Iraq GIS data

This map illustrates satellite-detected fires and smoke plumes at oil wells south of Mosul, and also east of Baiji, Iraq. The Mosul fires began with an initial fire at one or two wells on 8 May 2016, lasting less than one day, and intermittently burned in June. The current fire complex began on 3 July with daily fire detections occurring until about 12 July, when the fires greatly increased in number and continued to burn until gradual reductions in detected fires occurred starting in November 2016. The fires east of Baiji have been active since early January 2016. The frequency of smoke plumes (in days) is symbolized in shades of red and yellow, and was calculated using daily MODIS satellite images collected between 18 July 2016 and 13 March 2017. Note that as the plume dissipates then areas of thinner smoke are not detected in this process. The inset on the top right corner shows the infrared data from a Landsat image collected on 25 February 2017, indicating the Mosul fires in white. The inset on the top left corner, from 5 March 2017, shows the same area in real color. Additionally, precipitation data from NASA's IMERG algorithm was included to evaluate instances of rainfall intersecting the smoke plume. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

This map illustrates satellite-detected fires and smoke plumes at oil wells south of Mosul, and also east of Baiji, Iraq. The Mosul fires began with an initial fire at one or two wells on 8 May 2016, lasting less than one day, and intermittently burned in June. The current fire complex began on 3 July with daily fire detections occurring until about 12 July, when the fires greatly increased in number and continued to burn until gradual reductions in detected fires occurred starting in November 2016. The fires east of Baiji have been active since early January 2016. The frequency of smoke plumes (in days) is symbolized in shades of red and yellow, and was calculated using daily MODIS satellite images collected between 18 July 2016 and 13 March 2017. Note that as the plume dissipates then areas of thinner smoke are not detected in this process. The inset on the top right corner shows the infrared data from a Landsat image collected on 25 February 2017, indicating the Mosul fires in white. The inset on the top left corner, from 5 March 2017, shows the same area in real color. Additionally, precipitation data from NASA's IMERG algorithm was included to evaluate instances of rainfall intersecting the smoke plume. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

This map illustrates satellite-detected damage in Old City, Mosul, Ninawa Governorate, Iraq. Using satellite imagery acquired 18 July 2017, UNITAR - UNOSAT identified a total of 6,981 affected structures within this part of city. This marks an overall increase of 26% in damage affected structures from the previous assessment of the 30 June. Approximately 1,202 of these were destroyed (17% of the total affected buildings), 3,982 severely damaged (57% of the total affected buildings) and 1,797 moderately damaged (26% of the total affected buildings). This marks a 145% increase in destroyed buildings, 4% in moderately damaged buildings and 20% severely damaged buildings from the 30 June assessment. The most heavily impacted areas appear to be the Ras al-Kur (see inset) and the Maydan neighbourhoods. Due to the densely constructed nature of this part of the city, these values might be underestimated. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

Settlement locations representing cities, towns, villages, and neighborhoods P-coded in accordance with Iraq - Subnational Administrative Boundaries (COD-AB). The "Iraq populated places 2021 P-coded.xlsx" spreadsheet contains two tables . Table "IRQ populated places master" contains all 12,494 available records. Table '"RQ populated places filter" excludes 338 master records that have blank English (ADM3_EN) and blank Arabic (ADM4_AR) names, and is the basis for the shapefile. The "IRQ populated places V04" contains the 12,156 points of the "IRQ populated places filter" table. When names are verified for the 338 excluded records the spreadsheet and shapefile will be updated. The Iraq - Subnational Administrative Boundaries (COD-AB) administrative level 0-3 gazetteer is also included.

* These datasets are intended for coordination and operations in humanitarian activities. * The purpose of this settlements list in particular is as a gazetteer for geographical reference. It is not intended as a cartographic source or as an authoritative reference list. * The list is derived from IOM's placename database, which is the most extensive currently maintained placename source available for Iraq, and provides compatability with IOM's Displacement Tracking Matrix (DTM). * The list does NOT contain any designations of hierarchy (e.g. governorate / district capitals) or of size (e.g. settlement size or population size). Thus, the placenames refer to a variety of populated places including cities, towns, villages and neighbourhoods. * Arabic names are in the process of being added; some are included in this dataset.

This map illustrates satellite-detected shelters and other buildings at the Gawilan IDP Camp in Ninawa Province, Iraq. This camp is 7.2 kilometres northwest of Aski Kalak town and as of 28 June 2016, a total of 1,609 shelters were detected as well as 1,584 infrastructure and support buildings. Areas of shelters under construction are also visible in the image as of 28 June 2016. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes: - macroregion (admin-1 including region) - region (admin-2 including state, province, department, governorate) - macrocounty (admin-3 including arrondissement) - county (admin-4 including prefecture, sub-prefecture, regency, canton, commune) - localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb) The dataset also contains human settlement points and polygons for: - localities (city, town, and village) - neighbourhoods (borough, macrohood, neighbourhood, microhood) The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names. Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

This map illustrates satellite-detected damage in Old City, Mosul, Ninawa Governorate, Iraq. Using satellite imagery acquired 30 June 2017, UNITAR - UNOSAT identified a total of 5,536 affected structures within this part of city. This marks an overall increase of 37% in damage affected structures from the assessment fourteen days prior on 16 June. Approximately 490 of these were destroyed (9% of the total affected buildings), 3,310 severely damaged (60% of the total affected buildings) and 1,736 moderately damaged (31% of the total affected buildings). This marks a 150% increase in destroyed buildings, 0% in moderately damaged buildings and 57% severely damaged buildings from the 16 June assessment. The most heavily impacted area appears to be the Ammu Baqqal neighbourhood (see inset). Due to the densely constructed nature of this part of the city, these values might be underestimated. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

This map illustrates satellite-detected damaged and potentially damaged buildings in the town of Koeeke Hasan, Iran following the M 7.3 earthquake that struck the area on the 12 November 2017. The analysis was carried out comparing a post-event Pleiades image acquired on the 16 November 2017 with a pre-event WorldView3 image of 24 July 2017. The results indicate intense damage across the whole town, particularly in Koeeke Hasan, Koeeke Aziz and Koeeke Mahmod. A total of 1174 structures have been identified as damaged or collapesed across the area extent represented here. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

Satellite Data (1): Pleiades Imagery Dates: 16 November 2017 Resolution: 50 cm Copyright: CNES 2017, distribution AIRBUS Defense and Space Source: Airbus Defense and Space Satellite Data (2): Resurs-P Imagery Dates: 14 November 2017 Resolution:1m Copyright: Roscosmos Source: Roscosomos Baseline Data: GADM, OpenStreetMap Analysis :UNITAR - UNOSAT/Iranian Space Agency Production: UNITAR - UNOSAT

This map illustrates satellite-detected fires and smoke plumes at oil wells south of Mosul, and also east of Baiji, Iraq. The Mosul fires began with an initial fire at one or two wells on 8 May 2016, lasting less than one day, and intermittently burned in June. The current fire complex began on 3 July with daily fire detections occurring until about 12 July, when the fires greatly increased in number and continued to burn until gradual reductions in detected fires occurred starting in November 2016. The fires east of Baiji have been active since early January 2016. The frequency of smoke plumes (in days) is symbolized in shades of red and yellow, and was calculated using daily MODIS satellite images collected between 18 July 2016 and 7 January 2017. Note that as the plume dissipates then areas of thinner smoke are not detected in this process. The inset on the top right corner shows the infrared data from a Landsat image collected on 23 December 2016, indicating the Mosul fires in white. The inset on the top left corner, from 1 January 2017, shows the same area in real color. Additionally, precipitation data from NASA's IMERG algorithm was included to evaluate instances of rainfall intersecting the smoke plume. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

This map illustrates satellite-detected damaged and potentially damaged buildings and the related density of damage in the areas north of Sarpol-e-Zahab county in Kermanshah province, Iran following the M 7.3 earthquake that struck the area on the 12 November 2017. The analysis was carried out comparing a post-event Pleiades image acquired on the 16 November 2017 with a pre-event WorldView3 image of 24 July 2017. The results indicate damage across several localities and villages in this zone and about 3300 structures were identified as potentially damaged (e.g. 362 in Koeeke Hasan; 148 in Tapamaran and 98 in Jaberi). This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

This map illustrates satellite-detected fires and smoke plumes at oil wells south of Mosul, and also east of Baiji, Iraq. The Mosul fires began with an initial fire at one or two wells on 8 May 2016, lasting less than one day, and intermittently burned in June. The current fire complex began on 3 July with daily fire detections occurring until about 12 July, when the fires greatly increased in number and continued to burn until gradual reductions in detected fires occurred starting in November 2016. The fires east of Baiji have been active since early January 2016. The frequency of smoke plumes (in days) is symbolized in shades of red and yellow, and was calculated using daily MODIS satellite images collected between 18 July 2016 and 24 January 2017. Note that as the plume dissipates then areas of thinner smoke are not detected in this process. The inset on the top right corner shows the infrared data from a Landsat image collected on 24 January 2017, indicating the Mosul fires in white. The inset on the top left corner, from 21 January 2017, shows the same area in real color. Additionally, precipitation data from NASA's IMERG algorithm was included to evaluate instances of rainfall intersecting the smoke plume. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

This map illustrates satellite-detected fires and smoke plumes at oil wells south of Mosul, and also east of Baiji, Iraq. The Mosul fires began with an initial fire at one or two wells on 8 May 2016, lasting less than one day. Subsequently, on several dates in June small-scale fires burned for durations of less than one day. The current fire complex began on 3 July with daily fire detections occurring until about 12 July, when the fires greatly increased in number, continuing to the present at that scale. The fires east of Baiji have been active since early January 2016. The frequency of smoke plumes (in days) is symbolized in shades of red and yellow, and was calculated using 99 MODIS satellite images collected between July 18 and October 24, 2016. Note that as the plume dissipates then areas of thinner smoke are not detected in this process, and thus this analysis indicates only the areas of dense, relatively heavy smoke. The inset on the top right corner shows the thermal data from a Landsat image collected on October 20, indicating the Mosul fires in white. The inset on the top left corner shows the same area in natural color. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

This map illustrates satellite-detected fires and smoke plumes at oil wells south of Mosul, and also east of Baiji, Iraq. The Mosul fires began with an initial fire at one or two wells on 8 May 2016, lasting less than one day, and intermittently burned in June. The current fire complex began on 3 July with daily fire detections occurring until about 12 July, when the fires greatly increased in number and continued to burn to the present at that scale. The fires east of Baiji have been active since early January 2016. The frequency of smoke plumes (in days) is symbolized in shades of red and yellow, and was calculated using daily MODIS satellite images collected between 18 July and 14 December, 2016. Note that as the plume dissipates then areas of thinner smoke are not detected in this process. The inset on the top right corner shows the thermal data from a Landsat image collected on 7 December, indicating the Mosul fires in white. The inset on the top left corner, from 6 December, shows the same area in false color, displaying the near infrared band in red. Additionally, precipitation data from NASA's IMERG algorithm was included to evaluate instances of rainfall intersecting the smoke plume. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

This map illustrates potentially damaged structures/buildings and related density in Sarpol-e-Zahab, Kermanshah, Iran as detected by a Pleiades satellite image acquired on 16 November 2017. The UNITAR-UNOSAT analysis identified 683 damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap (~9700 structures), this represents about 7 % of the total number of structures within Sarpol-e-Zahab. The damage density highlights that the neighbourhood of Maskane Mahar and the area around the Post Office are amongst the most affected zones. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

This map illustrates satellite-detected fires and environmental damage at and around Al Qayyarah, approximately 60 Km south of Mosul, Iraq. The main frame shows fires and the oil spill as seen in imagery collected between 23 December 2016 and 24 January 2017. Marked in blue are fires that were detected in 23 December 2016 but appear inactive as of 24 January 2017. Insets on the left show the oil spill north of Al Qayyarah, detected with both radar and optical imagery from multiple dates. As seen in the imagery, the oil spill is very close to one of the streams which is incidentally a tributary to the Tigris River; therefore, it is possible that oil is spilling to the river. The spectral signature from thermal imagery also suggest that areas of the oil spill are on fire. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

This map illustrates satellite-detected damage in Old City, Mosul, Ninawa Governorate, Iraq. Using satellite imagery acquired 11 June 2017, UNITAR - UNOSAT identified a total of 2,589 affected structures within this part of city. Approximately 153 (6% of the total affected buildings) of these were destroyed, 950 (37% of the total affected buildings) severely damaged and 1,486 (57% of the total affected buildings) moderately damaged. The most heavily impacted location appears to be the Bazaar area (see inset). Due to the densely constructed nature of this part of the city, these values might be underestimated. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

This map illustrates satellite-detected damage in Old City, Mosul, Ninawa Governorate, Iraq. Using satellite imagery acquired 16 June 2017, UNITAR - UNOSAT identified a total of 4,040 affected structures within this part of city. This marks an overall increase of 56% in damage affected structures from the assessment five days prior on 11 June. Approximately 196 of these were destroyed (5% of the total affected buildings), 2,107 severely damaged (52% of the total affected buildings) and 1,737 moderately damaged (43% of the total affected buildings). This marks a 28% increase in destroyed buildings, 17% in moderately damaged buildings and 121% severely damaged buildings from the 11 June assessment. The most heavily impacted area appears to be the Bab al-Tub neighbourhood (see inset). Due to the densely constructed nature of this part of the city, these values might be underestimated. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Iraq **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.2839 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.026 million km (9.1664%) - Unpaved roads: 0.0089 million km (3.1261%) About 0.249 million km (87.7075%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0003 million km (0.1046%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org

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The actual EvapoTranspiration and Interception (ETIa) is the sum of the soil evaporation (E), canopy transpiration (T), and evaporation from rainfall intercepted by leaves (I). The value of each pixel represents the ETIa in a given month. **Data publication:** 2023-01-01 **Supplemental Information:** No data value: -9999 Unit: mm Scale Factor : 0.1 Map code : L3-AETI-M Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. License: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** See ETIa by dekad for further information. The annual total is obtained by taking the ETIa in mm/day, multiplying by the number of days in a dekad, and summing the dekads of each year. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The actual EvapoTranspiration and Interception (ETIa) is the sum of the soil evaporation (E), canopy transpiration (T), and evaporation from rainfall intercepted by leaves (I). The value of each pixel represents the average daily ETIa in a given dekad. **Data publication:** 2023-01-01 **Supplemental Information:** No data value: -9999 Unit: mm Scale Factor : 0.1 Map code : L3-AETI-D Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. License: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The calculation of the ETIa is based on the ETLook model described in Bastiaanssen et al. (2012). See the methodology of the three components (E, T and I) for further information. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The Evaporation (E) data component is the actual evaporation of the soil surface. The value of each pixel represents the total annual evaporation for that specific year. **Data publication:** 2023-01-01 **Supplemental Information:** No data value: -9999 Unit: mm Scale Factor : 0.1 Map code : L3-E-A Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. License: CC-BY-4.0 **Contact points:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** See evaporation (E) by dekad for further information. The annual total is obtained by taking the E in mm/day, multiplying by the number of days in a dekad, and summing the dekads of each year. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The Evaporation (E) data component (dekadal, in mm/day) is the actual evaporation of the soil surface. The value of each pixel represents the average daily actual evaporation for that specific dekad. **Data publication:** 2023-01-01 **Supplemental Information:** No data value: 255 Unit: mm Scale Factor : 0.1 Map code : L3-E-D Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The calculation of the evaporation is based on the ETLook model described in Bastiaanssen et al. (2012). It uses the Penman-Monteith equation, adapted to remote sensing input data. The Penman-Monteith equation predicts the rate of total evaporation and transpiration using commonly measured meteorological data (solar radiation, air temperature, vapour content and wind speed). It has become the FAO standard for calculating the actual and reference evapotranspiration (see FAO Irrigation and Drainage paper 56, Allen et al. 1998). The following data is used for calculating evaporation: • Daily: incoming solar radiation and weather data (temperature, humidity, wind speed and precipitation); • Dekadal: NDVI, surface albedo and soil moisture stress; • Seasonal: Land Cover; • Static: Digital Elevation Model. Data component developed through collaboration with the FRAME Consortium. More information can be found at: http://www.fao.org/in-action/remote-sensing-for-water-productivity/en/ **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The Transpiration (T) data component is the actual transpiration of the vegetation canopy. The value of each pixel represents the total annual transpiration for that specific year. **Data publication:** 2023-01-01 **Supplemental Information:** No data value: -9999 Unit: mm Scale Factor : 0.1 Map code : L3-T-A Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. License: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Metadata Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** See transpiration (T) by dekad for further information. The annual total is obtained by taking the T in mm/day, multiplying by the number of days in a dekad, and summing the dekads of each year. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The transpiration (T) data component (dekadal, in mm/day) is the actual transpiration of the vegetation canopy. The value of each pixel represents the average daily actual transpiration for that specific dekad. **Data publication:** 2023-01-01 **Supplemental Information:** No data value: 255 Unit: mm Scale Factor : 0.1 Map code : L3-T-D Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The calculation of the transpiration is based on the ETLook model described in Bastiaanssen et al. (2012). It uses the Penman-Monteith equation, adapted to remote sensing input data. The Penman-Monteith equation predicts the rate of total evaporation and transpiration using commonly measured meteorological data (solar radiation, air temperature, vapour content and wind speed). It has become the FAO standard for calculating the actual and reference evapotranspiration (see FAO Irrigation and Drainage paper 56, Allen et al. 1998). The following data is used for calculating transpiration: • Daily: incoming solar radiation and weather data (temperature, humidity, wind speed and precipitation); • Dekadal: NDVI, surface albedo and soil moisture stress; • Seasonal: Land Cover; • Static: Digital Elevation Model. Data component developed through collaboration with the FRAME Consortium. More information can be found at: http://www.fao.org/in-action/remote-sensing-for-water-productivity/en/ **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

Interception is the process where rainfall is captured by the leaves. Part of this captured rainfall will evaporate again. The Interception (I) data component represents the evaporation of intercepted rainfall from the vegetation canopy. The value of each pixel represents the total annual interception for that specific year. **Data publication:** 2023-01-01 **Supplemental Information:** No data value: -9999 Unit: mm Scale Factor : 0.1 Map code : L3-I-A Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. License: CC-BY-4.0 **Contact points:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** See interception (I) by dekad for further information. The annual total is obtained by taking the I in mm/day, multiplying by the number of days in a dekad, and summing the dekads of each year. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The Interception (I) data component (dekadal, in mm/day) represents the evaporation of intercepted rainfall from the vegetation canopy. Interception is the process where rainfall is captured by the leaves. Part of this captured rainfall will evaporate again. The value of each pixel represents the average daily evaporated interception for that specific dekad. **Data publication:** 2023-01-01 **Supplemental Information:** No data value: 255 Unit: mm Scale Factor : 0.1 Map code : L3-I-D Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** Evaporation of intercepted rainfall requires energy that is not available for transpiration or evaporation. Interception is a function of the vegetation cover and precipitation. Interception is relatively high with a small amount of precipitation, with the fraction intercepted decreasing quickly as precipitation increases. The maximum interception is determined by the LAI. The following data is used for calculating interception: • Daily: incoming solar radiation and weather data (temperature, humidity and precipitation); • Dekadal: NDVI. Data component developed through collaboration with the FRAME Consortium. More information can be found at: http://www.fao.org/in-action/remote-sensing-for-water-productivity/en/ **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

Net Primary Production (NPP) is a fundamental characteristic of an ecosystem, expressing the conversion of carbon dioxide into biomass driven by photosynthesis. The pixel value represents the total NPP for that specific month. **Data publication:** 2023-01-01 **Supplemental Information:** No data value: -9999 Unit: gC/m² Scale Factor : 0.001 Map code : L3-NPP-M Conversion factor: the pixel value in the downloaded data must be multiplied by 0.001 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The core of the methodology for deriving NPP is detailed in Veroustraete et al. (2002), whilst the practical implementation, as developed for the MARS Crop Yield Forecasting System, is described in Eerens et al. (2004). These methodologies were improved within the framework of the Copernicus Global Land Component, the most important change being the incorporation of biome-specific light-use efficiencies (LUEs). The FRAME project applies this updated methodology, adding improvements which include the addition of a reduction factor to account for reduced water availability (i.e. soil moisture stress). The following data is used to calculate NPP: • Daily: incoming solar radiation and temperature data (Tmin/Tmax); • Dekadal: fAPAR and soil moisture stress; • Seasonal: Land Cover. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

Net Primary Production (NPP) is a fundamental characteristic of an ecosystem, expressing the conversion of carbon dioxide into biomass driven by photosynthesis. The pixel value represents the mean daily NPP for that specific dekad. **Data publication:** 2023-01-01 **Supplemental Information:** No data value: -9999 Unit: gC/m² Scale Factor : 0.001 Map code : L3-NPP-D Conversion factor: the pixel value in the downloaded data must be multiplied by 0.001 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The core of the methodology for deriving NPP is detailed in Veroustraete et al. (2002), whilst the practical implementation, as developed for the MARS Crop Yield Forecasting System, is described in Eerens et al. (2004). These methodologies were improved within the framework of the Copernicus Global Land Component, the most important change being the incorporation of biome-specific light-use efficiencies (LUEs). The FRAME project applies this updated methodology, adding improvements which include the addition of a reduction factor to account for reduced water availability (i.e. soil moisture stress). The following data is used to calculate NPP: • Daily: incoming solar radiation and temperature data (Tmin/Tmax); • Dekadal: fAPAR and soil moisture stress; • Seasonal: Land Cover. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The actual EvapoTranspiration and Interception (ETIa) is the sum of the soil evaporation (E), canopy transpiration (T), and evaporation from rainfall intercepted by leaves (I). The value of each pixel represents the ETIa in a given year. **Data publication:** 2023-01-01 **Supplemental Information:** No data value: -9999 Unit: mm Scale Factor : 0.1 Map code : L3-AETI-A Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. License: CC-BY-4.0 **Contact points:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** See ETIa by dekad for further information. The annual total is obtained by taking the ETIa in mm/day, multiplying by the number of days in a dekad, and summing the dekads of each year. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage