Xiaoqin Wang

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.

Enhanced hyperspherical color space fusion technique preserving spectral and spatial content

Abstract. The newly developed hyperspherical color space (HCS) fusion method is a cost-effective panchromatic (pan) sharpening technique, but it still suffers from local spectral distortion and insufficient overall spatial details. This paper attempts to improve the HCS fusion method by combining the spectrum gain modulation and wavelet transformation and presents a novel fusion method called enhanced HCS (EHCS) to mitigate the mentioned issue. This method mainly contains three steps. (1) The proposed method extracts the angular component from multispectral images through hyperspherical color transformation (HCT). (2) A modulated multispectral image is generated by using the spectrum gain modulation. Then, the spatial details of the modulated multispectral image are substituted with the spatial details of the pan image by multiscale wavelet decomposition. (3) Finally, the angular component and modulated detail image are integrated to achieve the fused image by the inverse HCT. To validate the proposed method, three high spatial resolution images, i.e., two WorldView-2 and one QuickBird datasets, acquired at Fujian, China, were used. The comparison with HCS method was also done. Both the visual and quantitative analysis demonstrate that the EHCS method can significantly preserve the spectral characteristics and enhance the spatial detail as well.

Genetic algorithm optimized SVM in object-based classification of quickbird imagery

This paper presents a genetic algorithm (GA) approach for parameters optimization of support vector machine, which is used for the object-oriented classification of high spatial resolution images over urban area. The proposed method is a three-step routine involves the integration of 1) image segmentation, 2) GA-based parameter optimization of Support vector machine (SVM), and 3) objected-based classification. Experiments conducted on multi-spectral Quick-Bird image fused with panchromatic image in Fuzhou city. In addition, a traditional parameter searching method, Grid algorithm, was investigated to evaluate the effectiveness of the proposed approach. The results show that our proposed GA-based approach significantly outperforms the Grid algorithm both in terms of classification accuracy and time efficiency.

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.

Estimation of chlorophyll-a concentration in coastal waters with HJ-1A HSI data using a three-band bio-optical model and validation

Accurate assessment of phytoplankton chlorophyll-a (chl-a) concentration in turbid waters by means of remote sensing is challenging because of the optical complexity of case 2 waters. We applied a bio-optical model of the form [R–1(λ1) – R–1(λ2)](λ3), where R(λi) is the remote-sensing reflectance at wavelength λi, to estimate chl-a concentration in coastal waters. The objectives of this article are (1) to validate the three-band bio-optical model using a data set collected in coastal waters, (2) to evaluate the extent to which the three-band bio-optical model could be applied to the spectral radiometer (SR) ISI921VF-512T data and the hyperspectral imager (HSI) data on board the Chinese HJ-1A satellite, (3) to evaluate the application prospects of HJ-1A HSI data in case 2 waters chl-a concentration mapping. The three-band model was calibrated using three SR spectral bands (λ1 = 664.9 nm, λ2 = 706.54 nm, and λ3 = 737.33 nm) and three HJ-1A HSI spectral bands (λ1 = 637.725 nm, λ2 = 711.495 nm, and λ3 = 753.750 nm). We assessed the accuracy of chl-a prediction with 21 in situ sample plots. Chl-a predicted by SR data was strongly correlated with observed chl-a (R2 = 0.93, root mean square error (RMSE) = 0.48 mg m–3, coefficient of variation (CV) (RMSE/mean(chl-amea)) = 3.72%). Chl-a predicted by HJ-1A HSI data was also closely correlated with observed chl-a (R2 = 0.78, RMSE = 0.45 mg m–3, CV (RMSE/mean(chl-amea)) = 7.51%). These findings demonstrate that the HJ-1A HSI data are promising for quantitative monitoring of chl-a in coastal case-2 waters.

A processing method for rice crop inventory using multi-date ENVISAT-1 ASAR data

Synthetic Aperture Radar (SAR) is anticipated to be the dominant high-resolution remote sensing data source for agricultural applications in tropical and subtropical regions due to its independent from cloud cover. ENVISAT-1 ASAR is the most advanced satellite radar-imaging instrument, its capabilities include beam steering for acquiring images with different incidence angles, duel polarization and wide swath coverage. Agricultural crop inventory based on remote sensed data will be improved greatly by ASARs new capabilities. In this paper, a procedure has been developed using multi-date ASAR data for rice crop inventory. The procedure comprises two parts: data preprocessing and classification of multi-date data for rice field. In order to carry out the research, 6 scenes of ASAR images covering Fuzhou area year round of 2004 were used. PCI 9.1 is used for data preprocessing which includes data calibration, image co-registration, speckle suppression, orthorecitification and amplitude-to-dB conversion. Some novel methods are applied in this procedure such as correlation matching for image co-registration and multi-channel filtering for speckle suppression. Object-oriented classifier was used, compared with K-means supervised classifier and maximum likelihood classifier, and higher classification accuracy was achieved. By adopting the procedure presented in this paper, more than 90% classification accuracy for rice was achieved in Fuzhou city with multi-date Envisat ASAR data. This indicates that the procedure is feasible for rice crop inventory using multi-date ASAR data.

Study on the fusion of MODIS and TM images using the spectral response function and STARFM algorithm

To make full use of the advantages of the high spatial resolution of Landsat, along with the high temporal resolution of MODIS, we applied the spatial and temporal adaptive reflectance fusion model (STARFM) by means of comparing the difference of the spectral response functions, as well as normalizing the similar bands between these two types of sensors, to construct a time series images with high spatial resolution in this paper. In this research, the experiments were carried out on the TM and MODIS images in the Yongtai area of Fuzhou city. The results show that: 1) in terms of spatially manner, the predicated data well reflected the spatial details difference of the high resolution remote sensing imagery by analyzing the difference in histogram between the real data and the predicated data; 2) in terms of timely manner, the predicated data retained the time change trend of high temporal remote sensing imagery by comparing the values of NDVI between the MODIS image and the predicated image.

Vegetation monitoring in rugged terrain with one novel topography — Adjusted vegetation index (TAVI)

Vegetation monitoring with spectral vegetation index (VI) has made great advances over the last several decades, and numerous vegetation indexes have been developed as well. However, the monitoring accuracy with conventional VI in rugged mountains is disturbed some extent by topographic effects. This paper presents one novel topography - adjusted vegetation index (TAVI) to obtain correct vegetation information in rugged area, which is only based on near infrared and red wavebands data of optical remote sensing image, without the digital elevation model (DEM) data. The validation with Landsat TM image shows that the slope of linear regression equation (k) of TAVI and the solar incidence cosine and the correlation coefficient (r) between them are similar to those from NDVIac_tc (normalized difference vegetation index after atmospheric correction and topographic correction). TAVI achieves good results in rugged mountain vegetation monitoring, especially in resistance to topographic effect in rugged terrain and works well without the support of DEM data, which greatly extends its potentially applicable scope.

Application of adaptive weighted averaging method for ocean color data in East China Sea

The use of ocean color satellite image data to investigate oceanic processes has become essential for oceanographic research and monitoring. In order to exploit information from multiple sensors, a new data merger, i.e. adaptive weighted averaging was employed to merge MODIS Aqua and MODIS Terra chla product in the East China Sea and its performance was assessed. Comparison between chla products pairs reveal consistency below 10mg/m3, but discrepancy was obvious when turned to high values. The method present here will minimize the difference between the merged pixels and their adjoin pixels having single source. This will make the merged output more continuous.

Comparison of multi-sensor data application in algal bloom detection

Satellite remote sensing plays a significant role in algal bloom monitoring and detection over these decades. Different satellite data from SeaWiFS, MODIS/Aqua, MODIS/Terra and MERIS were processed for East China Sea in May 2008. Derived normalized water-leaving radiance (nLw), chlorophyll concentration (Chl) and fluorescence line height (FLH) were compared and analysed for these sensors. The comparison results show that nLw data from MODIS/Terra with SeaWiFS and MODIS/Terra with MERIS have more reliable correlation than those from MODIS/Terra with MODIS/Aqua. The Chl comparsions reveal the good capability of MODIS/Terra and MERIS better than others. Therefore, it would be feasible to continue the time series using multi-sensor data and improve the data merging in algal bloom detection.