Lebanon GIS data
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Lebanon administrative level 0-3 edge-matched gazetteer and boundary files. SEE CAVEATS COD-EM datasets do not replace the authoritative COD-AB available here; however COD-EM datasets may be preferred for cartographic purposes. See caveats. These layers are suitable for database or GIS linkage to the Lebanon - Subnational Population Statistics tables using the ADM0, ADM1, ADM2, and ADM3_PCODE fields. Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.
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.
**UNOSAT code: FR20191016LBN** This map illustrates satellite-detected burned areas in Aley and Chouf districts, in Lebanon, based on SPOT-6 imagery acquired on 19 October 2019. Approximately 1275 ha of potentially burned areas were detected by Copernicus-EMS as of 17 October 2019 (orange) and additional 92 ha of burned were detected by UNOSAT as of 19 October 2019 (red), which is equivalent to approximately 7% increase in affected surface area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
**UNOSAT code: FR20191016LBN** This map illustrates satellite-detected burned areas in Al-Haffa and Al-Qardaha Districts, Lattakia Governorate, Syria, based on SPOT-6 imagery acquired on 21 October 2019. Approximately 433 ha of potentially burned areas were detected by UNOSAT as of 21 October 2019 (red). This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
**UNOSAT code: FR20191016LBN** This map illustrates satellite-detected burned areas in South and El Nabatieh Governorates, in Lebanon, based on SPOT-6 imagery acquired on 19 October 2019. Approximately 80 ha of potentially burned area were detected by UNOSAT as of 19 October 2019 (red), This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
**UNOSAT code: FR20191016LBN** This map illustrates satellite-detected burned areas in Akkar, Tartous and Homs Governorates, in Lebanon and Syria, based on SPOT-6 imagery acquired on 21 October 2019. Approximately 549 ha of potentially burned areas were detected by UNOSAT as of 21 October 2019 (red). This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
**UNOSAT code: FR20191016LBN** This map illustrates satellite-detected fire hotspots based on the analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) accessed via NASA FIRMS, between October 15-16, 2019. 30 hotspots were detected in Lebanon and 121 hotspots were detected in three analysed Governorates (i.e. Homs, Lattakia and Tartous) in Syria. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
Lebanon administrative level 4 (Beirut neighborhoods) boundary shapefile and geodatabase
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# Lebanon Explosion: Damage Proxy Maps, Aug 2020 The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and Caltech, in collaboration with the Earth Observatory of Singapore (EOS), produced these damage proxy maps for Lebanon Explosion (Aug 2020) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **ARIA-SG Damage Proxy Map: Lebanon, Explosion, 4-5 Aug 2020, v0.5** - **ARIA-SG Damage Proxy Map: Lebanon, Explosion, 4-5 Aug 2020, v0.5** **More map details and files:**
This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['railway'] IN ('rail','station') Features may have these attributes: - name - name:en - railway - ele - operator:type - layer - addr:full - addr:city - source - name:ar This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
Number of rainy days calculated in the Litani River Basin area. For every pixel, the number of rainy days in a month is reported. This dataset is created by using daily rainfall data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). **Data publication:** 2019-06-01 **Supplemental Information:** No data value: -9999 Unit: days/month **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: [Bich Tran](mailto:b.tran@un-ihe.org) Resource Contact: [Bich Tran](mailto:b.tran@un-ihe.org) **Data lineage:** The daily precipitation data was used to compute the number of rainy days per month. The monthly data is obtained by counting the number of daily precipitation higher than 0.201 in a month for each pixel. Then, the data was resampled to 100m resolution. More information at https://wapor.apps.fao.org/catalog/WAPOR_2/1/L1_PCP_E. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage
Evapotranspiration from precipitation calculated over the Litani River Basin area. The evapotranspiration from precipitation (etrain) is the evapotranspiration of green water, in other words the fraction of the total evapotranspiration that is due to rainfall, calculated on a pixel-based soil moisture balance model. More information can be found on the Water Accounting report of Litani (FAO and IHE Delft, 2019). **Data publication:** 2019-06-01 **Supplemental Information:** No data value: -9999 Unit: mm/month **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: [Bich Tran](mailto:b.tran@un-ihe.org) Resource Contact: [Bich Tran](mailto:b.tran@un-ihe.org) **Data lineage:** The calculation of the Evapotranspiration from precipitation is based on a pixel-based soil moisture balance model. More information can be found on the Water Accounting report of Litani (FAO and IHE Delft, 2019). **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage
Incremental evapotranspiration calculated for the Litani River Basin area. The incremental evapotranspiration (ETINCR) is the evapotranspriation of blue water, in other words the incremental evapotranspiration is the fraction of the total actual evapotranspiration that is not due to rainfall. The calculation of Incremental Evapotranspiration is based on a pixel-based soil moisture balance model. More information can be found on the Water Accounting report of Litani (FAO and IHE Delft, 2019). **Data publication:** 2019-06-01 **Supplemental Information:** No data value: -9999 Unit: mm/month **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: [Bich Tran](mailto:b.tran@un-ihe.org) Resource Contact: [Bich Tran](mailto:b.tran@un-ihe.org) **Data lineage:** The calculation of Incremental Evapotranspiration is based on a pixel-based soil moisture balance model. More information can be found on the Water Accounting report of Litani (FAO and IHE Delft, 2019). **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage
Precipitation minus actual evapotranspiration (P-ET) calculated over the Litani River Basin area in Lebanon. The P-ET is the difference between WaPOR precipitation (based on CHIRPS) and the actual evapotranspiration and interception. **Data publication:** 2019-06-01 **Supplemental Information:** No data value: -9999 Unit: mm/month **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: [Bich Tran](mailto:b.tran@un-ihe.org) Resource Contact: [Bich Tran](mailto:b.tran@un-ihe.org) **Data lineage:** The calculation of Evapotranspiration minus Precipitation is done by subtracting WaPOR precipitaiton (CHIRPS) from WaPOR actual evapotranspiration. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage
Percolation calculated Litani River Basin area in Lebanon. The percolation (perco) is the amount of soil moisture in the root zone that leaks deeper contributing to groundwater recharge. The calculation of percolation is based on a pixel-based soil moisture balance model. More information can be found on the Water Accounting report of Litani (FAO and IHE Delft, 2019). **Data publication:** 2019-06-01 **Supplemental Information:** No data value: -9999 Unit: mm/month **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: [Bich Tran](mailto:b.tran@un-ihe.org) Resource Contact: [Bich Tran](mailto:b.tran@un-ihe.org) **Data lineage:** The calculation of percolation is based on a pixel-based soil moisture balance model. More information can be found on the Water Accounting report of Litani (FAO and IHE Delft, 2019). **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage
Surface runoff calculated for the Litani River Basin area. The surface runoff (sro) is the fraction of the effective rainfall that does infiltrate into the soil and contributes to overland flow. More information can be found on the Water Accounting report of Litani (FAO and IHE Delft, 2019). **Data publication:** 2019-06-01 **Supplemental Information:** No data value: -9999 Unit: mm/month **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: [Bich Tran](mailto:b.tran@un-ihe.org) Resource Contact: [Bich Tran](mailto:b.tran@un-ihe.org) **Data lineage:** The calculation of surface runoff is based on a pixel-based soil moisture balance model. More information can be found on the Water Accounting report of Litani (FAO and IHE Delft, 2019). **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage
Supplied water calculated over the Litani River Basin area. The supplied water is the amount of water that is artificially or naturally supplied to a pixel generating incremental evapotranspiration and return flow. More information can be found on the Water Accounting report of Litani (FAO and IHE Delft, 2019). **Data publication:** 2019-06-01 **Supplemental Information:** No data value: -9999 Unit: mm/month **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: [Bich Tran](mailto:b.tran@un-ihe.org) Resource Contact: [Bich Tran](mailto:b.tran@un-ihe.org) **Data lineage:** The calculation of supplied water is based on a pixel-based soil moisture balance model. More information can be found on the Water Accounting report of Litani (FAO and IHE Delft, 2019). **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage
Base flow calculated for the Litani River Basin area in Lebanon. Base flow (bf) or slow flow is the component of the total flow that is due to groundwater discharge. It mainly occurs during dry months. The calculation of base flow is based on a pixel-based soil water balance model. More information are available in the water accounting report of Litani (FAO and IHE Delft, 2019). **Data publication:** 2019-06-01 **Supplemental Information:** No data value: -9999 Unit: mm/month **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: [Bich Tran](mailto:b.tran@un-ihe.org) Resource Contact: [Bich Tran](mailto:b.tran@un-ihe.org) **Data lineage:** The calculation of base flow is based on a pixel-based soil moisture balance model. More information can be found on the Water Accounting report of Litani (FAO and IHE Delft, 2019). **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage
Total flow calculated over the Litani River Basin area in Lebanon. The total flow (tf) is the sum of surface runoff (sro) and the base flow (bf). More information can be found on the Water Accounting report of Litani (FAO and IHE Delft, 2019). **Data publication:** 2019-06-01 **Supplemental Information:** No data value: -9999 Unit: mm/month **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: [Bich Tran](mailto:b.tran@un-ihe.org) Resource Contact: [Bich Tran](mailto:b.tran@un-ihe.org) **Data lineage:** The calculation of total flow is based on a pixel-based soil moisture balance model. More information can be found on the Water Accounting report of Litani (FAO and IHE Delft, 2019). **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage
A geospatial impact assessment was conducted to detect the significant negative changes of vegetation cover for cereals during the conflict of 2023-2024 in Lebanon. The assessment used the baseline period of October 2019 to 7th October 2023 compared to October 2024 without field validation. Land cover and cropland data are critical resources for analysing the status of agricultural land. Sentinel-2 satellite imagery (10m resolution) was utilized for establishing baseline land cover for 2023. This map depicts the severity of damage in localized areas for cereals. Each hexagon shows the total cereals area with negative change. **Data creation:** 2025-09-15 **Supplemental Information:** This assessment was conducted in collaboration between FAO-NSL and CNRS, where the cropland/crop category datasets are derived from previous LULC maps produced by CNRS. Datasets for negative vegetation cover changes in other crop categories are also available, along with the aggregated dataset on cropland: [Negative vegetation cover changes in olive plantation (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/dataset/d9dcfdd8-7b7e-466b-b481-d18f482beda7) [Negative vegetation cover changes in field crops & grape field (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/iso/8e66aef8-db2d-463a-bbc4-1e1e8f3678f7) [Negative vegetation cover changes in fruit tree orchard (Lebanon, October 2024)]( https://data.apps.fao.org/catalog/dataset/e344d2d0-20f3-4773-a60d-9e3280139170) Negative vegetation cover changes in cropland (Lebanon, October 2024) More information on the damage to cropland and cropland categories can be found in the following fact-sheets: - Cropland with negative vegetation cover changes during the conflict in Lebanon as of October 2024 - Crop categories with negative vegetation cover changes during the conflict in Lebanon as of October 2024 **Contact points:** Resource Contact: [Matieu Henry](mailto:Matieu.Henry@fao.org) Metadata Contact: [Geospatial Unit](mailto:gis-manager@fao.org) **Data lineage:** Landsat 7 and 8 imageries (30m resolution) were used to analyse negative vegetation changes. The Normalized Difference Vegetation Index (NDVI) was used to compare cropland conditions between the baseline and the October 2024 periods. Additionally, very high-resolution Maxar satellite imagery (<0.5 m) was used to calibrate the impact assessment model for cropland. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:** Fact-sheet: Crop categories with negative vegetation cover changes during the conflict in Lebanon as of October 2024
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. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **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
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. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **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. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource 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 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. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **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
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. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **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. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource 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 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. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **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 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. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **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. Licence: 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 ETIa in a given month. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **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. Licence: 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 monthly 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 month. **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. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **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. **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 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. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **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. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource 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
This land cover dataset at sub-national scale (Level 3) shows a detailed classification with information on the the crops representing at least 10% of the area. It is published on a dekadal basis. The data is provided in near real time from January 2009 to 2021. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: class Map code : L3-LCC-D **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:** A supervised classification methodology is applied to assign a specific class to each pixel of the image. Training data consist of seasonal and long term metrics derived from dekadal NDVI time series, phenology and spectral reflectance data combined with reference data denoting the exact location of each of the classes specified in the table below. 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 mean daily NPP for that specific dekad. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: g/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
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 NPP for that specific month. The data is provided in near real time from January 2009 to 2023. **Data publication:** 2021-01-01 **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 **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); - Monthly: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
Total Biomass Production (TBP) is defined as the sum of the above-ground dry matter produced during the course of the growing season. The seasonal value represents the total accumulated biomass during one growing season, from start of season (SOS) to end of season (EOS). A pixel value of -9997 denotes "out of season". The data is provided in near real time from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: kg/ha Scale Factor : 10 Map code : L3-TBP-S 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. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** To derive the accumulation in biomass production over or during a growing season, first the start and the end of the growing season need to be identified using the phenology data component. TBP is then calculated as the sum of NPP, converted into dry matter productivity (DMP) units (kg/ha), between the start of the season (SOS) and the end of the season (EOS). At the end of the season, when the crops for the area can be assessed, the dekadal and seasonal TBP values are adjusted using an additional root-shoot correction factor data layer that allows the user to correct the TBP using the land cover specific root-shoot values. A limitation for the derivation of TBP is the dependency on phenological information, meaning that TBP can only be derived for areas where seasonality is detected. For ecosystems, such as tropical forests or deserts, that experience almost no seasonality, the start of season is theoretically set at January 1st and end of season is set at December 31st. 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