Surface properties Thematic Mapper and MODIS

For observations from space of the land, the key variables are the reflectivity (in the near-IR) and the emissivity (in the thermal-IR). Figure 10.8 shows how these 'fingerprints' vary for some common materials. Careful choice of wavelength filters (for example, isolating the chlorophyll bands) enhances sensitivity to vegetation types and different stages of growth, or can address the mineralogy and physical state of the surface, with a view to studying desertification, for example, or even prospecting for oil or other valuable deposits. Problems that have to be addressed include the contaminating and obscuring effects of the atmosphere, the relatively bland spectra of most minerals, and the fact that material of different kinds is always mixed and layered in any scene. Spectroscopic data are also sensitive to parameters such as fractional cover, shadowing and dampness, and analysis often requires the application of advanced image processing and empirical models to separate the wide range of variables.

flG. 10.8. Spectral signatures of some common materials in the near-IR. Similar differences apply in the thermal-IR and microwave spectral regions: for instance, the dielectric constants of snow, water, and ice are so different that the effect on emission is easily detected. At a wavelength of 1.55 cm, for example, the emissivity of sea water is 0.44, whereas that of ice is approximately twice as great.

flG. 10.8. Spectral signatures of some common materials in the near-IR. Similar differences apply in the thermal-IR and microwave spectral regions: for instance, the dielectric constants of snow, water, and ice are so different that the effect on emission is easily detected. At a wavelength of 1.55 cm, for example, the emissivity of sea water is 0.44, whereas that of ice is approximately twice as great.

One of the earliest instruments to work on this principle was the Thematic Mapper on the Landsat series of Earth satellites. This had six spectral channels in the visible and near-IR from 0.45 to 2.35 ¡m and one in the thermal-IR 'window' at 10.4 to 12.5 ¡m. The instantaneous field of view was 30x30 m2 at the surface from the orbital altitude of 705 km, with a swath width of 185 km comprising twenty simultaneous measurements using a linear array of detectors in the focal plane aligned perpendicular to the scan direction. The large aperture telescope - 50 cm across - makes for an instrument measuring 0.9x0.9x1.8 m3, weighing 325 kg, and with a power consumption of 250 W. In analyzing the data, the experimenters normally worked with the ratio of signals from pairs of channels, one inside the selected feature, the other just outside, to reduce unwanted effects such as those due to variations in surface illumination or the intervention of thin clouds.

Remote sensing from Landsat enabled the rapid acquisition of land-surface data that otherwise would have been costly, if not impossible, to collect. Recently, the Terra and Aqua satellites (the first two components of the Earth Observing System, EOS, launched in 1999 and 2002), have carried two new instruments, ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) and MODIS (Moderate Resolution Imaging Spectrometer) for multispectral thermal imaging. MODIS has 490 detectors in 36 spectral channels, 19 covering the visible and near-IR and the rest in the thermal-IR. As with ATSR, a contin-

Table 10.1 Summary of MODIS spectral bands and uses, from Ward (1997). In the RH column, the small numbers (<1) are noise equivalent temperature change (K) and the large numbers (>10) are signal-to-noise ratios, for typical targets in each case.

Primary use

Band

Bandwidth (^m)

SNR or NEAT

Land/cloud

1

.620 -

.670

128

boundaries

2

.841 -

.876

201

3

.459 -

.479

243

4

.545 -

.565

228

5

1.230 -

1.250

74

6

1.628 -

1.652

275

7

2.105 -

2.155

110

Ocean colour

8

.405 -

.420

880

Phytoplankton

9

.438 -

.448

838

Biogeochemistry

10

.483 -

.493

802

11

.526 -

.536

754

12

.546 -

.556

750

13

.662 -

.672

910

14

.673 -

.683

1087

15

.743 -

.753

586

16

.862 -

.877

516

Atmospheric

17

.890 -

.920

167

water vapour

18

.931 -

.941

57

19

.915 -

.965

250

Surface/cloud

20

3.660 -

3.840

0.05

temperature

21

3.929 -

3.989

2.00

22

3.929 -

3.989

0.07

23

4.020 -

4.080

0.07

Atmospheric

24

4.433 -

4.498

0.25

temperature

25

4.482 -

4.549

0.25

Cirrus clouds

26

1.360 -

1.390

150

Water vapour

27

6.535 -

6.895

0.25

28

7.175 -

7.475

0.25

29

8.400 -

8.700

0.05

Ozone

30

9.580 -

9.880

0.25

Surface/cloud

31

10.780 -

11.280

0.05

temperature

32

11.770 -

12.270

0.05

Cloud top

33

13.185 -

13.485

0.25

altitude

34

13.485 -

13.785

0.25

35

13.785 -

14.085

0.25

36

14.085 -

14.385

0.35

uously rotating mirror views the Earth and internal calibration targets in one rotation, but in this case also views cold space for a zero-radiance reference.

The radiation from the scan mirror is reflected via a telescope onto a series of beam splitters that divide the photons spectrally into four focal planes, as shown in Fig. 10.9. The energy is directed through focusing optics onto the arrays of detectors, each array coated with an individual bandpass filter. The 36 bands are summarized in Table 10.1.

The calibration philosophy for MODIS differs from that of ATSR because the former observes the sea surface, where the emissivity is quite well known in advance. For land observations, it is still useful to have rigorous pre-launch calibration against traceable sources and an onboard calibration system using an aluminium v-groove plate with a calibrated emissivity and accurately monitored temperature as one point and the space view as the other. In addition to knowing absolute radiance, a ground truth method is used in which an airborne imaging system, with similar characteristics as MODIS on an aircraft, images the same scene as MODIS at the same time. Since the error in the airborne system is much less than that of the satellite instrument, the airborne measurements are taken as truth and the calibration routine can be modified to correct for the difference between calculated and measured thermal imaging surface emissivity. At the same time, the surface campaign can identify visually and otherwise what the material is in the field-of-view of both instruments and allow a database of spectral fingerprints to be built up using real data.

MODIS data is finding wide application in many areas of natural and sustainable resource management, monitoring land-cover dynamics and land use, carbonbudget modelling, coastal and ocean-water quality monitoring, aerosol budgets, production in agriculture, forestry and fisheries including crop-yield monitoring and forecasting. It also has value in the emergency-response area, for the management of emergencies such as fires and floods and following the evolution of volcanic ash plumes.

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