Abstract When capturing distant targets, the video sequence images are affected by atmospheric turbulence, resulting in distortion and blur. In order to restore the degraded images due to atmospheric turbulence in video sequences, this article combines lucky imaging with generative adversarial networks for the first time. The idea of lucky imaging is employed to eliminate geometric distortions, followed by the use of generative adversarial networks to address the blur issue. Additionally, an adaptive restoration method targeting turbulence intensity is proposed to improve the computational efficiency of the proposed approach. Experimental results demonstrate that the combined restoration method of lucky imaging and generative adversarial networks outperforms classical lucky imaging. Specifically, compared to classical lucky imaging, the Brenner gradient function, Laplacian gradient function, Spatial Median Difference (SMD), Entropy, Energy gradient function, PIQE, and Brisque indicators improve by 7.7%, 13.1%, 3.6%, 4.1%, 2.1%, 26.6% and 21.54% (all evaluation indicators in the above improvement rates have undergone logarithmic transformation), respectively. Meanwhile, the proposed adaptive restoration method can improve efficiency by 28%, with greater efficiency gains observed with larger datasets.

Accurate and high spatiotemporal resolution water quality data are critical for the effective management of marine and coastal ecosystems. However, accurate atmospheric correction under high solar zenith angles (SZA) remains a challenge, introducing substantial uncertainties in satellite-derived water quality indicators (WQI) under high SZA. With an attempt to fill the gap, this study evaluated three types of strategies for satellite retrieval of suspended particulate matter (SPM) and chlorophyll-a (Chl-a) concentrations from top-of-atmosphere reflectance (ρt), Rayleigh-corrected reflectance (ρrc) and remote sensing reflectance (Rrs), respectively. The models, named XGBWQI, based on three types of remote sensing data were tested with in-situ data and compared with the Geostationary Ocean Color Imager (GOCI) standard algorithms. Results showed that: (i) ρt-based XGBWQI had the best accuracy (R2= 0.90 and MAPD = 14.65% for SPM, R2= 0.85 and MAPD = 5.34% for Chl-a); (ii) model testing results with in-situ data also confirmed the advantage of ρt-based XGBWQI over other models (R2= 0.88, MAPD = 26.9% and MRPD =11.8% for SPM, R2= 0.78, MAPD = 43.3% and MRPD = -15.5% for Chl-a); and (iii) the XGBWQI models obtained more valid WQI values for GOCI images under high SZA and successfully revealed the diurnal variations of a red tide event in the Yellow Sea and the SPM dynamics in the East China Sea. Therefore, ρt-based XGBWQI models were recommended as the best strategy for satellite retrievals of WQI under high SZA. The methods can serve as an effective tool in retrieving WQI in coastal waters under high SZA, and thus contribute to better and high-frequency water quality monitoring.

Estimating road traffic noise is essential for examining the quality of sounding environment and mitigating such a non-negligible pollutant in urban areas. However, existing estimated models often have limited applicability to specific traffic conditions, while the required parameters may not be readily available for city-wide collection. This paper proposes a data-driven approach for measuring road-level acoustic information of traffic with street view imagery. Specifically, we utilize portable vehicle-equipped hardware for in-situ noise acquisition and employ a deep learning model ResNet to learn high-level visual features from street view images that are closely associated with road traffic noise. The ResNet captures meaningful patterns from the input data, and the output probability vectors are then fed into a Random-Forest regression algorithm to quantitatively estimate the noise in decibels for different road segments. The MAE and RMSE of the DCNN-RF model are 2.01 and 2.71, respectively. Additionally, we employ a gradient-weighted Class Active Mapping approach to visually interpret our deep learning model and explore the significant elements in streetscapes that contribute to the model's estimations. Our proposed framework facilitates low-cost and fine-scale road traffic noise estimations and sheds light on how auditory information could be inferred from street imagery, which may benefit practices in geography and urban planning.

Accurate satellite retrieval of oceanic particulate organic nitrogen (PON) concentrations could contribute to a better and more comprehensive understanding of global marine biogeochemical processes. However, no satisfactory satellite PON retrieval model could be found in the literature. In an attempt to develop applicable PON models, large diverse matchups of synchronous global oceanic in situ PON measurements and ocean color satellite data were used to develop PON retrieval models using the Gaussian process regression (GPR) method for SeaWiFS, Terra-Moderate Resolution Imaging Spectroradiometer (MODIS), MERIS, Aqua-MODIS, and SNPP-Visible Infrared Imaging Radiometer Suite (VIIRS), respectively. The GPR PON models were compared with polynomial PON models based on single bio-optical properties or band index, and further used to retrieve spatiotemporal variations of global oceanic PON concentrations. Combined with the satellite-derived particulate organic carbon (POC) products, the possibility of deriving the POC to nitrogen ratio (POC:PON) was further explored. Results showed that GPR PON models, with$R^{2}$, root mean square error (RMSE), and mean absolute percentage error (MAPE) ranging from 0.76 to 0.87, 0.15 to 0.18, and 9.19% to 12.59%, respectively, had a comparable performance for both Case-1 and Case-2 waters and outperformed the polynomial PON models. The global PON concentrations derived from different satellite missions were generally consistent, with their mean relative differences all less than 14.70% between Aqua-MODIS and the other four missions. The GPR-derived POC:PON was acceptable, with most relative errors within ±40% compared to in situ POC:PON. The applicable satellite PON retrieval models and the readily available satellite PON products should be helpful for studying oceanic PON dynamics and ecological processes in marine biogeochemical cycles.

The Ocean and Land Color Instrument (OLCI) on Sentinel-3 is one of the most advanced ocean color satellite sensors for aquatic environment monitoring. However, limited studies have been focused on a comprehensive assessment of atmospheric correction (AC) methods for OLCI. In an attempt to fill the gap, this study evaluated seven different AC methods for OLCI using global automaticin situobservations from Aerosol Robotic Network-Ocean Color (AERONET-OC). Results showed that the POLYnomial-based algorithm applied to MERIS (POLYMER) had the best performance for bands with wavelength ≤ 443 nm, and the SeaDAS method based on 779 and 865 nm was the best for longer spectral bands; however, SeaDAS (SeaWiFS Data Analysis System) processing algorithm based on 779 and 1020 nm, as well as 865 and 1020 nm, obtained degraded AC performance; Case 2 Regional CoastColor (C2RCC) also produced large uncertainties; Baseline AC (BAC) method might be better than SeaDAS method; and simple subtraction method was the worst except for turbid waters. POLYMER and C2RCC underestimated high remote sensing reflectance (Rrs) at red and green bands; SeaDAS method based on 779 and 865 nm held an advantage for clear waters over the other two band combinations, while their difference turned small for turbid waters. AC uncertainties generally impacted the performance of chlorophyll retrievals. POLYMER outperformed other methods for chlorophyll retrieval. This study provides a good reference for selecting a suitable AC method for aquatic environment monitoring with Sentinel-3 OLCI.

As the only natural satellite of the Earth, the Moon provides vital location resources and supportive environment for Earth observations, and the Moon-based Earth observation (MEO) has unparalleled advantages in global climate change and large-scale phenomena. The ocean plays an important role in regulating climate and global water and carbon cycle. With an attempt to explore the feasibility of MEO-based marine environment monitoring, this study aimed to investigate the observing geometry and revisiting frequency of the MEO-based ocean color remote sensing and further to explore its quantitative application potentials. Results showed that MEO-based ocean color remote sensing, capturing the Earth on an hourly basis, could observe most part of the ocean for over five times per day; however, both solar zenith angle and view zenith angle were high at high-latitude regions; atmospheric reflectance accounted for most of sensor-measured signal, especially at high solar and view zenith angle, while surface-reflected glint reflectance was also notable at low solar zenith angle; and the remote sensing reflectance retrieved from MEO-based ocean color remote sensing could be used for chlorophyll retrieval. In further studies, more efforts should be paid on how to accurately retrieve remote sensing reflectance at high solar and view zenith angle, which would improve the application capability of MEO for polar regions. Overall, this study demonstrated the great potentials of MEO-based ocean color remote sensing, and MEO would be a new observing perspective and long-term consistent data source for marine environment monitoring.

Human emotion is an intrinsic psychological state that is influenced by human thoughts and behaviours. Human emotion distribution has been regarded as an important part of emotional geography research. However, it is difficult to form a global scaled map reflecting human emotions at the same sampling density because various emotional sampling data are usually positive occurrences without absence data. In this study, a methodological framework for mapping the global geographic distribution of human emotion is proposed and applied, combining a species distribution model with physical environment factors. State-of-the-art affective computing technology is used to extract human emotions from facial expressions in Flickr photos. Various human emotions are considered as different species to form their ‘habitats’ and predict the suitability, termed as ‘Emotional Habitat’. To our knowledge, this framework is the first method to predict emotional distribution from an ecological perspective. Different geographic distributions of seven dimensional emotions are explored and depicted, and emotional diversity and abnormality are detected at the global scale. These results confirm the effectiveness of our framework and offer new insights to understand the relationship between human emotions and the physical environment. Moreover, our method facilitates further rigorous exploration in emotional geography and enriches its content.

Hydrological drought is a global issue that many countries face. In this paper, we present a new hydrological index (SGI) based on the Gravity Recovery and Climate Experiment (GRACE) product. For comparison, we compare it with the documented drought in July 2010 and calculate the correlation coefficient between it and the standardized precipitation index (SPI). The result shows the new index can reflect spatiotemporal distribution of dryness and wetness, and it has good agreement with SPI in different time scales, and the new index can be used in the hydrological drought measurement in the global scale.

Accurate removal of atmospheric interference and precise retrieval of water-leaving reflectance is decisive for subsequent water color applications. As follow-up satellite of Envisat, Sentinel-3 will provide valuable observations of the earth. This study aims to adapt the shortwave infrared extrapolation (SWIRE) atmospheric correction algorithm for Sentinel-3 to derive remote sensing reflectance of turbid waters, and validation it using our in -situ data in Pearl River Estuary. Results showed that SWIRE algorithm could effectively remove atmospheric perturbations, and produced more accurate remote sensing reflectance over complex waters of PRE than NIR and SWIR algorithms.

Heavy metals that are present in soil are poisonous to both plants and animals. Measuring the heavy metal concentrations in wetland soil are of great significance for the assessment of wetland ecosystem health. This study was conducted in the Taihu Lake wetland region of China, and is aimed at comparing the partial least squares regression (PLSR) as well as support vector machine regression (SVMR) methods for estimating the zinc (Zn), arsenic (As) and copper (Cu) concentrations present in wetland soil utilizing hyperspectral technology. In total, there were 100 homogeneous wetland soil samples collected, and their Zn, As and Cu concentration models were developed based on laboratory-based hyperspectral data (350-2500 nm). According to independent validation, the SVMR method achieved better accuracies, which had determination coefficients of 0.61, 0.66 and 0.72 for Zn, As and Cu, respectively. It was concluded that the SVMR method combined with laboratory-based hyperspectral data has the cumulative potential to estimate heavy metal concentrations within homogeneous wetland soil.

Fitting polarimetric synthetic aperture radar (PolSAR) data with adaptive scattering models is a promising way to mitigate the deficiencies of the model-based decomposition. Recently, Lee et al. have proposed a generalized decomposition model with several adaptive parameters, whereas the generalized model introduces too much freedom to be solved. In this paper, based on the Lee generalized decomposition model, an adaptive two-component decomposition model is proposed. The PolSAR coherency matrix is represented as the sum of two scattering mechanisms: coherent ground scattering and incoherent volume scattering. The proposed model is under three assumptions: 1) Surface and double scattering are coherent; 2) surface and double scattering are integrated as the ground scattering; and 3) the average polarimetric orientation angle (POA) of the volume (or Bragg) scattering is zero. As the proposed model is very difficult to solve directly, we adopted the exhaustion technique to find the best fit parameter set. The proposed model has three advantages: 1) It can successfully avoid the negative power problem; 2) it is considered without the assumption of reflection symmetry; and 3) the dominant scattering mechanism criterion is not needed in the process of model inversion. However, the proposed model has two disadvantages: 1) the attribution of the volume model becomes ambiguous; and 2) the assumption that sets the POA of the Bragg scattering component to zero is inconsistent with the actual scattering mechanism when there is a slope in the rough surface. The polarimetric AIRSAR L-band data of San Francisco and ESAR L-band data of Oberpfaffenhofen were used to show the efficiency of the proposed decomposition model. Statistical properties of typical areas showed that, except the sea surface and the urban area with building orientation angle about 45°, the proposed model fits the PolSAR data very well.

The assumption of reflection symmetry is one of the major deficiencies for the model based decompositions. The introduction of helix scattering component can mitigate the impact of this assumption limitedly, while it generates some new negative power problems simultaneously. In this paper, we expand the techniques of symmetric scatterer transformation in the Cameron and Huynen decompositions to the multi-look coherency matrix. Two new models of nonlinear programming problem are proposed to mitigate the reflection symmetry assumption and negative power problems for the model based decomposition. Experimental results in the L-band AIRSAR San Francisco data show that the two new models can reduce the correlations between co-polarized and cross-polarized channels to an insignificant level.

Building orientations with respect to the radar look direction is a critical influence on the interpretation of PolSAR data in urban areas. In this paper, we try to analyze thoroughly the problems of polarimetric scattering mechanism in urban areas induced by building orientation. For multi-look PolSAR data, the polarimetric scattering mechanism in urban areas is modeled by two double-bounce scatterings from two orthogonal dihedral structures. From the model, it can be inferred that with the increase of the building orientation, the POA estimation circular-polarization method and the dominant scattering mechanism labeling technique based on the model-based decompositions will gradually become invalid. Moreover, the POA compensation processing is helpful to reduce the impacts of the building orientation, but when the building orientation is increased to some degree, it also become invalid. Three L-band datasets of San Francisco acquired by AIRSAR are used to verify the inferences.

Due to the diversity of geographical objects, it makes great sense to introduce multiscale segmentation/representation into the analysis and interpretation of high-spatial-resolution remote sensing images. However, with the increasing use of high-resolution images, traditional multiscale segmentation methods gradually show their lack in efficiency, particularly when handling large-scale images. In this paper, a novel bilevel scale-sets model (BSM) is proposed for multiscale region-based representation of large-scale remote sensing images. In the BSM, first, an image is divided into blocks with overlapped margins, and a low-level scale-sets model is blockwisely implemented. Second, a segmentation result is obtained by retrieving and mosaicking the blockwise segmentation results, based on which a high-level scale-sets model is implemented covering the whole image. To further improve the efficiency of the BSM, a parallel implementation is presented for the blockwise scale-sets model. In the experiments, first, the effectiveness of the BSM is validated using a WorldView2 image covering a coastal area of Shenzhen, where the BSM obtains accurate multiscale representation results without any mosaic artifacts. Then, the efficiency of the BSM is demonstrated by comparing with the state-of-the-art multiscale segmentation method, i.e., the one integrated in the commercial software eCognition v9.2, where the proposed BSM takes about 7 min to process a 24 000 × 24 000 multispectral ZY3 image and is two to three times faster than the competing method.

The assumption of reflection symmetry is one of the major deficiencies for model-based decompositions. The introduction of helix scattering components can mitigate some of the impact of this assumption while simultaneously generating some new negative power problems. The helix mechanism has been developed in the Krogager coherent target decomposition where there is no negative power problem. Why do these problems happen in model-based decompositions? In this paper, we review the Krogager decomposition based on the coherency matrix form of the unified CTD model and come to the conclusion that the imaginary part of T23is an overestimate of the helix contribution, which is the main cause of the problem. Furthermore, we review the techniques of symmetric scatterer transformation in the Cameron and Huynen decompositions and expand them to the multilook coherency matrix. Two new models of the nonlinear programming problem are proposed to get the transformed coherency matrix: 1) by subtracting the asymmetric component; and 2) by the procedure of con-diagonalization. A new parameter is established to measure the degree of reflection asymmetry of the coherency matrices. Experimental results in the L-band Airborne SAR (AIRSAR) San Francisco data show that the two new models can reduce the correlations between copolarized and cross-polarized channels significantly. In this case, the assumption of reflection symmetry is reasonable for the transformed coherency matrices, and negative power problems related to helix scattering no longer exist in the model-based decompositions; hence, the helix scattering component is not needed. Lastly, the Freeman and Durden decomposition has been applied to the transformed coherency matrices. Experimental results showed that the decomposition results can reflect the predominant characteristics of the ground objects.

Building orientation with respect to the radar look direction has a critical influence on the interpretation of multilook polarimetric synthetic aperture radar (PolSAR) data in urban areas. In this paper, its impacts on polarimetric orientation angle (POA) estimation and model-based decomposition are discussed. The discussion begins with the analysis of the general double-bounce scattering model, of which the characteristics are dependent on the electromagnetic and geometric parameters of the related dihedral structure. Then, for multilook PolSAR data, the polarimetric scattering mechanism in urban areas is modeled by two double-bounce scatterings from two orthogonal dihedral structures. From the model, the impacts of the building orientation on POA estimation can be revealed. With the increase of the building orientation, the POA difference between the two dihedral structures increases gradually, and the feasibility to estimate the building orientation via the estimated POA is reduced dramatically. Upon further analysis, we illustrate the impacts on the model-based decomposition. With the increase of the building orientation, the dominant scattering mechanism labeling technique based on the model-based decompositions will gradually become invalid. Moreover, the processing of POA compensation, which is helpful in reducing the impacts of the building orientation, also becomes invalid when the building orientation increases to a certain value. At last, three L-band data sets of San Francisco acquired by AIRSAR are used to verify the inferences. The experimental results show that, for L-band PolSAR data in urban areas, when the radar look angle is around 45, the threshold of building orientation for the validity of dominant scattering mechanism labeling is about ±3, and for the POA compensation, the threshold is about ±12.