Contributors:Chuyong Lin, Jason Cohen, Shuo Wang, Ruoyu Lan
These 10 attached datasets are what underly the data in the paper "Lin C., Cohen J.B., Wang S., and Lan R. (2020) "Application of a Combined Standard Deviation and Mean Based Approach to MOPITT CO Column Data, and Resulting Improved Representation of Biomass Burning and Urban Air Pollution Sources." Submitted to Remote Sensing of Environment.
In specific the data represent:
dataset1.mat: Map of classifications (2000-2016)
dataset2.mat: Map of classifications (2000-2009)
dataset3.mat: Map of classifications (2010-2016)
dataset4.mat: Weekly averaged CO Total Column
dataset5.mat: Climatological Mean of #4
dataset6.mat: Climatological Normalized Standard Deviation of #4
dataset7.mat: Weekly averaged AERONET AOD at 12 stations
dataset8.mat: MOPITT CO mean time series over the Yangtze River Delta region
dataset9.mat: MOPITT CO mean time series over the Upper, Lower, and Downwind Biomass Burning regions
dataset10.mat: MOPITT CO mean and standard deviation over the Chengdu Basin
fig_16.mat: EOF1 and the linear combination of EOF2 and EOF3
finn_year_2000_2018.mat: FINN CO emissions year by year
This R code allows to calculate three differernt functional diversity inidices (richness, regularity and divergence) based on a moving window approach applied to a raster file as done in Rossi et al. (2020)
The Shape-File „Morphological_Urban_Area.shp” contains the final delimitation of city extents based on the methodology described in section 3.2. The analysis was done for all cities on our planet with more than 300,000 inhabitants, i.e. a total of 1,692 cities were included in this study. However, as many urban regions across the globe have experienced a coalescence of multiple, once morphologically separate cities, the data sets in the Shape-File is reduced to 1569 MUA extents. This is due to the following approach: if MUAs from two (or more) neighboring cities overlap, we combine the MUAs from both (or more) cities into one.
This dataset is made of twelve netCDF files corresponding to the twelve climatological months of marine optical data. Each file contains the following mapped variables:
- x coordinate of the CIE1931 color space
- y coordinate of the CIE1931 color space
- Hue angle
- Forel-Ule index
Source data for these files are the ESA-OC-CCI v2.0 climatological remote-sensing reflectance of global surface waters (1997-2013), mapped at a resolution of 0.25 degrees latitudinally and longitudinally. These source files can be retrieved at: ftp://oceancolour.org/occci-v2.0/geographic/netcdf/climatology/lower_resolution/0.25degree/
Files in this dataset have directly copied the latitude and longitude variables of the corresponding source files. Absence of data is coded as not-a-number. File name format is 'ESACCI-OC-L3S-OC_PRODUCTS-CLIMATOLOGY-16Y_MONTHLY_0.25degree_GEO_PML_OC4v6_QAA-mMM-fv2.0_xy_Hue_FU.nc', where MM stands for the month of the year, in two-digits format.
The excel file contains the results of the content analysis of all social media photographs considered in the paper. In the file NT stand for the dominance of native tree species, and NNT refers to the dominance of non-native trees.
Summary tables of existing active and inactive retrogressive thaw slumps in northern Alaska.
1. About the visulization tool for the RossThick-LiSparseReciprocal-Snow (RTLSRS for short) BRDF model.
This is the description for the interface and operation of the # rtlsr_gui.sav application, which can be used to launch runtime IDL applications. Source code will be offerred on request from email: email@example.com. "Constrain" botton on this application interface inplements the function that does not allow negative model parameters, which are suggested by MODIS BRDF parameter product.
2. Environment：need to install IDL8.2 and above
3. Data format：this tool supports the input format of text file.
The specific input format is requested as follows: the first line defines the number of multi-angle observations and the number of bands for the multi-angle input dataset, respectively. From the beginning of the second line, each line defines an observation with each sample representing the viewing and solar geometries (in degree) and the reflectances in different bands. Specifically, from left to right, each sample represents view zenith angle,view azimuth angle,solar zenith angle,view azimuth angle and the reflectance of each band in sequence.
4. The exemplary data
A typical POLDER example was obtained in Apr. 2006 from the file "brdf_ndvi02.0216_3046.txt." in POLDER database. This pixel is located on the central North Greenland Ice Sheet (i.e., 52.1W, 78.03N) and was recorded to represent a relatively pure snow and ice IGBP class with a Normalized Difference Vegetation Index (NDVI) value of -0.03. This pixel incorporates more than 1,000 POLDER reflectances and 70 solar angles representing a very good BRDF sampling distribution. The method proposed by Breon (2005) should be utilized to correct the viewing geometry from the original POLDER measurements that offer two correction parameters (DVzC and DVzA), which was used in the paper. However, please note that this input example file including 6-band observations is not corrected for the viewing geometries.
Input directory: contains an input example file, e.g., "brdf_ndvi02.0216_3046.txt".
Output directory: contains the corresponding output example file, e.g.,"brdf_ndvi02.0216_3046_result.txt".
5. All validation data for the RTLSRS model are open and available to users following the details of the paper.
Contributors:David Frantz, Achim Röder, Marion Stellmes, Joachim Hill
This dataset contains seasonal, multi-annual MODIS reflectance composites generated across Zambia for 2005 +-2 years. The data is associated to following paper:
D. Frantz, A. Röder, M. Stellmes, and J. Hill (2017): Phenology-Adaptive Pixel-Based Compositing using Optical Earth Observation Imagery. Remote Sensing of Environment 190, 331-347. DOI: 10.1016/j.rse.2017.01.002
A parametric compositing technique was employed to produce composites from very dense MODIS reflectance images (MOD09GA product, 1-2 day temporal resolution). Composites were generated for three phenological seasons: peak of season (POS), end of season (EOS) and minimum of season (MOS). Two different sets were produced: (1) phenology-adaptive composites that explicitly consider the land surface phenology of each pixel and (2) static composites that use a fixed and global target DOY representative for the seasons.
The images are in Standard ENVI Format.
- First 8 digits: Mean date of selected observations; the temporal sequence is POS, EOS, and MOS
- PBC_INF / PBC_REF: composite criteria / reflectance composite
The reflectance composites are 6-band (0.469µm, 0.555µm, 0.645µm, 0.859µm, 1.640µm, 2.130µm), 16bit bsq images.
The composite criteria images are 4-band (number of observations, selected DOY, diff. to target DOY, diff. to target year), 16bit bsq images.
The MODIS MOD09GA data products were retrieved from the online Data Pool, courtesy of the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, https://lpdaac.usgs.gov/data_access/data_pool.
The MODIS MOD13Q1/MYD13Q1 data products were retrieved from the online Data Pool, courtesy of the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, https://lpdaac.usgs.gov/data_access/data_pool.
Land Surface Phenology was inferred from MOD13Q1/MYD13Q1 data with the Spline Analysis of Time Series (SpliTS) algorithm, courtesy of Dr. Sebastian Mader, Trier University, Trier, Germany.