Land cover classification (Africa and Near East - Annual - 250m) - WaPOR v2

This experimental land cover dataset at continental scale (Level 2) shows a broad classification aiming at identifying cultivated land and, more specifically, distinguishing between irrigated and rainfed areas. It is published on a yearly basis, while seasonal products are available upon request. The data is provided from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit : class Scale Factor : 1.0 Map code : L2-LCC-A Class - Caption 0 - n.a 20 - Shrubland 30 - Grassland 41 - Cropland, rainfed 42 - Cropland, irrigated or under water management 43 - Cropland, fallow 50 - Built-up 60 - Bare / sparse vegetation 70 - Permament snow / ice 80 - Water bodies 81 - Temporary water bodies 90 - Shrub or herbaceous cover, flooded 111 - Tree cover: closed, evergreen needle-leaved 112 - Tree cover: closed, evergreen broadleaved 114 - Tree cover: closed, deciduous broadleaved 115 - Tree cover: closed, mixed type 116 - Tree cover: closed, unknown type 121 - Tree cover: open, evergreen needle-leaved 122 - Tree cover: open, evergreen broadleaved 123 - Tree cover: open, deciduous needle-leaved 124 - Tree cover: open, deciduous broadleaved 125 - Tree cover: open, mixed type 126 - Tree cover: open, unknown type 200 - Sea water 255 - no data **Citation:** FAO 2018. WaPOR Database Methodology: Level 2. 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:** Land Cover Classification makes use of the dekadal reflectance time series and seasonal phenology information from the Crop Calendar. The Level 1 land cover products were derived from the Global Land Service of Copernicus, the Earth Observation programme of the European Commission. This product was generated from MODIS data, using the Copernicus training data and operational workflow, modified to account for differences in spatial resolution and the delivered land cover classes. In addition, irrigated areas are identified by applying a water deficit index that takes into consideration seasonal cumulated values of precipitation and actual evapotranspiration. The classification applied is based on the Land Cover Classification System (LCCS) that was developed by FAO. 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/. Until December 2019 the base input layers (NDVI, albedo, and fAPAR) for the Level 2 (100m) products were derived from the Proba-V satellite. Proba-V was decommissioned in June 2020. From January 2020 onwards the base input layers of NDVI, albedo and fAPAR for level 2 are derived from the Copernicus Sentinel-2 mission. **Class - Caption** 0 - n.a. 20 - Shrubland 30 - Grassland 20 - Shrubland 30 - Grassland 41 - Cropland, rainfed 42 - Cropland, irrigated or under water management 43 - Cropland, fallow 50 - Built-up 60 - Bare / sparse vegetation 70 - Permament snow / ice 80 - Water bodies 81 - Temporary water bodies 90 - Shrub or herbaceous cover, flooded 111 - Tree cover: closed, evergreen needle-leaved 112 - Tree cover: closed, evergreen broadleaved 114 - Tree cover: closed, deciduous broadleaved 115 - Tree cover: closed, mixed type 116 - Tree cover: closed, unknown type 121 - Tree cover: open, evergreen needle-leaved 122 - Tree cover: open, evergreen broadleaved 123 - Tree cover: open, deciduous needle-leaved 124 - Tree cover: open, deciduous broadleaved 125 - Tree cover: open, mixed type 126 - Tree cover: open, unknown type 200 - Sea water 255 - no data **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

This land cover dataset at sub-national scale (Level 3) shows a detailed classification with information on the the crops representing at least 10% of the area. It is published on a dekadal basis. The data is provided in near real time from January 2009 to 2021. **Data publication:** 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:** 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/ Classes: Class Caption 2 Tree cover (open) 4 Grassland 5 Bare 7 Artificial 8 Wheat 12 Fallow 17 Wetland 19 Water 24 Cotton 26 Onions 33 Sorghum 35 Groundnut 36 Pigeon pea 37 Chickpea 38 Okra 39 Tomatoes 42 Cowpea 43 Sesame 44 Soyabean 108 Irrigated wheat 124 Irrigated cotton 126 Irrigated onions 133 Irrigated sorghum 135 Irrigated groundnut 136 Irrigated pigeon pea 137 Irrigated chickpea 138 Irrigated okra 139 Irrigated tomatoes 142 Irrigated cowpea 143 Irrigated sesame 144 Irrigated soyabean 255 no data **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.KOG **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

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

The land use (LU) map based on Water Accounting Plus land use classification is produced based on WaPOR land cover class and protected areas from the World Database on Protected Areas. Please read the Land Cover Classification (Africa and Near East - Annual - 100m) metadata for more information on the data source. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 **Citation:** FAO 2018. WaPOR Database Methodology: Level 2. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Resource Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) Metadata Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) **Data lineage:** Read the Land Cover Classification (Africa and Near East - Annual - 100m) metadata for more information on the data source. **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:** 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/ Class Caption 1 Tree cover (closed) 7 Artificial 9 Maize 11 Vegetables 12 Fallow 13 Orchard (dense) 17 Wetland 18 Shrubland 19 Water 20 Rice 21 Mixed crops 28 Eggplant 30 Non vegetation (reclass) 32 Cassava 38 Okra 41 Bananas 42 Cowpea 43 Sesame 49 Black Gram 51 Rural area with trees 53 Coconut 63 Teak 111 Irrigated vegetables 113 Irrigated orchard (dense) 120 Irrigated rice 121 Irrigated mixed crops 141 Irrigated bananas 143 Irrigated sesame 153 Irrigated coconut 163 Irrigated teak 255 no data **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage