed, because of which frequent oceanic movements at the mesoscale affect the accuracy of inversion of its SSP. Method: In this study, we propose a method for the inversion of the SSP of the South China Sea based on a long short-term memory model. We use continuous-time data on the SSP of the South China Sea as well as satellite observations of the height and temperature of the sea surface to make use of the long-term and short-term memory-related capacities of the proposed model. Result: It can achieve highly accurate results while using a small number of samples by virtue of the unique structure of its memory. Compared with the single empirical orthogonal function regression method, the inversion accuracy of this model is improved by 24.5%, and it performed exceptionally well in regions with frequent mesoscale movements. Discussion: This enables it to effectively address the challenges posed by the sparse sample distribution and the frequent mesoscale movements of the South China Sea. [ABSTRACT FROM AUTHOR]

The sea surface temperature (SST) is one of the most important parameters that characterize the thermal state of the ocean surface, directly affecting the heat exchange between the ocean and the atmosphere, climate change, and weather generation. Generally, due to factors such as the weather, satellite scanning orbit range, and satellite sensor malfunction, there are large areas of missing satellite remote sensing SST data, greatly reducing data utilization. In this situation, how to use effective data or avenues to rebuild missing SST data has become a research hotspot in the field of ocean remote sensing. Based on the SST data from an FY-3C visible and infrared radiometer with a spatial resolution of 5 km (FY-3C VIRR), an improved data interpolation convolutional autoencoder (I-DINCAE) was used to reconstruct the missing SST data. Through cross-validation, the accuracy of the reconstruction results was quantitatively evaluated with an RMSE of 0.36 °C and an MAE of 0.24 °C. The results showed that the I-DINCAE algorithm outperformed the original DINCAE algorithm greatly. For further optimization, a deep neural network (DNN) was chosen to adjust the error between the reconstructed SST and the in situ data. The RMSE of the final adjusted SST and in situ data is 0.466 °C, and the MAE is 0.296 °C. Compared to the in situ data, the accuracy of the adjusted data has shown a significant improvement over the reconstructed data. This method successfully applies deep-learning technology to the reconstruction of SST data, achieving the full coverage and high accuracy of SST products, which can provide more reliable and complete SST data for marine scientific research. [ABSTRACT FROM AUTHOR]

The Earth's climate is largely determined by its energy budget. Since the 1960s, satellite remote sensing has been used in estimating these energy budget components at both the top of the atmosphere (TOA) and the surface. Besides the broadband sensors that have been traditionally used for monitoring Earth's Energy Budget (EEB), data from a variety of narrowband sensors aboard both polar-orbiting and geostationary satellites have also been extensively employed to estimate the EEB components. This paper provides a comprehensive review of the satellite missions, state-of-the art estimation algorithms and the satellite products, and also synthesizes current understanding of the EEB and spatio-temporal variations. The TOA components include total solar irradiance, reflected shortwave radiation/planetary albedo, outgoing longwave radiation, and energy imbalance. The surface components include incident solar radiation, shortwave albedo, shortwave net radiation, longwave downward and upwelling radiation, land and sea surface temperature, surface emissivity, all-wave net radiation, and sensible and latent heat fluxes. Some challenges, and outlook such as virtual constellation of different satellite sensors, temporal homogeneity tests of long time-series products, algorithms ensemble, and products intercomparison are also discussed. [ABSTRACT FROM AUTHOR]

Remote marine sensing systems, such as autonomous underwater vehicles or telemetry tags, can be limited in data collection and deployment duration due to the finite energy of the onboard battery. With these technologies migrating vast distances over long-term deployments, maintaining high data resolution is difficult and often yields nonideal data sets. Furthermore, electronics systems used in telemetry tags are often potted in epoxy, which makes replacing or recharging the battery impractical. The implementation of solar energy harvesting on these technologies could increase the fidelity of collected data and/or tag longevity. This paper presents an assessment model that estimates the energy output of a stationary or migrating solar cell above or below the ocean's surface. The theory and assumptions behind the model are explained, including a review of established concepts for the purpose of consolidation and variable consistency. The model is then compared to benchmark data for verification. Finally, a preliminary analysis is conducted on the previously collected time, depth, and global location data from a northern elephant seal deployment to demonstrate the resulting model outputs and how they can be used for analysis in future works. [ABSTRACT FROM AUTHOR]

• We propose a new sea surface temperature inversion model. • The model is based on deep neural network. • This model is more accurate than traditional method. • The model is applied to different sea areas and we get good results. The traditional sea surface temperature (SST) inversion model has a complicated parameter fitting process and poor adaptability in different sea areas. This paper presents an infrared remote sensing inversion model of SST based on deep neural network to refine the situation. The training data are the moderate-resolution imaging spectroradiometer (MODIS) infrared remote sensing data on sunny days and measured data from buoy in Bohai. The accuracy of inversion results is analyzed, the determination coefficient of inversion and measured values is 0.98, the standard error is 0.71 °C and the mean absolute deviation is 0.85 °C, the results show good accuracy of the model. The accuracy of Bohai SST inversion results is compared with SST products from the MODIS sensors and the inversion model is applied to other sea areas, demonstrating the credibility and portability of the model. The data experiments in this paper prove the feasibility of the model, which provides ideas for global SST inversion. [ABSTRACT FROM AUTHOR]

The Greenhouse gases Observing SATellite (GOSAT) carries the Thermal and Near-Infrared Sensor for carbon Observations Fourier Transform Spectrometer (TANSO-FTS). TANSO-FTS covers a wide spectral range from the shortwave infrared to the thermal infrared (TIR). This paper describes the updated calibration algorithm of the TANSO-FTS Level 1B TIR spectra and the radiometric validation of the new V210.210 products by comparison with the previous version of V201.202. The revised nonlinearity correction for V210.210 product creates a decade-long, well-calibrated radiance set while minimizing the effect of two major anomalies: rotation stop of the one of the solar paddles in 2014 and a cryocooler shutdown in 2015, which caused abrupt changes in the thermal environment of the TANSO-FTS sensor. To check the improved nonlinearity correction and onboard calibration in TANSO-FTS V210.210 processing, we validated the entire spectral range by comparing with aircraft-based Scanning High-resolution Interferometer Sounder coincident in time with the GOSAT overpass. Also selected CO2 and CH4 channels are validated with the Atmospheric infrared sounder and the window channel with the in situ SST Quality Monitor data at temporally coincident and spatially collocated points. We have confirmed that the new V210.210 products exhibit no significant time trend in the window channel and a reduced spectral bias in the CO2 and CH4 channels. There remains some spectral bias, especially in the CO $_{2} \nu _{2}$ channel and CH4 channel, which are attributed to the uncertainty of orbital and seasonal variations in the average direct current level of TANSO-FTS without photon input. [ABSTRACT FROM AUTHOR]

Many air‐sea interaction processes are quantified in terms of whitecap fraction W because whitecaps are the most visible and direct way of observing breaking waves with air entrainment in the open ocean. Photographs or video images of the sea state collected from towers, ships, and aircrafts have been used over the years to obtain W. Satellite remote sensing of W is a recent development that allows long‐term, consistent observations of whitecapping on a global scale. The method for estimating W uses the variations of ocean surface emissivity at microwave frequencies (6 to 37 GHz) due to presence of sea foam on a rough sea surface. Satellite‐borne microwave radiometers detect these variations at the ocean surface as changes of the brightness temperature TB at the top of the atmosphere. We present the physical and parameterized models, as well as the input data, necessary to calculate W from WindSat TB observations with our W (TB) algorithm. We describe the implementation versions through which the W (TB) algorithm has developed. We show that satellite‐based W data vary with the frequency and polarization of the TB observations. The wind speed dependence of the W retrievals at horizontal and vertical polarizations shows different behavior and compares differently with in situ W data and existing wind speed parameterizations W(U10). We discuss the applicability of our methodology to other radiometric data. We indicate possible modifications and tuning of the models in the W (TB) algorithm that can help to further improve the accuracy of the satellite W retrievals. Plain Language Summary: When out in the open sea, we all fancy the bright, fleeting sea foam capping the waves. In addition to their beauty, these whitecaps also play a critical role in the interaction between the ocean and the atmosphere. Oceanic whitecaps form when waves break and entrain air in the water. The entrained air breaks up into bubbles, which then rise to the surface clustering into patches of sea foam. Whitecaps on the surface and bubbles in the water enhance the exchange of gases and particles across the air‐sea interface. Accurate modeling of these exchange processes is necessary for weather prediction and climate studies. Whitecaps are traditionally measured from photographs of the ocean surface. However, these measurements are difficult and only on a local scale. We developed a method to measure whitecaps from satellites. This method allows observations of oceanic whitecaps, and the processes associated with them, on a global scale. In this paper, we describe the models and the data, which we use to measure whitecaps from satellites. We show how these satellite measurements compare to the traditional photographic measurements of whitecaps. We discuss how this methodology can be improved and applied to sensors on different satellites. Key Points: Passive microwave remote sensing of whitecap fraction is presentedAlgorithm obtaining whitecap fraction from satellite observations is describedSatellite‐based retrievals of whitecap fraction are evaluated [ABSTRACT FROM AUTHOR]

With the increasing demands of large maritime scene scattering prediction and also its high-resolution imaging simulation, the traditional two-scale scattering models, such as capillary wave modified facet scattering model (CWMFSM), appear to be inefficient and laborious when encountering scattering simulation of large maritime scene or long-time case. This paper proposes an effective and efficient approach, i.e. a statistical realisation of CWMFSM, for scattering simulation of space-time varying sea surface. Namely, this approach can give a fast and accurate simulation of large scene and long-time space-time varying sea surface scattering results using the memoryless non-linear transform method together with the statistic characteristics of results calculated by CWMFSM method. From the comparisons of texture feature, statistical characteristics and Doppler spectrum between the results derived from CWMFSM method and the statistical realisation, it is demonstrated that the proposed approach can give a pretty efficient simulation of space-time varying sea surface scattering with almost the same accuracy as the CWMFSM method itself. Similar with CWMFSM, the proposed approach can be applied to fields of sea surface scattering intensity distribution simulation, Doppler spectrum investigation of dynamic sea surface, composite scattering prediction of sea surface with multiple targets, SAR remote-sensing simulation of maritime scene, etc. [ABSTRACT FROM AUTHOR]