MODIS (or Moderate-Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.

The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).

This data set records the statistical data of consumer price index and ranking (2001-2010) of all regions in China, and the data are divided by year. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains 11 data tables with the same structure. For example, the data table in 2011 has five fields: Field 1: Province (city, district) Field 2: consumer price index Field 3: Rank Field 4: Food Field 5: Residence

Explanation of CPI and the Dataset:

What is CPI?

CPI (Consumer Price Index) measures the average change in prices over time that consumers pay for a basket of goods and services. It is a key indicator of inflation and is used by governments and central banks to monitor price stability and for inflation targeting. Components: The construction of CPI involves two main components: Weighting Diagrams: These represent the consumption patterns of households. Price Data: This is collected at regular intervals to track changes in prices.

Role of the Central Statistics Office (CSO):

The CSO, under the Ministry of Statistics and Programme Implementation, is responsible for releasing CPI data. The indices are released for Rural, Urban, and Combined sectors for all-India and individual States/UTs.

Dataset Alignment:

Sectors: The dataset includes a "Sector" column that categorizes data into "Rural," "Urban," and "Rural+Urban," aligning with the CPI data released by the CSO. Time Period: The "Year" and "Name" (which appears to represent months) columns in the dataset track the data over time, consistent with the monthly release schedule by the CSO starting from January 2011. State/UT Data: Each column corresponding to a state or union territory likely represents the CPI values for that region. The numeric values under each state/UT column represent the CPI index values, with a base of 2010=100. Purpose: This data can be used to analyze inflation trends, price stability, and the impact on economic policies, such as adjustments to dearness allowance for employees. Practical Use of This Data: Inflation Analysis: By examining the changes in CPI values across different states, analysts can study regional inflation trends and compare them to the national average. Policy Making: Governments and central banks can use this data to design and adjust policies aimed at controlling inflation, targeting specific regions or sectors that are experiencing higher inflation. Wage Indexation: Companies and governments can use CPI data to adjust wages and allowances in line with inflation, ensuring that purchasing power is maintained.

Based on the RWI-GEO-RED data that base on the data provided by ImmobilienScout24 hedonic housing price indices are estimated. The indices are on the grid level, LMR, district/county and municipality level. We conduct a hedonic price regression that covers characteristics of the object as well as regional fixed effects. The hedonic regression is estimated separately for houses for sale as well as apartments for rent and for sale. We also offer a combined index which combines the individual housing types into one index. There are three different specifications: First, the overall time development from 01/2008 to 05/2024 on grid level given yearly and quaterly; Second, cross-regional differences for each year separately and time development within one region from 01/2018 to 05/2024 (municipality, district, LMR, and grid level); third, the time-region fixed effect between 2008 and 2024, which is used to determine the price changes for all three region types to the base year of 2008. RWI-GEO-REDX Other The data is based on the data set RWI-GEO-RED, that collects all offers for private housing on ImmobilienScout24 between January 2008 and May 2024. ImmobilienScout24 is the largest listing website for real estate in Germany. The price indices are estimated labor market region, district and municipality level

The Kirwan Institute for the Study of Race and Ethnicity at Ohio State University developed the Detroit Regional Opportunity Index to compare levels of opportunity for people growing up in different parts of a region. The Index was developed by combining many different data indicators for opportunity into a single score. More information on the Detroit methodology and composite data can be found here: http://kirwaninstitute.osu.edu/wp-content/uploads/2014/08/20131211neighborhood.pdf

The full report from Kirwan on the Detroit Opportunity project can be found here: http://kirwaninstitute.osu.edu/?my-product=opportunity-for-all-inequity-linked-fate-and-social-justice-in-detroit-and-michigan/

Based on the RWI-GEO-RED data that base on the data provided by ImmobilienScout24 hedonic housing price indices are estimated. The indices are on the grid level, LMR, district/county and municipality level. We conduct a hedonic price regression that covers characteristics of the object as well as regional fixed effects. The hedonic regression is estimated separately for houses for sale as well as apartments for rent and for sale. We also offer a combined index which combines the individual housing types into one index. There are three different specifications: First, the overall time development from 01/2008 to 05/2024 on grid level given yearly and quaterly; Second, cross-regional differences for each year separately and time development within one region from 01/2018 to 05/2024 (municipality, district, LMR, and grid level); third, the time-region fixed effect between 2008 and 2024, which is used to determine the price changes for all three region types to the base year of 2008. Sampled Universe: The data is based on the data set RWI-GEO-RED, that collects all offers for private housing on ImmobilienScout24 between January 2008 and May 2024. ImmobilienScout24 is the largest listing website for real estate in Germany. The price indices are estimated labor market region, district and municipality level Sampling: Stratified random sampling Collection Mode: Other Unit Type: GeographicUnit Numer of Units: 1047014

The data set records the statistical data of various price indexes (2012-2018) in various regions of the country, and the data are divided by year. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of seven data tables All kinds of price indexes of all regions in China (2012). Xls Price indices of various regions in China (2013). Xls All kinds of price indexes of various regions in China (2014). Xls All kinds of price indexes of all regions in China (2015). Xls All kinds of price indexes of all regions in China (2016). Xls All kinds of price indexes of various regions in China (2017). Xls All kinds of price indexes in different regions of China (2018). XLS, with the same data table structure. For example, the data table in 2018 has five fields: Field 1: Region Field 2: consumer price index Field 3: retail price index Field 4: price index of agricultural means of production Field 5: fixed asset investment price index

This study, based on the Google Earth Engine (GEE) cloud platform, utilizes Landsat series satellite products to generate a dataset of the annual maximum Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Kernel Normalized Difference Vegetation Index (kNDVI) at a 30-meter resolution for the Yangtze River Delta region of China from 1984 to 2023. This dataset can reflect the regional vegetation growth conditions and long-term temporal change characteristics. To ensure the accuracy and reliability of the data, linear interpolation and the Savitzky-Golay filter are employed to smooth the bands, effectively removing noise in the spectral domain. The stable long-term image product MODIS13Q1 was employed to verify the occurrence dates of the maximum values of the three vegetation indices, enhancing the readability and usability of the data. The construction of this dataset provides robust data support for the spatiotemporal evolution of vegetation coverage and related research in the Yangtze River Delta region. The dataset consists of three vegetation index datasets, hence, there are three folders named csj_ndvi, csj_evi, and csj_kndvi. Each folder contains three subfolders named nolisg_ + ndvi/evi/kndvi + csj (original data), lisg + ndvi/evi/kndvi + csj (data processed by linear interpolation and SG filtering, referred to as processed data), and qa + ndvi/evi/kndvi + _csj (quality assessment). Within each subfolder, spatial distribution data of annual maximum vegetation indices from 1984 to 2023 are included, with data format in *.tif.

The data set is 2000-2023 monthly China area normalized difference vegetation index products with spatial resolution of 250 meters, maximum synthesis and 287 in total. This product is based on the Aqua/Terra - MODIS satellite sensor MOD13Q1 and land use. The processing includes initial reconstruction of the similar feature noise pixel , long sequence images S - G filter, keeping high quality, monthly synthesis and stitching. The data set provides data reference for regional ecological quality assessment and important ecological spatial survey and assessment.

Based on the RWI-GEO-RED data that base on the data provided by ImmobilienScout24 hedonic housing price indices are estimated. The indices are on the grid level, district/county and municipality level. We conduct a hedonic price regression that covers characteristics of the object as well as regional fixed effects. The hedonic regression is estimated separately for houses for sale as well as apartments for rent and for sale. We also offer a combined index which combines the individual housing types into one index. There are three different specifications: First, the overall time development from 01/2008 to 11/2023 on grid level given yearly and quaterly; Second, cross-regional differences for each year separately and time development within one region from 01/2018 to 11/2023 (municipality, district and grid level); third, the time-region fixed effect between 2008 and 2023, which is used to determine the price changes for all three region types to the base year of 2008 or year-quarter 2008-Q1. RWI-GEO-REDX Other The data is based on the data set RWI-GEO-RED, that collects all offers for private housing on ImmobilienScout24 between January 2008 and November 2023. ImmobilienScout24 is the largest listing website for real estate in Germany. The price indices are estimated labor market region, district and municipality level The data is based on the data set RWI-GEO-RED, that collects all offers for private housing on ImmobilienScout24 between January 2008 and November 2023. ImmobilienScout24 is the largest listing website for real estate in Germany. The price indices are estimated labor market region, district and municipality level. Stratified random sampling

Open Street Map (OSM) data were downloaded by CANUE staff from the Geofabrik site (see Supporting Documentation) in September 2020 as shapefiles for Canada. All data included in the gis_osm_pois_a_free_1 file were used for the calculations. These were made up of 138 different subcategories. Two different indicators were calculated: facility richness index and facility density index. Facility richness index is the number of different facility types present in a buffer area of 100, 250, 300, 500, 750 and 1000 around each 2019 DMTI Spatial single-link postal code divided by the maximum potential number of facility types specified (138). A higher value indicates a more availability of different facility types. Facility density index is the number of facilities present in a buffer area of 100, 250, 300, 500, 750 and 1000 around each 2019 DMTI Spatial single-link postal code divided by the buffer area. A higher value indicates a more availability of facilities in the area.

This dataset provides values for CONSUMER PRICE INDEX CPI reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Analysis of ‘Detroit Regional Opportunity Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ec26de4a-010d-4fcf-8015-a780bcee1963 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The Kirwan Institute for the Study of Race and Ethnicity at Ohio State University developed the Detroit Regional Opportunity Index to compare levels of opportunity for people growing up in different parts of a region. The Index was developed by combining many different data indicators for opportunity into a single score. More information on the Detroit methodology and composite data can be found here: http://kirwaninstitute.osu.edu/wp-content/uploads/2014/08/20131211neighborhood.pdf

The full report from Kirwan on the Detroit Opportunity project can be found here: http://kirwaninstitute.osu.edu/?my-product=opportunity-for-all-inequity-linked-fate-and-social-justice-in-detroit-and-michigan/

--- Original source retains full ownership of the source dataset ---

Urban areas play a very important role in global climate change. There is an increasing interest in comprehending global urban areas with adequate geographic details for global climate change mitigation. Accurate and frequent urban area information is fundamental to comprehending urbanization processes and land use/cover change, as well as the impact of global climate and environmental change. Defense Meteorological Satellite Program/Operational Line Scan System (DMSP/OLS) night-light (NTL) imagery contributes powerfully to the spatial characterization of global cities, however, its application potential is seriously limited by its coarse resolution. In this paper, we generate annual Normalized Difference Urban Index (NDUI) to characterize global urban areas at a 30 m-resolution from 2000 to 2021 by combining Landsat-5,7,8 Normalized Difference Vegetation Index (NDVI) composites and DMSP/OLS NTL images on the Google Earth Engine (GEE) platform. With the capability to delineate urban boundaries and, at the same time, to present sufficient spatial details within urban areas, the NDUI datasets have the potential for urbanization studies at regional and global scales.

The dataset is a vegetation index dataset for the Central Asia region in August, covering the years 2000-2004 and 2010-2014. The data is sourced from the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index data (MOD13A1), with a spatial resolution of 500 meters and a temporal resolution of 16 days. It includes the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). The data was processed using the maximum synthesis method to generate monthly composite vegetation index datasets. The application of the data has allowed us to gain a better understanding of the impact of climate change on vegetation and ecosystems by combining the analysis of meteorological factors and vegetation characteristics with other vegetation datasets. It also enables us to predict the distribution of specific vegetation types in different regions.

Amur honeysuckle bush (Lonicera maackii) and Morrow's honeysuckle (Lonicera morrowii) are two of the most aggressively invasive species to become established throughout areas along the Blue River in metropolitan Kansas City, Missouri. These two large, spreading shrubs (locally referred to as bush honeysuckle in the Kansas City metropolitan area) colonize the understory, crowd out native plants, and may be allelopathic, producing a chemical that restricts growth of native species. Removal efforts have been underway for more than a decade by local conservation groups such as Bridging The Gap and Heartland Conservation Alliance, who are concerned with the loss of native species diversity associated with the spread of bush honeysuckle. Bush honeysuckle produces leaves early in the spring before almost all other vegetation and retains leaves late in the fall after almost all other species have lost their leaves. Appropriately timed imagery can be used during early spring and late fall to map the extent of bush honeysuckle. Using multispectral imagery collected in February 2016 and true color aerial imagery collected in March 2016, a coverage map of bush honeysuckle in the study area was made to investigate the extent of bush honeysuckle in a study area along the middle reach of the Blue River in the Kansas City metropolitan area in Jackson County, Missouri. The coverage map was further classified into unlikely, low-, and high-density bush honeysuckle density at a 30-foot cell size. The unlikely density class correctly predicted the absence and approximate density of bush honeysuckle for 86 percent of the field-verification points, the low-density class predicted the presence and approximate density with 73-percent confidence, and the high-density class was predicted with 67-percent confidence. This data was used to support the project work described in: Ellis, J.T., 2018, Remote sensing of bush honeysuckle in the Middle Blue River Basin, Kansas City, Missouri, 2016–17: U.S. Geological Survey Scientific Investigations Map XXXX, 1 sheet., https://doi.org/xxxx.

SEN12VTS: Sentinel 1 and 2 Vegetation Time-Series Dataset

  Overview

The SEN12VTS (Sentinel-1 & Sentinel-2 Vegetation Time-Series) dataset has been created to support research on time-series analysis for vegetation indices, specifically targeting NDVI (Normalized Difference Vegetation Index) regression tasks. Recognizing the lack of datasets catering to this specific temporal and spatial need, SEN12VTS was developed to fill the gap with a high-quality, Europe-focused… See the full description on the dataset page: https://huggingface.co/datasets/links-ads/sen12vts.

Satellite images can be used to derive time series of vegetation indices, such as normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI), at global scale. Unfortunately, recording artifacts, clouds, and other atmospheric contaminants impacts a significant portion of the produced images, requiring the usage of ad-hoc techniques to reconstruct the time series in the affected regions. In literature, several methods have been proposed to fill the gaps present in the images, and some works also presented performance comparisons between them (Roerink et al., 2000; Moreno-Martínez et al., 2020; Siabi et al., 2022). Because of the lack of a ground truth for the reconstructed images, the performance evaluation requires the creation of datasets where artificial gaps are introduced in a reference image, such that metrics like the root mean square error (RMSE) can be computed comparing the reconstructed images with the reference one. Different approaches have been used to create the reference images and the artificial gaps, but in most cases, the artificial gaps are introduced using arbitrary patterns and/or the reference image is produced artificially and not using real satellite images (e.g. Kandasamy et al., 2013; Liu et al., 2017; Julien & Sobrino, 2018). In addition, to the best of our knowledge, few of them are openly available and directly accessible allowing for fully reproducible research.

We provide here a benchmark dataset for time series reconstruction method based on the harmonized Landsat Sentinel-2 (HLS) collection where the artificial gaps are introduced with a realistic spatio-temporal distribution. In particular, we selected six tiles that we considered representative for most of the main climate classes (e.g. equatorial, arid, warm temperature, boreal and polar), as depicted in the preview.

Specifically, following the relative tiling system shown above, we downloaded the Red, NIR and F-mask bands from both the HLSL30 and HLSS30 collections for the tiles 19FCV, 22LEH, 32QPK, 31UFS, 45WFV and 49MWM. From the Red and NIR band we derived the NDVI as:

(NDVI = {NIR - Red \over NIR + Red})

only for clear-sky on lend pixels (F-mask bits 1, 3, 4 and 5 equal zero), setting as not a number the remaining pixels. The images are then aggregated on a 16 days base, averaging the available values for each pixel in each temporal range. The so obtained data, are considered from us as the reference data for the benchmarking, and stored following the file naming convention

HLS.T..v2.0.NDVI.tif

where TILE_NAME is one between the above specified ones, YYYY is the corresponding year (spanning from 2015 to 2022) and DDD is the day of the year from which the corresponding 16 days range starts. Finally, for each tile, we have a time series composed of 184 images (23 images for 8 years) that can be easily manipulated, for example using the Scikit-Map library in Python.

Starting from those data, for each image we considered the mask of currently present gaps, we randomly rotated it by 90, 180 or 270 degrees and we added artificial gaps in the pixels of the rotated mask. Doing so, we believe that the spatio-temporal distribution will be still realistic, providing a solid benchmark for gap-filling methods that work on time series, on spatial pattern or combination of the both.

The data including the artificial gaps are stored with the naming structure

HLS.T..v2.0.NDVI_art_gaps.tif

following the previously mentioned convention. The performance metrics, such as RMSE or normalized RMSE (NRMSE), can be computed by applying a reconstruction method on the images with artificial gaps, and then comparing the reconstructed time series with the reference one only on the artificially created gaps locations.

This dataset was used to compare the performance of some gap-filling methods and we provide a Jupyter notebook that shows how to access and use the data. The files are provided in GeoTIFF format and projected in the coordinate reference system WGS 84 / UTM zone 19N (EPSG:32619).

If you succeed to produce higher accuracy or develop a new algorithm for gap filling, please contact authors or post on our GitHub repository. May the force be with you!

References:

Julien, Y., & Sobrino, J. A. (2018). TISSBERT: A benchmark for the validation and comparison of NDVI time series reconstruction methods. Revista de Teledetección, (51), 19-31. https://doi.org/10.4995/raet.2018.9749

Kandasamy, S., Baret, F., Verger, A., Neveux, P., & Weiss, M. (2013). A comparison of methods for smoothing and gap filling time series of remote sensing observations–application to MODIS LAI products. Biogeosciences, 10(6), 4055-4071. https://doi.org/10.5194/bg-10-4055-2013

Liu, R., Shang, R., Liu, Y., & Lu, X. (2017). Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability. Remote Sensing of Environment, 189, 164-179. https://doi.org/10.1016/j.rse.2016.11.023

Moreno-Martínez, Á., Izquierdo-Verdiguier, E., Maneta, M. P., Camps-Valls, G., Robinson, N., Muñoz-Marí, J., ... & Running, S. W. (2020). Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud. Remote Sensing of Environment, 247, 111901. https://doi.org/10.1016/j.rse.2020.111901

Roerink, G. J., Menenti, M., & Verhoef, W. (2000). Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9), 1911-1917. https://doi.org/10.1080/014311600209814

Siabi, N., Sanaeinejad, S. H., & Ghahraman, B. (2022). Effective method for filling gaps in time series of environmental remote sensing data: An example on evapotranspiration and land surface temperature images. Computers and Electronics in Agriculture, 193, 106619. https://doi.org/10.1016/j.compag.2021.106619

This dataset contains documentation on the 146 global regions used to organize responses to the ArchaeGLOBE land use questionnaire between May 18 and July 31, 2018. The regions were formed from modern administrative regions (Natural Earth 1:50m Admin1 - states and provinces, https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-1-states-provinces/). The boundaries of the polygons represent rough geographic areas that serve as analytical units useful in two respects - for the history of land use over the past 10,000 years (a moving target) and for the history of archaeological research. Some consideration was also given to creating regions that were relatively equal in size. The regionalization process went through several rounds of feedback and redrawing before arriving at the 146 regions used in the survey. No bounded regional system could ever truly reflect the complex spatial distribution of archaeological knowledge on past human land use, but operating at a regional scale was necessary to facilitate timely collaboration while achieving global coverage. Map in Google Earth Format: ArchaeGLOBE_Regions_kml.kmz Map in ArcGIS Shapefile Format: ArchaeGLOBE_Regions.zip (multiple files in zip file) The shapefile format is a digital vector file that stores geographic location and associated attribute information. It is actually a collection of several different file types: .shp — shape format: the feature geometry .shx — shape index format: a positional index of the feature geometry .dbf — attribute format: columnar attributes for each shape .prj — projection format: the coordinate system and projection information .sbn and .sbx — a spatial index of the features .shp.xml — geospatial metadata in XML format .cpg — specifies the code page for identifying character encoding Attributes: FID - a unique identifier for every object in a shapefile table (0-145) Shape - the type of object (polygon) World_ID - coded value assigned to each feature according to its division into one of seventeen ‘World Regions’ based on the geographic regions used by the Statistics Division of the United Nations (https://unstats.un.org/unsd/methodology/m49/), with small changes to better reflect archaeological scholarly communities. These large regions provide organizational structure, but are not analytical units for the study. World_RG - text description of each ‘World Region’ Archaeo_ID - unique identifier (1-146) corresponding to the region code used in the ArchaeoGLOBE land use questionnaire and all ArchaeoGLOBE datasets Archaeo_RG - text description of each region Total_Area - the total area, in square kilometers, of each region Land-Area - the total area minus the area of all lakes and reservoirs found within each region (source: https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-lakes/) PDF of Region Attribute Table: ArchaeoGLOBE Regions Attributes.pdf Excel file of Region Attribute Table: ArchaeoGLOBE Regions Attributes.xls Printed Maps in PDF Format: ArchaeoGLOBE Regions.pdf Documentation of the ArchaeoGLOBE Regional Map: ArchaeoGLOBE Regions README.doc