An operational data fusion framework was built to generate dense time-series Landsat-like images by fusing MODIS data products and Landsat imagery. The spatial and temporal adaptive reflectance fusion model (STARFM) was integrated in the framework. Compared with earlier implementations of the STARFM, several improvements have been incorporated in the operational data fusion framework. These include viewing an- gular correction on the MODIS daily bidirectional reflectance, precise and automated coregistration on MODIS and Landsat paired images, and automatic selection of Landsat and MODIS paired dates. Three tests that use MODIS and Landsat data pairs from the same season of the same year, the same season of two different years, and different seasons from adjacent years were performed over a Landsat scene in northern India using the integrated STARFM operational framework. The results show that the accuracy of the predicted results depends on the data consistency between the MODIS nadir bidirectional-reflectance- distribution-function-adjusted reflectance and Landsat surface reflectance on both the paired dates and the prediction dates. When MODIS and Landsat reflectances were consistent, the max- imum difference of the predicted results for all Landsat spectral bands, except the blue band, was about 0.007 (or 5.1% relatively). However, differences were larger (0.026 in absolute and 13.8% in relative, except the blue band) when two data sources were inconsistent. In an extreme case, the difference for blue-band reflectancewasaslargeas0.029(or39.1%relatively).Case studies focused on monitoring vegetation condition in central India and the Hindu Kush Himalayan region. In general, spatial and tem- poral landscape variation could be identified with a high level of detail from the fused data. Vegetation index trajectories derived from the fused products could be associated with specific land cover types that occur in the study regions. The operational data fusion framework provides a feasible and cost-effective way to build dense time-series images at Landsat spatial resolution for cloudy regions. http://kaashivinfotech.com/ http://inplanttrainingchennai.com/ http://inplanttraining-in-chennai.com/ http://internshipinchennai.in/ http://inplant-training.org/ http://kernelmind.com/ http://inplanttraining-in-chennai.com/ http://inplanttrainingchennai.com/

1. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE
SENSING, VOL. 52, NO. 11, NOVEMBER 2014,
Operational Data Fusion Framework for Building Frequent Land
sat-Like Imagery

2. Abstract
An operational data fusion framework was built to generate dense time-series Landsat-like
images by fusing MODIS data products and Landsat imagery.
The spatial and temporal adaptive reflectance fusion model (STARFM) was integrated in the
framework. Compared with earlier implementations of the STARFM, several improvements have
been incorporated in the operational data fusion framework.
These include viewing an- gular correction on the MODIS daily bidirectional reflectance, precise
and automated coregistration on MODIS and Landsat paired images, and automatic selection of
Landsat and MODIS paired dates. Three tests that use MODIS and Landsat data pairs from the
same season of the same year, the same season of two different years, and different seasons from
adjacent years were performed over a Landsat scene in northern India using the integrated
STARFM operational framework.
The results show that the accuracy of the predicted results depends on the data consistency
between the MODIS nadir bidirectional-reflectance- distribution-function-adjusted reflectance
and Landsat surface reflectance on both the paired dates and the prediction dates.
When MODIS and Landsat reflectances were consistent, the max- imum difference of the
predicted results for all Landsat spectral bands, except the blue band, was about 0.007 (or 5.1%
relatively). However, differences were larger (0.026 in absolute and 13.8% in relative, except the
blue band)
when two data sources were inconsistent. In an extreme case, the difference for blue-band
reflectancewasaslargeas0.029(or39.1%relatively).Case studies focused on monitoring vegetation
condition in central India and the Hindu Kush Himalayan region. In general, spatial and tem-poral
landscape variation could be identified with a high level of detail from the fused data.
Vegetation index trajectories derived from the fused products could be associated with specific
land cover types that occur in the study regions. The operational data fusion framework provides
a feasible and cost-effective way to build dense time-series images at Landsat spatial resolution
for cloudy regions.

3. Existing System
In the existing system the Landsat images was generally
used to monitor crop condition , yield estimates, forest fire
detection, land cover change mapping analysis alone.
Medium resolution sensors were used in the existing
approach which have an ideal spatial resolution for
vegetation mapping at the field scale in order to predict the
satellite detected images.
The captured images in the urban areas were so very cloudy
and with so many disturbances to capture , so in our system
we fails to identify the clarity of images.. (Apart from that
the urban areas tends to opt for more spatial resolution
Landsat scenes are about 35% cloud covered on average
globally and probability of taking two cloud-free
observations of a Landsat images at southern Asia within 48
days is less than 60%
Landsat is limited by a 16-day revisit cycle and this was made
worse by cloud contamination in those images

4. Proposed System
A possible solution for applications that require fine
spatial resolution (The spatial and temporal adaptive
reflectance fusion model) STARFM was introduced.
STARFM model blends Landsat and MODIS data to
generate synthetic “daily” surface reflectance products
at Landsat spatial resolution. It requires a minimum of
two image pairs as the inputs into the algorithm.
The STARFM approach can work with one image pair,
which is a more flexible approach for cloudy regions
where finding cloud-free Landsat scenes are very scarce.
The one image pair detection is useful in forward
prediction of Landsat imagery because new MODIS
data are available throughout the growing season

5. System Architecture
• HARWARE REQUIREMENT:
Processor : Core 2 duo
Speed : 2.2GHZ
RAM : 2GB
Hard Disk : 160GB
• SOFTWARE REQUIREMENT:
Platform : DOTNET (VS2010) , ASP.NET Dotnet
framework 4.0
Database : SQL Server 2008 R2

6. Architecture Diagram