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The vertical structure of wind is a key factor in modulating air quality, from which the determination of boundary layer height (BLH) remains a major challenge. In this paper, we developed an improved threshold method to determine the BLH from radar wind profiler (RWP) measurements. The normalized signal-to-noise ratio (SNR) profiles were used instead of the original SNR profiles to avoid instrumental inconsistencies. Additionally, a peak filter was designed to indicate the BLH based on the maximum SNR by taking into account the multiple peaks in the SNR profile. This algorithm was then applied to the RWP measurements taken in the summer (June–July–August) of 2018 in Beijing to obtain the BLHs. Validation analyses suggested that the BLH retrievals from RWP exhibited high consistency with those from radiosondes, with an average correlation coefficient of 0.69 (0.66) and a root mean squared error of 0.39 (0.41) in the daytime (nighttime). Additionally, the major features of summertime BLHs in Beijing were examined. In particular, a distinct diurnal variation in BLH was observed with a peak (1630 ± 510 m) occurring at 0600 universal time coordinated (UTC) and a minimum (587 ± 343 m) at 2300 UTC. Therefore, the algorithm presented here has great potential to be applied to other regions to obtain reliable BLHs. The findings obtained here highlight the importance of vertical wind structure in air quality studies. [ABSTRACT FROM AUTHOR]

Urban black and odorous water (BOW) is a serious global environmental problem. Since these waters are often narrow rivers or small ponds, the detection of BOW waters using traditional satellite data and algorithms is limited both by a lack of spatial resolution and by imperfect retrieval algorithms. In this paper, we used the Chinese high-resolution remote sensing satellite Gaofen-2 (GF-2, 0.8 m). The atmospheric correction showed that the mean absolute percentage error of the derived remote sensing reflectance ($R_{\mathrm {rs}}$) in visible bands is 25.19%. We first measured $R_{\mathrm {rs}}$ spectra of two classes of BOW [BOW with high concentrations of iron (II) sulfide, i.e., BOW1 and BOW with high concentrations of total suspended matter, i.e., BOW2] and ordinary water in Shenyang. Then, in situ $R_{\mathrm {rs}}$ data were converted into $R_{\mathrm {rs}}$ corresponding to the wide GF-2 bands using the spectral response functions. We used the converted $R_{\mathrm {rs}}$ data to calculate several band combinations, including the baseline height, [ $R_{\mathrm {rs}}$ (green) $- R_{\mathrm {rs}}$ (red))/($R_{\mathrm {rs}}$ (green) $+ R_{\mathrm {rs}}$ (red)], and the color purity on a Commission Internationale de L’Eclairage (CIE) chromaticity diagram. The color purity was found to be the best index to extract BOW from ordinary water. Then, $R_{\mathrm {rs}}$ (645) was applied to categorize BOW into BOW1 and BOW2. We applied the algorithm to two synchronous GF-2 images. The recognition accuracy of BOW2 and ordinary water are both 100%. The extracted river water type near Weishanhu Road was BOW1, which agreed well with ground truth. The algorithm was further applied to other GF-2 data for Shenyang and Beijing. [ABSTRACT FROM AUTHOR]

This paper addresses the estimation of adjacency effect in ground spectrum and Landsat-5 pixels. The adjacency effect influences the digital number value of a pixel by adding surface surrounding scatter signals and path scatter signals at the sensor. Along with the increasing use of satellite high-resolution imagery and quantitative remote sensing, much attention has been paid to the experimental measurement and estimation of the natural phenomena of adjacency effects. Based on the theory of radiation transfer, a procedure was designed to measure the reflectance from the surface target materials and the materials in a box which is 1.5 m above the surface to avoid upwelling reflectance. At every 3 × 3 sites, the measurement was carried out during 10:30 to 13:30 of local time at the Guanting Remote Sensing Test Site in north Beijing. The results show that the adjacency effect becomes stronger from visible, near infrared to shortwave infrared wavelength; the adjacency effect weakens with the increase of distance between testing site. At last, the adjacency effect of Landsat-5 image was corrected, and the quality of the resulting image was improved. [ABSTRACT FROM AUTHOR]