Ethiopia GIS data

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.

Data collated from Ethiopian Airports Agency, it contains data on airport facilities.

HydroBASINS represents a series of vectorized polygon layers that depict sub-basin boundaries at a global scale. The goal of this product is to provide a seamless global coverage of consistently sized and hierarchically nested sub-basins at different scales (from tens to millions of square kilometers), supported by a coding scheme that allows for analysis of catchment topology such as up- and downstream connectivity. HydroBASINS has been extracted from the gridded HydroSHEDS core layers at 15 arc-second resolution. More info: HydroBASINS

Water turbidity index analysis is a powerful, decision-oriented approach that converts raw water quality measurements into actionable insights for policymakers, planners, and environmental managers, and it is widely applied in hydrology, environmental monitoring, and GIS-based water resource management. In particular, the Normalized Difference Turbidity Index (NDTI) plays a critical role in detecting suspended sediments and assessing water clarity using remote sensing data. In this study, Sentinel-2 Level-2A (Surface Reflectance) imagery processed and atmospherically corrected by the European Space Agency through the Sen2Cor algorithm was utilized. The workflow includes cloud masking, water body extraction to ensure turbidity is only measured over water surfaces, and subsequent computation of the turbidity index. The NDTI is calculated using the formula: NDTI=(R_red-R_green)/(R_red+R_green )where R_redand R_greenrepresent the reflectance values in the red and green spectral bands, respectively. In Ethiopia, elevated turbidity levels often indicate soil erosion, surface runoff, or industrial discharge, making such analyses essential for reservoir sedimentation management, aquatic ecosystem protection, and ensuring safe drinking water supplies. However, it is important to interpret results contextually; for example, Lake Langano is naturally characterized by high turbidity due to its elevated mineral content (such as sulfur and soda) and suspended sediments, particularly during rainy seasons, which does not necessarily imply pollution or unsafe water conditions.

Agriculture cluster partner operational presence to the administrative boundary 3 (Woreda) level. The datasets cover partner operational presence by woreda Level (Admin 3) in all five crisis affected regions which are Afar, Amhara, Oromia, SNNPR and Somali.

Health Cluster Ethiopia, Clinical package provided by heath cluster partners as at February 2020. This map depict the the clinical packages provided and the service provider agency at facility level.

This file contains the location of refugee camps in Ethiopia.

**ADAM ID: **sm\_us6000pz7g Magnitude 5.5 earthquake at 10.0 depth occurred on Mar 16 2025 33km SE of Dulecha. The epicentre of the earthquake was located at latitude 9.3942 longitude 40.2111. It impacted 207052 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **sm\_us7000pdu6 Magnitude 6.0 earthquake at 10.0 depth occurred on Feb 14 2025 6km NE of Metahara. The epicentre of the earthquake was located at latitude 8.9563 longitude 39.934. It impacted 444470 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **sm\_us6000phrk Magnitude 5.8 earthquake at 10.0 depth occurred on Jan 04 2025 24km E of Dulecha. The epicentre of the earthquake was located at latitude 9.5045 longitude 40.169. It impacted 226735 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **sm\_us6000phnz Magnitude 5.5 earthquake at 10.0 depth occurred on Jan 03 2025 30km NW of Asebot. The epicentre of the earthquake was located at latitude 9.2986 longitude 40.4246. It impacted 459121 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **sm\_us6000rglb Magnitude 5.6 earthquake at 10.0 depth occurred on Oct 11 2025 7km NE of Womberta. The epicentre of the earthquake was located at latitude 13.6508 longitude 39.8628. It impacted 815772 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

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 Ethiopia **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.477 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0363 million km (7.6172%) - Unpaved roads: 0.079 million km (16.5703%) About 0.3616 million km (75.8125%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0018 million km (0.5095%) 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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Ethiopia Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2021

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

From the Demographic Health Survey ( DHS) this is the dataset for mobile ownership per cluster in the mode using the different Households - at the resolution of 5000 metres referenced ( 2016)

This dataset contains the Ethiopia Above Ground Biomass (AGDB)  Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFTs) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands). For each of the eight beams, additional data are reported with the AGBD estimates, including the associated uncertainty metrics, quality flags, model inputs, and other information about the GEDI L2A waveform for this selected algorithm setting group with a resolution of 0.05 pixels ,

This dataset is the Ethiopia cluster Anemia Levels (Mild, Severe,) from the DHS 2016- standard data in different clusters in Ethiopia at an resolution of  0.05 pixels approximately 5000 metres  referenced year -2016 -

This dataset contains the most up to date version of GLW 4 for the reference year 2020 for the following species: buffalo, cattle, sheep, goats, pigs and chicken,at the resolution of 0.05 ×0.05 ,  The fourth version of GLW, compared to the previous ones, reflects the most recently compiled and harmonized subnational livestock distribution data

Precipitation minus actual evapotranspiration (P-ET) calculated over the Awash River basin area in Ethiopia. The P-ET is the difference between WaPOR precipitation (based on CHIRPS) and the actual evapotranspiration and interception. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: mm 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: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The evapotranspiration from precipitation (ETRAIN) is the portion of the actual evapotranspiration and Interception (ETIa) that is satified from precipitation. It is computed from precipitation, actual evapotranspiration and soil moisture and calculated by spliting ETIa to rainfall and incremental ET using pixel based Soil Moisture Water Balance Model. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: mm 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: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The incremental evapotranspiration is part of the actual evapotranspiration and Interception (ETIa) that is not satified from precipitation but from supply or runoff from other cells or from groundwater. It is calculated by spliting ETIa to rainfall and incremental ET using pixel based soil moisture water balance model. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: mm 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: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

This map represents the Ethiopia Land Cover map for the development of Green House Gas Inventory. These products were derived from LandSat Imagery Data through a supervised classification method. The Ancillary data was provided by Ethiopia Mapping Agency. **Contact points:** Resource Contact: Ethiopia Mapping Agency Resource Contact: [USAID](mailto:https://www.usaid.gov/) Resource Contact: [RCMRD](mailto:https://www.rcmrd.org/en/) Resource Contact: [NASA](mailto:https://www.nasa.gov/) Resource Contact: [US EPA](mailto:https://www.epa.gov/) Metadata Contact: [Patrick Kabatha. ](mailto:pkabatha@rcmrd.org) **Data lineage:** Data quality information not available **Resource constraints:** This Ethiopia Land Cover 2003 Scheme I is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ **Online resources:** Ethiopia_LandCover_2013_Scheme_ii

This map represents the Ethiopia Land Cover map for the development of Green House Gas Inventory. These products were derived from LandSat Imagery Data through a supervised classification method. The Ancillary data was provided by Ethiopia Mapping Agency. **Contact points:** Resource Contact: Ethiopia Mapping Agency Resource Contact: [USAID](mailto:https://www.usaid.gov/) Resource Contact: [RCMRD](mailto:https://www.rcmrd.org/en/) Resource Contact: [NASA](mailto:https://www.nasa.gov/) Resource Contact: [US EPA](mailto:https://www.epa.gov/) Metadata Contact: [Patrick Kabatha. ](mailto:pkabatha@rcmrd.org) **Data lineage:** Data quality information not available **Resource constraints:** This Ethiopia Land Cover 2003 Scheme I is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ **Online resources:** Ethiopia_LandCover_2008_Scheme_i

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.AWA 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 long-term NDVI quality layer is produced as a summary of the quality for the entire time period (2009 to 2023). Pixel values indicate the percentage of good NDVI observations over the entire time period. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: 255 Unit: % Map code: L3-AWA-QUAL-NDVI-LT **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 long-term NDVI quality layer is produced as a summary of the quality for the entire time period (2009-2019). Pixel values indicate the percentage of good NDVI observations over the entire time period. While the dekadal NDVI Quality layer shows the quality of the data for a specific time of year, the long-term NDVI quality layer has been added to give users a quick overview of the areas that tend to suffer more cloud 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. The data is provided 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.AWA 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

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 seasonal basis. The layer indicates the quality (in percentage %) of the original classifier and whether a pixel was re-classified during post-processing. The data is provided from January 2009 to 2022. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: 255 Unit: % Map code : L3-QUAL-LCC-S.AWA **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:** A combination of factors influences the accuracy of the classification across a land cover classification map. All land cover maps contain a fraction of falsely classified pixels. At level 3, the land cover classification quality layer combines the result of the machine learning classifier quality output, with flags that indicate when pixels were reclassified during post-processing: - a flag value of 240 indicates that a pixel was reclassified using a fixed mask (used in isolated cases). - a flag value of 241 indicates that a pixel was reclassified during post-processing. Pixel values represent the accuracy of the classification expressed in percentage (%). A quality value close to 100 represents more certainty regarding the classification, whilst pixel values close to 0 indicate pixels for which the classification is less accurate. **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 from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: class Map code : L3-LCC-D.AWA **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

Phenology indicates the cycle or season of a crop and, in this case, is defined by the dekad (D) corresponding to the start, maximum and end of the growing season. This information can be derived from satellite-based vegetation index time series. Phenology is delivered for a maximum of two growing seasons annually. The phenology for one growing season is therefore delivered as three raster files. The first raster indicates the Start of Season (SOS), the second the Maximum of Season (MOS) and the third represents the End of Season (EOS). With a maximum of 2 growing seasons annually, a full year is described by 6 raster files. The pixel values of the phenology data components are expressed in dekad numbers. The data is provided from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: D (dekad number) Map code : L3-PHE-S.AWA 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:** The year that contains the Maximum of season determines the year a growing season is attributed to (i.e. the target year). As the crop calendar is determined from a three-year NDVI time series with the target year in the middle, dekad numbers range between 1 and 108 (3 x 36 = 108 dekads). Since it occurs in the target year, MOS has a value between 37 and 72. Start of season pixel values must be smaller than 72, whilst End of season pixel values must be larger than 36. 254 denotes "no season" (if no growing season can be distinguished), and 253 "out of season". The Start, Maximum and End of up to two seasons for a given calendar year (January - December) is derived by applying the methodology described by Van Hoolst et al. (see methodology document for references). This methodology can derive phenological information from a time series of dekadal vegetation index composites, in this case NDVI. The following data is used to calculate Phenology: - NDVI **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The layer gives an indication of the quality of the NDVI input data. It shows whether a valid observation was available for that dekad, and if not, the length of the data gap since the nearest valid observation, in dekads. The data is provided from January 2009 to 2023. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: Number of Dekads (D) Map code : L3-QUAL-NDVI-D.AWA **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 quality layer is produced during the compositing and smoothing of the NDVI. The quality index (QI) for every pixel in each dekad depicts the number of dekads since the last valid observation used for reconstruction. The fundamental idea is that the longer the gap, the less reliable the smoothing is. When a number of consecutive dekads did not have direct observations for calculating the NDVI, the quality layer pixel value of the consecutive dekadal NDVI quality layers will represent the cumulative number of dekads since a valid observation. The user can use consecutive NDVI Quality layers to calculate the length of the gap between valid observations. In the case that the pixel observation is usable and no reconstruction was needed, the QI is set to 0 (ideal situation). For the observations at the profile edges, it is assumed that the (a priori unknown) observations preceding the first valid observation and following the last valid observations are "good".\n\nThis quality layer depicts the quality for the NDVI, fAPAR, albedo and NPP, as all these data components rely on the same input, i.e. the spectral reflectance data. Furthermore, the length of the data gap is the same. 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/ **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The seasonal Net Biomass Water Productivity expresses the quantity of output (total biomass production) in relation to the total volume of water beneficially consumed (through canopy transpiration) during the growing cycle of the vegetation. Contrary to gross water productivity, net water productivity is particularly useful in monitoring how effectively vegetation (and, more importantly, crops) uses water to develop biomass (and thus yield). The data is provided from January 2010 to 2019. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: kg/m³ Scale Factor : 0.001 Map code : L3-NBWP-S.AWA 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:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The calculation of net biomass water productivity (NBWP) is as follows: NBWP = TBP/T Where TBP is seasonal Total Biomass Production in kg/ha and T is the Transpiration 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 T - 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