Exercise 3 Visualization of MODIS LAI/FPAR product at local scale at ORNL DAAC

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Phoebe Barton
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Exercise 3 Visualization of MODIS LAI/FPAR product at local scale at ORNL DAAC The MODIS ASCII subsets visualization tool was developed by Oak Ridge National Lab (ORNL) DAAC.

1 Exercise 3 Visualization of MODIS LAI/FPAR product at local scale at ORNL DAAC The MODIS ASCII subsets visualization tool was developed by Oak Ridge National Lab (ORNL) DAAC. The tool allows visualization (and downloading) the MODIS Land products over 7x7 km spatial subsets over about 300 sites, mostly from FLUXNET network ( This tutorial discuss three major fetaures of the tool ( 1) vizualization of time series; 2) vizualization of data spatial distribution; 3) product vizualization with advanced Quality Control (QC) filtering. The functionality of the tool is demonstrated with Terra MODIS LAI/FPAR product (8-day composites, 1km). Note, that MODIS LAI/FPAR products distributed in the HDF format, includes not only LAI and FPAR data layers, but also several QC layers, which document pixel-by-pixel information about cloud, aerosol, snow contamination, algorithm path, overall product quality, etc. Using QC information, the user can design custom data mask approprite to his/her application and filter out undesirable data.. A) Vizualization of Time Series. At the front page of the website you can select the desirable product (LAI/FPAR), sensor (Terra) and visualization type (Time series) as shown below.

(Estonia, Toravere BSRN Station) as shown below.

as shown below for Toravere BSRN Station.

a line) of the data, which passed filter.

2 Next, select Country (Estonia). From the next menue selct a site from the list of available sites for the particular country (Estonia, Toravere BSRN Station) as shown below. Next, time series of 8-day LAI and FPAR data from February 2000 to the present will be displayed as shown below for Toravere BSRN Station. Each plot shows individual values (blue dots) and corresponding averages (red dots, connected with a line) of the data, which passed filter. The default filter usually passes through the best quality retrievals with main RT algorithm and traps poor quality retrievals with empirical back-up algorithm. The bottom portion of each plot also indicates the proportion of data which passed the filter (rate of retrievals). LAI and rate of retrievals FPAR and rate of retrievals

Finally, the tool provides an option to download LAI/FPAR and corresponding QC time series data over 7x7 km subsets (49 values for each 8-day composites). Data are in plain ASCII format.

Next you need to select a particular 8-day composite, for which you want to display the spatial subset (7x7 pixels). The menue specifiles both Julian day and calendar day.

3 Finally, the tool provides an option to download LAI/FPAR and corresponding QC time series data over 7x7 km subsets (49 values for each 8-day composites). Data are in plain ASCII format. B) Visualization of the Spatial Subsets. Now return back to the the front page of the website and select spatial subset option instead of time series. Next, select country and site. Next you need to select a particular 8-day composite, for which you want to display the spatial subset (7x7 pixels). The menue specifiles both Julian day and calendar day. The particular day corresponds to the starting date of composite. Lai_1km Raw/UnFiltered(from HDF) Lai_1km Scale Factor¹ and Filter Applied Fpar_1km Raw/UnFiltered(from HDF) Fpar_1km Scale Factor² and Filter Applied Next, 7x7 pix spatial subsets of LAI and FPAR will be displayed as shown below for Toravere BSRN Station, Jul. 28, Aug. 4, For each pixel the tool shows DN (digital numbers) values corresponding to that, stored in the product files. Additionally, the tool presents corresponding biophysical variables (scaled DN values) which passed the default filter (see previous section). For convenience of analysis of spatial variation of LAI/FPAR as function of vegetation type, the tool also shows the corresponding 6-biome MODIS land cover product as shown below.

MODIS Land Cover Classification (Collection 3 LAI/fPAR Type_3) MODIS Land Cover Legend (Collection 3 LAI/fPAR Type_3 In the spatial subsets mode the tool also provides an option to download LAI/FPAR

QC information provided with the LAI/FPAR products allows to set up custom data filtering suitable for particular application. The default filter uses QC called SCF_QC, which specifies algorithm path.

4 MODIS Land Cover Classification (Collection 3 LAI/fPAR Type_3) MODIS Land Cover Legend (Collection 3 LAI/fPAR Type_3 In the spatial subsets mode the tool also provides an option to download LAI/FPAR and corresponding QC time series data over 7x7 km subsets (49 values for each 8-day composites). C) Advanced filtering of the LAI/FPAR product. QC information provided with the LAI/FPAR products allows to set up custom data filtering suitable for particular application. The default filter uses QC called SCF_QC, which specifies algorithm path. Five states of SCF_QC are: (1) main algorithm retrievals without saturation, (2) main algorithm retrievals with saturation (3) back up algorithm retrievals due to geometry problems; (4) back up algorithm retrievals due to other problems, (5) complete failure of the algorithm. Under the default filter (see snapshot below), if SCF_QC indicates retrievals by back up algorithm or complete failure of retrievals the data will not pass the filter and will not be displayed. If the user would like to modify the default filter this can be done in the Display Spatial Subsets mode, by pressing button Advanced Version: User Selected QC Conditions as shown above. The following screen (shown below) allows selecting any combination of the states of

four QC flags to set up a custom filter. The flags are as follows. MODLAND_QC- this flag can be seen in each MODIS Land product, and it indicates overall quality of the retrievals.

5 four QC flags to set up a custom filter. The flags are as follows. MODLAND_QC- this flag can be seen in each MODIS Land product, and it indicates overall quality of the retrievals. DEADDETECTOR indicates if majority of sensors were in nominal working conditions. CLOUDSTATE indicates pixel-by-pixel cloud contamination of MODIS measurements. SCF_QC indicates algorithm paths as described above. End of Exercise 3

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