INNOVATION March-April 2015

Sensor type

Spatial resolution

Repeat cycles Potential uses

Examples: sensor (satellite)

Potential limitations

AVHRR (NOAA), MODIS (Aqua & Terra), VIIRS (NPP & JPSS), ASTER (Terra), MISR (Terra), ETM+ (Landsat), GOES (GOES)

cloud cover, forest canopy; visible requires daylight

Visible- Infrared

15 m– 8 km

0.5–16 days

albedo, normalized-difference vegetation index, snow cover

Passive Microwave


5 km– 15 km

0.5–2 days

complex terrain, deep and/or wet snow

snow cover, depth, SWE

Active Microwave

KaRIN (SWOT), TerraSar-X/TanDEM-X, RADARSAT, SIRAL (Cryosat)

0.8 m– 250 m

11–369 days

water levels, snow cover, SWE, land cover, DEMs, vegetation

complex terrain, price, low heritage

groundwater storage, glacier mass balance, snow mass variations

Gravimetric GRACE

400 km 30 days


Laser Altimetry

closed canopy, clouds, rain


70 m 91 days ice cover, snow depth, water level

TABLE 1: Brief summary of some satellite remote sensors and a few of their possible hydrologic uses and limitations.

(LEO) of about 700 km altitude; many are in polar or near-polar orbits that provide good planetary coverage. The ribbon-like path that a sun-synchronous satellite traces out on the earth as it orbits is referred to as its swath. Instruments are divided into active and passive sensors. Active sensors emit energy and monitor the return signal from the earth, synthetic-aperture radar (SAR) being an example. As their name implies, passive sensors instead passively monitor some charac- teristic of the earth; visible wavelength imagery—that is, digital photographs from space—are examples. Active sensors can be useful but tend to be more expensive. Not all sensors use electromagnetic (EM) energy—the GRACE gravity mission, widely used to monitor changes in basin-scale water balance, is a notable exception—but most do. Any given sensor generally monitors only some portion of the EM spectrum, such as microwave bands, or the visible- near-infrared (VNIR). Each such frequency range presents capabilities and limitations: for instance, microwave sensors cut through cloud cover and operate at night, but have poor spatial resolution. The energy corresponding to very specific, narrow portions lying within the general EM range monitored by a given instrument may in turn be measured as distinct bands or channels, and the resulting spectra can provide critically useful information. For instance, MODIS primarily uses the Normalized Difference Snow Index (NDSI), developed by Dr. Jeff Dozier of UC Santa Barbara, to automatically detect snow cover on Terra (a similar but not identical index is used by the MODIS instrument on Aqua): Differentiation between snow and cloud is the primary purpose of the NDSI. Specifically, the NDSI takes advantage of the fact that snow has a high level of reflectance in the visible range (band4) and a low level of reflectance in the near infrared (band6) when compared with clouds, which appear similar in the visible range.

A Visitor’s Guide to the Satellite Zoo A wide, and perhaps bewildering, variety of satellites and sen- sors exist. Additional satellites and instruments gathered large volumes of potentially still-useful data prior to reaching the end of their design lifetimes and failing, and still others are in the works. Table 1 provides a taste of some of the key technologies as might be relevant to water resource studies in British Columbia, and illustrates the wide range of hydrologic uses to which differ- ent types of satellite remote sensing might be put (see above). A Practical Application in British Columbia Operational river forecasting is a central element of hydrology, yielding powerful insights in practical contexts ranging from flood hazard response to optimal hydroelectric reservoir plan- ning. Given that snowmelt is generally a leading component of the catchment water cycle in British Columbia, we briefly present an applied example of how mountain snowpack mapping by satellite remote sensing is being used to support hydrologic forecasting. For the past five years, BC Hydro and the BC River Forecast Centre (RFC) have used near-real-time MODIS Terra binary snow observations for monitoring SCA in selected watersheds through- out British Columbia. Automated downloading, re-projection, analysis and mapping procedures were developed by Dr. Joseph Shea of ICIMOD. These procedures produce eight-day composite snow cover maps and SCA stratified by the elevation bands of BC Hydro and RFC’s respective hydrologic models, and deliver them via FTP on a daily basis for 33 operational basins. These maps have been used qualitatively on a routine opera- tional basis for several years by hydrologic forecasters at both RFC and BC Hydro to provide information on the amount of snow still available to melt within a watershed. This information has proven most useful in small watersheds where the snowcover has nearly disappeared in spring, and prior to an expected rain-on-snow


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