Atmospheric correction is the basis for quantitative analysis of satellite remote sensing images, such as monitoring land surface changes. However, precise atmospheric correction is still challenging. Landsat 8 is a satellite used for surface monitoring launched by the United States National Aeronautics and Space Administration (NASA) in 2013, with good spatial resolution. Rich spectral information. In this paper, an improved dense dark vegetation(DDV)aerosol retrieval algorithm is developed, and the retrieved AOD map will be used to process the aerosol impact factor of remote sensing images. Atmospheric correction is performed based on a lookup table generated by the 6SV model. Validation with the Land Surface Reflectance Code (LaSRC) algorithm produced atmospheric correction images, correction images of the paper algorithm showed a good agreement with high correlation(Correlation R exceeds 95%). Meanwhile, a reliable software prototype system for processing atmospheric correction on Landsat 8 OLI images was developed. This system is based on C++ language and can perform atmospheric correction automatically, low-latency, and accuracy. The data products corrected by the software prototype are helpful for the widespread application of remote sensing data in emergency response, environmental monitoring, and national defense.
As so far, studies based on remote sensing to explore ozone column concentration keep a watchful eye on the stratosphere or troposphere, while few focus on the near-surface, though it directly correlative to human health. In this paper, the regional near-surface total column ozone was inversed based on the moderate-resolution imaging spectroradiometer (MODIS) for its extraordinary spatial resolution. First, the statistical synthetic regression algorithm was utilized to retrieve the first guess. A nonlinear physical iterative method was then employed to acquire final ozone profiles. Finally, after creating a unique database, the ozone column concentration was obtained by using the multivariable linear regression model. Compared with the measurements of ground monitoring sites, the retrieval results were over 95% accurate and its distribution consists with the actual situation. The method proposed in this paper can be applied to monitor air pollution.
An empirical multilinear model was developed for estimating ground-level PM2.5
concentration at city scale (Chengdu, China) using Landsat 8 data. In this model, the improved DDV (dense
dark vegetation) algorithm (V5.2) was used to retrieve aerosol optical thickness (AOT), Image-based Method
(IBM) was used to compute the land surface temperature (LST), and TVDI was calculated to reflect the air
humidity. The three parameters (AOT, LST, TVDI) and in-situ measured PM2.5 (particulate matter) data
were then utilized to establish the empirical model by partial least square (PLS) regression. In the
computation, the band 9, cirrus band, was used to reduce the influence of atmospheric vapor to LST retrieval.
The results show that when considering moisture and temperature, the correlation between AOT and PM2.5
would be efficiently improved; furthermore, moisture shows more impact on the relationship than
temperature. Station record hourly average PM2.5 also shows higher correlation coefficients than 24-hr
average. As a result, PM2.5 concentration distribution across Chengdu was retrieved using this model
developed in this paper. The method could be a beneficial complement to ground-based measurement and
implicate that remote sensing data has enormous potential to monitor air quality at city scale.
This paper proposes a new spatial scale conversion method, which validates moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) product when geometry information from the MODIS 1B product and classification result is combined. The in situ LAI data, Landsat Thematic Mapper (TM), and MODIS 1B product were utilized in this research. An object-oriented method was used to classify TM imaging, where each class was computed using an empirical model to achieve LAI respectively. The 30-m TM LAI image was aggregated into the MODIS 1B product based on the geometry information of MODIS 1B. The simulated MODIS 1B image was then converted into a MODIS LAI product and compared with the simulated LAI map pixel by pixel. The results showed a lower root mean square error and higher normalization of the absolute error with the new method. In addition, the field LAI was not significantly correlated with MODIS LAI, but it did show a strong correlation with TM LAI. The new method achieved a higher correlate coefficient with the MODIS product than the conventional methods. Using this validation method based on classification and image simulation can improve the accuracy of product certification.
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