Combining DDV and SYNTAM Methods to Retrieval Aerosol Optical Thickness from MODIS for Land Region in China.
ABSTRACT Aerosol particles, water vapor and clouds are the three important factors that affect the signals received by sensors. To remove the aerosol effect, some methods have been developed. MODIS aerosol products provided by NASA are based on the Dark Dense Vegetation (DDV) algorithm. The Synergy of Terra and Aqua MODIS (SYNTAM) method is developed recently that can be used to retrieval aerosol optical thickness over land from MODIS data, no matter whether the land is dark or bright. The experiments prove that the DDV method is better for dark targets and the SYNTAM method is better for bright surfaces. By combining the DDV method and SYNTAM method, it will provide more details about the aerosols over land by mutually compensating for each other.
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ABSTRACT: A new technique for remote sensing of aerosol over the land and for atmospheric correction of Earth imagery is developed. It is based on detection of dark surface targets in the blue and red channels, as in previous methods, but uses the 2.1 μm channel, instead of the 3.75 μm for their detection. A 2.1-μm channel is present on ADEOS OCTS and GLI and planned on EOS-MODIS and EOSP, and a similar 2.2-μm channel is present on Landsat TM. The advantage of the 2.1-μm channel over the 3.75-μm channel is that it is not affected by emitted radiation. The 2.1-μm channel is transparent to most aerosol types (except dust) and therefore can be used to detect dark surface targets. Correlation between the surface reflection in the blue (0.49 μm), red (0.66 μm), and 2.1 μm is established using atmospherically corrected Landsat TM and AVIRIS aircraft images collected over the Eastern United States, Maine, and California and spectral data obtained from the ground and light aircraft near San Diego, CA. Results from a variety of surface covers show that the surface reflectance at 0.49 μm (ρ<sub>0.49</sub>) and 0.66 μm (ρ<sub>0.66</sub>) can be predicted from that at 2.2 μm (ρ<sub>2.2</sub>) within Δρ=±0.06 for ρ<sub>2.2</sub>⩽0.10, using ρ <sub>0.49</sub>=ρ<sub>2.2</sub>/4 and ρ<sub>0.66</sub>=ρ<sub>2.2</sub>/2. Error in surface reflectance of 0.006 corresponds to an error in remote sensing of aerosol optical thickness, τ, of Δτ~±0.06. These relationships were validated using spectral data taken close to the surface over vegetated areas in a different biome. This method expends application of dark targets for remote sensing of aerosol to brighter, nonforested vegetation. The higher reflection of the surface at 2.2 μm than that of 3.75 μm may even enable remote sensing of dust above surfaces with reflectivity ρ<sub>2.2</sub>=0.15±0.05. For this reflectivity range the dust radiative effect at 2.2 μm is small, and the surface reflectance in the blue and red channels can be retrievedIEEE Transactions on Geoscience and Remote Sensing 10/1997; · 3.47 Impact Factor
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ABSTRACT: The launch of ADEOS in August 1996 with POLDER, TOMS, and OCTS instruments on board and the future launch of EOS-AM 1 in mid-1998 with MODIS and MISR instruments on board start a new era in remote sensing of aerosol as part of a new remote sensing of the whole Earth system (see a list of the acronyms in the Notation section of the paper). These platforms will be followed by other international platforms with unique aerosol sensing capability, some still in this century (e.g., ENVISAT in 1999). These international spaceborne multispectral, multiangular, and polarization measurements, combined for the first time with international automatic, routine monitoring of aerosol from the ground, are expected to form a quantum leap in our ability to observe the highly variable global aerosol. This new capability is contrasted with present single-channel techniques for AVHRR, Meteosat, and GOES that although poorly calibrated and poorly characterized already generated important aerosol global maps and regional transport assessments. The new data will improve significantly atmospheric corrections for the aerosol effect on remote sensing of the oceans and be used to generate first real-time atmospheric corrections over the land. This special issue summarizes the science behind this change in remote sensing, and the sensitivity studies and applications of the new algorithms to data from present satellite and aircraft instruments. Background information and a summary of a critical discussion that took place in a workshop devoted to this topic is given in this introductory paper. In the discussion it was concluded that the anticipated remote sensing of aerosol simultaneously from several space platforms with different observation strategies, together with continuous validations around the world, is expected to be of significant importance to test remote sensing approaches to characterize the complex and highly variable aerosol field. So far, we have only partial understanding of the information content and accuracy of the radiative transfer inversion of aerosol information from the satellite data, due to lack of sufficient theoretical analysis and applications to proper field data. This limitation will make the anticipated new data even more interesting and challenging. A main concern is the present inadequate ability to sense aerosol absorption, from space or from the ground. Absorption is a critical parameter for climate studies and atmospheric corrections. Over oceans, main concerns are the effects of white caps and dust on the correction scheme. Future improvement in aerosol retrieval and atmospheric corrections will require better climatology of the aerosol properties and understanding of the effects of mixed composition and shape of the particles. The main ingredient missing in the planned remote sensing of aerosol are spaceborne and ground-based lidar observations of the aerosol profiles.01/1997; 102830(27):815-16.
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ABSTRACT: Air pollution and aerosol characteristics are of considerable interest to studies in climate change and air quality. Although the reference methodology will undoubtedly remain in situ measurements, the synoptic view and detailed spatial coverage potentially offered by space sensors would constitute some very desirable complementary information, even if of a qualitative nature. Effective retrieval of information about the atmospheric and surface characteristics necessitates an atmospheric correction step, which takes into account the effects of multiple scattering. These are greatly dependant on aerosol properties. Here we address the possibility of remote sensing the aerosols over land, mostly with space sensors currently in orbit that are imagers in the visible and the near‐infared. One popular method is the concept of dark dense vegetation (DDV). The dark targets in the blue and red allow retrieval of the spectral aerosol path radiance and inference of the aerosol loading and size distribution, once the refractive index is known. For the Medium Resolution Imaging Spectrometer (MERIS) land product algorithm, they are selected by a thresholding on Atmospherically Resistant Vegetation Index (ARVI) (> ∼0.78). However, as shown on Modular Optoelectronic Scanner (MOS) images acquired over western Europe, pure DDV targets are sparse (< 1% of land pixels) and of little use for aerosol characterization, at least for Europe.This problem is tackled by extending the DDV concept to brighter targets that have a lower ARVI (down to 0.6). It is shown that these new targets have a reflectance in the red that is very well correlated with the ARVI, and a quite constant reflectance in the blue. The idea of using a DDV reflectance in the red dependant on ARVI in the aerosol retrieval step is tested on several MOS images, on which an atmospheric correction algorithm similar to the one developed for MERIS is applied. The result is that aerosol optical properties are now retrieved over˜ 10% of the land area with little loss of accuracy compared to pure DDV. The proposed ARVI‐red reflectance empirical relationship also includes correction for the adjacency effects that arise when a dark area is surrounded with some brighter surfaces.International Journal of Remote Sensing - INT J REMOTE SENS. 01/2003; 24(7):1439-1467.