Zhaoming Zhang

A practical DOS model-based atmospheric correction algorithm

Atmospheric correction is of great importance in quantitative remote sensing studies. However, many of the atmospheric correction algorithms proposed in the literature are not easily applicable in real cases. In order to develop a practical atmospheric correction algorithm, Moderate Resolution Imaging Spectroradiometer (MODIS) imagery is employed to obtain aerosol optical depth and the total atmospheric water vapour content, which are used to compute the transmittances in a dark object subtraction (DOS) model. An improved DOS atmospheric correction method combining MODIS imagery with the conventional DOS technique is proposed. A Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image acquired on 21 October 2001 in Wuyi mountain, south-eastern China, and a CBERS 02 CCD image acquired on 24 August 2005 in Dunhuang, north-western China, were atmospherically corrected with this new approach. Various tests are performed, from spectral signature analysis, to vegetation index spatial profile and image information content comparisons, and by direct comparison with ground-measured reflectances, to evaluate the performance of the improved DOS model. The evaluation shows it can generally achieve a good atmospheric correction result.

A Fast and Reliable Matching Method for Automated Georeferencing of Remotely-Sensed Imagery

Due to the limited accuracy of exterior orientation parameters, ground control points (GCPs) are commonly required to correct the geometric biases of remotely-sensed (RS) images. This paper focuses on an automatic matching technique for the specific task of georeferencing RS images and presents a technical frame to match large RS images efficiently using the prior geometric information of the images. In addition, a novel matching approach using online aerial images, e.g., Google satellite images, Bing aerial maps, etc., is introduced based on the technical frame. Experimental results show that the proposed method can collect a sufficient number of well-distributed and reliable GCPs in tens of seconds for different kinds of large-sized RS images, whose spatial resolutions vary from 30 m to 2 m. It provides a convenient and efficient way to automatically georeference RS images, as there is no need to manually prepare reference images according to the location and spatial resolution of sensed images.

Generation of Landsat surface temperature product for China, 2000–2010

Land surface temperature (LST) is a key parameter in the physics of land surface processes on regional and global scales. Although there are MODIS and Landsat land surface reflectance products, there is no LST product for Landsat data due in part to many challenges in the development of an operational Landsat LST product generating system because Landsat possesses only one thermal infrared channel. The aim of this article is to describe the Landsat LST product generation project launched by the Centre for Earth Observation and Digital Earth (CEODE), Chinese Academy of Sciences. The generalized single-channel (SC) algorithm proposed by Jiménez-Muñoz et al. is used for LST retrieval. It is fully operational, requires minimal input data requirements, and has acceptable precision. Total atmospheric water vapour content is the key input parameter required by the SC algorithm. In this project, the MODIS water vapour product is employed to derive total atmospheric water vapour content. In this way, an operational Landsat LST product generation program was constructed by integration of MODIS and Landsat satellite imagery.

Towards an operational method for land surface temperature retrieval from Landsat 8 data

ABSTRACT Land surface temperature (LST) is a key parameter in the physics of land surfaces through the processes of energy and water exchange with the atmosphere. For Landsat data with only one thermal infrared channel (Landsat 4 to Landsat 7), LST cannot actually be retrieved, and external data sources, such as meteorological observations or Moderate Resolution Imaging Spectroradiometer (MODIS) data, are needed to obtain the water vapour content parameter (an important input parameter for the LST retrieval algorithm); this results in limitations on deriving LST. However, the band designations of the Landsat 8 sensors enable the derivation of LST from the Landsat 8 data. This article demonstrates an LST retrieval methodology that makes use of only Landsat 8 image data. In this methodology, the split-window covariance-variance ratio (SWCVR) technique is introduced to derive water vapour content from Landsat 8. A comparison between the retrieved LST and the in situ LST measurements shows good accuracy, with a root mean squared error (RMSE) of 0.83 K. The fact that the proposed LST estimation method utilizing solely Landsat 8 image data does not rely on any external data is a significant advantage for the development of an operational Landsat 8 LST product generating system.

Leaf area index estimation of bamboo forest in Fujian province based on IRS P6 LISS 3 imagery

Leaf area index (LAI) is an important surface biophysical parameter as an input to many process-oriented ecosystem models. Much work has been reported in the literature on LAI estimation in boreal forests using remotely sensed imagery. However, few if any explicit LAI retrieval studies on bamboo forests in Asian subtropical monsoon-climate regions based on remote sensing technology have been performed. Our goal is to carry out a comparative study on the LAI estimation methods of bamboo forest in Fujian province, China, based on IRS P6 LISS 3 imagery. Both the traditional empirical–statistical approach and the newly proposed normalized distance (ND) method were employed in this study, and a total of 18 modelling parameters were regressed against ground-based LAI measurements. The results show that simple ratio (SR) is the best predictor for LAI estimation in this study area, with the highest R 2 (coefficient of determination) value of 0.68; modified simple ratio (MSR) and normalized difference vegetation index (NDVI) ranked second and third, respectively. The good performance of these three vegetation indices (VIs) can be explained by the ratioing principle. The overall good modelling performance of the ND method in our study area also indicates it is a promising method.

Study on atmospheric correction approach of Landsat-8 imageries based on 6S model and look-up table

Abstract. Atmospheric correction is a necessary step for deriving surface geophysical parameters. The aim of this paper is to study the atmospheric correction of Landsat-8 imageries released by the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. A look-up table (LUT)-based atmospheric correction method on Landsat-8 OLI is proposed. The LUT is generated with 6S model with inputs including total atmospheric water vapor content, ozone, and aerosol optical thickness (AOT) from MODIS atmospheric level 2 products. The conventional method to build up the atmospheric parameter LUT usually only takes part of the factors (e.g., AOT) into consideration, whereas it is not applicable in the atmospheric correction using per pixel of MODIS products as input atmospheric parameters. Thus, a five-dimensional LUT, which considers most input parameters, is built up and has high universality for the Landsat-8 OLI sensor. Finally, spectral analysis, comparison to U.S. Geological Survey-released surface reflectance (SR) products, and field observations are used to validate the quality of the model-computed SR. The validation results indicate that the proposed method can effectively generate accurate and reliable SR results, although there is an overcorrected problem in the costal blue region when the AOT value is very high.

An enhanced single‐channel algorithm for retrieving land surface temperature from Landsat series data

Land surface temperature (LST) is a critical parameter in the physics of earth surface processes and is required for many applications related to ecology and environment. Landsat series satellites have provided more than 30 years of thermal information at medium spatial resolution. This paper proposes an enhanced single-channel algorithm (SCen) for retrieving LST from Landsat series data (Landsat 4 to Landsat 8). The SCen algorithm includes three atmospheric functions (AFs), and the latitude and acquisition month of Landsat image were added to the AF models to improve LST retrieval. Performance of the SCen algorithm was assessed with both simulated and in situ data, and accuracy of three single-channel algorithms (including the mono-window algorithm developed by Qin et al. [Z-h Qin et al., 2001a], SCQin, and the generalized single-channel algorithm developed by Jimenez-Munoz and Sobrino [Jimenez-Munoz and Sobrino, 2003], SCJ&S) were compared. The accuracy assessments with simulated data had root mean square deviations (RMSDs) for the SCen, SCJ&S and SCQin algorithms of 1.363 K, 1.858 K and 2.509 K, respectively. Validation with in situ data showed RMSDs for the SCen and SCJ&S algorithms of 1.04 K and 1.49 K, respectively. It was concluded that the SCen algorithm is very operational, has good precision, and can be used to develop an LST product for Landsat series data.

Detecting Decadal Land Cover Changes in Mining Regions based on Satellite Remotely Sensed Imagery: A Case Study of the Stone Mining Area in Luoyuan County, SE China

Abstract Mining regions often undergo abrupt and extensive land coverchanges that pose serious environmental and social impacts.In this study, decadal land cover changes in stone mining areas of Luoyuan County, southeastern China from 2001 to2010 were examined based on multi-source satellite remote sensing imageries including ALOS, SPOT2, and Landsat-7.Object-oriented classifications combined with decision tree and retrospective approaches are employed to extract land cover and change information for the ten-year period. The study results show that the stone mining area nearly quadrupled over the ten-year period. It is found that the digging area accounts for only 14.3 percent of the stone mining region. However, mine dumps and tailings occupy the majority of the region, a remarkable characteristic distinct from other mining regions. Reclaimed land in the mined region is very limited.An evident increase in the extent of urbanized land cover is also observed in the study area for the last decade.

Leaf Area Index Estimation Using MESMA Based on EO-1 Hyperion Satellite Imagery

—Leaf area index (LAI) is an important surface biophysical parameter used by many process-oriented ecosystem models. Traditionally, remote sensing based techniques to estimate LAI have either been based on the empirical–statistical approach that relates ground-measured LAI to the spectral vegetation indices, or on a radiative transfer modeling approach. However, both approaches have their limitations. In recent years, much effort has been expended to develop new remote sensing based LAI estimation methods. Multiple endmember spectral mixture analysis (MESMA) is an important one in the newly developed LAI retrieval methods. The aim of this study is to test the effectiveness of MESMA in LAI retrieval of broad-leaf forest in Asian subtropical monsoon climate region. In this study, EO-1 hyperion hyper-spectral imagery acquired on May 22rd, 2012 was employed to carry out an experiment on the MESMA method to estimate LAI in the forested area of Yongan county, Fujian province, located in southeast of China. MESMA based LAI estimation model for broad-leaf forest in the study area were finally formulated. The result shows MESMA method can achieve a good LAI estimation result.

A Novel Image Registration Method Based on Phase Correlation Using Low-Rank Matrix Factorization With Mixture of Gaussian

Image registration is a critical process for the various applications in the remote sensing community, and its accuracy greatly affects the results of the subsequent applications. Image registration based on phase correlation has been widely concerned due to its robustness to gray differences and efficiency. After calculating the normalized cross-relation matrix