Quality land surface temperature (Africa and Near East - Dekadal - 250m) - WaPOR v2

The quality layer gives an indication on the quality of the Land Surface Temperature (LST) input data. It shows the time between the date of the data file and the earlier remote sensing observation on which the data is based. The data is provided from January 2009 to 2023. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: day Map code:L2-QUAL-LST-D 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 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:** The LST input uses each clear observation. For each clear LST observation a separate "observation quality" is calculated which is dependent on the viewing angle. Smaller viewing angles provide a better LST observation. For each day the following procedure is followed to calculate the LST: If there is no clear LST observation, then the previous value of LST is used. The observation quality is reduced 10% each day. If there is a clear LST observation then the new LST value will be a weighted mean of the previous observation and current observation, based on their observation quality. The Quality Indicator provides the number of days since the last clear observation, regardless of its observation quality. 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 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 with the nearest valid observation. The data is provided from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: Number of Dekads (D) Scale Factor: 1 Map code: L1-QUAL-NDVI-D 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 1. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC BY 4.0 IGO **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 smoothing of the NDVI. The quality index (QI) for every pixel in each dekad depicts if and how a new value was created for that pixel. The procedure can be described as follows: 1) First all the valid observations (not flagged) are treated. If the final estimate of the smoothed NDVI is very close to the (pre-cleaned) NDVI value, the QI is set to 0 (ideal situation). Where a valid observation was present but it was adapted by the smoothing, the QI is set to 250 ("valid-smoothed" in the legend). 2) For the remaining (flagged) observations, the QI is set to the number of days in-between the surrounding valid observations (i.e. with QI=0 or 250). If the length of the data gap exceeds 240 days, it is saturated to 240. The fundamental idea is that the longer the gap, the less reliable the smoothing is. 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" This 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/en/ **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 with the nearest valid observation. The data is provided from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: Number of Dekads (D) Scale Factor : 1.0 Map code : L2-QUAL-NDVI-D 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 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:** The quality layer is produced during the smoothing of the NDVI. The quality index (QI) for every pixel in each dekad depicts if and how a new value was created for that pixel. The procedure can be described as follows: 1) First all the valid observations (not flagged) are treated. If the final estimate of the smoothed NDVI is very close to the (pre-cleaned) NDVI value, the QI is set to 0 (ideal situation). Where a valid observation was present but it was adapted by the smoothing, the QI is set to 250 ("valid-smoothed" in the legend). 2) For the remaining (flagged) observations, the QI is set to the number of days in-between the surrounding valid observations (i.e. with QI=0 or 250). If the length of the data gap exceeds 240 days, it is saturated to 240. The fundamental idea is that the longer the gap, the less reliable the smoothing is. 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" This 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/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. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage 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 2022). Pixel values indicate the percentage of good NDVI observations over the entire time period. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: Percentage (%) Scale Factor: 1.0 Map code: L1-QUAL-NDVI-LT **Citation:** FAO 2018. WaPOR Database Methodology: Level 1. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC BY 43.0 IGO **Contact points:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** 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 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 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 2009 to 2023. **Data publication:** 2020-01-01 **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:** 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