Understanding the concept of EO time series
- the case of Urban Heat Islands
What are Urban Heat Islands?
- a difference in temperature of urban and suburban areas compared to their outlying rural surroundings
- E.g. the annual mean air temperature of a city with one million or more people can be 1 to 3°C warmer than its surroundings,
- Usually the difference decreases as city size decreases,
- We can differ surface and atmospheric urban heat islands.
Surface vs atmospheric UHI
|
Feature |
Surface UHI |
Atmospheric UHI |
|
Temporal Development |
Present at all times of the day and night |
May be small or non-existent during the day |
|
Most intense during the day and in the summer |
Most intense at night or predawn and in the winter |
|
|
Peak Intensity (Most intense UHI conditions) |
More spatial and temporal variation: |
Less variation: |
|
Typical Identification Method |
Indirect measurement: |
Direct measurement: |
|
Typical Depiction |
Thermal image |
Isotherm map Temperature graph |
Surface UHI
- On a sunny day – exposed urban surfaces heated 27 to 50°C more than the air while shaded or moist surfaces (more often in rural areas) remain close to air temperatures
- The magnitude of temperature differences of developed and rural areas varies with seasons – UHI larges during summer (especially when the sky is clear and winds are calm)!
Summer UHI in Zurich
See video: https://youtu.be/HNqm0ae6DY4
Atmospheric UHI
- Canopy layer urban heat islands exist in the layer of air where people live, from the ground to below the tops of trees and roofs.
- Boundary layer urban heat islands start from the rooftop and treetop level and extend up to the point where urban landscapes no longer influence the atmosphere. This region typically extends no more than 1.5 km from the surface
Surface vs atmospheric temperatures
Surface temperatures have an indirect, but significant, influence on air temperatures, especially in the canopy layer, which is closest to the surface. For example, parks and vegetated areas, which typically have cooler surface temperatures, contribute to cooler air temperatures. Dense, built-up areas, on the other hand, typically lead to warmer air temperatures. Because air mixes within the atmosphere, though, the relationship between surface and air temperatures is not constant, and air temperatures typically vary less than surface temperatures across an area
Why UHI are important?
Most impacts (if not all) of the UHI are negative:
- Increased energy consumption
- Elevated emissions of air pollutants and greenhouse gases
- Compromised human health and comfort
- Impaired water quality
Details on the UHI and the mitigating strategies presented here are defined and shown in:
Akbari, H., Bell, R., Brazel, T., Cole, D., Estes, M., Heisler, G., ... & Oke, T. (2008). Reducing Urban Heat Islands: Compendium of Strategies Urban Heat Island Basics. Environmental Protection Agency: Washington, DC, USA, 1-22.
Remote sensing
- the acquisition of information about an object or phenomenon without making physical contact
- used in numerous fields, including geography, land surveying and most Earth science disciplines (for example, hydrology, ecology, meteorology, oceanography, glaciology, geology); it also has military, intelligence, commercial, economic, planning, and humanitarian applications.
- repeating satellite orbits enable continuous monitoring of the object, phenomenon or area in general, i.e. gathering EO time series
EO time series
- The same phenomenon given in different time, example in gGIF image
- For the analyis of the time series we need images from different epoch – often some preprocessing is needed due to:
- Different image resolution
- Different sensor
- Different angle
- Different coordinate system
- Different color/signal interpretation
EO time series
- Efficiently provides information on
- Land cover change
- Vertical and horizontal land movements
- Sea level change
- Ice sheet balance change
- Temperature change over the season or different time
- Specific events such as fires, floods, landslides, earthquakes, etc.
- Enable global and local analysis (such as detecting and monitoring Urban Heat Islands, UHIs)
EO time series for UHI monitoring
|
 |
EO time series for UHI monitoring
- Different sensing instruments
- ASTER
- Advanced Spaceborne Thermal Emission and Reflection Radiometer
- Imaging device on the TERRA satellite
- ETM+
- Enhanced Thematic Mapper
- Sensor platform on the LANDSAT satellite
- ESA Copernicus Sentinel-3A (focus of this presentation)
- Carried out by EUMETSAT
- Toolbox available
- ASTER
ASTER and LANDSAT
- Thermal infrared band
- ASTER
- Bands 10-14, 8.125-11.65 µm
- Spatial resolution 90x90 m
- LANDSAT
- Band 6, 10.4-12.5 µm
- Spatial resolution 60x60 m
- ASTER
Following slides on ASTER and LANDSAT data and procedures are designed based on the Christopher S Martin’s (csmartin@buffalo.edu) presentation: Remote Sensing of the Urban Heat Island Effect
ASTER and LANDSAT
- Procedure (1)
- Calibration
- L = 0.0370588 ΣDN + 3.2
- Convert to BT
- BT = K2 / {ln [(k1 / L) + 1]}
- Calculate LST (Land Surface Temperature)
- Ts = BT / {1 + [(λ * BT / ρ) * ln ε]}
- Calibration
ASTER and LANDSAT
- Procedure (2)
- Separation of sources
- Computed LST contains
- Radiation
- Anthropogenic sources
- Different methods
- E.g. Kato and Yamaguchi (2005)
- Rn = G + LE + H vs Rn + A = G + LE + H
- Estimate the values of each variable
- G varies with material
- H – total heat flux (final result)
- Has = H – Hn
- Hn – heat from „natural causes”, i.e. radiant heat flux
- E.g. Kato and Yamaguchi (2005)
- Computed LST contains
- Separation of sources
Vegetation – UHI relationship
- More the vegetation less the UHI effect
- Methods on quantifying this
- NDVI (Normalized Difference Vegetation Index)
- Vegetation fraction
- LAI (Leaf Area Index)
- Other methods
- Handheld temperature sensors
- Aerial temperature measurements
Vegetation – UHI relationship
|
NDVI Numerical indicator showing live green vegetation NDVI = (NIR – RED) / (NIR + RED) |
|
Vegetation fraction Derived from spectral mixture model Uses LSMA to determine vegetation – at sub-pixel level Sometimes more accurate than NDVI |
|
LAI Can be computed from NDVI Can be derived from field measurements (collection of leaf litterfall or destructive harvesting of leaves within a vertical column passing upward through the entire tree canopy) |
UHI detection from Sentinel-3A with the case study
- Methods and details presented here are taken from RUS (Research and User Support for Sentinel Core products), the service used to promote the uptake of Copernicus data and to support the scaling up of R&D activities with Copernicus data
- Video webinar on UHI detection available at: https://youtu.be/5UHghHjP6ZQ
- Details on webinar available at: https://rus-training.eu/training/urban-heat-island-with-sentinel-3
- Some of the figures are screenshotted from the webinar
- Instead of the following slides, you are advised to take a look at the webinar
Sentinel-3A
- The SENTINEL-3 mission is jointly operated by ESA and EUMETSAT to deliver operational ocean and land observation services.
- The spacecraft carries four main instruments:
- OLCI: Ocean and Land Colour Instrument
- SLSTR: Sea and Land Surface Temperature Instrument
- SRAL: SAR Radar Altimeter
- MWR: Microwave Radiometer.
- These are complemented by three instruments for Precise Orbit Determination (POD):
- DORIS: a Doppler Orbit Radio positioning system
- GNSS: a GPS receiver, providing precise orbit determination and tracking multiple satellites simultaneously
- LRR: to accurately locate the satellite in orbit using a Laser Retro-Reflector system.
Sentinel-3A
If video does not load automatically, please go to https://youtu.be/zL7Ge6BWd98
Sentinel-3
Sentinel products
|
Name of |
Main Content |
Availability |
Latency |
Estimated Size per orbit |
Product Dissemination Unit |
|
SL_0_SLT |
Full resolution ISPs |
Internal |
NRT |
7.2 GB |
N/A |
|
SL_1_RBT |
Brightness temperatures and radiances |
User |
NRT/NTC |
44.5 GB |
Frame 3 mn |
|
SL_2_WCT |
Sea surface temperature |
Internal |
NRT/NTC |
4.1 GB |
N/A |
|
SL_2_WST |
Level-2P sea surface temperature (GHRSST-like) |
User |
NRT/NTC |
2.2 GB |
NRT = Frame 3 mn |
|
SL_2_LST__ |
Land surface temperature parameters |
User |
NRT/NTC |
5 GB |
NRT = Frame 3 mn |
|
SL_2_FRP |
Fire radiative power |
User |
NRT/NTC |
4.4 GB (TBC) |
NRT = Frame 3 mn (TBC) |
|
SL_2_AOD |
Aerosol Optical Depth |
User |
NRT |
0.079 GB (TBC) |
NRT + Frame 3 min (TBC) |
Sentinel-3A SLSTR
- SLSTR – Sea and Land Surface Temperature Radiometer
- The main characteristics of the SLSTR are:
- swath width: dual view scan, 1 420 km (nadir) / 750 km (backwards)
- spatial sampling: 500 m (VIS, SWIR), 1 km (MWIR, TIR)
- spectrum: nine bands [0.55-12] µm
- noise equivalent dT: 50 mK (TIR) at 270 K
- launch mass: 90 kg
- size: 2.116 m3
- design lifetime: 7.5 years.
Sentinel-3A SLSTR
The radiometric bands of SLSTR are presented in the table below:
|
Band |
Central Wavelength (nm) |
Bandwidth (nm) |
Function |
Comments |
Resolution (metres) |
|
|
S1 |
554.27 |
19.26 |
Cloud screening, vegetation monitoring, aerosol |
VNIR |
Solar Reflectance Bands |
500 |
|
S2 |
659.47 |
19.25 |
NDVI, vegetation monitoring, aerosol |
|||
|
S3 |
868.00 |
20.60 |
NDVI, cloud flagging,Pixel co-registration |
|||
|
S4 |
1374.80 |
20.80 |
Cirrus detection over land |
SWIR |
||
|
S5 |
1613.40 |
60.68 |
loud clearing, ice, snow,vegetation monitoring |
|||
|
S6 |
2255.70 |
50.15 |
Vegetation state and cloud clearing |
|||
|
S7 |
3742.00 |
398.00 |
SST, LST, Active fire |
Thermal IR Ambient bands (200 K -320 K) |
1000 |
|
|
S8 |
10854.00 |
776.00 |
SST, LST, Active fire |
|||
|
S9 |
12022.50 |
905.00 |
SST, LST |
|||
|
F1 |
3742.00 |
398.00 |
Active fire |
Thermal IR fire emission bands |
||
|
F2 |
10854.00 |
776.00 |
Active fire |
|||
Sentinel-3 SLSTR
Product types
- Describes the granularity of SENTINEL-3 SLSTR products distributed to users: Level-1B(radiance and brightness temperature), Level-2 WST(SST following the GHRSST specifications) and Level-2 LST (Land Surface Temperature). The Level-2 WCT (Sea Surface Temperature (SST) for single and dual view) is not distributed to the users.
In addition, two new products are under specification. They should be available shortly after the end of the commissioning phase: the Fire Radiative Power (FRP) and the Global Aerosol Optical Depth (AOD).
Sentinel-3 SLSTR - hands on
- Data access: Copernicus Open Access Hub (registration required and free)
Sentinel-3 SLSTR - hands on
- Data acquisition
Sentinel-3 SLSTR - hands on
- Data acquisition (SL_2_LST – level 2 data)
Sentinel-3 SLSTR - hands on
- Data acquisition – data download – each scene up to 2GB
Sentinel-3 SLSTR - hands on
- Data processing – SNAP (Sentinel Application Platform) software
- Published under GPL-3 licence
- Common architecture for all Toolboxes
- Very fast image display and navigation even of giga-pixel images
- Graph Processing Framework (GPF): for creating user-defined processing chains
- Advanced layer management allows adding and manipulation of new overlays such as images of other bands, images from WMS servers or ESRI shapefiles
- Rich region-of-interest definitions for statistics and various plots
- Easy bitmask definition and overlay
- Flexible band arithmetic using arbitrary mathematical expressions
- Accurate reprojection and ortho-rectification to common map projections,
- Geo-coding and rectification using ground control points
- Automatic SRTM DEM download and tile selection
- Product library for scanning and cataloguing large archives efficiently
- Multithreading and Multi-core processor support
- Integrated WorldWind visualisation
Sentinel-3 SLSTR - hands on
Data preview (select two downloaded images of the same scene)
Sentinel-3 SLSTR - hands on
Making a subset for both images of the same scene (Raster… Subset… Band subset – NDVI, LST, x_in, y_in, biome, lat, lon)
Sentinel-3 SLSTR - hands on
- Adapting the coordinate projection (Raster… Geometric Operation… Reprojection – select desired)
- Changing color ramp
Sentinel-3 SLSTR - hands on
- Changing color ramp – sensor detection of temperature in one image (°K) – day scene
Sentinel-3 SLSTR - hands on
- Changing color ramp – sensor detection of temperature in one image (°K) – night scene
Sentinel-3 SLSTR - hands on
- Changing color ramp – temperature day vs night comparison
Sentinel-3 SLSTR - hands on
- Overlapping the night temperature map with land cover map (Add Land Cover Band – automatic process in SNAP)
Sentinel-3 SLSTR - hands on
- Overlapping the night temperature map with land cover map (Add Land Cover Band – automatic process in SNAP)
Sentinel-3 SLSTR - hands on
- Within overlapping process select manually some of the pixels with known rural position (e.g. import .shp with points)
Sentinel-3 SLSTR - hands on
- Results
Sentinel-3 SLSTR - hands on
- Results presented in QGIS for London, 2018 (see RUS webinar for a whole procedure) – if processed for different dates – TIME SERIES ANALYSIS
Take-aways
- Time series serve different purposes one of which is monitoring of the UHIs
- UHI detection and monitoring is routine job that can be performed for local or global purposes
- Sentinel 3 satellite mission provides data freely available for such purposes
- On top of that EU and ESA are trying hard to get people use Sentinel data – beside the data, they provide software and trainings, sometimes even grants to pay off the Copernicus investments
Thanks!
Feel free to contact EO4GEO for more!