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The document titled "Remote Sensing of Coastal Waters, Land Use/Cover, Lakes, Rivers, and Watersheds II" explores the significance of remote sensing in monitoring and analyzing coastal regions, land masses, lakes, rivers, and watersheds. It emphasizes the advancements in remote sensing technologies that have improved data collection and understanding of environmental changes. The document includes 22 research papers covering topics such as land cover change, ecological environments, wetlands and habitats, water quality, and marine disasters and environmental impacts. These papers offer valuable insights into the interactions between human and natural factors and aim to inform sustainable resource management efforts. The integration of artificial intelligence with remote sensing data further enhances accuracy and understanding. The authors express their gratitude to the contributors and reviewers for their valuable contributions to this Special Issue. [Extracted from the article]

The two prerequisites for monitoring SDG11.A "support positive economic, social and environmental links between urban, peri-urban and rural areas by strengthening national and regional development planning" are the classification of the urban–rural continuum and the extraction of spatial links. However, the complexity and diversity of urban patch distribution make it difficult to achieve a global rapid assessment. Based on the self-developed high-resolution global impervious surface area 2021 (Hi-GISA 2021) product, this study combined the complex network with remote sensing technology to propose a new method to delineate and evaluate the pattern and inner spatial links of the urban–rural continuum for five typical urban agglomerations in China, including the Beijing–Tianjin–Hebei urban agglomeration (BTHUA), the Yangtze River Delta urban agglomeration (YRDUA), the Greater Bay Area (GBAUA), the Chengdu–Chongqing urban agglomeration (CYUA), and the Middle Reaches of Yangtze River urban agglomeration (MRYRUA). The research results are in good agreement with Chinese government documents. First, the five urban agglomerations are all small-world networks with a low degree of overall polycentricity, and the urbanization degrees of GBAUA and YRDUA are higher than BTHUA, CYUA, and MRYRUA. Second, the imbalanced development of YRDUA is higher than the other regions, and the siphon effects of BTHUA and MRYRUA are more significant than YRDUA, CYUA, and GBAUA. Third, some multi-centers show significant siphon effects. The urbanization degree is highly correlated with the urbanization potential but not positively correlated with the degree of balanced development. The results can provide data, methods, and technical support for monitoring and evaluating SDG11.A. [ABSTRACT FROM AUTHOR]

As the most relevant indicator of global warming, the ocean heat content (OHC) change is tightly linked to the Earth's energy imbalance. Therefore, it is vital to study the OHC and heat absorption and redistribution. Here we analyzed the characteristics of global OHC variations based on a previously reconstructed OHC dataset (named OPEN) with four other gridded OHC datasets from 1993 to 2021. Different from the other four datasets, the OPEN dataset directly obtains OHC through remote sensing, which is reliable and superior in OHC reconstruction, further verified by the Clouds and the Earth's Radiant Energy System (CERES) radiation flux data. We quantitatively analyzed the changes in the upper 2000 m OHC of the oceans over the past three decades from a multisource and multilayer perspective. Meanwhile, we calculated the global ocean heat uptake to quantify and track the global ocean warming rate and combined it with the Oceanic Niño Index to analyze the global evolution of OHC associated with El Niño–Southern Oscillation variability. The results show that different datasets reveal a continuously increasing and non-decaying global ocean warming from multiple perspectives, with more heat being absorbed by the subsurface and deeper ocean over the past 29 years. The global OHC heating trend from 1993 to 2021 is 7.48 ± 0.17, 7.89 ± 0.1, 10.11 ± 0.16, 7.78 ± 0.17, and 12.8 ± 0.26 × 1022 J/decade according to OPEN, IAP, EN4, Ishii, and ORAS5, respectively, which shows that the trends of the OPEN, IAP, and Ishii datasets are generally consistent, while those of EN4 and ORAS5 datasets are much higher. In addition, the ocean warming characteristics revealed by different datasets are somewhat different. The OPEN OHC dataset from remote sensing reconstruction shows a unique remote sensing mapping advantage, presenting a distinctive warming pattern in the East Indian Ocean. Meanwhile, the OPEN dataset had the largest statistically significant area, with 85.6% of the ocean covered by significant positive trends. The significant and continuous increase in global ocean warming over the past three decades, revealed from remote sensing reconstruction, can provide an important reference for projecting ocean warming in the context of global climate change toward the United Nations Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

Background: Road network data are crucial in various applications, such as emergency response, urban planning, and transportation management. The recent application of deep neural networks has significantly boosted the efficiency and accuracy of road network extraction based on remote sensing data. However, most existing methods for road extraction were designed at local or regional scales. Automatic extraction of large-scale road datasets from satellite images remains challenging due to the complex background around the roads, especially the complicated land cover types. To tackle this issue, this paper proposes a land cover background-adaptive framework for large-scale road extraction. Method: A large number of sample image blocks (6820) are selected from six different countries of a wide region as the dataset. OpenStreetMap (OSM) is automatically converted to the ground truth of networks, and Esri 2020 Land Cover Dataset is taken as the background land cover information. A fuzzy C-means clustering algorithm is first applied to cluster the sample images according to the proportion of certain land use types that obviously negatively affect road extraction performance. Then, the specific model is trained on the images clustered as abundant with that certain land use type, while a general model is trained based on the rest of the images. Finally, the road extraction results obtained by those general and specific modes are combined. Results: The dataset selection and algorithm implementation were conducted on the cloud-based geoinformation platform Google Earth Engine (GEE) and Google Colaboratory. Experimental results showed that the proposed framework achieved stronger adaptivity on large-scale road extraction in both visual and statistical analysis. The C-means clustering algorithm applied in this study outperformed other hard clustering algorithms. Significance: The promising potential of the proposed background-adaptive network was demonstrated in the automatic extraction of large-scale road networks from satellite images as well as other object detection tasks. This search demonstrated a new paradigm for the study of large-scale remote sensing applications based on deep neural networks. [ABSTRACT FROM AUTHOR]

Rooftop greening stands as a distinctive approach to delivering green space without occupying ground-level areas. The spatial configuration of rooftop greenery and its socioeconomic associations are critical for sustainable urban development and environmental justice. However, historical research trends have predominantly concentrated on open, ground-level environments, neglecting the vertical manifestation of urban greenery. Investigations into green roofs have typically been circumscribed to their environmental benefits, while little attention has been paid to the spatial distribution of rooftop greenery and the drivers behind it. This study aims to fill the gap by implementing a multiscale mapping approach with satellite imagery to quantify rooftop greenness in Hong Kong. An integrated analysis incorporating geographic and demographic data reveals a correlation between rooftop greenness and factors such as income levels, housing types, residential area sizes, and household compositions. This association exhibits pronounced environmental inequality, where lower-income individuals, solitary residents, and households with smaller living spaces in densely populated and aged neighborhoods are deprived of rooftop green spaces, in contrast to their counterparts in more affluent neighborhoods. Government-subsidized public housing initiatives have significantly contributed to enhancing the green living environment for low-income families. Buildings administered by government have demonstrated exceptional performance in rooftop greening efforts. This evidence provides nuanced insights into the advantages and deficiencies of green roof planning in Hong Kong and could serve as a critical reference for other densely urbanized cities working towards a sustainable and equitable urban green environment. • Precise city-scale mapping of rooftop greenness at the pixel level. • Revealing spatial disparities in rooftop greenness across Hong Kong's landscape. • Socioeconomic inequality reflected in uneven rooftop greenness distribution. • Low to middle-income groups in cramped quarters face minimal rooftop greenness. • Affluent areas consistently exhibit elevated levels of rooftop greenness. [ABSTRACT FROM AUTHOR]

Aim: Mangrove forests are important coastal wetlands for the blue carbon budget and play a significant role in mitigating global climate change. However, spatial patterns of carbon stocks in mangrove forests on an island scale have not been quantified owing to methodological limitations and lack of understanding of controlling factors. We took the entire Hainan Island as a case study and aimed to carry out a comprehensive investigation of the spatial patterns and driving factors of carbon stocks in mangrove forests. Location: Southern China. Time period: 2017–2020. Major taxa studied: Mangrove forest. Methods: The upscaling method combined with field surveys and Sentinel‐2 imagery analysis were used to compare different models for optimization of mangrove ecosystem carbon stock estimations. We also used structural equation modelling (SEM) to evaluate the factors driving the distributional patterns of mangrove carbon stocks on an island scale. Results: The current total mangrove carbon stock of the entire Hainan Island was estimated to be 703,181 Mg C (with a mean density of 192 Mg C/ha), with the above‐ and below‐ground carbon stocks averaging at 44.7 and 147.3 Mg C/ha, respectively. The mangrove carbon storage in the north‐eastern region of the island was the highest and in the west region the lowest. Sediment nitrogen content and plant species diversity had the most positive driving effects on the distribution of total carbon stock for Hainan Island mangroves. Main conclusions: The combination of field surveys and Sentinel‐2 imagery analysis can be applied to regional‐scale estimations of carbon stocks for mangrove forests. Spatial pattens of mangrove carbon stocks vary among locations on Hainan Island, and soil nutrient (especially nitrogen) availability is the dominant factor regulating carbon stock variations along the land‐to‐sea gradient. Our findings have significant implications for better understanding the distribution of mangrove carbon stocks and quantification of the global blue carbon budget. [ABSTRACT FROM AUTHOR]

Terrestrial evapotranspiration (ET) is a crucial link between Earth's water cycle and the surface energy budget. Accurate measurement and estimation remain a major challenge in geophysical, biological, and environmental studies. Pioneering work, represented by Dalton and Penman, and the development of theories and experiments on turbulent exchange in the atmospheric boundary layer (ABL), laid the foundation for mainstream methodologies in ET estimation. Since the 1990s, eddy covariance (EC) systems and satellite remote sensing have been widely applied from cold to tropical and from arid to humid regions. They cover water surfaces, wetlands, forests, croplands, grasslands, barelands, and urban areas, offering an exceptional number of reports on diverse ET processes. Surface nocturnal ET, hysteresis between ET and environmental forces, turbulence intermittency, island effects on heterogeneous surfaces, and phase transition between underlying surfaces are examples of reported new phenomena, posing theoretical and practical challenges to mainstream ET methodologies. Additionally, based on non-conventional theories, new methods have emerged, such as maximum entropy production and nonparametric approaches. Furthermore, high-frequency on-site observation and aerospace remote sensing technology in combination form multi-scale observations across plant stomata, leaves, plants, canopies, landscapes, and basins. This promotes an insightful understanding of diverse ET processes and synthesizes the common mechanisms of the processes between and across spatial and temporal scales. All the recent achievements in conception, model, and technology serve as the basis for breaking through the known difficulties in ET estimation. We expect that they will provide a rigorous, reliable scientific basis and experimental support to address theoretical arguments of global significance, such as the water-heat-carbon cycle, and solve practical needs of national importance, including agricultural irrigation and food security, precise management of water resources and eco-environmental protection, and regulation of the urban thermal environment and climate change adaptation. [ABSTRACT FROM AUTHOR]

Sonneratia have posed a threat to native mangrove species in Hong Kong. Early detection of individual Sonneratia when they are introduced and naturalized before invasion is essential for native mangrove species protection, especially for Sonneratia with a strong ability of propagation. This letter aims to provide an effective way to the accurate detection of individual Sonneratia. Specifically, using very high spatial resolution remotely sensed data, we adapt the RetinaNet, incorporating multiscale features for sapling detection and convolutional neural networks for detecting the Sonneratia distributed scatteredly among native species. The Sonneratia were detected with a higher mean average precision (mAP) 0.50 of 0.3891 with a precision of 0.5465 than that from the deformable part model. In addition, 3678 Sonneratia were detected at early stage. This letter can support the government for mangrove forest management and offer a scientific guidance for adequate response to the species invasion, like annual removal of Sonneratia, and then reduce the consumption of labor and time over a large scale. In addition, it can provide a quantitative survey for Sonneratia management. [ABSTRACT FROM AUTHOR]

Incorporating optical and polarimetric synthetic-aperture radar (SAR) data to estimate impervious surface is useful but challenging due to their different geometric imaging mechanism and the high diversity of urban land covers. The recent development of deep convolutional networks (DCN) opens a promising opportunity by automatically extracting the deep features from both data sets. In this study, a small-patched DCN (SDCN) was designed to estimate the impervious surface from optical and SAR data. Benchmark methods, e.g., GoogLeNet, VGG16, ResNet50, and the support vector machine were employed for comparison. Two study sites in the most complex metropolitan of China, the Guangdong-Hong Kong-Macau Greater Bay Area, were selected to assess the proposed method. Experimental results indicated the effectiveness of proposed SDCN with a better accuracy outperforming other benchmark methods. Furthermore, we found that 60%–80% of training samples performed comparably with the whole training set, indicating that a large number of training samples may not be necessary in all cases, depending on the settings of some factors (e.g., number of epochs). Generally, SDCN appears more suitable than other methods in terms of combining the optical and SAR data and improved the accuracy of estimating impervious surface. [ABSTRACT FROM AUTHOR]

Numerous studies on environmental modeling and ecological process lay emphasis on the fundamental information of the impervious surface area (ISA). However, accurate ISA extraction from high-resolution satellite images remains challenging due to both the high diversity of land covers and shadow effects from tall buildings and trees. To address the problem, a discriminative Optical-SAR-LiDAR dictionary sparse representation classification (OSLD-SRC) method was proposed using high-resolution WorldView-2, GeoEye-1, TerraSAR-X, and airborne LiDAR data. First, it used multisource data and fuzzy samples by low-pass filtering (LPF) to solve the problem of roads and buildings misclassification; second, it learned the Optical-SAR-LiDAR dictionary for nonshadow and shadow classes, and then used discriminative sparse coding method for classification to reduce the shadow effects and improve the ISA extraction accuracy. Experimental results demonstrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Numerous studies have demonstrated the benefits of fusing optical and synthetic aperture radar (SAR) data for urban impervious surfaces estimation. Given the increasing availability of SAR satellite data, the synergized use of optical and SAR data is increasingly important. However, previous studies have focused primarily on a single scale. This study aims to evaluate the impervious surfaces estimation using different combinations of optical and SAR data, as well as to quantify the scale effects of combining optical and SAR data under various resolutions from 30 to 1050 m. Six combinations of optical and SAR data in three cities, Shenzhen, Mumbai, and São Paulo, were compared. Experimental results illustrated the effectiveness of using texture and SAR data in improving impervious surface estimation with producer's accuracy improvement of 5%, 0%, and 4% in Shenzhen, Mumbai, and São Paulo at 30 m. The correlation coefficient assessment between the estimated and referenced impervious surfaces at various scales demonstrated that TerraSAR-X data generally outperformed ENVISAT advanced SAR of impervious surface estimation. Estimation accuracy of impervious surfaces in Shenzhen had an improvement of 4% to 5%. Moreover, the texture information of SAR data was observed to provide positive contributions to the impervious surfaces estimation in three study cases at different scales. [ABSTRACT FROM PUBLISHER]

Multisource remote sensing data have been widely used to improve land-cover classifications. The combination of synthetic aperture radar (SAR) and optical imagery can detect different land-cover types, and the use of genetic algorithms (GAs) and support vector machines (SVMs) can lead to improved classifications. Moreover, SVM kernel parameters and feature selection affect the classification accuracy. Thus, a GA was implemented for feature selection and parameter optimization. In this letter, a GA-SVM algorithm was proposed as a method of classifying multifrequency RADARSAT-2 (RS2) SAR images and Thaichote (THEOS) multispectral images. The results of the GA-SVM algorithm were compared with those of the grid search algorithm, a traditional method of parameter searching. The results showed that the GA-SVM algorithm outperformed the grid search approach and provided higher classification accuracy using fewer input features. The images obtained by fusing RS2 data and THEOS data provided high classification accuracy at over 95%. The results showed improved classification accuracy and demonstrated the advantages of using the GA-SVM algorithm, which provided the best accuracy using fewer features. [ABSTRACT FROM PUBLISHER]

Urban trees store and sequester large amounts of carbon and are a vital component of natural climate solutions. Despite the well-recognized carbon benefits of urban trees, there is limited effort to examine how spatial distribution of carbon density varies across distinctive social, demographic, and built dimensions of urban landscapes. Moreover, it is unclear whether specific aspects of landscape structure and design could help increase carbon densities in urban trees. Here, we produced a fine-resolution carbon density map of urban trees in New York City (NYC) by integrating high-resolution land cover map, LiDAR-derived tree metrics, i-Tree Eco, and field survey data. We then explored spatial variations of carbon density across the gradients of urban development intensity, social deprivation index, and neighborhood age, and we examined the relationships between carbon density, and fragmentation, aggregation, size, and shape of tree canopy cover. We find that carbon stored in urban trees in NYC is estimated as 1078 Gg, with an average density of 13.8 Mg/ha. This large amount of carbon is unevenly distributed, with carbon densities being highest in Bronx and in open parks and street trees. Furthermore, carbon densities are negatively associated with urban development intensity and the social gradient of deprivation. Regarding the impacts of tree morphology on carbon density, our results show that while the amount of tree cover is the most influential factor in determining carbon density, small-sized forest patches and moderate levels of forest edges are also conductive to increasing carbon densities of urban trees. To incorporate urban forestry into developing innovative, effective, and equitable climate mitigation strategies, planners and decision makers need to identify the optimal spatial configuration of urban forests and invest in tree planting programs in marginalized communities. • Carbon density in urban trees in New York City is estimated as 13.8 Mg/ha. • Socially deprived areas are associated with lower carbon densities. • Small-sized forest patches and moderate levels of forest edges are beneficial to increase carbon densities of urban trees. • The findings provide implications to incorporate urban forestry to develop effective and equitable climate mitigation strategies. [ABSTRACT FROM AUTHOR]

Land use/land cover (LULC) classification using optical and synthetic aperture radar (SAR) remote sensing images is becoming increasingly important to produce more accurate LULC products. As an important step, feature normalization techniques have been studied by the areas of pattern recognition. Nevertheless, because of the totally different imaging mechanisms of optical and SAR sensors, most of the existing normalization approaches are not suitable for optical and SAR data fusion. Moreover, whether normalization is a significant step remains unclear regarding optical and SAR fusion. Taking the Satellite Pour l'Observation de la Terre (SPOT-5) and the Advanced Land Observing Satellite (ALOS)/Phased Array type L-band SAR (PALSAR) (HH and HV polarizations) as the optical and SAR data, this letter aims to evaluate the impact of feature normalization. Experimental results indicated that feature normalization is not necessarily significant depending on fusion methods. For instance, distribution-dependent classifiers (e.g., a maximum likelihood classifier) are independent of feature normalization; thus, it has no impact on the results when using these classifiers. Moreover, advanced classifiers (e.g., a support vector machine) with built-in normalization are also not influenced by feature normalization. In contrast, a minimum distance classifier and an artificial neural network (ANN) depend on the input values of optical and SAR features and thus can be influenced by feature normalization. However, our experiments showed a fluctuation in classification accuracy using an ANN with normalized features. Therefore, more experiments are required to investigate the optimal normalization approaches for the optical and SAR images when using an ANN as the fusion method. [ABSTRACT FROM PUBLISHER]

Urban tree species identification is the basis for studying the urban-environment coordination mechanism at the species level. Although the gradual maturity of remote sensing data and related research including light detection and ranging (LiDAR) provides a good foundation for the realization of this technology, multiple reasons such as cost, data openness, study scope limitations, and weakness of traditional morphological features make such data still challenging to apply to subtropical urban trees with heterogeneous canopy structures and high biodiversity. To address the problem, we developed two large-scale LiDAR morphological features in this research by, 1) modifying the rotate image method based on the axisymmetric structure to make it easier to use, and 2) developing an innovative adaptive ellipsoid method to extract the canopy features of the non-axisymmetric structure effectively. We evaluated the ability of these two morphological features to describe 12 common subtropical urban tree (SUT) species in Hong Kong growing in urban parks and streets, obtaining an accuracy of 88%. And the advantages of the proposed method are demonstrated by comparison with existing LiDAR morphological features and mean decrease accuracy (MDA) analysis. Our results illustrated that the rotate image feature based on the axisymmetric structure did not perform as well as the adaptive ellipsoid feature based on the non-axisymmetric structure in SUT, and the combined application of these two new morphological features got further accuracy improvement. The method proposed in this study had significant advantages in terms of accuracy, the number of species included, and generalisation capability compared to existing studies on the identification of subtropical urban trees. [ABSTRACT FROM AUTHOR]

An improved reconstruction algorithm is proposed for compact polarimetric (CP) synthetic aperture radar (SAR) on oil spill detection. Based on the differences in statistical behavior between open and oil-covered sea surfaces, the proposed algorithm can iteratively reconstruct quad-pol SAR images from CP SAR data. During the experiment, it outperformed two existing compact SAR reconstruction algorithms in terms of both statistical and information theoretical analysis. [ABSTRACT FROM PUBLISHER]

Feature extraction is highly important for classification of remote-sensing (RS) images. However, extraction of comprehensive spatial features from high-resolution imagery is still challenging, leading to many misclassifications in various applications. To address the problem, a shape-adaptive neighbourhood (SAN) technique is presented based on human visual perception. The SAN technique is an adaptive feature-extraction method that not only considers spectral feature information but also the spatial neighbourhood as well as the shape of features. The distinct advantage of this approach is that it can be adjusted to different feature sizes and shapes. Assessment experiments on a Système Pour l'Observation de la Terre 5 (SPOT-5) image were conducted to perform classification of land use/land cover. Results showed that improvement with SAN features is not significant for supervised classifiers due to the spectral confusion problem that resulted from similar spectral signatures between farmland and green areas, but a particularly significant improvement is observed for the unsupervised classifier. For the unsupervised classification, the SAN features noticeably improved the overall accuracy from 0.58 to 0.86, and the kappa coefficient from 0.45 to 0.80, indicating promise in the application of SAN features in the auto-interpretation of RS images. [ABSTRACT FROM PUBLISHER]

Abstract: The estimation of impervious surface area (ISA) is becoming increasingly important because of its environmental and socio-economic significance. However, accurate ISA estimation remains challenging due to the diversity of impervious materials, as well as the occurrence of clouds in subtropical humid areas. In order to address these challenges and provide an accurate estimation of ISA in cloudy areas, it is advantageous to use both optical and microwave remote sensing which can penetrate cloud coverage. Our study aims to conduct a comprehensive comparison between these two data sources and between different methods for mapping ISA. Both the classification results and accuracy assessment provide a better understanding about the differences between Landsat ETM+ and ENVISAT ASAR images and between artificial neural network (ANN) and support vector machine (SVM) classifier for estimating the impervious surfaces. The comparison demonstrates that ETM+ images alone provide a better ISA estimation (OA: about 90%; Kappa: about 0.88) than the estimation from ASAR images alone (OA: about 85%; Kappa: about 0.77). Additionally, the experiment indicates that SVM should be a better choice for ISA estimation using Landsat ETM+ images, while ANN turns out to be more sensitive to the confusion between dry soils and bright impervious surfaces, and between shade and dark impervious surfaces. For ENVISAR ASAR images, ANN gets a better result with higher accuracy, while the SVM classifier produces more noise and has some edge effects. [Copyright &y& Elsevier]

The upcoming Surface Water and Ocean Topography (SWOT) mission will measure rivers wider than 50–100 m using a 21-day orbit, providing river reach derived discharges that can inform applications like flood forecasting and large-scale hydrologic modelling. However, these discharges will not be uniform in time or coincident with those of neighboring reaches. It is often assumed discharge upstream and downstream of a river location are highly correlated in natural conditions and can be transferred using a scaling factor like the drainage area ratio between locations. Here, the applicability of the drainage area ratio method to integrate, in space and time, SWOT-derived discharges throughout the observable river network of the Mississippi River basin is assessed. In some cases, area ratios ranging from 0.01 to 100 can be used, but cumulative urban area and/or the number of dams/reservoirs between locations decrease the method's applicability. Though the mean number of SWOT observations for a given reach increases by 83% and the number of peak events captured increases by 100%, expanded SWOT sampled time series distributions often underperform compared to the original SWOT sampled time series for significance tests and quantile results. Alternate expansion methods may be more viable for future work. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (chl-a) is an important parameter of water quality and its concentration can be directly retrieved from satellite observations. The Ocean and Land Color Instrument (OLCI), a new-generation water-color sensor onboard Sentinel-3A and Sentinel-3B, is an excellent tool for marine environmental monitoring. In this study, we introduce a new machine learning model, Light Gradient Boosting Machine (LightGBM), for estimating time-series chl-a concentration in Fujian's coastal waters using multitemporal OLCI data and in situ data. We applied the Case 2 Regional CoastColour (C2RCC) processor to obtain OLCI band reflectance and constructed four spectral indices based on OLCI feature bands as supplementary input features. We also used root-mean-square error (RMSE), mean absolute error (MAE), median absolute percentage error (MAPE), and R2 as performance indicators. The results indicate that the addition of spectral indices can easily improve the prediction accuracy of the model, and normalized fluorescence height index (NFHI) has the best performance, with an RMSE of 0.38 µg/L, MAE of 0.22 µg/L, MAPE of 28.33%, and R2 of 0.785. Moreover, we used the well-known band ratio and three-band methods for chl-a estimation validation, and another two OLCI chl-a products were adopted for comparison (OC4Me chl-a and Inverse Modelling Technique (IMT) Neural Net chl-a). The results confirmed that the LightGBM model outperforms the traditional methods and OLCI chl-a products. This study provides an effective remote sensing technique for coastal chl-a concentration estimation and promotes the advantage of OLCI data in ocean color remote sensing. [ABSTRACT FROM AUTHOR]

The area, distribution, and temporal dynamics of anthropogenic impervious surface (AIS) at large scale are significant for environmental, ecological and socio-economic studies. Remote sensing has become an important tool for monitoring large scale AIS, while it remains challenging for accurate extraction of AIS using optical datasets alone due to the high diversity of land covers over large scale. Previous studies indicated the complementary use of synthetic aperture radar (SAR) to improve the AIS estimation, while most of them were limited to local and small scales. The potential of SAR for large scale AIS mapping is still uncertain and underexplored. In this study, first, a machine learning framework incorporating both optical and SAR data based on Google Earth Engine platform was developed for mapping and analyzing the annual dynamics of AIS in China. Feature-level fusion for SAR and optical data across large scale was tested applicable considering the backscattering coefficients, texture measures and spectral characteristics. Improved accuracy (averaged 2% increased overall accuracy and averaged 4% increased Kappa coefficient) and better delineation between the bright impervious surface and bare land was observed comparing with using optical data alone. Second, comprehensive assessment was conducted using high-resolution samples from Google Earth, census data from China Statistic Yearbook and benchmark datasets from the GlobeLand30 and GHSL, demonstrating the feasibility and reliability of the proposed method and results. Last but not the least, we analyzed the spatial and temporal patterns of AIS in China from national, regional and provincial levels. • We incorporated SAR and optical images for large scale impervious surfaces mapping. • Results were validated with high resolution samples, census and benchmark data. • SAR generally improved the overall accuracy by 2%. • SAR better delineated the impervious surface from bare land. [ABSTRACT FROM AUTHOR]

Urbanization has become one of the most important human activities modifying the Earth's land surfaces; and its impacts on tropical and subtropical cities (e.g., in South/Southeast Asia) are not fully understood. Colombo; the capital of Sri Lanka; has been urbanized for about 2000 years; due to its strategic position on the east–west sea trade routes. This study aims to investigate the characteristics of urban expansion and its impacts on land surface temperature in Colombo from 1988 to 2016; using a time-series of Landsat images. Urban land cover changes (ULCC) were derived from time-series satellite images with the assistance of machine learning methods. Urban density was selected as a measure of urbanization; derived from both the multi-buffer ring method and a gravity model; which were comparatively adopted to evaluate the impacts of ULCC on the changes in land surface temperature (LST) over the study period. The experimental results indicate that: (1) the urban land cover classification during the study period was conducted with satisfactory accuracy; with more than 80% for the overall accuracy and over 0.73 for the Kappa coefficient; (2) the Colombo Metropolitan Area exhibits a diffusion pattern of urban growth; especially along the west coastal line; from both the multi-buffer ring approach and the gravity model; (3) urban density was identified as having a positive relationship with LST through time; (4) there was a noticeable increase in the mean LST; of 5.24 °C for water surfaces; 5.92 °C for vegetation; 8.62 °C for bare land; and 8.94 °C for urban areas. The results provide a scientific reference for policy makers and urban planners working towards a healthy and sustainable Colombo Metropolitan Area. [ABSTRACT FROM AUTHOR]

The Philippines is rich in mangrove forests, containing 50% of the total mangrove species of the world. However, the vast mangrove areas of the country have declined to about half of its cover in the past century. In the 1970s, action was taken to protect the remaining mangrove forests under a government initiative, recognizing the ecological benefits mangrove forests can bring. Here, we examine two mangrove areas in the Philippines—Coron in Palawan and Balangiga-Lawaan in Eastern Samar over a 30-year period. Sets of Landsat images from 1987 to 2016 were classified and spatially analyzed using four landscape metrics. Additional analyses of the mangrove areas' spatiotemporal dynamics were conducted. The impact of typhoon landfall on the mangrove areas was also analyzed in a qualitative manner. Spatiotemporal changes indicate that both the Coron and Balangiga-Lawaan mangrove forests, though declared as protected areas, are still suffering from mangrove area loss. Mangrove areal shrinkage and expansion can be attributed to both typhoon occurrence and management practices. Overall, our study reveals which mangrove forests need more responsive action, and provides a different perspective in understanding the spatiotemporal dynamics of these mangrove areas. [ABSTRACT FROM AUTHOR]

Short-term characteristics of urban land cover change have been observed and reported from satellite images, although urban landscapes are mainly influenced by anthropogenic factors. These short-term changes in urban areas are caused by rapid urbanization, seasonal climate changes, and phenological ecological changes. Quantifying and understanding these short-term characteristics of changes in various land cover types is important for numerous urban studies, such as urbanization assessments and management. Many previous studies mainly investigated one study area with insufficient datasets. To more reliably and confidently investigate temporal variation patterns, this study employed Fourier series to quantify the seasonal changes in different urban land cover types using all available Landsat images over four different cities, Melbourne, Sao Paulo, Hamburg, and Chicago, within a five-year period (2011–2015). The overall accuracy was greater than 86% and the kappa coefficient was greater than 0.80. The R-squared value was greater than 0.80 and the root mean square error was less than 7.2% for each city. The results indicated that (1) the changing periods for water classes were generally from half a year to one and a half years in different areas; and, (2) urban impervious surfaces changed over periods of approximately 700 days in Melbourne, Sao Paulo, and Hamburg, and a period of approximately 215 days in Chicago, which was actually caused by the unavoidable misclassification from confusions between various land cover types using satellite data. Finally, the uncertainties of these quantification results were analyzed and discussed. These short-term characteristics provided important information for the monitoring and assessment of urban areas using satellite remote sensing technology. [ABSTRACT FROM AUTHOR]

Mangrove deforestation is occurring globally at a rapid rate, and is causing serious ecological and economic losses on all scales. Monitoring mangrove changes is the first important step for mangrove management and conservation. Zonation of mangrove species (ZMS) is the predictable and discrete ordering of mangrove species caused by a unique, intertidal environment. Mapping the ZMS is critical to understanding the mangrove community at a species level. In this paper, the Standard Deviational Ellipse (SDE) was proposed as a method to evaluate mangrove species from a new dimension of directional changes. Three dominant mangrove species, Kandelia obovata (KO), Avicennia marina (AM), and Acanthus ilicifolius (AI), in Mai Po, Hong Kong were analyzed using SDEs based on the time series Système Pour l’Observation de la Terre (SPOT) and Gaofen-1 (GF-1) satellite images. The SDE results demonstrated that in the past 25 years: (1) The overall spatial extent of the mangroves in Mai Po expanded significantly, approximately from 150 to 350 Ha, and show a zonation pattern with a clear sequence of species perpendicular to the shoreline; (2) KO was the dominant species in most years, showing the strongest directional characteristic; (3) All three species zones have moved toward the north and west, as observed by the SDE centers. The SDE was proved to be a useful tool for understanding the temporal and spatial changes of mangrove zonation. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) remote sensing technology has the advantage of all-weather observation and can acquire time-series images with crop growth period, which has great potential for applications such as crop phenology analysis. However, available studies primarily focus on conducting statistical and crop growth analyses based on the polarization or backscatter intensities of SAR images, and the exploration of polarization scattering information in SAR images is not sufficient. To comprehensively reflect the polarization characteristics and scattering mechanisms of crop at different growth stages, we propose a new method for extracting vegetation descriptors from Sentinel-1 dual-polarimetric SAR data. The method combines the backscattering intensity and polarization decomposition information to construct a normalized index q , which is used to generate three vegetation descriptors: the co-pol purity parameter (m cp ), the pseudo-scattering angle (θ cp ), and the pseudo-scattering entropy (H cp ). Further, a novel unsupervised clustering framework, founded on H cp and θ cp , has been proposed. This framework establishes six zones (named as Z1 to Z6) representing distinct physical scattering mechanisms, and by statistically sampling point data, it can determine the growth stage of the crops For validating the performance of the proposed vegetation descriptors and clustering framework, we conducted a three-year experiment using four crops from two publicly available datasets, namely wheat and canola from the Carman in Canada (Test site-1), corn and soybeans from Iowa in the United States (Test site-2). The experimental results indicate that m cp , θ cp , and H cp exhibit regular changes at different growth stages of crops from planting to maturity, with m cp and θ cp gradually decreasing while H cp gradually increasing. Within the entire phenology window, θ cp changes by approximately 42°, while both m cp and θ cp varies by about 0.9, and the sampling points shift from the Z2 to the Z5 zone. The vegetation descriptors are highly sensitive to the growth status of crops, and the clustering framework can also effectively respond to different growth stages of vegetation. Furthermore, the vegetation descriptors and clustering framework proposed in this study have the potential for extended application to different crop types and other polarimetric SAR data sources. [ABSTRACT FROM AUTHOR]

Chromophoric dissolved organic matter (CDOM), the key component in aquatic environment, plays an important role in biogeochemical processing. In this study, in situ reflectance and water samples were used to develop models for estimating CDOM absorption in a complex freshwater environment in Poyang Lake, China. The in situ water reflectance spectra, CDOM absorption spectra and other water-color parameters from 92 samples collected in four representative study areas between 2015 and 2016 were analysed. Band ratio models were established to estimate CDOM absorption coefficient at 355 nm [a g (355)] based on the correlation analysis between reflectance and a g (355). The results indicated that the models performed well for estimating a g (355) when the 92 samples were divided into two datasets with the threshold of concentration of total suspended matter (TSM) as 10 mg/L. The band ratios of R(689)/R(497) and R(767)/R(826) were selected to establish models for retrieval of a g (355) in clean water (TSM < 10 mg/L) and turbid water (TSM ≥ 10 mg/L), respectively. The determination coefficients (r 2 ) of calibration models for clean and turbid water were 0.70 and 0.73, respectively. The percentage root-mean-square errors (%RSME) of validation models for clean and turbid water were 13.2% and 11.6%, respectively. The simulated Sentinel-2 and Landsat-8 bands based on reflectance spectra were used to examine potential capability for retrieving CDOM using these sensors. The results indicated that Sentinel-2 performed better than Landsat-8 for estimating a g (355). The Sentinel-2 band ratio of B4/B2 or B5/B2 and B7/B8 or B7/B8A are effective for retrieval of CDOM in clean and turbid water of Poyang Lake, respectively. [ABSTRACT FROM AUTHOR]

Extraction of drainage networks is an important element of river flow routing in hydrology and large-scale estimates of river behaviors in Earth sciences. Emerging studies with a focus on greenhouse gases reveal that small rivers can contribute to more than half of the global carbon emissions from inland waters (including lakes and wetlands). However, large-scale extraction of drainage networks is constrained by the coarse resolution of observational data and models, which hinders assessments of terrestrial hydrological and biogeochemical cycles. Recognizing that Sentinel-2 satellite can detect surface water up to a 10-m resolution over large scales, we propose a new method named Remote Sensing Stream Burning (RSSB) to integrate high-resolution observational flow location with coarse topography to improve the extraction of drainage network. In RSSB, satellite-derived input is integrated in a spatially continuous manner, producing a quasi-bathymetry map where relative relief is enforced, enabling a fine-grained, accurate, and multitemporal extraction of drainage network. RSSB was applied to the Lancang-Mekong River basin to derive a 10-m resolution drainage network, with a significant reduction in location errors as validated by the river centerline measurements. The high-resolution extraction resulted in a realistic representation of meanders and detailed network connections. Further, RSSB enabled a multitemporal extraction of river networks during wet/dry seasons and before/after the formation of new channels. The proposed method is fully automated, meaning that the network extraction preserves basin-wide connectivity without requiring any postprocessing, hence facilitating the construction of drainage networks data with openly accessible imagery. The RSSB method provides a basis for the accurate representation of drainage networks that maintains channel connectivity, allows a more realistic inclusion of small rivers and streams, and enables a greater understanding of complex but active exchange between inland water and other related Earth system components. • A new method is developed to extract high-resolution drainage network. • The proposed method integrates 10-m Sentinel-2 imagery and 90-m resolution DEM. • A large basin-scale application demonstrates effective and robust extractions. • The inclusion of small streams at high-resolution improves drainage density scaling. • The results have implications for potential use in hydromorphologic research. [ABSTRACT FROM AUTHOR]