Yanning Guan

Regularized inversion method for retrieval of aerosol particle size distribution function in W 1,2 space

A determination of the aerosol particle size distribution function by using the particle spectrum extinction equation is an ill-posed integral equation of the first kind. To overcome this, we must incorporate regularization techniques. Most of the literature focuses on the Phillips-Twomey regularization or its variations. However, there are drawbacks for some applications in which the real aerosol distributions have large oscillations in a Junge-type distribution. The reason for this is that the scale matrix based on the norm of the second differences in the Phillips-Twomey regularization is too ill- conditioned to filter the large perturbations induced by the small algebraic spectrum of the kernel matrix and the additive noise. Therefore we reexamine the aerosol particle size distribution function retrieval problem and solve it in W1,2 space. This setting is based on Sobolevs embedding theorem in which the approximate solution best simulates the true particle size distribution functions. For choosing the regularization parameters, we also develop an a posteriori parameter choice method, which is based on the discrepancy principle. Our numerical results are based on the remote sensing data measured by the CE318 sunphotometer in Jia Xiang County, Shan Dong Province, China, and are performed to show the feasibility of the proposed algorithms.

Aerosol Optical Thickness determination by exploiting the synergy of TERRA and AQUA MODIS (SYNTAM)

Aerosol retrieval over land remains a difficult task because the solar light reflected by the Earth-Atmospheric system mainly comes from the ground surface. Dark Dense Vegetation (DDV) for MODIS data has showed excellent competence at the aerosol distribution and properties retrieval, which is, however, restrictedly used for lower reflectance ground surface such as water body and dense vegetation. In this paper, we attempt to derive Aerosol Optical Thickness (AOT) by exploiting the synergy of TERRA and AQUA MODIS data (SYNTAM), which can be used for various ground surfaces, including high reflective surface. Preliminary validation result compared with AERONET data shows good accuracy and a promising potential

Algorithms for the estimation of the concentrations of chlorophyll a and carotenoids in rice leaves from airborne hyperspectral data

Algorithms based on reflectance band ratios and first derivative have been developed for the estimation of chlorophyll a and carotenoid content of rice leaves by using airborne hyperspectral data acquainted by Pushbroom Hyperspectral Imager (PHI). There was a strong R680/R825 and chlorophyll a relationship with a linear relationship between the ratio of reflectance at 680nm and 825nm. The first derivative at 686 nm and 601 nm correlated best with carotenoid. The relationship between the ratio of R680/R825 and chlorophyll a relationship, the first derivative at 686 nm and carotenoid concentration were used to develop predictive regression equations for the estimation of canopy chlorophyll a and carotenoid concentration respectively. The relationship was applied to the imagery, where a chlorophyll a concentration map was generated in XueBu, which is one of the sites for rice.

Middleware development for remote sensing data sharing and image processing on HIT-SIP system

Sharing spatial data derived from remote sensing is a very significant thing. Grid computing and Web Service technology provides fundamental support for it. In this paper we mainly discuss architecture and middleware of sharing spatial data derived from remote sensing and processing. Because middleware of automatically transferring and task execution on grid is the key of the architecture, we study the middleware. It can effectively protect the owner of data and middlewares property through giving users their required result not just simply copying data and codes resource to them. Based on this sharing architecture and middleware technology, a data and middleware transferring example is showed.

Application of airborne hyperspectral data for precise agriculture

Hyperspectral remote sensing exploits the fact that all material reflects, absorb, and emit electromagnetic energy, at specific wavelengths, in distinctive patterns related to their molecular composition. Hyperspectral algorithms for the estimation of the concentrations of chlorophyll A and carotenoids can be developed using statistical approaches. Some algorithms for the estimation of the concentrations of chlorophyll A and carotenoids in rice leaves from airborne hyperspectral data were developed in this research. Algorithms based on reflectance band ratios and first derivative have been developed for the estimation of chlorophyll A and carotenoid content of rice leaves by using airborne hyperspectral data acquainted by Pushbroom Hyperspectral Imager (PHI). There was a strong R680/R825 and chlorophyll A relationship with a linear relationship between the ratio of reflectance at 680 nm and 825 nm. The first derivative at 686 nm and 601 nm correlated best with carotenoid. The relationship between the ratio of R680/R825 and chlorophyll A relationship, the first derivative at 686 nm and carotenoid concentration were used to develop predictive regression equations for the estimation of canopy chlorophyll A and carotenoid concentration respectively. The relationship was applied to the imagery and a chlorophyll A concentration map was generated

A new approach to generate the look-up table for aerosol remote sensing on grid platform

Grid computing seeks to aggregate computing resources which are geographically distributed or heterogeneous and leverage on resources one dont own for oneself computational intensive applications. The procedure to generate the look-up table (LUT), which is very commonly used for aerosol remote sensing retrieval, is computational intensive even though the aims to take it are mainly to speedup the retrieval computation. This work focuses on realization of the compute-intensive look-up table generation on GCP-ARS (Grid Computation Platform for Aerosol Remote Sensing), which is one grid middleware we are developing based on Condor system. We discuss our approach to parameterization, task partitioning, generated methodology, and the collection of result. Experimental results obtained using Condor-pool consisted of commodity PCs are discussed.

Data standardization for the Chinese Resources and Environment Remote Sensing Database

The International Organization for Standardization (ISO) defines standards as documented agreements containing technical specifications or other precise criteria to be used consistently as rules, guidelines, or definition characteristics, to ensure that materials, products, procedures, and services are fit for their purpose. Further, standards contribute to making life simpler, and to increasing the reliability and effectiveness of the goods and services we use. The Scientific Database (SDB) of the Chinese Academy of Sciences (CAS) is comprehensive scientific information service system. It has been built by different disciplines of many institutes of CAS in the past ten years. The Chinese Resources and Environment Remote Sensing Database is one of subsidiary database of SDB. The purpose of data standardization for the Chinese Resources and Environment Remote Sensing Database is to facilitate data sharing and increase interoperability among resources and environment remote sensing applications. The primary objective of this proposed standard is to define the content for remote sensing data, thereby providing a solid basis from which to develop interoperable data formats for this common form of remote sensing data