Data Layers

Layer Name	Update Frequency	Long Description	Display Name	Citation	Source	Standards	Unit	Short Description	Url
Normalized Difference Vegetation Index (NDVI)	16 days	NDVI quantifies vegetation by measuring the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs)	Normalized Difference Vegetation Index (NDVI)	Didan, K. (2021). MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2025-01-03 from https://doi.org/10.5067/MODIS/MOD13Q1. 061	LP DAAC	All data distributed by the LP DAAC contain no restrictions on the data reus		Normalized difference vegetation index	https://lpdaac.usgs.gov/products/mod13q1v061/
Normalized Difference Water Index	Monthly	The Normalized Difference Water Index (NDWI) is sensitive to changes in liquid water content of vegetation canopies.	Normalized Difference Water Index	Vermote, E., Wolfe, R. (2015). MOD09GA MODIS/Terra Surface Reflectance Daily L2G Global 1kmand 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2025-01-03 from https: //doi.org/10.5067/MODIS/MOD09GA.006	LP DAAC	All data distributed by the LP DAAC contain no restrictions on the data reus		Normalized Difference Water Index	https://lpdaac.usgs.gov/products/mod09gav006/
Leaf Area Index (LAI)	8 days	The MOD15A2H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the ?best? pixel available from all the acquisitions of the Terra sensor from within the 8- day period	Leaf Area Index (LAI)	Myneni, R., Knyazikhin, Y., Park, T. (2021). MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2025-01-03 from https://doi.org/10. 5067/MODIS/MOD15A2H.061	LP DAAC	All data distributed by the LP DAAC contain no restrictions on the data reus		LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies.	https://lpdaac.usgs. gov/products/mod15a2hv061/
Soil Moisture	Monthly	This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) L-Band radiometer.	Soil Moisture	MAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture V006. NASA National Snow and Ice Data Center Distributed Active Archive Center. DOI: 10.5067/M20OXIZHY3RJ	Earthdata	Free to use with Citation		The total amount of water, including the water vapor, in an unsaturated soil	https://cmr.earthdata.nasa. gov/search/concepts/C2776463943-NSIDC_ECS. html
Land Surface Temperature (LST)	Monthly	The radiative skin temperature of the land surface during daytime expressed in degree Celsuis. It plays an important role in the exchange of energy and water between the ground and vegetation.	Land Surface Temperature (LST)	Wan, Z., Hook, S., Hulley, G. (2021). MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2025-01-03 from https://doi.org/10.5067/MODIS/MOD11A1.061	LP DAAC	MODIS data and products acquired through the LP DAAC have no restrictions on subsequent use, sale, or redistribution.		The radiative skin temperature of the land surface during daytime.	https://lpdaac.usgs.gov/products/mod11a1v061
Particulate Matter (PM2.5)	Monthly	Fine particles primarily come from vehicle exhausts, or burning of fuels such as wood, or coal and natural sources such as forest fires.	Particulate Matter (PM2.5)	Keller, C. A., Knowland, K. E., Duncan, B. N., Liu, J., Anderson, D. C., Das, S., ... & Pawson, S. (2021). Description of the NASA GEOS composition forecast modeling system GEOS-CF v1. 0. Journal of Advances in Modeling Earth Systems, 13(4), e2020MS002413. doi:10.1029/2020MS002413	GMAO		ug/m^3	Fine inhalable particles, with diameters that are generally 2.5 micrometers and smaller (ug/m3).	https://gmao.gsfc.nasa. gov/weather_prediction/GEOS-CF/
Temperature - Monthly Avg.	Monthly	Temperature of air at 2m above the surface of land, sea or in- land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Temperature measured in kelvin can be converted to degrees Celsius (deg C) by subtracting 273.15.	Temperature - Monthly Avg.	Mu'oz Sabater, J. (2019): ERA5-Land monthly averaged data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.68d2bb30	https://cds. climate. copernicus. eu/cdsapp#! /dataset/rean		deg C	Temperature Monthly Average	https://cds.climate.copernicus.eu/cdsapp#! /dataset/reanalysis-era5-land-monthly-means
Total Precipitation - Monthly	Monthly	Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large- scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.	Total Precipitation - Monthly	Mu'oz Sabater, J. (2019): ERA5-Land monthly averaged data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.68d2bb30	https://cds. climate. copernicus. eu/cdsapp#! /dataset/rean		mm	Total Precipitation Monthly	https://cds.climate.copernicus.eu/cdsapp#! /dataset/reanalysis-era5-land-monthly-means
Land Use/Land Cover	Yearly	This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated from Impact Observatory's deep learning AI land classification model used a massive training dataset of billions of human-labeled image pixels developed by the National Geographic Society. The global maps were produced by applying this model to the Sentinel-2 scene collection on Microsoft's Planetary Computer, processing over 400,000 Earth observations per year.	Land Use/Land Cover	Karra, Kontgis, et al. ?Global land use/land cover with Sentinel-2 and deep learning.? IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.	https: //livingatlas. arcgis. com/landcove	Creative Commons by Attribution (CC BY 4.0) license		Land Use/Land Cover	https://env1.arcgis. com/arcgis/rest/services/Sentinel2_10m_LandC
Nitrogen Dioxide	Monthly	Nitrogen oxides (NO2 and NO) are important trace gases in the Earth's atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (notably fossil fuel combustion and biomass burning) and natural processes (wildfires, lightning, and microbiological processes in soils). Here, NO2 is used to represent concentrations of collective nitrogen oxides because during daytime, i.e. in the presence of sunlight, a photochemical cycle involving ozone (O3) converts NO into NO2 and vice versa on a timescale of minutes.	Nitrogen Dioxide (NO2)	Copernicus Sentinel-5P (processed by ESA), 2021, TROPOMI Level 2 Nitrogen Dioxide total column products. Version 02. European Space Agency. https://doi. org/10.5270/S5P-9bnp8q8	Copernicus Data Space Ecosystem	The use of Sentinel data is governed by the Copernicus Sentinel Data Terms and Conditions.	mol/m2	Gaseous air pollutant composed (molecules per m2)	https://dataspace.copernicus.eu/
Population	Yearly	WorldPop produces different gridded population layers	Population	Christopher T. Lloyd, Heather Chamberlain, David Kerr, Greg Yetman, Linda Pistolesi, Forrest R. Stevens, Andrea E. Gaughan, Jeremiah J. Nieves, Graeme Hornby, Kytt MacManus, Parmanand Sinha, Maksym Bondarenko, Alessandro Sorichetta & Andrew J. Tatem (2019) Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets, Big Earth Data, 3:2, 108-139, DOI: 10.1080/20964471.2019.1625151	WorldPop	Creative Commons Attribution 4.0 International License	Nos	The Spatial Distribution of population	https://www.worldpop.org/
Soil Organic Carbon	As per data source	Sequestering carbon in SOC has been suggested as one way to mitigate climate change by reducing atmospheric carbon dioxide.The argument is that small increases of SOC over very large areas in agricultural and pastoral lands will significantly reduce atmospheric carbon dioxide. SOC is crucial to soil health, fertility and ecosystem services, including food production ? making its preservation and restoration essential for sustainable development.	Soil Organic Carbon	He G, Zhang Z, Zhang J, Huang X. Soil Organic Carbon Dynamics and Driving Factors in Typical Cultivated Land on the Karst Plateau. Int J Environ Res Public Health. 2020 Aug 6;17(16):5697. doi: 10.3390/ijerph17165697. PMID: 32781763; PMCID: PMC7459649. Hengl T, Mendes de Jesus J, Heuvelink GB, Ruiperez Gonzalez M, Kilibarda M, Blagoti? A, Shangguan W, Wright MN, Geng X, Bauer-Marschallinger B, Guevara MA, Vargas R, MacMillan RA, Batjes NH, Leenaars JG, Ribeiro E, Wheeler I, Mantel S, Kempen B. SoilGrids250m: Global gridded soil information based on machine learning. PLoS One. 2017 Feb 16;12 (2):e0169748. doi: 10.1371/journal.pone.0169748. PMID: 28207752; PMCID: PMC5313206.	SoilGrids	Creative Commons Attribution 4.0 International License	dg/kg	Soil organic carbon (SOC) refers only to the carbon component of organic compounds.	https://soilgrids.org/
Crop Intensity	Yearly	Crop intensity is a measure of how much land is devoted to agricultural production. It is calculated by dividing the total area of crops planted by the total area of land suitable for cultivation in a given region or period. Crop intensity can increase by reducing the time that land is left fallow, growing more than one crop on the same land in a year, or using irrigation to extend the cropping season. Crop intensity is an indicator of crop intensification, which aims to increase food output per unit of land area.	Crop Intensity	Gumma, M.K., Thenkabail, P.S., Teluguntla, P., Rao, M.N., Mohammed, I.A. and Whitbread, A.M., 2016. Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data. International Journal of Digital Earth, 9(10), pp.981-1003.	ICRISAT	Data obtained from LP DAAC, methodology developed by ICRISAT and layer is distributed by UNDP which contain no restrictions on the data reuse	Categorical (Single/Doubl e/Triple )	Crop intensity is the percentage of arable land that is used for growing crops in a given region or period.	https://www.tandfonline.com/doi/full/10. 1080/17538947.2016.1168489
Croplands	As per data source	Cropland is a category of land use that refers to the land area that is cultivated for agricultural production. Cropland includes both arable land (land under temporary crops) and permanent crops (land under crops that do not need to be replanted for several years). Cropland is an important source of food, feed, and fiber for human consumption and animal husbandry.	Croplands	Gumma, M.K., Thenkabail, P.S., Teluguntla, P.G., Oliphant, A., Xiong, J., Giri, C., Pyla, V., Dixit, S. and Whitbread, A.M., 2020. Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud. GIScience & Remote Sensing, 57(3), pp.302-322.	ICRISAT	Data obtained from LP DAAC, methodology developed by ICRISAT and layer is distributed by UNDP which contain no restrictions on the data reuse	Categorical (Crop vs Non- Crop)	Cropland is a type of land use that is mainly used for growing crops for food, feed, or fiber.	https://www.tandfonline.com/doi/full/10. 1080/15481603.2019.1690780
Relative Wealth Index	As per data source	The Relative Wealth Index predicts the relative standard of living within countries using privacy protecting connectivity data, satellite imagery, and other novel data sources.	Relative Wealth Index	Microestimates of wealth for all low- and middle-income countries Guanghua Chi, Han Fang, Sourav Chatterjee, Joshua E. Blumenstock Proceedings of the National Academy of Sciences Jan 2022, 119 (3) e2113658119; DOI: 10.1073 /pnas.2113658119	Facebook Data for Good Relative Wealth Index	Creative Commons Attribution-Non Commercial 4.0 International (CC BY-NC 4.0)		Relative Wealth Index	https://dataforgood.facebook. com/dfg/tools/relative-wealth-index#accessdata
Crop Stress	As per data source	Crop stress is a term that describes the negative effects of environmental factors, such as drought, heat, cold, salinity, pests, diseases, or nutrient deficiency, on crop performance. Crop stress can impair the physiological processes of crops, such as photosynthesis, respiration, transpiration, or nutrient uptake, and cause visible symptoms, such as wilting, yellowing, stunting, or necrosis. Crop stress can result in lower crop yields, poorer crop quality, or crop failure.	Crop Stress	Gumma, M.K., Nelson, A. and Yamano, T., 2019. Mapping drought-induced changes in rice area in India. International journal of remote sensing, 40(21), pp.8146-8173.	ICRISAT	Data obtained from LP DAAC, methodology developed by ICRISAT and layer is distributed by UNDP which contain no restrictions on the data reuse	Categorical (Severe Moderate Mild)	Crop stress is a condition that reduces the growth, yield, or quality of crops due to adverse environmental factors.	https://research.utwente. nl/en/publications/mapping-drought-induced- changes-in-rice-area-in-india
Warehouses Geolocation (WH)	As per data source	This dataset contains information about the details of individual warehouses maintained by the State with geo-locations, names, their address, type, capacities and other related information.	Warehouses Geolocation	Department of Agriculture and Co-operation, Telangana Warehouses Geolocation Data, Open Government Data Platform Telangana	Department of Agriculture and Co- operation, Telangana Warehouses Geolocation Data, Open Government Data Platform Telangana	Open Government License, India		WH	https://data.telangana.gov.in/dataset/telangana- warehouses-geolocation-data
Fire Events	As per data source	Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies / Fire locations - Collection 6/61 processed by NASA's Science Computing Facility (SCF) at the University of Maryland (UMD) and distributed by Fire Information for Resource Management System (FIRMS), using swath products (MOD14/MYD14) rather than the tiled MOD14A1 and MYD14A1 products. The thermal anomalies / active fire represent the center of a 1km pixel that is flagged by the MODIS MOD14/MYD14 Fire and Thermal Anomalies algorithm (Giglio 2003) as containing one or more fires within the pixel. This is the most basic fire product in which active fires and other thermal anomalies, such as volcanoes, are identified	Fire Events	This data set was provided by LANCE FIRMS operated by NASA ESDIS with funding provided by NASA Headquarters. See https://earthdata.nasa.gov/earth- observation-data/near-real-time/citation#ed-firms-citation	Fire Information for Resource Management Systems	Citation, Acknowledgements and Disclaimer		Fire Events Data
Soil Moisture DPPD	6 Months	Contains mandal level, district level deviance values of soil moisture in Telangana. Deviance can be defined as positive or negative change in value over time.	Soil Moisture	MAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture V006. NASA National Snow and Ice Data Center Distributed Active Archive Center. DOI: 10.5067/M20OXIZHY3RJ	Earthdata	Free to use with Citation		Contains deviances in Soil moisture over time.
Leaf Area Index (LAI) DPPD	6 Months	Half of the total area of green leafs of the vegetation per unit of ground. This measure is a dimensionless indicator for the thickness of the vegetation cover.	Leaf Area Index (LAI)	Myneni, R., Knyazikhin, Y., Park, T. (2021). MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2025-01-03 from https://doi.org/10. 5067/MODIS/MOD15A2H.061	LP DAAC	All data distributed by the LP DAAC contain no restrictions on the data reus		The amount of leaf material in a canopy.
Normalized Difference Water Index DPPD	6 Months	The Normalized Difference Water Index (NDWI) is sensitive to changes in liquid water content of vegetation canopies.	Normalized Difference Water Index	Vermote, E., Wolfe, R. (2015). MOD09GA MODIS/Terra Surface Reflectance Daily L2G Global 1kmand 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2025-01-03 from https: //doi.org/10.5067/MODIS/MOD09GA.006	LP DAAC	All data distributed by the LP DAAC contain no restrictions on the data reus		Normalized Difference Water Index
Normalized Difference Vegetation Index (NDVI) DPPD	6 Months	NDVI measures the vegetation health by the reflectance if visible and near-infrared light by the vegetation. The higher the reflectance, the healthier the vegetation.	Normalized Difference Vegetation Index (NDVI)	Didan, K. (2021). MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2025-01-03 from https://doi.org/10.5067/MODIS/MOD13Q1. 061	LP DAAC	All data distributed by the LP DAAC contain no restrictions on the data reus		NDVI is a measure of vegetation health
Land Surface Temperature DPPD	6 Months	The radiative skin temperature of the land surface during daytime expressed in degree Celsius. It plays an important role in the exchange of energy and water between the ground and vegetation.	Land Surface Temperature (LST)	Wan, Z., Hook, S., Hulley, G. (2021). MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2025-01-03 from https://doi.org/10.5067/MODIS/MOD11A1.061	LP DAAC	MODIS data and products acquired through the LP DAAC have no restrictions on subsequent use, sale, or redistribution.		The radiative skin temperature of the land surface during daytime.
Crop Fires DPPD	6 Months	The scale shows the change in fires on monthly basis. For example, a negative score shows a positive deviant as it indicates a reduction in the number of fires.	Crop Fires	This data set was provided by LANCE FIRMS operated by NASA ESDIS with funding provided by NASA Headquarters. See https://earthdata.nasa.gov/earth- observation-data/near-real-time/citation#ed-firms-citation	Active Fire Data https: //firms. modaps. eosdis.nasa. gov/active_fir e/			Changes in the number of fires over time.
Nitrogen Dioxide DPPD	6 Months	The scale shows the change in NO2 on monthly basis. For example, a negative score shows a positive deviant as it indicates a reduction in NO2 values	Nitrogen Dioxide (NO2)	Copernicus Sentinel-5P (processed by ESA), 2021, TROPOMI Level 2 Nitrogen Dioxide total column products. Version 02. European Space Agency. https://doi. org/10.5270/S5P-9bnp8q8	Copernicus Data Space Ecosystem	The use of Sentinel data is governed by the Copernicus Sentinel Data Terms and Conditions.		Changes in Nitrogen Dioxide values in molecules per m2
Particulate Matter DPPD	6 Months	The scale shows the change in PM2.5 values on monthly basis. For example, a negative score shows a positive deviant as it indicates a reduction in PM2.5 values	Particulate Matter (PM2.5)	Keller, C. A., Knowland, K. E., Duncan, B. N., Liu, J., Anderson, D. C., Das, S., ... & Pawson, S. (2021). Description of the NASA GEOS composition forecast modeling system GEOS-CF v1. 0. Journal of Advances in Modeling Earth Systems, 13(4), e2020MS002413. doi:10.1029/2020MS002413	GMAO			Changes in PM2.5 values in ug/m3