Over the past decade, China has experienced a rapid increase in urbanization. The urban built-up areas
(population) of Shanghai increased by 16.1% (22.9%) from 2006 to 2015. This study aims to analyze
the variations of tropospheric NO2 over Yangtze River Delta region and the impacts of rapid
urbanization during 2006-2015. The results indicate that tropospheric NO2 vertical column density
(VCD) of all cities in the study area showed an increasing trend during 2006-2011 whereas a
decreasing trend during 2011-2015. Most cities showed a lower tropospheric NO2 VCD value in 2015
compared to that in 2006, except for Changzhou and Nantong. Shanghai and Ningbo are two hotspots
where the tropospheric NO2 VCD decreased most significantly, at a rate of 22% and 19%, respectively.
This effect could be ascribed to the implementation of harsh emission control policies therein. Similar
seasonal variability was observed over all cities, with larger values observed in the summer and smaller
values shown in the winter. Further investigations show that the observed increasing trend of
tropospheric NO2 during 2006-2011 could be largely explained by rapid urbanization linked to car
ownership, GDP, power consumption, population and total industrial output. Such effect was not
prominent after 2011, mainly due to the implementation of emission control strategies.
As Ozone Monitoring Instrument (OMI) onboard the Aura satellite has provided global scale ozone measurements on a daily basis since 2004, the long-term stability and consistency of ozone retrievals is thus of critical importance, especially for the ozone recovery assessment. This study aims to evaluate the long-term stability of total ozone derived from the OMI Total Ozone Mapping Spectrometer (OMI-TOMS) algorithm, by comparing with collocated ground-based total ozone measurements recorded from 42Dobson spectrophotometers during the period 2004-2015. It is indicative that the OMI-TOMS total ozone is in good agreement with collocated ground-based measurements, with a R2 of 0.96 and root mean square error (RMSE) of 3.3%. Further investigations show that the OMI-TOMS total ozone is of quality, as no significant latitude dependence is observed. In the past 12 years, the OMI-TOMS total ozone is highly consistent with the ground-based Dobson total ozone, with a variation of mean relative difference less than 1%. In general, the OMI-TOMS total ozone performs well and can be used with confidence.
This study evaluates the accuracy of total ozone column derived from Ozone Monitoring Instruments (OMI) with two algorithms: OMI Total Ozone Mapping Spectrometer (OMI-TOMS) and OMI Differential Optical Absorption Spectroscopy (OMI-DOAS), compared to ground-based Brewer and Dobson spectrophotometers located at eight China stations from July 2009 to December 2013, including Xianghe, Kunming, Mt.Waliguan, Lhasa, Taipei, Chengkung, Cape D'Aguilar and Longfengshan. Results showed that the agreement between OMI ozone data and ground-based measurements is excellent. Total ozone columns from both OMI-TOMS and OMI-DOAS data are on average about 1.5% lower than ground-based data. For both OMI ozone data products the SZA dependence of the mean relative differences (RD) between satellite data and the ground-based data is relative obvious when the SZA is larger than 50°. Similar to the SZA, the satellite view zenith angle (VZA) dependence of the mean relative differences (RD) between satellite and ground is relatively markedly when the VZA is smaller than 10° in eight stations. Finally, the dependence of the mean relative differences (RD) (-4.28% to 0.818%) between OMI-DOAS data and ground-based data for the total ozone column is remarkable. While for OMI-TOMS data the dependence is not obvious (the RD value varies from -3.30% to -0.676%).
Remote sensing is an effective tool to estimate foliar pigments contents with the analysis of vegetation index. The crucial issue is how to choose the optimal bands-combination to conduct the vegetation index. In this study, RVI, a vegetation index computed by the reflectance of Red and NIR bands, has been used to estimate the contents of chlorophyll and carotenoid. The reflectance of the two bands forming the narrow band RVI was simulated by the PROSPECT model. The possible combinations of narrow band RVI were examined from 400 nm to 800 nm. The results showed that: At the leaf level, estimation of chlorophyll content can be identified in narrow band RVI. Ranges for these bands included: (1) 549-589nm, 616-636nm or 729-735nm combined with 434-454nm; (2) 663-688nm, 710-717nm, 719-728nm or 730- 739nm combined with 549-561nm; (3) 663-688nm combined with 569-615nm. However, no valid narrow-band RVI for the estimation of carotenoid content was successfully identified. Our results also showed that two rules should be followed when choosing optimal bands-combination: (1) the selected bands must have minimal interference from other biochemical constituents; (2) there should be distinct differences between the sensitivities of the bands selected for particular pigments.
KEYWORDS: Reflectivity, Vegetation, Data modeling, Remote sensing, Spectroscopy, Ecosystems, Spectral resolution, Data acquisition, Information science, Radiative transfer
The aim of this work is to estimate leaf chlorophyll concentration with 6 different normalized difference vegetation indices (NDVIs) under 4 bandwidths (1, 5, 10 and 20 nm). A popular leaf radiative transfer model(i.e. PROSPECT) was used to simulate the leaf reflectance spectra from 400-800nm under varying chlorophyll concentrations. Then 6 combinations of bands sensitive to chlorophyll concentrations were chosen for the calculation of their NDVIs. Simulated spectral response functions were applied to calculate the synthesis reflectance spectra at the intervals of 5, 10 and 20 nm respectively, and then corresponding NDVIs were calculated. The change of correlation coefficients between the NDVIs and the leaf chlorophyll concentrations were examined. Results showed that some NDVIs had a good performance with increasing bandwidth, whereas response of different NDVIs to the 4 bandwidths were different generally. Our results suggested that the improvement of spectral resolution was not necessary for some NDVIs to estimate leaf chlorophyll.
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