Limits...
Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring.

Wu M, Huang W, Niu Z, Wang C - Int J Environ Res Public Health (2015)

Bottom Line: In this study, we address this problem by applying the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Spatial and Temporal Data Fusion Approach (STDFA) to combine Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field of view camera (GF-1 WFV) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate daily high spatial resolution synthetic data for land surface process monitoring.The results show that both the ESTARFM and STDFA can be applied to combine HJ CCD and MODIS reflectance data, and GF-1 WFV and MODIS reflectance data, to generate synthetic HJ CCD data and synthetic GF-1 WFV data that closely match actual data with correlation coefficients (r) greater than 0.8989 and 0.8643, respectively.Synthetic red- and near infrared (NIR)-band data generated by ESTARFM are more suitable for the calculation of Normalized Different Vegetation Index (NDVI) than the data generated by STDFA.

View Article: PubMed Central - PubMed

Affiliation: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China. wumq@radi.ac.cn.

ABSTRACT
The limitations of satellite data acquisition mean that there is a lack of satellite data with high spatial and temporal resolutions for environmental process monitoring. In this study, we address this problem by applying the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Spatial and Temporal Data Fusion Approach (STDFA) to combine Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field of view camera (GF-1 WFV) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate daily high spatial resolution synthetic data for land surface process monitoring. Actual HJ CCD and GF-1 WFV data were used to evaluate the precision of the synthetic images using the correlation analysis method. Our method was tested and validated for two study areas in Xinjiang Province, China. The results show that both the ESTARFM and STDFA can be applied to combine HJ CCD and MODIS reflectance data, and GF-1 WFV and MODIS reflectance data, to generate synthetic HJ CCD data and synthetic GF-1 WFV data that closely match actual data with correlation coefficients (r) greater than 0.8989 and 0.8643, respectively. Synthetic red- and near infrared (NIR)-band data generated by ESTARFM are more suitable for the calculation of Normalized Different Vegetation Index (NDVI) than the data generated by STDFA.

No MeSH data available.


Related in: MedlinePlus

Comparison of MODIS, HJ CCD and synthetic data generated by the ESTARFM and STDFA acquired on 7 October 2013: (a) MODIS data; (b) synthetic HJ CCD data generated by STDFA; (c) synthetic HJ CCD data generated by ESTARFM; (d) actual HJ CCD data.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4555320&req=5

ijerph-12-09920-f002: Comparison of MODIS, HJ CCD and synthetic data generated by the ESTARFM and STDFA acquired on 7 October 2013: (a) MODIS data; (b) synthetic HJ CCD data generated by STDFA; (c) synthetic HJ CCD data generated by ESTARFM; (d) actual HJ CCD data.

Mentions: By inputting a base HJ CCD image, two days of MOD09GA data and two days of multi-spectral HJ CCD images, a synthetic multi-spectral HJ CCD image was generated by STDFA. By inputting a base HJ CCD image, two days of multi-spectral MOD09GA data and two days of multi-spectral MODIS data and HJ CCD datasets, a synthetic multi-spectral HJ CCD image was also generated by ESTARFM. These synthetic multi-spectral HJ CCD images contained four bands, including blue, green, red and NIR. Figure 2a shows the actual observed MODIS surface reflectance in the red band acquired on 7 October 2013 in Kuche and Luntai. Figure 2b, c show the synthetic surface reflectance imagery in the red band generated by STDFA and ESTARFM, respectively, for the two study areas. Figure 2d shows the actual observed HJ CCD red-band surface reflectance acquired on 7 October 2013 in Kuche and Luntai.


Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring.

Wu M, Huang W, Niu Z, Wang C - Int J Environ Res Public Health (2015)

Comparison of MODIS, HJ CCD and synthetic data generated by the ESTARFM and STDFA acquired on 7 October 2013: (a) MODIS data; (b) synthetic HJ CCD data generated by STDFA; (c) synthetic HJ CCD data generated by ESTARFM; (d) actual HJ CCD data.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4555320&req=5

ijerph-12-09920-f002: Comparison of MODIS, HJ CCD and synthetic data generated by the ESTARFM and STDFA acquired on 7 October 2013: (a) MODIS data; (b) synthetic HJ CCD data generated by STDFA; (c) synthetic HJ CCD data generated by ESTARFM; (d) actual HJ CCD data.
Mentions: By inputting a base HJ CCD image, two days of MOD09GA data and two days of multi-spectral HJ CCD images, a synthetic multi-spectral HJ CCD image was generated by STDFA. By inputting a base HJ CCD image, two days of multi-spectral MOD09GA data and two days of multi-spectral MODIS data and HJ CCD datasets, a synthetic multi-spectral HJ CCD image was also generated by ESTARFM. These synthetic multi-spectral HJ CCD images contained four bands, including blue, green, red and NIR. Figure 2a shows the actual observed MODIS surface reflectance in the red band acquired on 7 October 2013 in Kuche and Luntai. Figure 2b, c show the synthetic surface reflectance imagery in the red band generated by STDFA and ESTARFM, respectively, for the two study areas. Figure 2d shows the actual observed HJ CCD red-band surface reflectance acquired on 7 October 2013 in Kuche and Luntai.

Bottom Line: In this study, we address this problem by applying the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Spatial and Temporal Data Fusion Approach (STDFA) to combine Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field of view camera (GF-1 WFV) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate daily high spatial resolution synthetic data for land surface process monitoring.The results show that both the ESTARFM and STDFA can be applied to combine HJ CCD and MODIS reflectance data, and GF-1 WFV and MODIS reflectance data, to generate synthetic HJ CCD data and synthetic GF-1 WFV data that closely match actual data with correlation coefficients (r) greater than 0.8989 and 0.8643, respectively.Synthetic red- and near infrared (NIR)-band data generated by ESTARFM are more suitable for the calculation of Normalized Different Vegetation Index (NDVI) than the data generated by STDFA.

View Article: PubMed Central - PubMed

Affiliation: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China. wumq@radi.ac.cn.

ABSTRACT
The limitations of satellite data acquisition mean that there is a lack of satellite data with high spatial and temporal resolutions for environmental process monitoring. In this study, we address this problem by applying the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Spatial and Temporal Data Fusion Approach (STDFA) to combine Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field of view camera (GF-1 WFV) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate daily high spatial resolution synthetic data for land surface process monitoring. Actual HJ CCD and GF-1 WFV data were used to evaluate the precision of the synthetic images using the correlation analysis method. Our method was tested and validated for two study areas in Xinjiang Province, China. The results show that both the ESTARFM and STDFA can be applied to combine HJ CCD and MODIS reflectance data, and GF-1 WFV and MODIS reflectance data, to generate synthetic HJ CCD data and synthetic GF-1 WFV data that closely match actual data with correlation coefficients (r) greater than 0.8989 and 0.8643, respectively. Synthetic red- and near infrared (NIR)-band data generated by ESTARFM are more suitable for the calculation of Normalized Different Vegetation Index (NDVI) than the data generated by STDFA.

No MeSH data available.


Related in: MedlinePlus