Remote Sensing Basics - Shorter version
Objectives
Establish baseline vocabulary/concepts:
Satellite vs sensor Active vs passive sensor
Geophysical variable/product
Polar vs geostationary orbit
Spatial, temporal resolution, swath width
Wavelength band/channel
Data levels
Temporal composites vs cloud cover
Near-Real Time vs. Science Quality
Advantages of satellite remote sensing
Satellite remote sensing provides information where surface-based measurements are not available and augments existing measurements. Because of its ability to collect data over large spatial areas, satellite remote sensing provides global/near-global coverage with consistent observations.
Satellite vs Sensor
A satellite is a space-borne platform holding one or more sensors (instruments) making measurements. Some satellites are single-mission, carrying only one sensor: e.g. the SeaWiFS sensor on the GeoEye/OrbImage satellite. Other satellites have multiple sensors on them: e.g. MODIS is one of 6 sensors on the Aqua satellite. There is also a MODIS sensor on the Terra satellite.
What is measured by the sensors?
Sensors can NOT directly measure populations of most fish, whales, turtles, monk seals, etc.
Satellite data can provide information about oceanic parameters that influence marine resources: SST, Currents, Wind, Ocean color, Salinity
Satellite sensors measure electromagnetic radiation (EMR) that is emitted or reflected by the ocean (and land).
Sensors target specific sets of wavelengths depending on their application.
After (many) corrections and calibrations, algorithms are used to calculate geophysical products (SST, chla concentration, wind speed, …).
Several regions of the electromagnetic spectrum (EMS) are useful for satellite remote sensing for different applications. Most sensors focus on the part of the EMS comprised between portions of the UV to Microwaves.
Sensor types (active vs passive)
There are two main types of sensors:
Passive sensors measure electromagnetic radiation that has a natural origin: - Solar radiation reflected or scattered from the Earth’s surface - Thermal radiation emitted from the Earth’s surface
Active sensors measure electromagnetic radiation generated by the satellite that is sent down to the Earth’s surface and reflected or scattered back to the satellite
The influence of the atmosphere
Most solar energy comes to the earth as short wavelength electromagnetic radiation (UV, visible light) and is re-radiated (emitted) back to space as long wavelength electromagnetic radiation (infrared, microwaves)
All radiation is influenced by the atmosphere in various ways: The sun's radiation is scattered, reflected or absorbed by particles in the atmosphere as is the radiation reflected by the Earth’s surface.
Satellites look at the earth surface through the atmosphere.
Ray 1 - the useful signal
Ray 2 - the radiation leaving the sea that is absorbed by the atmosphere
Ray 3 - the radiation that is scattered by the atmosphere out of the sensor field of view
Ray 4 - the energy emitted by the constituents of the atmosphere
Ray 5 - the energy reflected by scattering into the field of vision of the sensor
Ray 6 - the energy that left the sea surface but from outside the field of view.
Because of the influence of the atmosphere on the signal received by the sensors, atmospheric correction of the satellite data is necessary to derive accurate satellite data products.
The ocean area within the sensor’s field of view emits rays 1+2+3
Rays 4, 5, and 6 reach the sensor from outside of the sea surface in the field of view, and therefore constitute extraneous "noise" on top of the signal.
The sensor receives rays 1+4+5+6
The complete atmospheric correction should result in the sum of rays 1+2+3.
How is EMR measured by sensors?
Satellite sensors measure the intensity of EMR at specific wavelengths using a telescope to focus the EMR onto a series of light detectors (radiometers). The sensor views the earth in a swath of individual scan lines as the satellite is moving
The configuration of the light detectors and telescope varies between sensor types, but in the end, all sensors produce a set of equally spaced boxes/pixels (i.e., a 2-D array of measured values) where each individual box/pixel contains the value of the intensity of EMR (Watts m-2) for a specific wavelength from each location on earth covered by the satellite.
The resulting 2-D array can be displayed as an image or it can be analyzed as a 2-D array of numbers.
Multispectral radiometers measure EMR intensity at multiple discrete wavelength regions. Each wavelength region is defined by a central wavelength and a small range of wavelengths around it (a bandwidth).
The intensity of EMR at each wavelength region is stored separately in a stack of digital images. Each separate image is called a wavelength band or a wavelength channel.
Each band or channel can be referred to by its central wavelength value or by sequential numbering (1, 2, 3, …) of each band from shortest to longest wavelength
Higher order products
Several bands can be combined to produce higher order products. Below is an example of making a true color image from the addition of separate red, green and blue bands.
Types of Applications of Satellite Data
Sensor characteristics (passive vs active, range of the EMS target by the sensor) are selected depending on the application the instrument is designed for.
Resolution
Spatial resolution is defined as the pixel size of an image representing the size of the surface area being measured on the ground and is determined by the sensors' instantaneous field of view (IFOV).
Temporal resolution is defined by the amount of time that passes between imagery collection periods.
Swath width of the satellite refers to the width of the area observed by the satellite. Satellites with larger swath widths will take less time to acquire global spatial coverage.
Satellite orbits
The two major types of satellite orbits are:
Polar orbiting: a single polar orbiting satellite can view the entire earth once a day
Geostationary: a single geostationary satellite can view a limited region of the earth, but can do so continuously throughout the day
Polar Orbiting
Credit: EUMETSAT
Satellites in a polar orbit travel from North to South, passing roughly over Earth's poles.
Altitude: 700 -800km
~ 14 orbits a day
Depending on the width of the swath, will cover almost the whole Earth in a day
Provide global coverage
High spatial resolution (< 1 km)
Low temporal resolution (≥ 1 day): only provide repeat observations of the same spot on Earth once a day.
Geostationary orbit
Credit: Omega Open Course
Credit: http://rammb-slider.cira.colostate.edu
Altitude: 35,800km
Always sees same part of the Earth (no global coverage)
Lower spatial resolution (2-4 km)
High temporal resolution (every minute!)
Poor coverage of the poles
Because geostationary satellites only observe a portion of the Earth, at least 5 geostationary satellites are necessary to achieve global coverage.
Levels of Data
Satellite data is processed at different levels:
Level 0: Raw data received from satellite, in standard binary form
Level 1: Unprocessed data in sensor’s geographic coordinates, containing calibration information
Level 2: Derived geophysical variables atmospherically corrected and geolocated, but presented in sensor’s geographic coordinates (granules). Also sometimes referred to as “along-track” data.
Level 3: Derived geophysical variables mapped on uniform space-time grid scales: gridded products. Spatial and temporal composites.
Level 4: Model output or results from analyses of lower-level data (e.g., variables derived from multiple measurements, like primary productivity, derived from chl-a, SST and PAR), or interpolation to provide cloud-free product
Levels of Data: L0 -> L3 examples
Level-2 vs. Level-3 data
The sensor views the earth in a swath of individual scan lines as the satellite is moving
Temporal composites
Individual segments of scan lines or granules can be combined to generate a composite (or average) of all data collected during a certain time period. Typically, hourly, daily, 3-day, weekly and monthly composites can be generated depending on the frequency of data collection.
Observations in the visible light and the infra-red are sensitive to clouds, resulting in missing data in cloudy regions. Composites allow to gather more data over a certain area which can help with getting cloud-free data.
Levels of Data: L3 -> L4 examples
Near Real Time vs Science Quality
For some purposes, data is needed in near-real time (NRT), i.e. as quickly after being measured as possible. For many datasets, it is possible to obtain yesterday’s data (or from a few hours ago).
However, the processing of NRT data is a little on the “quick & dirty” side, to ensure fast through-put. To look at trends over time, or for data used in publications, Science Quality data, which has been processed more carefully for more accuracy, might be more adequate.
Different agencies have different definitions of NRT vs. Science Quality (or Climate Quality) data. Some science quality data are available several days after NRT, others perhaps only when the entire mission data is reprocessed.
References
Bruce Monger. Remote Sensing Training Program. Cornell University.
Robinson, Discovering the Ocean from Space. Springer. 2010
Chassot et al. 2011. Satellite remote sensing for an ecosystem approach to fisheries management. ICES Journal of Marine Science, Volume 68, Issue 4
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