Sri Lanka imagery and land cover data

satellite imagery by Vantor, acquired 2025-12-01 to 2025-12-01, 30.7 cm per pixel, published on OpenAerialMap.

satellite imagery by Vantor, acquired 2025-11-30 to 2025-11-30, 30.7 cm per pixel, published on OpenAerialMap.

satellite imagery by Vantor, acquired 2025-11-30 to 2025-11-30, 30.7 cm per pixel, published on OpenAerialMap.

satellite imagery by Vantor, acquired 2025-12-04 to 2025-12-04, 30.5 cm per pixel, published on OpenAerialMap.

satellite imagery by Vantor, acquired 2025-12-04 to 2025-12-04, 30.5 cm per pixel, published on OpenAerialMap.

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

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:** 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