Downscaling MODIS images with area-to-point regression kriging

Wang, Qunming; Shi, Wenzhong; Atkinson, Peter M.; Zhao, Youbing

doi:10.1016/j.rse.2015.06.003

Cited by 140 publications

(126 citation statements)

References 34 publications

Supporting

Mentioning

126

Contrasting

Order By: Relevance

“…In this study, the most suitable PAN-like band was determined by measuring the correlation coefficient between them and the SWIR band. The 10-m band with the greatest correlation coefficient is chosen as the PAN-like band [37,38].…”

Section: Algorithm Implementationmentioning

confidence: 99%

“…More importantly, Bands 2, 3, 4 and 8 in the Sentinel-2 imagery all have 10-m resolution. Therefore, the 10-m bands in the Sentinel-2 imagery can be treated as PAN-like bands [37,38], and they can provide important fine spatial resolution information to downscale the 20-m bands to 10 m. Motivated by this, in this paper, the pan-sharpening technique was chosen to increase the spatial resolution of SWIR Band sharpening technique was chosen to increase the spatial resolution of SWIR Band 11 to 10 m to match the 10-m green Band 3, using the information provided by directly observed 10-m Bands 2, 3, 4 and 8.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

et al. 2016

Self Cite

View full text Add to dashboard Cite

Abstract:Monitoring open water bodies accurately is an important and basic application in remote sensing. Various water body mapping approaches have been developed to extract water bodies from multispectral images. The method based on the spectral water index, especially the Modified Normalized Difference Water Index (MDNWI) calculated from the green and Shortwave-Infrared (SWIR) bands, is one of the most popular methods. The recently launched Sentinel-2 satellite can provide fine spatial resolution multispectral images. This new dataset is potentially of important significance for regional water bodies' mapping, due to its free access and frequent revisit capabilities. It is noted that the green and SWIR bands of Sentinel-2 have different spatial resolutions of 10 m and 20 m, respectively. Straightforwardly, MNDWI can be produced from Sentinel-2 at the spatial resolution of 20 m, by upscaling the 10-m green band to 20 m correspondingly. This scheme, however, wastes the detailed information available at the 10-m resolution. In this paper, to take full advantage of the 10-m information provided by Sentinel-2 images, a novel 10-m spatial resolution MNDWI is produced from Sentinel-2 images by downscaling the 20-m resolution SWIR band to 10 m based on pan-sharpening. Four popular pan-sharpening algorithms, including Principle Component Analysis (PCA), Intensity Hue Saturation (IHS), High Pass Filter (HPF) and À Trous Wavelet Transform (ATWT), were applied in this study. The performance of the proposed method was assessed experimentally using a Sentinel-2 image located at the Venice coastland. In the experiment, six water indexes, including 10-m NDWI, 20-m MNDWI and 10-m MNDWI, produced by four pan-sharpening algorithms, were compared. Three levels of results, including the sharpened images, the produced MNDWI images and the finally mapped water bodies, were analysed quantitatively. The results showed that MNDWI can enhance water bodies and suppressbuilt-up features more efficiently than NDWI. Moreover, 10-m MNDWIs produced by all four pan-sharpening algorithms can represent more detailed spatial information of water bodies than 20-m MNDWI produced by the original image. Thus, MNDWIs at the 10-m resolution can extract more accurate water body maps than 10-m NDWI and 20-m MNDWI. In addition, although HPF can produce more accurate sharpened images and MNDWI images than the other three benchmark pan-sharpening algorithms, the ATWT algorithm leads to the best 10-m water bodies mapping results. This is no necessary positive connection between the accuracy of the sharpened MNDWI image and the map-level accuracy of the resultant water body maps.

show abstract

Section: Algorithm Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…Second, the calculation of regression coefficient required pixel subset within similar natural condition (e.g., similar climatic and ecological zones, vegetation type, geolocation and sun-view geometry effects). Wang et al [17] used one set of regression coefficient for the entire MODIS tile. Small sub-regions and classification of each region were also explored by Trishchenko et al [52] to build non-linear regression model for every vegetation type, which indicated complex land cover classification, and even the same vegetation type cannot guarantee the spectral similarity owe to larger geolocation deviation.…”

Section: Usability Of CC Ow Psf For Downscaling Methodsmentioning

confidence: 99%

Mapping Extent Dynamics of Small Lakes Using Downscaling MODIS Surface Reflectance

et al. 2017

View full text Add to dashboard Cite

Lake extent is an indicator of water capacity as well as the aquatic ecological and environmental conditions. Due to the small sizes and rapid water dynamics, monitoring the extent of small lakes fluctuating between 2.5 and 30 km 2 require observations with both high spatial and temporal resolutions. The paper applied an improved surface reflectance (SR) downscaling method (i.e., IMAR (Improved Modified Adaptive Regression model)) to downscale the daily SR acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra platform to a consistent 250-m resolution, and derived monthly water extent of four small lakes in the Tibetan Plateau (Longre Co, Ayonggongma Co, Ayonggama Co, and Ayongwama Co)) from 2000 to 2014. Using Landsat ETM+ acquired on the same date, the downscaled MODIS SR and identified water extent were compared to the original MODIS, observations downscaled using an early SR downscaling method (MAR (Modified Adaptive Regression model)) and Wavelet fusion. The results showed IMAR achieved the highest correlation coefficients (R 2 ) (0.89-0.957 for SR and 0.79-0.933 for water extent). The errors in the derived water extents were significantly decreased comparing to the results of MAR and Wavelet fusion, and lakes morphometry of IMAR is more comparable to Landsat results. The detected lake extents dynamic between 2000 and 2014 were analyzed using the trend and season decomposition model (BFAST), indicating an increasing trend after 2005, and it likely had higher correlations with temperature and precipitation variation in the Tibetan region (R 2 : 0.598-0.728 and 0.61-0.735, respectively).

show abstract

“…The MODIS bands 1 and 2 and bands 3 to 7 have different spectral range and a highly reliable image fusion approach is required for this issue. In our previous research, area-to-point regression kriging (ATPRK) was proposed for fusion of 500 m MODIS bands 3 to 7 and 250 m bands 1 and 2 [23]. ATPRK is an accurate approach for reproduction of spatial detail in bands 3 to 7 and is superior to some state-of-the-art benchmark methods.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Spatio-Temporal Fusion of MODIS and Landsat Data by Incorporating 250 m MODIS Data

Wang

Zhang

Onojeghuo

et al. 2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

Downscaling MODIS images with area-to-point regression kriging

Cited by 140 publications

References 34 publications

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

Mapping Extent Dynamics of Small Lakes Using Downscaling MODIS Surface Reflectance

Enhancing Spatio-Temporal Fusion of MODIS and Landsat Data by Incorporating 250 m MODIS Data

Contact Info

Product

Resources

About