Kenya GIS data

**UNOSAT code: FL20240426KEN, GDACS ID: 1102561** This map illustrates satellite-detected surface waters in Garissa City and Mororo Village, Kenya, as observed from a Pléiades image acquired on 5 May 2024 at 07:36 UTC Within the analysed area , a total of about 22 km² of land appears to be flooded. Furthermore, within the analyzed area, 3,377 buildings are identified as affected by the floods, along with about 90 km of roads, as of 5 May 2024 This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20240426KEN, GDACS ID: 1102561** This map illustrates satellite-detected surface waters in Nairobi, Kenya, as observed from a Pléiades image acquired on 1 May 2024 at 11:06 local time (08:06 UTC). Within the analyzed area, about 2 km² of land appears to be flooded. UNITAR-UNOSAT identified 3,387 affected structures, about 7 km of affected roads, and 9 affected bridges. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20240426KEN, GDACS ID: 1102561** This map illustrates satellite-detected surface waters in Nairobi and Machakos County, Kenya, as observed from a Pléiades image acquired on 1 May 2024 at 11:06 local time (08:06 UTC). Within the analyzed area, about 22 km² of land appears to be flooded. UNITAR-UNOSAT identified 4,246 affected structures, about 94 km of affected roads. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20240426KEN, GDACS ID: 1102561** This map illustrates satellite-detected surface waters in Nairobi and Kiambu County, Kenya, as observed from a Pléiades image acquired on 1 May 2024 at 11:06 local time (08:06 UTC). Within the analyzed area, about 3 km² of cropland appears to be inundated This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20240426KEN, GDACS ID: 1102561** This map illustrates satellite-detected surface waters in Kiambu County, Kenya, as observed from a Pléiades image acquired on 1 May 2024 at 11:06 local time (08:06 UTC). Within the analyzed area, about 2 km² of cropland appears to be inundated. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20240426KEN, GDACS ID: 1102561** This map illustrates satellite-detected surface waters in Kiambu County, Kenya, as observed from a Pléiades image acquired on 1 May 2024 at 11:06 local time (08:06 UTC). Within the analyzed area, about 13 km² of land appears to be flooded. UNITAR-UNOSAT identified 1,815 affected structures, about 24 km of affected roads, and 6 affected bridges. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20240426KEN, GDACS ID: 1102561** This map illustrates satellite-detected surface waters in Nairobi and Machakos County, Kenya, as observed from a Pléiades image acquired on 1 May 2024 at 11:06 local time (08:06 UTC). Within the analyzed area, about 4 km² of cropland appears to be inundated. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Kenya. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

Kenya Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2022

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 Kenya **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.5791 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.023 million km (3.9642%) - Unpaved roads: 0.1669 million km (28.8242%) About 0.3892 million km (67.2116%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0021 million km (0.5353%) 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

No description yet.

No description yet.

No description yet.

No description yet.

No description yet.

Kenya administrative level 0-2 boundaries (COD-AB) dataset version 01. This dataset is structured into 2 levels: - Admin 1: 47 - Admin 2: 290 Dates associated with this dataset: - 28 January 2025: dataset reviewed for accuracy and completeness - 31 October 2019: valid for use by the humanitarian community - 03 July 2018: last edit to the dataset before publishing - 03 July 2018: boundaries created by the source Contributed by OCHA Regional Office for Southern and Eastern Africa (ROSEA). Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries

Over the past three decades, large areas of gazetted forest reserves have been deforested and converted to other uses while unprotected indigenous forests are continuously degraded by illegal and legal of logging of valuable timber trees resulting in reduced carbon stocks and degraded biodiversity values. Forest cover loss in Kenya can be largely attributed to agricultural expansion which is motivated by the subsistence or the market economy. The best example of agricultural expansion fuelled by subsistence and commercial farming interests is in the Mau Conservancy. Unprecedented levels of officially sanctioned excisions of gazetted forests by 2001 was motivated by interests in commercial tea and wheat cultivation, in addition to small scale farming for subsistence and surplus production for the market. Landuse Landcover maps are useful in monitoring and tracking changes within forest ecosystems.

Over the past three decades, large areas of gazetted forest reserves have been deforested and converted to other uses while unprotected indigenous forests are continuously degraded by illegal and legal of logging of valuable timber trees resulting in reduced carbon stocks and degraded biodiversity values. Forest cover loss in Kenya can be largely attributed to agricultural expansion which is motivated by the subsistence or the market economy. The best example of agricultural expansion fuelled by subsistence and commercial farming interests is in the Mau Conservancy. Unprecedented levels of officially sanctioned excisions of gazetted forests by 2001 was motivated by interests in commercial tea and wheat cultivation, in addition to small scale farming for subsistence and surplus production for the market. Landuse Landcover maps are useful in monitoring and tracking changes within forest ecosystems.

Over the past three decades, large areas of gazetted forest reserves have been deforested and converted to other uses while unprotected indigenous forests are continuously degraded by illegal and legal of logging of valuable timber trees resulting in reduced carbon stocks and degraded biodiversity values. Forest cover loss in Kenya can be largely attributed to agricultural expansion which is motivated by the subsistence or the market economy. The best example of agricultural expansion fuelled by subsistence and commercial farming interests is in the Mau Conservancy. Unprecedented levels of officially sanctioned excisions of gazetted forests by 2001 was motivated by interests in commercial tea and wheat cultivation, in addition to small scale farming for subsistence and surplus production for the market. Landuse Landcover maps are useful in monitoring and tracking changes within forest ecosystems.

Kenya Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2020

Kenya Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2021

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.

Water features across the area: rivers, streams, canals, lakes, ponds, and water bodies tagged with `waterway`, `water`, or `natural=water`. Useful for flood-risk modelling, watershed analysis, and environmental basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: waterway, water, natural - Part of the country exports on HDX by HOT

The seasonal Gross Biomass Water Productivity expresses the quantity of output (above ground biomass production) in relation to the total volume of water consumed (actual EvapoTranspiration) during the growing cycle of the vegetation. By relating biomass production to total EvapoTranspiration (sum of soil evaporation, canopy transpiration and interception), this indicator provides insights on the impact of vegetation development on consumptive water use and thus on water balance in a given domain. When the focus is on monitoring performance of irrigated agriculture in relation to water consumption, it is more appropriate to use transpiration alone as a denominator, as a measure of water beneficially consumed by the plant. This latter indicator, for which we use the term \"net water productivity\", provides useful information on how effectively vegetation (and particularly crops) uses water to develop its biomass (and thus yield). **Data publication:** 2025-02-06 **Supplemental Information:** No data value: -9999 Unit : kg/m³ Scale Factor : 0.001 Map code : L3-GBWP-S 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. 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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The calculation of gross biomass water productivity (GBWP) is as follows: GBWP = TBP/ETIa Where TBP is seasonal Total Biomass Production in kg/ha and ETIa is the Actual EvapoTranspiration and Interception in m³/ha cumulated during the course of the growing season, as defined by the phenology information. The following data is used for calculating it: - Seasonal TBP - Dekadal ETIa - Phenology (start of season and end of season) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The Mwea Irrigation Scheme Rice Yield Map (MIS-RYM) is a geospatial vector data layer presenting section-level rice yield estimates for the Mwea Irrigation Scheme in Kenya for the main growing seasons, from 2019 to 2022. The dataset represents five sections (Karaba, Mwea, Tebere, Thiba, and Wamumu) with rice yield estimates stored in the attribute table and spatially linked to section polygons. The rice yield estimates are derived from WaPOR v3 Net Primary Productivity (NPP) layer at 20 m spatial resolution and dekadal (10-day) temporal resolution, aggregated by growing season to support consistent spatial analysis and mapping of yield variability within the scheme. The MIS-RYM is intended for data analysis, visualization, and comparison of seasonal and interannual rice productivity at the irrigation section scale, where detailed spatial assessment is required. **Data publication:** 2025-12-31 **Supplemental Information:** **Attibutes Table** - Id - identity (*Section name*) - Yield_mean - AETI_2019 - AETI_2020 - AETI_2021 - AETI_2022 - Yield_2019 - Yield_2020 - Yield_2021 - Yield_2022 **Unit of measure** - Yield mean: ton/ha/season - AETI: mm/season - Yield: ton/ha/season **Citation:** FAO WaPOR database. [Accessed on [Day/Month/Year]]. [URL] Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [Preeeti Koirala](mailto:preeti.koirala@fao.org) **Data lineage:** MIS-RYM geo-spatial vector dataset was produced by aggregating section-level rice yield estimates derived from WaPOR v3 Net Primary Productivity (NPP) data at 20 m spatial and dekadal (10day) temporal resolution for the main growing seasons (2019–2022). Yield values were spatially summarized using irrigation section boundaries for Karaba, Mwea, Tebere, Thiba, and Wamumu. The dataset is based on remotely sensed, model-derived inputs and is subject to uncertainties from NPP estimation, temporal aggregation, and boundary accuracy; it is intended for comparative spatial analysis rather than field-scale validation. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:**

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 2022. **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 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 2022. **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

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 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 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

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:** 2020-01-01 **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); - 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 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 2022. **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 2022. **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