A common method for synthesizing turbulent imagery is to model phase perturbations on a wavefront and then propagate the wavefront to the entrance pupil of an imaging system. The point spread function (PSF) that results from the wavefront in the pupil is then computed and used to synthesize images by the usual means of convolution. In a recent publication, a method was disclosed using sparse and redundant dictionaries of turbulent characteristics to construct PSFs directly in the image plane and simulate image formation without making phase models and computing wavefront propagation. However, the dictionary method, as disclosed in the recent publication, is limited to modeling PSFs characterized by the Fried parameter of the data used to construct the dictionary. Herein, we demonstrate that a dictionary constructed from data with a given Fried parameter can be scaled to construct turbulent PSFs corresponding to larger and smaller values of the Fried parameter. This enables a single dictionary, or a small number of dictionaries, to serve for the simulation of turbulent images over a range of turbulence conditions.

The history of the development of works on the creation of the elemental base of adaptive optics for a solar Russian telescope is briefly described. We consider separate issues of the development of the wavefront sensor (WFS) based on the Shack–Hartmann correlation sensor to provide measurements under conditions of strong turbulence. The parameters of the WFS are calculated on the basis of long-term observations of meteorological characteristics and optical measurements of the Fried parameter. A special adjusting device with a block of fast rasters changing is developed. The use of rasters of various dimensions is provided in the WFS. The sensor uses the original algorithm for determining the maximum of the correlation function. The effect on the accuracy of measuring the phase distortions of turbulence occurring in the rooms of the telescope itself is analyzed.

Deleterious effects of the atmosphere on remotely acquired images includes absorption and scattering of light by aerosol particulates, which not only attenuates the signal but can potentially cause blurring due to forward-scattered light accepted by the imaging system. Proposed aerosol scattering models (e.g., Ishimaru) provide a method for simulating the contrast and spatial detail expected when imaging through atmospheres with significant aerosol optical depth. This work explores closure between modulation transfer functions (MTFs) obtained from directly measured images and MTFs calculated from theory using measured cloud properties. The closure experiments are performed in a laboratory cloud chamber in which cloud droplet number density and size distribution are directly measured. Images of a binary knife-edge target were taken with an optical detector on the other side of a water cloud generated through reduction of pressure in the humidified chamber. The key results of this closure experiment are: the theoretical expression for the aerosol MTF is likely overly simplistic and does not account for broad particle size distributions. The significance of optical blurring from light scattering by aerosol particles depends sensitively on the properties of both the particles and the imaging system.

Coherent properties of diffraction-free Bessel-like optical beams propagating in the turbulent atmosphere are theoretically studied. An analytical solution of the equation for the second-order transverse function of mutual coherence of the optical radiation field, which was obtained from paraxial approximation of the scalar wave equation, is analyzed. The behavior of the degree of coherence, coherence radius, and integral scale of the degree of coherence of the Bessel–Gaussian beam and conic wave obtained through conic focusing of a Gaussian beam by an axicon is analyzed for different beam parameters and characteristics of the turbulent atmosphere. Significant qualitative and quantitative differences are discovered between the studied coherence characteristics of the Bessel–Gaussian beam and the conic wave. In general, under identical propagation conditions in the turbulent atmosphere, the coherence of the conic wave is higher than that of the Bessel–Gaussian beam. The comparison of two spatial scales of the degree of coherence of optical beams reveals that the integral scale for diffraction-free beams is a more representative characteristic than the coherence radius. The integral scale of the degree of coherence of diffraction-free beams is more unequivocally connected with the conditions of propagation of optical radiation in the turbulent atmosphere.

Satellite remote sensing technology provides the only viable means for global monitoring of atmosphere systems, such as ozone. The ozone mapping and profiler suite (OMPS) onboard Suomi-NPP satellite, which was launched in the year 2011, has a primary purpose of measuring ozone. Suomi-NPP has been on operation for more than 3 years, and it is crucial to keep the satellite data precise and trusted. By using 17 months of satellite and ground-based total ozone column (TOC) data, this study performs an evaluation of OMPS products. Ozone monitoring instrument (OMI) data generated from a similar satellite instrument were also used to compare with the OMPS TOC data, and both the TOC products were generated using TOMS version 8.5 (TOMS-V8.5) algorithm. The evaluation consists of intercomparisons with ground-based Brewer measurements, similar satellite instruments, and accuracy analysis as a function of time and solar zenith angle. Results show that after 3 years of operation, OMPS-derived TOC data still have good correlation (R2  >  0.99, RMSE  =  1.51  %  ) with ground-based measurements. The results also give some evidence that the OMPS TOC data have better accuracy than those from OMI using the same algorithm.

Inhalation of airborne particulate matter (PM) is associated with a variety of adverse health outcomes. However, the relative toxicity of specific PM types—mixtures of particles of varying sizes, shapes, and chemical compositions—is not well understood. A major impediment has been the sparse distribution of surface sensors, especially those measuring speciated PM. Aerosol remote sensing from Earth orbit offers the opportunity to improve our understanding of the health risks associated with different particle types and sources. The Multi-angle Imaging SpectroRadiometer (MISR) instrument aboard NASA’s Terra satellite has demonstrated the value of near-simultaneous observations of backscattered sunlight from multiple view angles for remote sensing of aerosol abundances and particle properties over land. The Multi-Angle Imager for Aerosols (MAIA) instrument, currently in development, improves on MISR’s sensitivity to airborne particle composition by incorporating polarimetry and expanded spectral range. Spatiotemporal regression relationships generated using collocated surface monitor and chemical transport model data will be used to convert fractional aerosol optical depths retrieved from MAIA observations to near-surface PM10, PM2.5, and speciated PM2.5. Health scientists on the MAIA team will use the resulting exposure estimates over globally distributed target areas to investigate the association of particle species with population health effects.

Aerosol observations with ceilometers have been made worldwide recently. To use ceilometer data to retrieve aerosol profiles, raw signals should be accurately converted to the attenuated backscattering coefficient. Hence, the calibration coefficient for the system constant has to be determined correctly. We conducted a ceilometer–lidar comparative experiment to evaluate the Lufft CHM15k Nimbus product. The attenuated backscattering coefficient using CHM15k was smaller by a factor of 1.48 compared to that of lidar. The calibration coefficient should be periodically corrected using the ceilometer signal itself since lidar data are generally unavailable in the field observations. We recalibrated the product using both Rayleigh fitting and cloud attenuation methods. The correction factor, determined from the recalibration, was 15% (9%) smaller when using the Rayleigh fitting (cloud attenuation) method than the factor determined from lidar. Uncertainties from backscattering ratios at the reference height and the lidar ratio can cause systematic errors in the correction factor determined from the Rayleigh fitting method. Uncertainties due to the multiple scattering factor contribute to systematic errors for the cloud attenuation method. We propose a calibration method using depolarization ratios for future polarization-sensitive ceilometers, which can estimate the calibration coefficient without multiple scattering factors.

Numerous studies employed remote sensing techniques on regional air quality in terms of aerosols, in particular, the observations from polar-orbiting satellites offer more detail on spatial distribution. Since the air pollutants/aerosols dramatically vary in location with time, diurnal observations on a timescale are restricted by the temporal resolution of the polar-orbiting satellite. To address this issue, this research proposes a spatially and temporally adaptive reflectance fusion model for measuring atmospheric properties to synthesize high-spatial–temporal resolution images from polar and geostationary satellite imagery for air quality monitoring. The reflectivity from short-wave infrared is employed to preserve the atmospheric effect within the fused image in the green band for further aerosol optical depth (AOD) retrieval. Taking the Landsat-8 Operational Land Imager as the reference, the spatial resolution of the Himawari-8 Advanced Himawari Imager (in kilometers) can thus be resampled into 30 m every 10 min during the daytime, by considering the surface bidirectional reflectivity from the variation of the solar zenith angle. The AOD retrieved with fused images containing atmospheric effect could have a better performance after comparison with in situ measurements, and therefore, be suggested for high-spatial–temporal aerosol monitoring.

GaoFen-4 (GF-4) is China’s first optical remote sensing geostationary satellite, which observes the Earth’s surface every 20 s. GF-4 has a gaze camera with a ground resolution of 50 m in the visible spectral bands that are sensitive to aerosol optical depth (AOD). AOD retrieval was completed using initial prior surface reflectance in ground-atmosphere decoupling. Surface reflectance was then updated using satellite measurements. The algorithm used in this study was based on two time-adjacent images of GF-4 (which have a constant viewing angle in the same area) and on the following two assumptions: (1) AOD varies quickly with time, but slowly with location and (2) surface reflectance varies quickly with location, but slowly with time. AOD retrieval was then accomplished using a lookup table strategy. The data from June 2016, in the North China Plain were selected for the AOD retrieval test. GF-4-derived AOD was validated using ground measurements from the aerosol robotic network and Sun–Sky radiometer network with a correlation coefficient of R  =  0.794. The derived results agreed reasonably well with the moderate resolution imaging spectroradiometer collection 6.0 aerosol product, with a correlation coefficient of R  =  0.893. The atmospheric distribution and changes in some parts of the North China Plain were analyzed using the aerosol products of GF-4. The results showed that it is feasible to use the time-series imaging method to conduct high spatiotemporal resolution aerosol inversion using GF-4’s data, which can be used for detecting changes in air pollution.

Satellites offer an unprecedented opportunity to evaluate patterns and trends in air pollution, especially in regions with few or no ground-based monitors. We evaluate the 100 most populous cities worldwide, comparing nitrogen dioxide (NO2) vertical column densities from the Aura satellite to population, gross urban product (GUP), and emissions estimates. We find a positive relationship between GUP and NO2 for 38 of the 56 low-income cities, where NO2 increases with GUP, and a negative relationship for the 7 high-income cities, where NO2 decreases with GUP. This trend is consistent with the environmental Kuznets curve model. However, we found the GUP of 36 cities in the middle range of incomes did not display any consistent relationship with NO2. This partial Kuznets curve relationship arises across the 2005 to 2011 years of our study period, where specific cities show a positive or negative trend in NO2 with GUP growth over time. We analyze a global emissions inventory to compare the relationship between GUP per capita and pollution, which shows a similar relationship. The difference between observed NO2 and the emissions inventory could be due to atmospheric processes or could be due to city-specific changes in energy that are not well captured in the inventory.

This study proposed a green–red band quasianalytical algorithm, QAA-GRI, and evaluates its performance using an in situ dataset from Lake Qiandaohu, a drinking water source, in China. First, by shifting the reference wavelength from 555 to 510 nm, a green–red index (GRI) can be calculated from remote sensing reflectance at 510, 560, and 620 nm, and the index was then used to retrieve the total absorption coefficients at the reference band, a  (  510  )  . Second, a semianalytical model based on a  (  510  )   and the GRI was deduced to establish the QAA-GRI, replacing the empirical model in quasianalytical algorithm version 5 (QAA-v5). The QAA-GRI was applied to retrieve absorption coefficients from an in situ dataset of Lake Qiandaohu, and the QAA-GRI’s performance was compared with that of QAA-v5 and another red–green QAA-based approach (QAA-RGR). The results demonstrate that, for this dataset, the QAA-GRI exhibited better performance (R2  =  0.81, mean absolute percentage error, MAPE  =  15.7  %  ), which indicated a clear improvement in the accuracy of absorption coefficient retrieval, compared with QAA-v5 (R2  =  0.56, MAPE  =  21.2  %  ) and QAA-RGR (R2  =  0.67, MAPE  =  22.6  %  ). In addition, to illustrate the QAA-GRI’s assumptions and its applicability, the QAA-GRI was also applied to an in situ dataset from Taihu Lake, a highly turbid and eutrophic water source. As predicted, when applied to Taihu Lake, the QAA-GRI did not perform as well as it did when applied to Lake Qiandaohu because of the former’s extremely high turbidity, which can cause greater uncertainty with regard to backscattering coefficients. This study suggests that the QAA-GRI is better suited to the retrieval of absorption coefficients from drinking water resources. Our algorithm will also have potential applications to satellite data from the Medium Resolution Imaging Spectrometer or Ocean and Land Color Instrument.

Land use/land cover (LULC) changes create the need for regular monitoring of soil properties. Modern technology and data sources offer possibilities to perform it. In this context, the objective of the study is to explore the possibility of predicting soil organic matter (SOM) content and texture from the spectral information of the three last generation satellites [Landsat-8, Sentinel-2, and the Environmental Mapping and Analysis Program (EnMAP)]. Soil samples (113) were collected in areas affected by wildfires and cropland abandonment in Aragón, Northern Spain. Reflectance spectra of soils were obtained in controlled laboratory conditions using the analytical spectral device Fieldspec4 spectroradiometer (spectral range 350 to 2500 nm). Reflectances simulated for Landsat-8, Sentinel-2, and EnMAP bands were used as predictors in multivariate models developed using partial least squares regression (PLSR) and step-down variable selection algorithm (SD). Modeling of all soil variables was performed simultaneously. The EnMAP models employed few predictors (10 to 58 of 244) and demonstrated good fit (Rcal2>0.8 and Rval2∼0.8), especially for SOM (Rcal2 and Rval2=0.9; RMSEP ∼1.6). Landsat models showed the least reliable estimates (Rcal2 0.54 to 0.77 and Rval2 0.42 to 0.80), whereas Sentinel-2 models showed Rcal2 of 0.70 to 0.81 and Rval2 between 0.67 (clay) and 0.79 (SOM). The results confirm high potential of spectral data from multispectral and hyperspectral satellites for soil monitoring. Application of PLSR combined with SD results in sparser and better-fit models. SOM and soil texture can be estimated with an acceptable accuracy from EnMAP and Sentinel-2 data enabling soil status monitoring in areas of wildfire burns and cropland abandonment.

Convolutional neural network (CNN) models achieve state-of-the-art performance for natural image semantic segmentation. An approach for extracting vegetation from Gaofen-2 (GF-2) remote sensing imagery based on the CNN model is presented. We constructed a convolutional encoder neural networks (CENN) consisting of two layers. The first layer has two sets of convolutional kernels for extracting the features of farmland and woodland, respectively. The second layer consists of two encoders that use nonlinear functions to encode the learned features and map the encoding results to the corresponding category number. In the training stage, samples of farmland, woodland, and other lands are categorically used to train the CENN. After training is accomplished, the CENN would acquire enough ability to accurately extract farmland and woodland from GF-2 imagery. The CENN was trained on 36 GF-2 images and tested on three other GF-2 images. We compared the proposed method to a deep belief network, a fully convolutional network, and a DeepLab model using the same images. The experiments demonstrate that the proposed approach improves upon the accuracy of existing approaches. The average precision, recall, and kappa coefficient of the proposed approach were 0.91, 0.87, and 0.86, respectively. Thus, the proposed approach is proven to effectively extract vegetation from GF-2 imagery.

Mexico’s largest freshwater body, Lake Chapala, is the main source of drinking water for over 4-million inhabitants. This study used satellite and field data to obtain annual estimates of Lake Chapala’s area and water volume from 1973 to 2009, as well as water intakes from 1934 to 2013. Twenty-one Landsat images (multispectral scanner system, 2, 5, and 7) for the period of 1973 to 2009 were processed. Gap filling correction was applied to Landsat 7 enhanced thematic mapper plus scan line corrector-off images. Binary water/no-water segmentation was obtained with a Markov random field model using the normalized difference water index. An area-based model was developed to calculate water volume, which used the lake’s area estimated based on the images as well as an exponential function obtained from area-elevation curves. Three of the resulting water/no-water layers were validated against reference points taken from aerial photography. Overall accuracies of 77% to 90% were obtained. A correlation coefficient of 0.9 resulted when comparing the results from using remote sensing to calculate water volume against field measurements. Given the observed reduction in volume, we concluded that it will be difficult to continue to use the lake as the main source of freshwater for the Guadalajara metropolitan area without substantial interventions.