Lin Sun

Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images

Conventional methods for Aerosol Optical Depth (AOD) retrieval are limited to areas with low reflectance such as water or vegetated areas because the satellite signals from the aerosols in these areas are more obvious than those in areas with higher reflectance such as urban and sandy areas. Land Surface Reflectance (LSR) is the key parameter that must be estimated accurately. Most current methods used to estimate AOD are applicable only in areas with low reflectance. It has historically been difficult to estimate the LSR for bright surfaces because of their complex structure and high reflectance. This paper provides a method for estimating LSR for AOD retrieval in bright areas, and the method is applied to AOD retrieval for Landsat 8 Operational Land Imager (OLI) images at 500 m spatial resolution. A LSR database was constructed with the MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product (MOD09A1), and this database was also used to estimate the LSR of Landsat 8 OLI images. The AOD retrieved from the Landsat 8 OLI images was validated using the AOD measurements from four AErosol RObotic NETwork (AERONET) stations located in areas with bright surfaces. The MODIS AOD product (MOD04) was also compared with the retrieved AOD. The results demonstrate that the AOD retrieved with the new algorithm is highly consistent with the AOD derived from ground measurements, and its precision is better than that of MOD04 AOD products over bright areas.

A Universal Dynamic Threshold Cloud Detection Algorithm (UDTCDA) supported by a prior surface reflectance database

Conventional cloud detection methods are easily affected by mixed pixels, complex surface structures, and atmospheric factors, resulting in poor cloud detection results. To minimize these problems, a new Universal Dynamic Threshold Cloud Detection Algorithm (UDTCDA) supported by a priori surface reflectance database is proposed in this paper. A monthly surface reflectance database is constructed using long-time-sequenced MODerate resolution Imaging Spectroradiometer surface reflectance product (MOD09A1) to provide the surface reflectance of the underlying surfaces. The relationships between the apparent reflectance changes and the surface reflectance are simulated under different observation and atmospheric conditions with the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) model, and the dynamic threshold cloud detection models are developed. Two typical remote sensing data with important application significance and different sensor parameters, MODIS and Landsat 8, are selected for cloud detection experiments. The results were validated against the visual interpretation of clouds and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation cloud measurements. The results showed that the UDTCDA can obtain a high precision in cloud detection, correctly identifying cloudy pixels and clear-sky pixels at rates greater than 80% with error rate and missing rate of less than 20%. The UDTCDA cloud product overall shows less estimation uncertainty than the current MODIS cloud mask products. Moreover, the UDTCDA can effectively reduce the effects of atmospheric factors and mixed pixels and can be applied to different satellite sensors to realize long-term, large-scale cloud detection operations.

Retrieving land surface temperature from Landsat 8 TIRS data using RTTOV and ASTER GED

Land surface temperature (LST) is a key parameter for a wide number of applications, which include hydrology, meteorology and model validation. In this paper a physical single channel algorithm was developed for retrieving LST from the Landsat 8 TIRS data. ASTER Global Emissivity Dataset (GED) and Vegetation Cover Method (VCM) were chosen to improve the accuracy of land surface emissivity and the fast radiative transfer model RTTOV was utilized for atmospheric correction which uses MERRA reanalysis data as inputs. The algorithm is evaluated by the ground measurements collected from in situ sites during the HiWATER experiment. The LST result shows a dynamical variation with the phenological changes and the average Bias and RMSE of the estimated LST for all sites after remove outliers are 0.09K and 2.20K, respectively. This indicates that the algorithm is suitable for producing LST product from Landsat 8 TIRS data and ASTER GED can be used to improve the accuracy of land surface emissivity in arid and semi-arid area.

Validation of the land surface temperature derived from HJ-1B/IRS data with ground measurements

Land surface temperature (LST) is required for a wide variety of scientific studies, from climatology to hydrology and ecology. The feasibility of using atmospheric profile extracted from NCEP data for LST retrieval from HJ-1B/IRS data was analyzed in this paper. A series of ground measurements were carried out to validate the IRS LST results in Hebei province, China, from May to September, 2010. The results indicate that the LST derived from IRS data by using NCEP data showed a good agreement with the ground LSTs, with RSEM lower than 1.5K. Therefore, it can be concluded that the profile extracted from NCEP data is a useful source for LST retrieval from HJ-1B/IRS data.

Retrieving BRDF of desert using time series of MODIS imagery

Desert plays a very import role on earth radiation budget and calibration research. In this paper, we propose a new algorithm for retrieving BRDF of desert using time series of MODIS imagery. The central idea of this algorithm is to detect the “clearest” observation during a temporal window for each pixel. For desert, the temporal window can be one year since its surface is highly stable. The clear observations are then used to fit the desert BRDF. Finally, the fitted BRDF is used to simulate the MODIS images under “real” conditions and the simulated MODIS images are compared with MODIS surface reflectance product (MOD09 and MYD09), which shows that the R2 and RMSE of the simulated surface reflectance is much better than those of MOD09 product. Therefore, the derived BRDF from this new algorithm much more accurately describe the directional characterization of the desert site.

Land surface emissivity retrieval from HJ-1B satellite data using a combined method

Land surface emissivity (LSE) is an essential parameter in deriving land surface temperature form remote sensing data. According to the single channel characteristics of HJ-1B Infrared Scanner (IRS), a combined method for estimating LSE was proposed based on the vegetation cover method and classification-based method. The proposed method requires inputs such as static land cover product, vegetation and ground emissivity for each land cover and vegetation cover product. The sensitivity analysis indicates that this method could achieve good accuracy with LSE relative errors vary from 0.4% to 2%.

BRDF of Badain Jaran Desert retrieval using Landsat TM/ETM+ and ASTER GDEM data

In this paper, we propose a method to extract the feature of Bi-directional Reflectance Distribution Functions (BRDF) over Badain Jaran Desert using Landsat-TM/ETM+ and ASTER GDEM data. Badain Jaran Desert is characterized with homogeneous and rugged terrain, which forms a natural Bi-directional Reflectance data sets with hypotheses that the surface structure of each slope element does not vary with the variations of slope and aspect; therefore, we can use nadir view Landsat-TM/ETM+ imagery reconstruct the BRDF characterization of this experimental site. The results show that this method can simulate the BRDF feature of land surface accurately.

High resolution AOT retrieval based on MODIS surface reflectance product

The resolution of current MODIS aerosol optical thickness (AOT) product is 10km. This product is suitable for global research, but it faces difficulty in local area research, especially in a city. In order to get detail aerosol distribution in local area or a city, this article mainly discussed how to retrieve 1km resolution AOT and how to estimate surface reflectance in the visible from archived MODIS surface reflectance product. The archived MODIS surface reflectance product is mainly used to build surface reflectance database that is used to estimate surface reflectance in the visible. Based on the database, the surface reflectance of band blue was first estimated and then the AOT of Beijing was retrieved using Dense Dark Vegetation (DDV) method and the surface reflectance estimated using the database.

Retrieval of aerosol optical thickness from HJ-1A/B images using structure function method

Aerosol optical thickness (AOT) is retrieved from HJ-1A/B images using Structure Function Method (SFM) over Beijing and its surrounding area. SFM is discussed by establishing structure function formula, choosing window size and distance value. Retrieved result is validated by the ground-based observation.

A comparison of the cloud detection results between the UDTCDA mask and MOD35 cloud products

UDTCDA (universal dynamic threshold cloud detection algorithm) is a new cloud detection method which was proposed recently. This cloud detection method is supported by priori surface reflectance obtained from MODIS surface reflectance product (MOD09). Reflectance of four bands in the wavelength of visible to near infrared are used to detect the cloudy pixels. Because there is a priori reference data on the ground surface, pixels of thin cloud and the fractional cloud can be well detected from the clear pixels. MOD35 is the MODIS cloud mask product, combined use of reflectance and brightness temperature to determine the cloud pixels at 250m, and 1km resolutions. 22 out of 36 bands in the visible, near-infrared, and thermal infrared bands are used to create a high quality cloud mask. The methods of visual interpretation and comparison with the CALIPSO data are used to evaluate the two cloud detection algorithms.