Senegal GIS data
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**UNOSAT code: FL20261008SEN, GDACS ID: 1104203** This application provides geospatial information on ongoing satellite based-assessment related to the floods in Senegal.
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 in near real time from January 2015 to 2023. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: 255 Unit: Number of Dekads (D) Scale Factor : 0.1 Map code : L3-QUAL-NDVI-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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata 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 layer indicates the quality (in percentage %) of the original classifier and whether a pixel was re-classified during post-processing. The data is provided in near real time from January 2015 to 2022. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: 255 Unit: % Scale Factor : 0.1 Map code : L3-QUAL-LCC-S **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 in near real time from January 2015 to 2022. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: 255 Unit: class Scale Factor : 0.1 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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata 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/ Classes: Class Caption 11 Vegetables 12 Fallow 17 Wetland 19 Water 20 Rice 21 Mixed crops 30 Non vegetation (reclass) 72 Spontaneously re-vegetated surfaces 111 Irrigated vegetables 120 Irrigated rice 121 Irrigated mixed crops 165 Irrigated market gardens 25 no data **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 in near real time from January 2015 to 2022. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: 255 Unit: D (dekad number) Scale Factor : 0.1 Map code : L3-PHE-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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata 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 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 in near real time from January 2016 to 2019. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: -9999 Unit: kg/m³ Scale Factor : 0.1 Map code : L3-NBWP-S 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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata 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
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). The data is provided in near real time from January 2016 to 2019. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: -9999 Unit: kg/m³ Scale Factor : 0.1 Map code : L3-GBWP-S 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:** 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
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 2015 to 2022. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: -9999 Unit: kg/ha Scale Factor : 0.1 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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata 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
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 2015 to 2023. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: -9999 Unit: gC/m² Scale Factor : 0.1 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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata 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
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 2015 to present. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: -9999 Unit: gC/m² Scale Factor : 0.1 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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata 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
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 2015 to 2023. **Data publication:** 2021-01-01 **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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata 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 2015 to 2023. **Data publication:** 2021-01-01 **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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** 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. See evaporation (E) by dekad 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 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 2015 to 2023. **Data publication:** 2021-01-01 **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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata 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 2015 to 2023. **Data publication:** 2021-01-01 **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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** 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. See transpiration (T) by dekad 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 2015 to 2023. **Data publication:** 2021-01-01 **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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata 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 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 2015 to 2023. **Data publication:** 2021-01-01 **Supplemental Information:** No data value: -999 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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** 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. See evaporation (E) by dekad 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 average daily ETIa in a given dekad. The data is provided in near real time from January 2015 to 2023. **Data publication:** 2021-01-01 **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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** 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. See ETIa by dekad for further information. 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. **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 2015 to 2023. **Data publication:** 2021-01-01 **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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** 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. See ETIa by dekad for further information. 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. **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 2015 to 2023. **Data publication:** 2021-01-01 **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:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** 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. See ETIa by dekad for further information. See ETIa by dekad for further information. 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. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage
The raster dataset represents top location score areas suitable for fruits storage, filtered by exclusive criteria: access to finance, distance to major roads, and access to IT (mobile broadband connection). Access to finance and roads are defined using a linear distance threshold: • Banks - 20km buffer radius. • Major roads - 2km buffer radius. Access to IT is characterized by applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs that characterize production, logistical and socio-economic factors for warehouse/storage locations: • Supply: Production. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. • Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2022-07-04 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates crop distribution within disaggregated units. 3. OpenStreetMap. 4. Altas AI - Asset Wealth Index 2020. 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Crop Storage Final Location: Fruits (Senegal - ~ 500 m)
The raster dataset represents top location score areas suitable for groundnut storage, filtered by exclusive criteria: access to finance, distance to major roads, and access to IT (mobile broadband connection). Access to finance and roads are defined using a linear distance threshold: • Banks - 20km buffer radius. • Major roads - 2km buffer radius. Access to IT is characterized by applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs that characterize production, logistical and socio-economic factors for warehouse/storage locations: • Supply: Production. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. • Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2022-07-04 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates crop distribution within disaggregated units. 3. OpenStreetMap. 4. Altas AI - Asset Wealth Index 2020. 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Crop Storage Final Location: Groundnut (Senegal - ~ 500 m)
The raster dataset represents top location score areas suitable for millet storage, filtered by exclusive criteria: access to finance, distance to major roads, and access to IT (mobile broadband connection). Access to finance and roads are defined using a linear distance threshold: • Banks - 20km buffer radius. • Major roads - 2km buffer radius. Access to IT is characterized by applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs that characterize production, logistical and socio-economic factors for warehouse/storage locations: • Supply: Production. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. • Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2022-07-04 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates crop distribution within disaggregated units. 3. OpenStreetMap. 4. Altas AI - Asset Wealth Index 2020. 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Crop Storage Final Location: Millet ( Senegal - ~ 500 m)
The raster dataset represents top location score areas suitable for oilcrops storage, filtered by exclusive criteria: access to finance, distance to major roads, and access to IT (mobile broadband connection). Access to finance and roads are defined using a linear distance threshold: • Banks - 20km buffer radius. • Major roads - 2km buffer radius. Access to IT is characterized by applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs that characterize production, logistical and socio-economic factors for warehouse/storage locations: • Supply: Production. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. • Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2022-07-04 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates crop distribution within disaggregated units. 3. OpenStreetMap. 4. Altas AI - Asset Wealth Index 2020. 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Crop Storage Final Location: Oilcrops (Senegal - ~ 500 m)
The raster dataset represents top location score areas suitable for rice storage, filtered by exclusive criteria: access to finance, distance to major roads, and access to IT (mobile broadband connection). Access to finance and roads are defined using a linear distance threshold: • Banks - 20km buffer radius. • Major roads - 2km buffer radius. Access to IT is characterized by applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs that characterize production, logistical and socio-economic factors for warehouse/storage locations: • Supply: Production. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. • Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2022-07-04 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates crop distribution within disaggregated units. 3. OpenStreetMap. 4. Altas AI - Asset Wealth Index 2020. 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Crop Storage Final Location: Rice (Senegal - ~ 500 m)
The raster dataset represents top location score areas suitable for vegetables storage, filtered by exclusive criteria: access to finance, distance to major roads, and access to IT (mobile broadband connection). Access to finance and roads are defined using a linear distance threshold: • Banks - 20km buffer radius. • Major roads - 2km buffer radius. Access to IT is characterized by applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs that characterize production, logistical and socio-economic factors for warehouse/storage locations: • Supply: Production. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. • Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2022-07-04 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates crop distribution within disaggregated units. 3. OpenStreetMap. 4. Altas AI - Asset Wealth Index 2020. 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Crop Storage Final Location: Vegetables (Senegal - ~ 500 m)
The raster dataset consists of a 500 m score grid for the crop storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize production, logistical and socio-economic factors for warehouse/storage locations: • Supply: Production. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. • Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + (“Major Cities Accessibility” * 0.1) + (”Poverty” * 0.1) + ("Human Population Density" * 0.2) + (“Regional Cities Accessibility” * 0.1) +(“Port Accessibility” * 0.1). **Data publication:** 2022-04-11 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty – Atlas AI 2020. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Crop Storage Location Score: Vegetables (Senegal - ~ 500 m)
The 500 m raster dataset represents selected top location score areas filtered by exclusive criteria: access to finance, distance to major roads, access to IT, and electricity. The layer was produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The score is achieved by processing sub-model outputs that characterize location factors: 1. Supply - Livestock distribution. 2. Demand/Infrastructure - Transportation network (accessibility) 3. Atlas AI Asset Wealth Index. It consists of an arithmetic weighted sum of normalized grids (0 to 100). The top score is selected/clipped using the exclusive criteria: Access to finance and distance to roads are defined using a linear distance threshold: • Banks - 20km buffer radius. • Major roads - 2km buffer radius. Access to IT and electricity are characterized by applying the mobile broadband coverage map and the Atlas AI Electrification map. **Data publication:** 2022-07-04 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2). 2. OpenStreetMap. 3. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. 4. Atlas AI - Asset Wealth Index 2020 and Electrification 2021. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Dairy Processing Final Location: Cattle (Senegal - ~ 500 m)
The 500 m raster dataset represents selected top location score areas filtered by exclusive criteria: access to finance, distance to major roads, access to IT, access to electricity. The layer was produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The score is achieved by processing sub-model outputs that characterize, production, logistical and socio-economic factors: 1. Supply - livestock distribution. 2. Infrastructure - Transportation network (accessibility) 3. Atlas AI Asset Wealth Index. The top score is selected/clipped using the exclusive criteria. Access to finance, distance to roads, and urban areas are defined using a linear distance threshold: • Banks - 20km buffer radius. • Major roads - 2km buffer radius. Access to IT and electricity is characterized by applying the mobile broadband coverage map and the Atlas AI Electrification map. **Data publication:** 2022-07-04 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2). 2. OpenStreetMap. 3. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. 4. Atlas AI - Asset Wealth Index 2020 and Electrification 2021. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Dairy Processing Final Location: Goat and Sheep (Senegal - ~ 500 m)
The 500 m raster dataset represents selected top location score areas filtered by exclusive criteria: access to finance, distance to major roads, access to IT, electricity, and distance to urban areas. The layer was produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The score is achieved by processing sub-model outputs that characterize, production, logistical and socio-economic factors: 1. Supply - Feed and livestock distribution. 2. Infrastructure - Transportation network (accessibility) 3. Atlas AI Asset Wealth Index. The top score is selected/clipped using the exclusive criteria. Access to finance, distance to roads, and urban areas are defined using a linear distance threshold: • Banks - approx. 20km • Major roads - approx. 2km • Urban centers - 10km buffer radius Access to IT and electricity is characterized by applying the mobile broadband coverage map and the Atlas AI Electrification map. **Data publication:** 2022-07-04 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. 5. Atlas AI - Asset Wealth Index 2020 and Electrification 2021. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Slaughterhouse Final Location (Senegal - ~ 500 m)
This harmonized set of soil parameter estimates for Senegal and The Gambia. It was derived from the Soil and Terrain Database for Senegal and The Gambia (SENSOTER ver. 1.0) and the ISRIC-WISE soil profile database, using standardized taxonomy-based pedotransfer (taxotransfer) procedures. The land surface of the study area, covering some 200 800 km2, has been characterized using 149 unique SOTER units. Each SOTER unit consists of up to four different soil components. In so far as possible, each soil component has been characterized by a regionally representative profile, selected and classified by national soil experts. Conversely, in the absence of any measured legacy data, soil components were characterized using synthetic profiles for which only the FAO-Unesco (1988) classification is known. Soil components in SENSOTER have been characterized using 90 profiles of which 34 are synthetic. The latter represent some 37 per cent of the territory. Comprehensive sets of measured attribute data are not available for most of the measured profiles (56) collated in SENSOTER. Consequently, to permit modelling, gaps in the soil analytical data have been filled using consistent taxotransfer procedures. Modal soil parameter estimates necessary to populate the taxotransfer procedure were derived from statistical analyses of soil profiles held in the ISRIC-WISE database. The current procedure only considers profiles in WISE that have FAO soil unit names identical to those mapped for SOTER-Senegal (41) and that originate from the Tropics (n= 4510). Parameter estimates are presented for 18 soil variables by soil unit for fixed depth intervals of 0.2 m to 1 m depth. Thes include: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminium saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity (ECE), bulk density, content of sand, silt and clay, content of coarse fragments (less than 2 mm), and available water capacity (-33 kPa to -1.5 MPa). These attributes have been identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil carbon stocks and change, and analyses of global environmental change. The current parameter estimates should be seen as best estimates based on the current selection of soil profiles and data clustering procedure. Taxotransfer rules have been flagged to provide an indication of the confidence in the derived data. Soil parameter estimates are presented as summary files (in MS-Access format) which can be linked to the SENSOTER map using GIS, through the unique SOTER-unit code (NEWSUID). The derived (secondary) soil data for Senegal and The Gambia are considered appropriate for exploratory studies at national scale (1:1 million); these should consider the full map unit composition.
The Soil and Terrain database for Senegal and The Gambia primary data (version 1.0), at scale 1:1 million (SOTER_Senegal_Gambia), was compiled of enhanced soil information within the framework of the FAO's program Land Degradation Assessment in Drylands (LADA). Primary soil and terrain data for Senegal were obtained through the Institut National de Pédologie Dakar; the digital soil map from the Centre de Suivi Ecologique. Adjustments made in the SOTER units were based on the SRTM-DEM derived data interpretations. SOTER forms a part of the ongoing activities of ISRIC, FAO and UNEP to update the world's baseline information on natural resources.The project involved collaboration with national soil institutes from the countries in the region as well as individual experts.