Post calibration of channel 1 of NOAA-14 AVHRR: Implications on aerosol optical depth retrieval

In order to produce long-term aerosol optical depth (AOD) dataset over land from the Advanced Very High Resolution Radiometer (AVHRR), AVHRR data quality in terms of radiometric calibration must be maintained. A vicarious calibration method have been developed by incorporating well calibrated Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) radiance data over several pseudo-invariant targets to inter-calibrate the channel 1 of AVHRR based on Bidirectional Reflectance Distribution Functions (BRDFs) and spectral band adjustment factor (SBAF) models for different targets. Comparison of our calibration coefficients with those of Pathfinder Atmospheres Extended (PATMOS-x) indicate the calibration accuracy to be within 2.5%. The operational L1B and recalibrated AVHRR radiance are applied to derive AOD maps over East America (dark surface) and West Africa (bright surface) using the land aerosol and bidirectional reflectance inversion by times series technique (LABITS) algorithm. Preliminary comparisons show that significant difference in the retrieved AOD from the two different calibration is expected, while the spatial distribution of AOD difference is complicated due to different surface brightness and deficiencies of numeric solutions

An atmospheric correction algorithm for FY3/MERSI data over land in China

Feng-Yun (FY-3) is the second generation of the Chinese Polar Orbiting Meteorological Satellites with global, three-dimensional, quantitative, and multispectral capabilities. Medium Resolution Spectral Imager (MERSI) has 20 channels onboard the FY-3A and FY-3B satellites, including five channels (four VIS and one thermal IR) with a spatial resolution of 250m. The top of the atmosphere signal are necessary to be radiometrically calibrated and corrected for atmospheric effects based on surface reflectance, especially in land surface remote sensing and applications. This paper presents an atmospheric correction algorithm for FY3/MERSI data over land in China, taking into account the directional properties of the observed surface by a kernel-based Bi-directional Reflectance Distribution Function (BRDF) model. The comparison with MODGA and ASD reflectance showed that there is a good agreement. Therefore, FY3/MERSI can serve a reliable and new data source for quantifying global environment change

Technical note: Intercomparison of three AATSR Level 2 (L2) AOD products over China

One of four main focus areas of the PEEX initiative is to establish and sustain long-term, continuous, and comprehensive ground-based, airborne, and seaborne observation infrastructure together with satellite data. The Advanced Along-Track Scanning Radiometer (AATSR) aboard ENVISAT is used to observe the Earth in dual view. The AATSR data can be used to retrieve aerosol optical depth (AOD) over both land and ocean, which is an important parameter in the characterization of aerosol properties. In recent years, aerosol retrieval algorithms have been developed both over land and ocean, taking advantage of the features of dual view, which can help eliminate the contribution of Earth's surface to top-of-atmosphere (TOA) reflectance. The Aerosol_cci project, as a part of the Climate Change Initiative (CCI), provides users with three AOD retrieval algorithms for AATSR data, including the Swansea algorithm (SU), the ATSR-2ATSR dual-view aerosol retrieval algorithm (ADV), and the Oxford-RAL Retrieval of Aerosol and Cloud algorithm (ORAC). The validation team of the Aerosol-CCI project has validated AOD (both Level 2 and Level 3 products) and AE (Ångström Exponent) (Level 2 product only) against the AERONET data in a round-robin evaluation using the validation tool of the AeroCOM (Aerosol Comparison between Observations and Models) project. For the purpose of evaluating different performances of these three algorithms in calculating AODs over mainland China, we introduce ground-based data from CARSNET (China Aerosol Remote Sensing Network), which was designed for aerosol observations in China. Because China is vast in territory and has great differences in terms of land surfaces, the combination of the AERONET and CARSNET data can validate the L2 AOD products more comprehensively. The validation results show different performances of these products in 2007, 2008, and 2010. The SU algorithm performs very well over sites with different surface conditions in mainland China from March to October, but it slightly underestimates AOD over barren or sparsely vegetated surfaces in western China, with mean bias error (MBE) ranging from 0.05 to 0.10. The ADV product has the same precision with a low root mean square error (RMSE) smaller than 0.2 over most sites and the same error distribution as the SU product. The main limits of the ADV algorithm are underestimation and applicability; underestimation is particularly obvious over the sites of Datong, Lanzhou, and Urumchi, where the dominant land cover is grassland, with an MBE larger than 0.2, and the main aerosol sources are coal combustion and dust. The ORAC algorithm has the ability to retrieve AOD at different ranges, including high AOD (larger than 1.0); however, the stability deceases significantly with increasing AOD, especially when AOD > 1.0. In addition, the ORAC product is consistent with the CARSNET product in winter (December, January, and February), whereas other validation results lack matches during winter

Dust aerosol optical depth retrieval and dust storm detection for Xinjiang Region using Indian National Satellite Observations

The Xinjiang Uyghur Autonomous Region (Xinjiang) is located near the western border of China. Xinjiang has a high frequency of dust storms, especially in late winter and early spring. Geostationary satellite remote sensing offers an ideal way to monitor the regional distribution and intensity of dust storms, which can impact the regional climate. In this study observations from the Indian National Satellite (INSAT) 3D are used for dust storm detection in Xinjiang because of the frequent 30-min observations with six bands. An analysis of the optical properties of dust and its quantitative relationship with dust storms in Xinjiang is presented for dust events in April 2014. The Aerosol Optical Depth (AOD) derived using six predefined aerosol types shows great potential to identify dust events. Cross validation between INSAT-3D retrieved AOD and MODIS AOD shows a high coefficient of determination (R2 = 0.92). Ground validation using AERONET (Aerosol Robotic Network) AOD also shows a good correlation with R2 of 0.77. We combined the apparent reflectance (top-of-atmospheric reflectance) of visible and shortwave infrared bands, brightness temperature of infrared bands and retrieved AOD into a new Enhanced Dust Index (EDI). EDI reveals not only dust extent but also the intensity. EDI performed very well in measuring the intensity of dust storms between 22 and 24 April 2014. A visual comparison between EDI and Feng Yun-2E (FY-2E) Infrared Difference Dust Index (IDDI) also shows a high level of similarity. A good linear correlation (R2 of 0.78) between EDI and visibility on the ground demonstrates good performance of EDI in estimating dust intensity. A simple threshold method was found to have a good performance in delineating the extent of the dust plumes but inadequate for providing information on dust plume intensity