![Oxford/MAP/LST_Day_5km_Monthly](https://developers.google.com/earth-engine/datasets/images/Oxford/Oxford_MAP_LST_Day_5km_Monthly_sample.png?authuser=6)
- Dataset Availability
- 2001-03-01T00:00:00Z–2015-06-01T00:00:00Z
- Dataset Provider
- Oxford Malaria Atlas Project
- Earth Engine Snippet
-
ee.ImageCollection("Oxford/MAP/LST_Day_5km_Monthly")
- Tags
Description
The underlying dataset for this daytime product is MODIS land surface temperature data (MOD11A2), which was gap-filled using the approach outlined in Weiss et al. (2014) to eliminate missing data caused by factors such as cloud cover. Gap-free outputs were then aggregated temporally and spatially to produce the monthly ≈5km product.
This dataset was produced by Harry Gibson and Daniel Weiss of the Malaria Atlas Project (Big Data Institute, University of Oxford, United Kingdom, https://malariaatlas.org/).
Bands
Resolution
5000 meters
Bands
Name | Units | Min | Max | Description |
---|---|---|---|---|
Mean |
°C | -74.03* | 63.87* | The mean value of daytime land surface temperature for each aggregated pixel. |
FilledProportion |
% | 0* | 100* | A quality control band that indicates the percentage of each resulting pixel that was comprised of raw data (as opposed to gap-filled estimates). |
Terms of Use
Terms of Use
Citations
Weiss, D.J., P.M. Atkinson, S. Bhatt, B. Mappin, S.I. Hay & P.W. Gething (2014) An effective approach for gap-filling continental scale remotely sensed time-series. ISPRS Journal of Photogrammetry and Remote Sensing, 98, 106-118.
Explore with Earth Engine
Code Editor (JavaScript)
var dataset = ee.ImageCollection('Oxford/MAP/LST_Day_5km_Monthly') .filter(ee.Filter.date('2015-01-01', '2015-12-31')); var daytimeLandSurfaceTemp = dataset.select('Mean'); var visParams = { min: -20.0, max: 50.0, palette: [ '800080', '0000ab', '0000ff', '008000', '19ff2b', 'a8f7ff', 'ffff00', 'd6d600', 'ffa500', 'ff6b01', 'ff0000' ], }; Map.setCenter(-88.6, 26.4, 1); Map.addLayer( daytimeLandSurfaceTemp, visParams, 'Daytime Land Surface Temperature');