Remote Sensing of Environment

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: jiaozt@bnu.edu.cn. "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.

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. Naming convention: 20050228_PBC_INF_zambia.dat - 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. Acknowledgements 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.

The file includes time series of VGCF, SOILCF, SNOWCF, WaterBodyCF, fAPARchl, fAPARnon-chl, fAPARcanopy, NDVI, EVI, NIRv, EVI2, and NDSI for the study area from 2001 - 2014. These data were retrieved from the physical model LVS3 with MODIS images.

Data includes: 1) Landsat and DEM imagery 2) The study area polygons 3) M. micrantha detection info

This dataset encompasses a large number (> 700) of in-situ observations over elemantary sampling units (about 20m2) of LAI (effective and actual), fAPAR and fraction of vegetation cover (fCover) collected over a network of agricultural sites during the ImagineS project (http://fp7-imagines.eu/) in the period 2013-2016. The ground dataset was collected with digital hemispherical photography (DHP), LAI2200, and AccuPAR devices following well stablished protocols in agreement with CEOS LPV good practices. The ground data is complemented with concomitant Landsat-8/OLI observations, the sun zenith angle at the acquisition and the NDVI. This results in a unique database to calibrate and validate algorithms for retrieval of biophysical variables over crops.

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 is the database used to relate spatial variability of spectral severity indices (dNBR, RBR and RdNBR) derived from Sentinel 2 MSI and Landsat 8 OLI in Yeste fire (SE, Spain) with prefire vegetation data derived from Lidar, National Forest Inventory and biophysical variables derived from prefire Sentinel 2 MSI image; and burning conditions (weather conditions, aligned winds and topography to the fire-front direction and fire progression map).

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 dataset depicts Belize's national mangrove cover for the period November 1980 through February 2017, based on the ~1990 baseline established by Simon Zisman. The dataset was developed by using satellite imagery to detect where how mangrove cover has changed between 1980 and 2017. The source data for this effort include Landsat-3 Multispectral Scanner (MSS) imagery, Landsat-5 Thematic Mapper (Landsat TM) imagery, Landsat-7 Enhanced Thematic Mapper Plus (Landsat ETM+) imagery, and Landsat-8 Operational Land Imager (OLI) imagery. A full description of the process by which the data were generated is provided in “Use of public Earth Observation data for tracking progress in sustainable management of coastal forest ecosystems in Belize, Central America,” by Cherrington et al.

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