Remote Sensing Review Lecture 1

What is remote sensing  Remote Sensing: remote sensing is science of acquiring, processing, and interpreting images and related data that are obtained from ground-based, air-or space-borne instruments that record the interaction between matter (target) and electromagnetic radiation.  Remote Sensing: using electromagnetic spectrum to image the land, ocean, and atmosphere.

Electromagnetic Spectrum Source:

Ways of Energy Transfer Energy is the ability to do work. In the process of doing work, energy is often transferred from one body to another or from one place to another. The three basic ways in which energy can be transferred include conduction, convection, and radiation. Most people are familiar with conduction which occurs when one body (molecule or atom) transfers its kinetic energy to another by colliding with it (physical contact). This is how a pan gets heated on a stove. In convection, the kinetic energy of bodies is transferred from one place to another by physically moving the bodies. A good example is the convectional heating of air in the atmosphere in the early afternoon (less dense air rises). The transfer of energy by electromagnetic radiation is of primary interest to remote sensing because it is the only form of energy transfer that can take place in a vacuum such as the region between the Sun and the Earth. Energy is the ability to do work. In the process of doing work, energy is often transferred from one body to another or from one place to another. The three basic ways in which energy can be transferred include conduction, convection, and radiation. Most people are familiar with conduction which occurs when one body (molecule or atom) transfers its kinetic energy to another by colliding with it (physical contact). This is how a pan gets heated on a stove. In convection, the kinetic energy of bodies is transferred from one place to another by physically moving the bodies. A good example is the convectional heating of air in the atmosphere in the early afternoon (less dense air rises). The transfer of energy by electromagnetic radiation is of primary interest to remote sensing because it is the only form of energy transfer that can take place in a vacuum such as the region between the Sun and the Earth. Jensen, 2000

Wave model of EMR  Electromagnetic wave consists of an electrical field (E) which varies in magnitude in a direction perpendicular to the direction in which the radiation is traveling, and a magnetic field (M) oriented at right angles to the electrical field. Both these fields travel at the speed of light (c). Jensen, 2000

Three characteristics of electromagnetic wave  Velocity is the speed of light, c=3 x 10 8 m/s  wavelength ( ג ) is the length of one wave cycle, is measured in metres (m) or some factor of metres such as centimetres (cm)10 -2 m micrometres (µm)10 -6 m nanometres (nm)10 -9 m  Frequency (v) refers to the number of cycles of a wave passing a fixed point per unit of time. Frequency is normally measured in hertz (Hz), equivalent to one cycle per second, and various multiples of hertz. unlike c and ג changing as propagated through media of different densities, v remains constant. Hertz (Hz)1 kilohertz (KHz)10 3 megahertz (MHz)10 6 gigahertz (GHz)10 9 The amplitude of an electromagnetic wave is the height of the wave crest above the undisturbed position Travel time from the Sun to Earth is 8 minutes

Particle model of EMR  Sir Isaac Newton (1704) was the first person stated that the light had not only wavelike characteristics but also light was a stream of particles, traveling in straight lines.  Niels Bohr and Max Planck (20’s) proposed the quantum theory of EMR: Energy content: Q (Joules) = hv (h is the Planck constant x 10 –34 J s) = c/v=hc/Q or Q=hc/  The longer the wavelength, the lower its energy content, which is important in remote sensing because it suggests it is more difficult to detect longer wavelength energy Newton’s experiment in 1966

Energy of quanta (photons) Jensen, 2000

EMR details  m) Red: Orange: Yellow: Green: Blue: Violet: Bees and some other insects can see near UV. The Sun is the source of UV, but only > 0.3  m (near UV) can reach the Earth.

EMR details (2)

Source of EMR  All objects above absolute zero emit electromagnetic energy, including water, soil, rock, vegetation, and the surface of the Sun. The Sun represents the initial source of most of the electromagnetic energy remote sensing systems (except radar and sonar)  Total radiation emitted M (Wm –2 ) = σT 4 (Stefan-Boltzmann Law), where T is in degrees K and σ is the “Stefan-Boltzmann” constant, 5.67×10 –8 K –4 Wm –2 -- Energy at Sun enormous, 7.3×10 7 Wm –2, reduced to 459 Wm –2 by Earth-Sun distance  Wavelength λ max of peak radiation, in μm = 2897/T (Wien’s Displacement Law) Examples: -- Peak of Sun’s radiation λ max = 2897/6000 = 0.48 μm -- Peak of Earth’s radiation λ max = 2897/300 = 9.7 μm Jensen, 2000

Paths and Interactions If the energy being remotely sensed comes from the Sun, the energy: is radiated by atomic particles at the source (the Sun), propagates through the vacuum of space at the speed of light, interacts with the Earth's atmosphere (3A), interacts with the Earth's surface (3B), interacts with the Earth's atmosphere once again (3C), finally reaches the remote sensor where it interacts with various optical systems, filters, emulsions, or detectors (3D). Jensen, miles or 100km

Several concepts  Planck’s equation - if a blackbody transforms heat into radiant energy, then the radiation received at a sensor is given by Planck’s equation.  Spectral Emissivity

 Spectral reflectivity is the percentage of EMR reflected by the object in a each wavelength or spectral bands  Albedo is ratio of the amount of EMR reflected by a surface to the amount of incident radiation on the surface. Fresh Snow has high albedo of , old snow , forest , Earth system 0.35 EMR

Some others  Pixel  FOV and IFOV  Solid angle  Radiance  Cross track and along track  Whiskbroom and Push broom  Dwell time  Nadir and off-nadir

Remote sensing platforms

Ground and Aircraft Based  Ground repeat or continuous sampling regional or local coverage example: NEXRAD for precipitation  Aircraft repeat sampling, any sampling interval regional or local coverage examples: AVIRIS for minerals exploration LIDAR for ozone and aerosols

Space Based  Sun-synchronous polar orbits global coverage, fixed crossing, repeat sampling typical altitude 500-1,500 km example: MODIS, Landsat  Low-inclination, non-Sun-synchronous orbits tropics and mid-latitudes coverage, varying sampling typical altitude 200-2,000 km example: TRMM  Geostationary orbits regional coverage, continuous sampling over equator only, altitude 35,000 km example: GOES

Types of remote sensing  Passive: source of energy is either the Sun, Earth, or atmosphere Sun - wavelengths: µm Earth or its atmosphere - wavelengths: 3 µm - 30 cm  Active: source of energy is part of the remote sensor system Radar - wavelengths: mm-m Lidar - wavelengths: UV, Visible, and near infrared

Measurement scales constrained by physics and technology  Spatial resolution (IFOV/GSD) and coverage (FOV) Optical diffraction sets minimum aperture size  Spectral resolution (  ) and coverage ( min to max ) Narrow bands need bigger aperture, more detectors, longer integration time  Radiometric resolution (S/N, NE , NE  T ) and coverage (dynamic range) Aperture size, detector size, number of detectors, and integration time  Temporal resolution (site revisit) and coverage (global repeat) Pointing agility, period for full coverage

Basics of Bit  Computer store everything in 0 or 1 bitsMax. num (2 bits ) Bit no bits as an example

The size of a cell we call image resolution, depending on… Such as 1 m, 30 m, 1 km, or 4 km

Digital Image Data Formats  Each band of image is stored as a matrix (array) format;  To efficiently handle the multi-bands (and hyperspectral) imagery in an image processing software, BSQ (band sequential), BIL (band interleaved by line), BIP (band interleaved by pixel) are common image data format (see an example in p103 of the text book).

Procedures of image processing  Preprocessing Radiometric correction is concerned with improving the accuracy of surface spectral reflectance, emittance, or back-scattered measurements obtained using a remote sensing system. Atmospheric and topographic corrections Geometric correction is concerned with placing the above measurements or derivative products in their proper locations.  Information enhancement Point operations change the value of each individual pixel independent of all other pixels Local operations change the value of individual pixels in the context of the values of neighboring pixels. They are image reduction, image magnification, transect extraction, contrast adjustments (linear and non-linear), band ratioing, spatial filtering, fourier transformations, principle components analysis, and texture transformations  Information extraction  Post-classification  Information output Image or enhanced image itself, thematic map, vector map, spatial database, summary statistics and graphs

Remote Sensing Applications  Land: rocks, minerals, faults, land use and land cover, vegetation, DEM, snow and ice, urban growth, environmental studies, …  Ocean: ocean color, sea surface temperature, ocean winds, …  Atmosphere: temperature, precipitation, clouds, ozone, aerosols, …

Applications driving remote sensing Various application demands as driving forces for the resolution improvemen ts of remote sensing Jensen, 2000

From Terra, Aqua to NPP to JPSS NPP (2011, Oct) CrIS/ATMS VIIRS OMPS Terra (1999) Aqua (2002) AIRS, AMSU & MODIS METOP (2006) IASI/AMSU/MHS & AVHRR JPSS/ (2016, 2019) CrIS/ATMS, VIIRS, CMIS, OMPS & ERBS Coriolis (2003) WindSat NWS/NCEP GSFC/DAO ECMWF UKMO FNMOC Meteo-France BMRC-Australia Met Serv Canada NWS/NCEP GSFC/DAO ECMWF UKMO FNMOC Meteo-France BMRC-Australia Met Serv Canada NWP Forecasts NWP Forecasts NOAA Real-Time Data Delivery Timeline Ground Station Scenario NOAA Real-time User C3S IDPS Joint Center for Satellite Data Assimilation Use of Advanced Sounder Data for Improved Weather Forecasting & Numerical Weather Prediction

29 NPP Goals The NPP mission has two major goals:  To provide a continuation of the EOS record of climate- quality observations after EOS Terra, Aqua, and Aura (i.e., it will extend key Earth system data records and/or climate data records of equal or better quality and uncertainty in comparison to those of the Terra, Aqua, and Aura sensors), and  To provide risk reduction for JPSS instruments, algorithms, ground data processing, archive, and distribution prior to the launch of the first JPSS spacecraft (but note that there are now plans to use NPP data operationally)

NPP sensors

31 NPP Satellite Scheduled for Launch Nadir facing antennas –T&C –HRD –SMD VIIRS CrIS ATMS OMPS Launched: October 28,

Data Products  Level 1 products VIIRS, CrIS, ATMS and OMPS Sensor Data Records (SDRs) are full resolution sensor data that are time referenced, Earth located, and calibrated by applying the ancillary information, including radiometric and geometric calibration coefficients and geo- referencing parameters such as platform ephemeris. These data are processed to sensor units (e.g., radiances). Calibration, ephemeris, and other ancillary data necessary to convert the sensor data back to sensor raw data (counts) are included.  Level 2 (EDR/CDRs) products EDR emphasis will be on generating products with a more rapid data delivery that necessarily involves high-speed availability of ancillary data and high-performance execution of the sensor contractors' state-of-the-art science algorithms for civilian and military applications. CDR, the requirement of timeliness can be relaxed, thereby allowing for the implementation of complex algorithms using diverse ancillary data. As understanding of sensor calibration issues and radiative transfer from the Earth and Atmosphere improves, algorithms can be improved, and products can be generated via reprocessing

EDR: environmental data records

Major image processing software  ENVI/IDL:  ERDAS Imagine: geosystems.com/Products/Imagine/ geosystems.com/Products/Imagine/  PCI Geomatics:  ER Mapper:  INTEGRAPH:  IDRIS:  Ecognition: imaging.com/ecognition/pro/40.htmhttp:// imaging.com/ecognition/pro/40.htm  See5 and decision tree