Search Results (4)

The pollution caused by nitrogen dioxide is a major environmental problem in China. This study introduces a new type of atmospheric trace gas remote-sensing instrument, an airborne fiber imaging spectrometer. This spectrometer has a spectral range of 300–410 nm and works in push-broom mode with a 30° field of view on a flight path. Flight experiments were conducted on 30 December 2022 and 5 January 2023, covering heavily polluted areas east of Beijing and Tangshan. This equipment obtained the density distribution of NO₂ over the flight area. The results showed that pollution was mainly concentrated in the Caofeidian area and at the power station in the north, and the main source of pollution was anthropogenic. Satellite and airborne data near the pollution points were compared, and the two datasets showed a positive correlation with a correlation coefficient of 0.78 and 0.7, on the two days, respectively. This study demonstrates the capability of an airborne fiber imaging spectrometer for NO₂ regional emission remote sensing and identifying the pollution points. Full article

A new retrieval of total column water vapour (TCWV) from daytime measurements over land of the Ocean and Land Colour Instrument (OLCI) on-board the Copernicus Sentinel-3 missions is presented. The Copernicus Sentinel-3 OLCI Water Vapour product (COWa) retrieval algorithm is based on the differential absorption technique, relating TCWV to the radiance ratio of non-absorbing band and nearby water vapour absorbing band and was previously also successfully applied to other passive imagers Medium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectroradiometer (MODIS). One of the main advantages of the OLCI instrument regarding improved TCWV retrievals lies in the use of more than one absorbing band. Furthermore, the COWa retrieval algorithm is based on the full Optimal Estimation (OE) method, providing pixel-based uncertainty estimates, and transferable to other Near-Infrared (NIR) based TCWV observations. Three independent global TCWV data sets, i.e., Aerosol Robotic Network (AERONET), Atmospheric Radiation Measurement (ARM) and U.S. SuomiNet, and a German Global Navigation Satellite System (GNSS) TCWV data set, all obtained from ground-based observations, serve as reference data sets for the validation. Comparisons show an overall good agreement, with absolute biases between 0.07 and 1.31 kg/m² and root mean square errors (RMSE) between 1.35 and 3.26 kg/m². This is a clear improvement in comparison to the operational OLCI TCWV Level 2 product, for which the bias and RMSEs range between 1.10 and 2.55 kg/m² and 2.08 and 3.70 kg/m², respectively. A first evaluation of pixel-based uncertainties indicates good estimated uncertainties for lower retrieval errors, while the uncertainties seem to be overestimated for higher retrieval errors. Full article

This study aims to compare the ground-based Brewer spectrophotometer total ozone column measurements with the Dobson spectrophotometer and various satellite overpass data available at Marambio Base during the period 2011–2013. This station provides a unique opportunity to study ozone variability near the edge of the southern polar vortex; therefore, many institutions, such as the National Meteorological Service of Argentina, the Finnish Meteorological Institute and the Czech Hydrometeorological Institute, have been carrying out various scientific activities there. The intercomparison was performed using total ozone column data sets retrieved from the ground-based instruments and from Ozone Monitoring Instrument (OMI)—Total Ozone Mapping Spectrometer (TOMS), OMI–Differential Optical Absorption Spectroscopy (DOAS), Global Ozone Monitoring Experiment 2 (GOME2), and Scanning Imaging Absorption Spectrophotometer for Atmospheric Cartography (SCIAMACHY) satellite observations. To assess the quality of the selected data products, comparisons with reference to the Brewer spectrophotometer single observations were made. The performance of the satellite observational techniques was assessed against the solar zenith angle and effective temperature, as well as against the actual shape of the vertical ozone profiles, which represent an important input parameter for the satellite ozone retrievals. The ground-based Dobson observations showed the best agreement with the Brewer data set (R² = 1.00, RMSE = 1.5%); however, significant solar zenith angle (SZA) dependency was found. The satellite overpass data confirmed good agreement with the Brewer observations but were, however, overestimated in all cases except for the OMI(TOMS), when the mean bias differed from −0.7 DU in the case of the OMI(TOMS) to 6.4 DU for the SCIAMACHY. The differences in satellite observational techniques were further evaluated using statistical analyses adapted for depleted and non-depleted conditions over the ozone hole period. Full article

Remote sensing imagery has been successfully used to map seagrass in clear waters, but here we evaluate the advantages and limitations of different remote sensing techniques to detect eelgrass in the tidal embayment of Elkhorn Slough, CA. Pseudo true-color imagery from Google Earth and broadband satellite imagery from Sentinel-2 allowed for detection of the various beds, but retrievals particularly in the deeper Vierra bed proved unreliable over time due to variable image quality and environmental conditions. Calibrated water-leaving reflectance spectrum from airborne hyperspectral imagery at 1-m resolution from the Portable Remote Imaging SpectroMeter (PRISM) revealed the extent of both shallow and deep eelgrass beds using the HOPE semi-analytical inversion model. The model was able to reveal subtle differences in spectral shape, even when remote sensing reflectance over the Vierra bed was not visibly distinguishable. Empirical methods exploiting the red edge of reflectance to differentiate submerged vegetation only retrieved the extent of shallow alongshore beds. The HOPE model also accurately retrieved the water column absorption properties, chlorophyll-a, and bathymetry but underestimated the particulate backscattering and suspended matter when benthic reflectance was represented as a horizontal eelgrass leaf. More accurate water column backscattering could be achieved by the use of a darker bottom spectrum representing an eelgrass canopy. These results illustrate how high quality atmospherically-corrected hyperspectral imagery can be used to map eelgrass beds, even in regions prone to sediment resuspension, and to quantify bathymetry and water quality. Full article