Guest editors Kaixu Bai, Simone Lolli, and Yuanjian Yang introduce the Special Section on Integrating Remote Sensing, Machine Learning, and Data Science for Air Quality Management.
In complex urban environments, the information of high-resolution Aerosol Optical Depth (AOD) is of great importance for effective air pollution control, air navigation, public health assessment, and meteorological forecasting. High resolution AOD may be produced by merging and/or fusing existing AOD products or reproduced by merging and/or fusing the reflectance data at the top of atmosphere (TOA) and ground levels through the deep blue method. However, the former can only lead to the production of AOD with 500 m~1 km spatial resolution at best. To overcome this barrier, it is necessary to fuse the reflectance values of Landsat and MODIS imageries at the TOA level to be in concert with the fused land surface reflectance values for advanced synthesis. Such a collective endeavor can lead to the production of AOD with daily 30m spatial resolution via the deep blue method. This paper thus presents such a synthetic effort that synergizes the spatial and temporal advantages of two satellite sensors (MODIS Terra and Landsat 8) to reach the goal with the aid of machine learning and high-performance computing. Based on the deep blue method, the practical implementation of the synthetic image processing was assessed by a case study of the downtown Atlanta area in the United States. 10-fold cross validation was applied stepwise to control the uncertainty via machine learning. The predictions of AOD at the ground level were calibrated using the AErosol RObotic NETwork (AERONET) AOD data and finally validated by the AERONET) AOD data too.
Data fusion algorithms help extract information from “asynchronous” time series satellite data whereas data merging data help extract information from “synchronous” time series satellite data into a series of synthetic images by using the temporal, spatial, or even spectral properties. Such data fusion algorithms including Bayesian maximum entropy (BME) and spatial and temporal adaptive reflectance fusion model (STARFM) have greatly improved the coverage, enhancing data application potential with higher spatiotemporal resolution via multi-sensor earth observations. The goal of this study is to assess the utility of BME and modified BME algorithm with the aid of a data merging algorithm called Modified Quantile-Quantile Adjustment (MQQA), in comparison with STARFM for the retrieval of Aerosol Optical Depth in an urban environment. MQQA heavily counts on big data to support the systematic bias correction from “synchronous” time series satellite data. Such assessment of algorithmic efficiency needs to be carried out for both top of atmosphere reflectance and ground reflectance levels in support of the deep blue method for the retrieval of atmospheric optical depth at the ground level.
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.
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.
Identification of teleconnection patterns at a local scale is challenging, largely due to the coexistence of non-stationary and non-linear signals embedded within the ocean-atmosphere system. This study develops a method to overcome the problem of non-stationarity and nonlinearity and investigates how the non-leading teleconnection signals as well as the known teleconnection patterns can affect precipitation over three pristine sites in the United States. It is presented here that the oceanic indices which affect precipitation of specific site do not have commonality in different seasons. Results also found cases in which precipitation is significantly affected by the oceanic regions of two oceans within the same season. We attribute these cases to the combined physical oceanic-atmospheric processes caused by the coupled effects of oceanic regions. Interestingly, in some seasons, different regions in the South Pacific and Atlantic Oceans show more salient effects on precipitation compared to the known teleconnection patterns. Results highlight the importance of considering the seasonality scale and non-leading teleconnection signals in climate prediction.
Leaf pigments are key elements for plant photosynthesis and growth. Traditional manual sampling of these pigments is labor-intensive and costly, which also has the difficulty in capturing their temporal and spatial characteristics. The aim of this work is to estimate photosynthetic pigments at large scale by remote sensing. For this purpose, inverse model were proposed with the aid of stepwise multiple linear regression (SMLR) analysis. Furthermore, a leaf radiative transfer model (i.e. PROSPECT model) was employed to simulate the leaf reflectance where wavelength varies from 400 to 780 nm at 1 nm interval, and then these values were treated as the data from remote sensing observations. Meanwhile, simulated chlorophyll concentration (Cab), carotenoid concentration (Car) and their ratio (Cab/Car) were taken as target to build the regression model respectively. In this study, a total of 4000 samples were simulated via PROSPECT with different Cab, Car and leaf mesophyll structures as 70% of these samples were applied for training while the last 30% for model validation. Reflectance (r) and its mathematic transformations (1/r and log (1/r)) were all employed to build regression model respectively. Results showed fair agreements between pigments and simulated reflectance with all adjusted coefficients of determination (R2) larger than 0.8 as 6 wavebands were selected to build the SMLR model. The largest value of R2 for Cab, Car and Cab/Car are 0.8845, 0.876 and 0.8765, respectively. Meanwhile, mathematic transformations of reflectance showed little influence on regression accuracy. We concluded that it was feasible to estimate the chlorophyll and carotenoids and their ratio based on statistical model with leaf reflectance data.
This paper discusses the analysis of the severe dust storm that occurred over Beijing from 26th April to 3rd May in 2012 with the use of combined satellite observations and ground-based measurements. In this study, we analyze the pollution characteristics of particulate matters near ground, with the main focus on spatio-temporal and vertical distributions of aerosol during this event by using ground-based Aerosol Robotic Network (AERONET), MODerate resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data. Results show that the Aerosol Optical Depth (AOD) measured at 550 nm from the AERONET Beijing station has an ascending trend with a peak value of 2.5 on 1st May. Moreover, the AOD variation from the MODIS data agrees well with AERONET observations during the same time period. In addition, the vertical distribution of total attenuated backscatter coefficient (TABC), volume depolarization ratio (VDR) and color ratio (CR) of CALIPSO data are comprehensively analyzed. Results from these analyses show that the dust mainly accumulates in the layer at altitudes of 1.5 to 4.5 km on 1st May. In this dust layer, the values of TABC are generally around 0.002~0.0045 km-1sr-1 and VDR and CR are typically around 0.1~0.5 and 0.6~1.4 respectively. Thus, the combined satellite and ground-based observations are of great use for monitoring and analyzing air quality with high accuracy.
Water vapor represents a small but environmentally significant constituent of the atmosphere. This study retrieved
columnar water vapor (CWV) with the 939.3 nm band of a Multi-filter Rotating Shadowband Radiometer (MFRSR)
using the modified Langley technique from September 23, 2004 to June 20, 2005 at the XiangHe site.To improve the
credibility, the MFRSR results were compared with those obtained from the AERONET (AErosol RObotic NETwork)
CIMEL sun-photometer measurements, co-located at the XiangHe site, and the Moderate Resolution Imaging
Spectroradiometer (MODIS) Near-Infrared Total Precipitable Water Product (MOD05), respectively. These comparisons
show a good agreement in terms of correlation coefficients, slopes, and offsets, revealing that the accuracy of CWV
estimation using the MFRSR instrument is reliable and suitable for extended studies in northern China.
This study aims to retrieve aerosol optical depth (AOD) from ground-based MultiFilter Rotating Shadowband
Radiometer (MFRSR) measurements at Xianghe site in Hebei Province from September 2004 to October 2005. Based on
Langley regression, calibrations of MFRSR are carried out and then AODs at Xianghe is derived. In order to evaluate the
precision of retrieved AOD, correlations between MFRSR AODs and Aerosol Robotic Network (AERONET) AODs
which has been generally approved and used are analyzed. The result suggests that MFRSR AODs and AERONET
AODs have a significant linear correlation. The correlation coefficients at 500nm, 670nm and 870nm band are 0.9764,
0.9712 and 0.954, respectively. Meanwhile, comparisons between Moderate resolution imaging spectroradiometer
(MODIS) AOD at Xianghe site and MFRSR AODs are carried out. Finally, monthly mean MODIS AODs in the study
area are derived from September 2004 to August 2005. Moreover, their spatial distribution and monthly variations are
demonstrated.
Water vapor is an important component in hydrological processes that basically involve all types of seasons, including dry (e.g., drought) or wet (e.g., hurricane or monsoon). This study retrieved columnar water vapor (CWV) with the 939.3 nm band of a multifilter rotating shadowband radiometer (MFRSR) using the modified Langley technique. Such an investigation was in concert with the use of the atmospheric transmission model MODTRAN for determining the instrument coefficients required for CWV estimation. Results of the retrieval of CWV by MFRSR from September 23, 2004 to June 20, 2005 at the XiangHe site are presented and analyzed in this paper. To improve the credibility, the MFRSR results were compared with those obtained from the AErosol RObotic NETwork CIMEL sun-photometer measurements, co-located at the XiangHe site, and the moderate resolution imaging spectroradiometer (MODIS) near-infrared total precipitable water product (MOD05), respectively. These comparisons show good agreement in terms of correlation coefficients, slopes, and offsets, revealing that the accuracy of CWV estimation using the MFRSR instrument is reliable and suitable for extended studies in northern China.
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