Ethiopia imagery and land cover data

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 Land Cover 2013 Scheme I

**UNOSAT code FL20230327SOM** This map illustrates satellite-detected surface waters in Shabelle Zone, Somali Region of Ethiopia and Beledweyne District, Hiraan Region of Somalia as observed from a Sentinel-2 image acquired on 29 April 2023 at 07:06 UTC. Within the analyzed area of about 5,300 km², about 180 km² of land appear to be flooded. Water extent appears to have increased by about 60 km² since 19 April 2023. Based on Worldpop population data and the detected surface waters in the analyzed area, 7,500 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

**UNOSAT code FL20230327SOM** This map illustrates satellite-detected surface waters in Shabelle Zone, Somali Region, Ethiopia as observed from a Sentinel-2 image acquired on 2 April 2023 at 07:38 UTC. Within the analyzed area of about 22,000 km², about 180 km² of land appear to be flooded. Water extent appears to have decreased by about 230 km² since 28 March 2023. Based on Worldpop population data and the detected surface waters in the analyzed area, 19,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

**UNOSAT code FL20230327SOM** This map illustrates satellite detected surface waters in Shabelle District, Shabelle Region, Ethiopia as observed from a Sentinel-2 image acquired on 28 March 2023 at 07:49 UTC. Within the analyzed area of about 44,000 km2, about 485 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters in the analyzed area, 40,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

**UNOSAT code FL20230327SOM** This map illustrates satellite detected surface waters in Ferfer Woreda, Shabelle District, Shabelle Region, Ethiopia as observed from a SPOT-6 image acquired on 30 March 2023 at 07:01 UTC. Within the analyzed area of about 260km2, about 4 km2 of lands appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

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

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

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:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: % Map code : L3-QUAL-LCC-S.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 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.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