Channel Brightness Research Articles

Global evaluation of fast radiative transfer model coefficients for early meteorological satellite sensors

Abstract. RTTOV (the Radiative Transfer for TOVS code, where TOVS is the TIROS Operational Vertical Sounder) coefficients are evaluated using a large, independent dataset of 25 000 atmospheric model profiles as a robust test of the diverse 83 training profiles typically used. The study is carried out for nine historical satellite instruments: the InfraRed Interferometer Spectrometer D (IRIS-D), Satellite Infrared Spectrometer B (SIRS-B), Medium Resolution Infrared Radiometer (MRIR) and High Resolution Infrared Radiometer (HRIR) for the infrared part of the spectrum, and the Microwave Sounding Unit (MSU), Special Sensor Microwave Imager (SSM/I), Special Sensor Microwave – Humidity (SSM/T-2), Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager/Sounder (SSMI/S) for the microwave. Simulated channel brightness temperatures show similar statistics for both the independent and the 83-profile datasets, confirming that it is acceptable to validate the RTTOV coefficients with the same profiles used to generate the coefficients. Differences between the RTTOV and the line-by-line models are highest in water vapour channels, where mean values can reach up to 0.4 ± 0.2 K for the infrared and 0.04 ± 0.13 K for the microwave. Examination of the latitudinal dependence of the bias reveals different patterns of variability for similar channels on different instruments, such as the channel centred at 679 cm−1 on both IRIS-D and SIRS-B, showing the importance of the specification of the instrumental spectral response functions (ISRFs). Maximum differences of up to several kelvin are associated with extremely non-typical profiles, such as those in polar or very hot regions.

FY‐4A/AGRI Infrared Brightness Temperature Estimation of Precipitation Based on Multi‐Model Ensemble Learning

AbstractSatellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data.

Probabilistic Convective Initiation Nowcasting Using Himawari-8 AHI with Explainable Deep Learning Models

Abstract Convective initiation (CI) nowcasting is crucial for reducing loss of human life and property caused by severe convective weather. A novel deep learning method based on the U-Net model (named as CIUnet) was developed for forecasting CI during the warm season with eight interest fields of Himawari-8 Advanced Himawari Imager (AHI) and terrain height. The results showed that the CIUnet model produced probability forecasts of CI occurrence location and time with probability of detection (POD) at 93.3% ± 0.3% and false alarm ratio (FAR) at 18.3% ± 0.4% at a lead time of 30 min. Sensitivity and permutation importance experiments on the input fields of the CIUnet model revealed that the differences in brightness temperature for spectral channels were more critical for CI nowcasts than the original infrared channel brightness temperatures. The brightness temperature difference between band 10 (7.3 μm) and band 13 (10.4 μm), which represents the cloud-top height relative to the lower troposphere, is identified as the most important input fields for CI nowcasting. The tri-spectral brightness temperature difference (TTD), which represents cloud-top glaciation, is ranked the second and it significantly reduced the FAR of the CI forecast. Using terrain heights as an extra input feature improved the POD, but slightly overestimated CI over complex terrain. In addition, a layer-wise relevance propagation (LRP) analyses was performed, and confirmed that the CIUnet model can effectively identify the crucial regions and features of the input fields for accurate CI prediction. Therefore, both permutation importance experiments and LPR analyses are useful for improving the CIUnet model and advancing the understanding of CI mechanisms.

Spatiotemporal Dynamics of Betula pendula Crown Cover on Abandoned Arable Land in a Broad-Leaved Forest Zone of Bashkir Cis-Ural

Silver birch (Betula pendula Roth) is one of the fast-growing tree species that often colonize abandoned agricultural lands in Europe and the European part of Russia. The purpose of this article is to analyze the spatiotemporal dynamics of Betula pendula crown cover on abandoned arable lands in a zone of broad-leaved forests of the Bashkir Cis-Ural (Russia, Republic of Bashkortostan). The assessment of current and retrospective values of crown cover was carried out using a regression model of the dependence of crown cover on the values of red channel brightness in early-spring images from Landsat 5–8 and Sentinel-2 satellites from 2012–2022. To estimate the number and height of trees, a survey was carried out using a LiDAR camera mounted on a UAV. After calculating the crown cover in grid squares and their distance from the seed source in QGIS, variance analysis was carried out to assess the influence of the factor of distance from the seed source on the crown cover. The influence of the factor of distance from the seed source on the crown cover was higher at earlier stages of overgrowth of abandoned agricultural lands. An exception to this dependence was only one sample plot where the prevailing wind direction was opposite to the direction of seed dispersal. The leading factors affecting the distribution of birch on abandoned agricultural lands were wind direction, height of seed trees, and grazing. In the parts of the sample plots that were furthest away from seed sources, the trees were 1–3 years younger or the same age, and stand density was lower than in sites located closer to the seed trees. In general, the results of the present study indicate two opposite relationships between seedling survival and distance to seed trees: (1) seed fall and seedling density decrease with increasing distance from the seed tree, and (2) the probability of seed/seedling survival increases due to decreased competition.

Harmonization of Meteosat First and Second Generation Datasets for Fog and Low Stratus Studies

Operational weather satellites, dating back to 1970s, currently provide the best basis for climatological investigations, such as an analysis of changes in the cloud cover. Because clouds are highly dynamic in time, temporally high-resolution data from the geostationary orbit are preferred in order to take variations in the diurnal cycles into account. For such studies, a consistent dataset in space and time is mandatory, but not yet available. Ground-based point measurements of various cloud parameters, such as ceiling, visibility, and cloud type are often sparsely spread and inconsistent, making it difficult to derive reliable spatio-temporal information over large areas. The Meteosat program has generally provided suitable data from over Europe since 1977, but different spatial, spectral, and radiometric resolution of the instruments of the individual satellites, including early-years calibration uncertainties, makes harmonization necessary to finally derive a time series applicable to any kind of climatological study. In this study, a machine learning-based approach has been employed to generate a long-term consistent dataset with high spatio-temporal resolution and extensive coverage over Europe by the harmonization of Meteosat First Generation (MFG) and Meteosat Second Generation (MSG) satellite datasets (1991–2020). A random forest (RF) regressor is trained on the overlap period (2004–2006), where datasets of both satellite generation (MFG and MSG) are available to predict MFG Water Vapour (WV) and Infrared (IR) channels brightness temperature (BT) values based on MSG channels. The aim of the study is to synthesize MFG MVIRI data from MSG SEVIRI to generate a consistent MFG time series. The results indicate a good match of MFG synthesized data with the original MFG data with a mean absolute error of 0.7 K for the WV model and 1.6 K for the IR model, and an out-of-bag (OOB) R² score of 0.98 for both the models. Based on the trained models, the MFG scenes are synthesized from the MSG scenes for the years from 2006 to 2020. The long-term homogeneity of the generated time series is analyzed. The harmonized dataset will be applied to generate a continuous time series on fog and low stratus (FLS) occurrence for a climatological time scale of 30 years.

A ratiometric hypochlorite-specific nanoprobe constructed by rationally designed AIE luminogen for fluorescence imaging of alcoholic liver disease

There is a lack of effective and friendly diagnostic technique for alcoholic liver disease (ALD). Fluorescence imaging, which has been widely applied in disease diagnostic ascribed to the merits of high sensitivity, excellent temporal resolution and minimal invasiveness, is probably a good candidate for ALD studying. Herein, by precise molecular optimization, an aggregation-induced emission (AIE) probe of TCPQ was designed to show bright near-infrared (NIR) emission centered at 680 nm, and selective response towards ClO− for ALD diagnosis. In comparison with PNQ, the started fluorophore constructed with ClO− oxidizable donor phenothiazine (PZ) and AIE active acceptor quinoline-malononitrile (QM), the emission wavelength and fluorescence quantum yield of TCPQ were 63 nm red-shifted and 2.3-fold enhanced after structural optimization. Once exposed to ClO−, the S atom in PZ donor of TCPQ could be oxidized to sulfoxide, leading to blue-shifted emission, which made TCPQ be suitable for ratiometirc imaging with high accuracy, while the such weak emissions of the other probes here in presence of ClO− made the ratiometric imaging impossible. After forming nanoparticles (NPs) with amphiphilic F-127, TCPQ NPs have successfully imaged the exogenous and endogenous ClO− fluctuations through the ratio between brightness of green channel and red channel, and have been further utilized to quantitatively evaluate the ClO− levels in four types of cells. The possibility of ClO− as a biomarker for ALD was confirmed in the cellular ALD model. Subsequently, TCPQ NPs were further applied in ALD diagnosis and pharmacodynamic evaluation of an anti-ALD drug in the mouse model.

A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data

Since 2013, frequent haze pollution events in China have been attracting public attention, generating a demand to identify the haze areas using satellite observations. Many studies of haze recognition algorithms are based on observations from space-borne imagers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Himawari Imager (AHI). Since the haze pixels are frequently misidentified as clouds in the official cloud detection products, these algorithms mainly focus on recovering them from clouds. There are just a few studies that provide a more precise distinction between haze and clear pixels. The Medium Resolution Imaging Spectrometer II (MERSI-II), the imager aboard the FY-3D satellite, has similar bands to those of MODIS, hence, it appears to have equivalent application potential. This study proposes a novel MERSI haze mask (MHAM) algorithm to directly categorize haze pixels in addition to cloudy and clear ones. This algorithm is based on the fact that cloudy and clear pixels exhibit opposing visible channel reflectance and infrared channel brightness temperature characteristics, and clear pixels are relative brighter, and as well as this, there is a positive difference between their apparent reflectance values, at 0.865 μm and 1.64 μm, respectively, over bright surfaces. Compared with the Aqua/MODIS and MERSI-II official cloud detection products, these two datasets treat the dense aerosol loadings as certain clouds, possible clouds and possible clear pixels, and they treat distinguished light or moderate haze as possible clouds, possible clear pixels and certainly clear pixels, while the novel algorithm is capable of demonstrating the haze region’s boundary in a manner that is more substantially consistent with the true color image. Using the PM2.5 (particle matter with a diameter that is less than 2.5 μm) data monitored by the national air quality monitoring stations as the test source, the results indicated that when the ground-based PM2.5 ≥ 35 μg/cm3 is considered to be haze days, the samples with the recognition rate that is higher than 85% accounted for 72.22% of the total samples. When PM2.5 ≥ 50 μg/cm3 is considered as haze days, 83.33% of the samples had an identification rate that was higher than 85%. A cross-comparison with similar research methods showed that the method proposed in this study had better sensitivity to bright surface clear and haze areas. This study will provide a haze mask for subsequent quantitative inversion of aerosol characteristics, and it will further exert the application benefits of MERSI-II instrument aboard on FY3D satellite.

Presentation in self-posted facial images can expose sexual orientation: Implications for research and privacy.

Recent research has found that facial recognition algorithms can accurately classify people's sexual orientations using naturalistic facial images, highlighting a severe risk to privacy. This article tests whether people of different sexual orientations presented themselves distinctively in photographs, and whether these distinctions revealed their sexual orientation. I found significant differences in self-presentation. For example, gay individuals were on average more likely to wear glasses compared to heterosexual individuals in images uploaded to the dating website. Gay men also uploaded brighter images compared to heterosexual men. To further test how some of these differences drove the classification of sexual orientation, I employed image augmentation or modification techniques. To evaluate whether the image background contributed to classifications, I progressively masked images until only a thin border of image background remained in each facial image. I found that even these pixels classified sexual orientations at rates significantly higher than random chance. I also blurred images, and found that merely three numbers representing the brightness of each color channel classified sexual orientations. These findings contribute to psychological research on sexual orientation by highlighting how people chose to present themselves differently on the dating website according to their sexual orientations, and how these distinctions were used by the algorithm to classify sexual orientations. The findings also expose a privacy risk as they suggest that do-it-yourself data-protection strategies, such as masking and blurring, cannot effectively prevent leakage of sexual orientation information. As consumers are not equipped to protect themselves, the burden of privacy protection should be shifted to companies and governments. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

An Extendable Half-Bridge Dimmable Multi-Channel LED Driver for DC/DC Grid lighting Applications

In this paper, a light emitting diode (LED) driver with multiple outputs, suitable to use with DC grid supply, is proposed. The proposed LED driver provides the ability to individually control the brightness of each output channel. In order to control the output power of each channel, a burst control mode is employed resulting in reduction of conduction losses. Moreover, the half bridge switches are turned on and off at zero voltage (ZV) with a fixed switching frequency; therefore, the switching losses is reduced and efficiency is further increased. Design and implementation have been simplified by choosing the same structure of the resonant series for outputs. A two-channel DC/DC converter is simulated and tested to verify the theoretical analyses.The presented converter operates above resonant frequency to decrease total harmonic distortion and reduce the size and weight of the components. Therefore, it is suitable for high power application of multi-channel drivers.

Potential Impacts of Assimilating All-Sky Satellite Infrared Radiances on Convection-Permitting Analysis and Prediction of Tropical Convection

AbstractGeostationary infrared satellite observations are spatially dense [>1/(20 km)2] and temporally frequent (>1 h−1). These suggest the possibility of using these observations to constrain subsynoptic features over data-sparse regions, such as tropical oceans. In this study, the potential impacts of assimilating water vapor channel brightness temperature (WV-BT) observations from the geostationary Meteorological Satellite 7 (Meteosat-7) on tropical convection analysis and prediction were systematically examined through a series of ensemble data assimilation experiments. WV-BT observations were assimilated hourly into convection-permitting ensembles using Penn State’s ensemble square root filter (EnSRF). Comparisons against the independently observed Meteosat-7 window channel brightness temperature (Window-BT) show that the assimilation of WV-BT generally improved the intensities and locations of large-scale cloud patterns at spatial scales larger than 100 km. However, comparisons against independent soundings indicate that the EnSRF analysis produced a much stronger dry bias than the no data assimilation experiment. This strong dry bias is associated with the use of the simulated WV-BT from the prior mean during the EnSRF analysis step. A stochastic variant of the ensemble Kalman filter (NoMeanSF) is proposed. The NoMeanSF algorithm was able to assimilate the WV-BT without causing such a strong dry bias and the quality of the analyses’ horizontal cloud pattern is similar to EnSRF’s analyses. Finally, deterministic forecasts initiated from the NoMeanSF analyses possess better horizontal cloud patterns above 500 km than those of the EnSRF. These results suggest that it might be better to assimilate all-sky WV-BT through the NoMeanSF algorithm than the EnSRF algorithm.

Typhoon Maria Precipitation Retrieval and Evolution Based on the Infrared Brightness Temperature of the Feng-Yun 4A/Advanced Geosynchronous Radiation Imager

Recognizing the importance and challenges inherent in the remote sensing of precipitation in typhoon monitoring, a study of the Advanced Geosynchronous Radiation Imager (AGRI) data from Feng-Yun 4A on typhoon precipitation was conducted. First, Typhoon Maria was selected to statistically analyze the AGRI infrared brightness temperature in the “precipitation” and “nonprecipitation” channels of the field of view. When there was precipitation, the brightness temperature of the AGRI channel changed significantly. Second, the shrunken locally linear embedding algorithm (SLLE) was adopted to carry out the retrieval of precipitation based on the brightness temperatures of AGRI infrared channels 8–14. The contribution rate of the brightness temperature at different channels to the objective function of precipitation retrieval model was obtained by the Bayesian model averaging (BMA). Based on the preliminary experimental “quantification” evaluation index, we concluded that the method adopted in this paper can be used to retrieve precipitation in infrared data and to retrieve the spiral cloud rain bands of a typhoon. Finally, based on the AGRI channel brightness temperature of a 10.8-micron window channel, we applied the membership degree information of a typhoon’s dominant cloud system from the fuzzy c-means (FCM) clustering method to modify precipitation retrieval results. The results were used to obtain the main morphological structure of typhoon precipitation. By further analyzing the temporal variation of dominant cloud system development using the FCM method, we concluded that the brightness temperature gradient can assist in the analysis of the variation of a typhoon’s intensity. This method can be applied to the continuous retrieval of large-scale precipitation. Precipitation retrieval via the AGRI can yield indicators for typhoon precipitation warnings and forecasts, thus providing a reliable reference tool for disaster prevention and mitigation.

Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network

Abstract. Snow lying on top of sea ice plays an important role in the radiation budget because of its high albedo and the Arctic freshwater budget, and it influences the Arctic climate: it is a fundamental climate variable. Importantly, accurate snow depth products are required to convert satellite altimeter measurements of ice freeboard to sea ice thickness (SIT). Due to the harsh environment and challenging accessibility, in situ measurements of snow depth are sparse. The quasi-synoptic frequent repeat coverage provided by satellite measurements offers the best approach to regularly monitor snow depth on sea ice. A number of algorithms are based on satellite microwave radiometry measurements and simple empirical relationships. Reducing their uncertainty remains a major challenge. A High Priority Candidate Mission called the Copernicus Imaging Microwave Radiometer (CIMR) is now being studied at the European Space Agency. CIMR proposes a conically scanning radiometer having a swath >1900 km and including channels at 1.4, 6.9, 10.65, 18.7 and 36.5 GHz on the same platform. It will fly in a high-inclination dawn–dusk orbit coordinated with the MetOp-SG(B). As part of the preparation for the CIMR mission, we explore a new approach to retrieve snow depth on sea ice from multi-frequency satellite microwave radiometer measurements using a neural network approach. Neural networks have proven to reach high accuracies in other domains and excel in handling complex, non-linear relationships. We propose one neural network that only relies on AMSR2 channel brightness temperature data input and another one using both AMSR2 and SMOS data as input. We evaluate our results from the neural network approach using airborne snow depth measurements from Operation IceBridge (OIB) campaigns and compare them to products from three other established snow depth algorithms. We show that both our neural networks outperform the other algorithms in terms of accuracy, when compared to the OIB data and we demonstrate that plausible results are obtained even outside the algorithm training period and area. We then convert CryoSat freeboard measurements to SIT using different snow products including the snow depth from our networks. We confirm that a more accurate snow depth product derived using our neural networks leads to more accurate estimates of SIT, when compared to the SIT measured by a laser altimeter at the OIB campaign. Our network with additional SMOS input yields even higher accuracies, but has the disadvantage of a larger “hole at the pole”. Our neural network approaches are applicable over the whole Arctic, capturing first-year ice and multi-year ice conditions throughout winter. Once the networks are designed and trained, they are fast and easy to use. The combined AMSR2 + SMOS neural network is particularly important as a precursor demonstration for the Copernicus CIMR candidate mission highlighting the benefit of CIMR.

Bottleneck Channels Algorithm for Satellite Data Dimension Reduction: A Case Study for IASI

The infrared atmospheric sounder interferometer (IASI) onboard the Metop-A and Metop-B satellites is an essential instrument for numerical weather prediction centers. The IASI, with its 8461 channels, enables the observation of the surface and atmospheric geophysical variables twice a day around the world. This data volume will increase in the future, stressing the need for practical and efficient dimension reduction techniques for storage and transmission but also for inversion or assimilation. In a previous paper by Aires et al. , a new dimension reduction method, the so-called bottleneck channel (BC), has been introduced. BCs are a good compromise between compression and channel selection techniques. In this paper, we introduce several new technical configurations of the BC approach, comparing their advantages/drawbacks. Comparisons are also made with two classical methodologies widely used in the satellite community: principal component analysis (PCA) compression and entropy reduction channel selection. Beyond the compression ability, the methodologies are tested on the temperature, water vapor, and ozone retrieval application using IASI measurements. Two approaches have been tested. First, theoretical retrieval improvement has been measured when using the various data reduction techniques. The BC achieves good theoretical improvements, comparable to noise-adjusted PCA (NAPCA), for the retrieval of the temperature, water vapor, and ozone. Second, a neural network inversion scheme is tested on real IASI observations using the several data reduction techniques. It is shown that BCs provide similar or better results than the other methods. The BCs present compression, denoising, and information content results comparable to the NAPCA, while preserving the physical meaning (i.e., actual channel brightness temperatures) of a channel selection approach. This has numerous advantages for inversion and assimilation: the mixing problem, contamination effects, minor constituents representation, or radiative transfer speed, and each of these will be discussed.

Image dehazing based on dark channel prior and brightness enhancement for agricultural remote sensing images from consumer-grade cameras

Remote sensing technology has been widely used for monitoring crop fields and other agricultural applications. However, the clarity of remote sensing images is often affected by clouds and chaotic media in the atmosphere. Image dehazing can be achieved through the dark channel prior method (DCP), but there is always a brightness distortion problem after image dehazing. To address the problem, this study proposed an improved image dehazing approach based on the DCP method and determined optimal enhancement parameters. Four evaluation indices, including mean square error (MSE), peak signal to noise ratio (PSNR), average gradient and program running time, were first calculated to evaluate the quality of enhanced images. An example image was dehazed by the DCP method initially using the four indices to determine optimal dehazing parameters. Results showed that image enhancement achieved the best effect when the dark channel window size Ω(x) is 5, atmospheric light A is 215/255, and the lower limit t0 of transmission factor t(x) is 0.1. Next, these indices were applied to evaluate the enhancement methods used in this research. The logarithmic enhancement method was finally selected as the optimal method with the base number (1 + r) = 11 and enhancement parameter m = 0.5. To verify the effectiveness of the selected method, 50 airborne images from a consumer-grade camera flown by an agricultural aircraft were used to evaluate the improved method. Both the original and the enhanced images after dehazing were mosaicked by Adobe Photoshop software. The mosaicked images before and after image dehazing were compared. Results showed that the mosaicked image without dehazing had an entropy of 6.359 and an average gradient of 6.513. In comparison, the mosaicked image with dehazing had an entropy of 6.668 and an average gradient of 11.305, which were 4.86% and 73.58% higher than the respective values for the mosaicked image without dehazing. These results indicate that the proposed method in this study is effective and can be applied to dehaze remote sensing images.

Spark Stage of Welding Arc Discharge Binding on an Aluminum Surface

The article presents a study of the spark stage of binding of the welding arc discharge on the surface of aluminum covered by oxide film. The experiments have shown the formation of cathode- and anodedirected leaders in a nonuniform field between the pin cathode (tungsten) and the plate (aluminum). It has been found that, if the cathode spot occurs against the background of uniform discharge glow, then the spark channel forms, as a rule, in two stages. At the first stage, a diffuse channel bound to the cathode spot that forms in the gap. At the second stage, a highly conductive contracted spark channel spreads from the cathode side along the diffuse channel; the brightness of that spark channel is comparable to that of the cathode plasma glow. From the color spectrum, it has been found that intensive aluminum emission takes place in the domain of binding spots already at the stage of avalanche-streamer spark formation. The estimated calculation of the heat flux rate in the binding spot of the spark discharge has shown values of 106–108 W/cm2, i.e., comparable to laser heating parameters. The spark discharge exerts a significant thermal impact consisting of melting of the surface in the spark binding zone and the development of the recrystallization process of an amorphous film matrix in the zone of thermal effect. Electron diffraction through the thin film layer in the thermal effect zone has shown clear concentric rings corresponding to the polycrystal γ-phase of Al2O3. According to transmission electron microscope data, the average size of γ-phase grains in the surface film layer after impact by the spark is d = 8–15 nm, whereas the volume of the produced γ-phase is at least 70%. The stable thermodynamic α-phase in the melting zone has been fixed. By the moment of arc discharge excitation, the entire aluminum surface in the spark-binding zone has been free of oxide film.

Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring

Obtaining clear and true images is a basic requirement for agricultural monitoring. However, under the influence of fog, haze and other adverse weather conditions, captured images are usually blurred and distorted, resulting in the difficulty of target extraction. Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion. In order to address the above-mentioned problems caused by traditional image dehazing methods, an improved image dehazing method based on dark channel prior (DCP) was proposed. By enhancing the brightness of the hazed image and processing the sky area, the dim and un-natural problems caused by traditional image dehazing algorithms were resolved. Ten different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm, and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method. Three image evaluation indicators including mean square error (MSE), peak signal to noise ratio (PSNR), and entropy were used to evaluate the dehazing performance. Results showed that the PSNR and entropy with the proposed method increased by 21.81% and 5.71%, and MSE decreased by 40.07% compared with the original DCP method. It performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95% and entropy by 2.04% and a decrease of MSE by 84.78%. The results from this study can provide a reference for agricultural field monitoring. Keywords: agricultural monitoring, image dehazing, monitoring image, dark channel prior (DCP), brightness promoting DOI: 10.25165/j.ijabe.20181102.3357 Citation: Wang X Y, Yang C H, Zhang J, Song H B. Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring. Int J Agric & Biol Eng, 2018; 11(2): 170–176.

Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the SARTA all-sky infrared radiative transfer algorithm

Abstract. One-dimensional variational retrievals of temperature and moisture fields from hyperspectral infrared (IR) satellite sounders use cloud-cleared radiances (CCRs) as their observation. These derived observations allow the use of clear-sky-only radiative transfer in the inversion for geophysical variables but at reduced spatial resolution compared to the native sounder observations. Cloud clearing can introduce various errors, although scenes with large errors can be identified and ignored. Information content studies show that, when using multilayer cloud liquid and ice profiles in infrared hyperspectral radiative transfer codes, there are typically only 2–4 degrees of freedom (DOFs) of cloud signal. This implies a simplified cloud representation is sufficient for some applications which need accurate radiative transfer. Here we describe a single-footprint retrieval approach for clear and cloudy conditions, which uses the thermodynamic and cloud fields from numerical weather prediction (NWP) models as a first guess, together with a simple cloud-representation model coupled to a fast scattering radiative transfer algorithm (RTA). The NWP model thermodynamic and cloud profiles are first co-located to the observations, after which the N-level cloud profiles are converted to two slab clouds (TwoSlab; typically one for ice and one for water clouds). From these, one run of our fast cloud-representation model allows an improvement of the a priori cloud state by comparing the observed and model-simulated radiances in the thermal window channels. The retrieval yield is over 90 %, while the degrees of freedom correlate with the observed window channel brightness temperature (BT) which itself depends on the cloud optical depth. The cloud-representation and scattering package is benchmarked against radiances computed using a maximum random overlap (RMO) cloud scheme. All-sky infrared radiances measured by NASA's Atmospheric Infrared Sounder (AIRS) and NWP thermodynamic and cloud profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF) forecast model are used in this paper.

Estimate of Hurricane Wind Speed from AMSR-E Low-Frequency Channel Brightness Temperature Data

Two new parameters (W6H and W6V) were defined that represent brightness temperature increments for different low-frequency channels due to ocean wind. We developed a new wind speed retrieval model inside hurricanes based on W6H and W6V using brightness temperature data from AMSR-E. The AMSR-E observations of 12 category 3–5 hurricanes from 2003 to 2011 and corresponding data from the H*wind analysis system were used to develop and validate the AMSR-E wind speed retrieval model. The results show that the mean bias and the overall root-mean-square (RMS) difference of the AMSR-E retrieved wind speeds with respect to H*wind (HRD Real-time Hurricane Wind Analysis System) analysis data were −0.01 m/s and 2.66 m/s, respectively. One case study showed that W6H and W6V were less sensitive to rain than the observed AMSR-E C-band and X-band brightness temperature data. The AMSR-E retrieval model was further validated by comparing the retrieved wind speeds against stepped-frequency microwave radiometer (SFMR) measurements. The comparison showed an RMS difference of 3.41 m/s and a mean bias of 0.49 m/s.

Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring

Obtaining clear and true images is a basic requirement for agricultural monitoring. However, under the influence of fog, haze and other adverse weather conditions, captured images are usually blurred and distorted, resulting in the difficulty of target extraction. Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion. In order to address the above-mentioned problems caused by traditional image dehazing methods, an improved image dehazing method based on dark channel prior (DCP) was proposed. By enhancing the brightness of the hazed image and processing the sky area, the dim and un-natural problems caused by traditional image dehazing algorithms were resolved. Ten different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm, and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method. Three image evaluation indicators including mean square error (MSE), peak signal to noise ratio (PSNR), and entropy were used to evaluate the dehazing performance. Results showed that the PSNR and entropy with the proposed method increased by 21.81% and 5.71%, and MSE decreased by 40.07% compared with the original DCP method. It performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95% and entropy by 2.04% and a decrease of MSE by 84.78%. The results from this study can provide a reference for agricultural field monitoring. Keywords: agricultural monitoring, image dehazing, monitoring image, dark channel prior (DCP), brightness promoting DOI: 10.25165/j.ijabe.20181102.3357 Citation: Wang X Y, Yang C H, Zhang J, Song H B. Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring. Int J Agric & Biol Eng, 2018; 11(2): 170–176.

基于电磁理论的水下图像色彩补偿

The invention provides an underwater image color compensation method based on the electromagnetic theory and belongs to the technical field of image restoration. According to the technical scheme, anunderwater optical imaging model is established according to the propagation characteristics of light in water, and the absorption of light of a red band is absorbed by water to seriously cause R channel brightness attenuation of an image. The distance between a scene and a camera and the attenuation coefficient of an R channel caused by water absorption are obtained according to the electromagnetic wave transmission theory, and the transmission coefficient of the R channel is finally obtained. According to the method, the background light and the transmission coefficient of each channel can be effectively estimated, and the color of the image is compensated. Experimental results show that the image contrast and sharpness can be effectively improved and color distortion can be improved inthe invention compared with a traditional underwater image restoration algorithm.