(determined by measuring the amount of incident light remaining as a function of
depth with an underwater light meter). K d and α are spectrally and depth averaged
values for a given water body.
Values of K d at specific wavelengths, most commonly K d,490 , have been
retrieved from ORS data by marine scientists as part of efforts to develop analytical
methods to retrieve water quality information from satellite imagery (e.g., [10, 15,
16]). Plug-in algorithms to compute K d,490 were developed for several MERIS
processors in the BEAM software system, including the Case 2 Regional and Boreal
lakes processors [17–19]. K d is not a common water quality variable in inland
waters, however, and there seems to have been little interest among freshwater
remote sensing scientists in using the algorithms for K d in inland lakes. This
situation is likely to change when improved satellite sensors (see Sect. 2) that
allow for more analytical retrieval methods become available for inland water
ORS measurements.
In summary, only a few water quality variables are amenable to direct measurement by ORS, but they include two variables, chlorophyll a and CDOM, that are
critically important for understanding lake metabolism and carbon cycling. A third
variable, SD, probably is the most widely measured lake water quality parameter
because its simplicity and low cost facilitates use by citizen monitoring programs.
SD also is important because it is related directly to water quality as perceived by
lake users and to trophic conditions and chlorophyll levels. TSS and turbidity round
out the common water quality variables amenable to measurement by ORS.
2.2.2 Potential Variables with Improved Spectral Characteristic
Sensors
As noted above, a few other variables could become important in applications of
ORS to regional-scale measurements of inland lake water quality when sensors with
improved spectral characteristics and adequate spatial resolution become available.
These include SS min , K d , and specific plant pigments indicative of various classes of
algae (e.g., see [10]), such as phycocyanin for cyanobacteria. Identification and
measurement of the abundance of submerged and emergent aquatic plants also can
be achieved using ORS [20–22], but details of this topic are beyond our scope.
2.2.3 Non-optical Variables Sometimes Correlated with Variables
Having Optical Properties
Many examples can be found in the remote sensing literature that claim the ability
to measure water quality variables that do not directly affect light reflectance or are
present in natural waters at such low concentrations that they do not affect reflectance signals measured by satellite sensors. Examples include mercury, bacteria
(e.g., Escherichia coli) [23], and total phosphorus (TP) [24, 25]. In all cases, the
reported relationships involve empirical regression equations. Despite the fact that
Remote Sensing for Regional Lake Water Quality Assessment: Capabilities and. . .
117
depth with an underwater light meter). K d and α are spectrally and depth averaged
values for a given water body.
Values of K d at specific wavelengths, most commonly K d,490 , have been
retrieved from ORS data by marine scientists as part of efforts to develop analytical
methods to retrieve water quality information from satellite imagery (e.g., [10, 15,
16]). Plug-in algorithms to compute K d,490 were developed for several MERIS
processors in the BEAM software system, including the Case 2 Regional and Boreal
lakes processors [17–19]. K d is not a common water quality variable in inland
waters, however, and there seems to have been little interest among freshwater
remote sensing scientists in using the algorithms for K d in inland lakes. This
situation is likely to change when improved satellite sensors (see Sect. 2) that
allow for more analytical retrieval methods become available for inland water
ORS measurements.
In summary, only a few water quality variables are amenable to direct measurement by ORS, but they include two variables, chlorophyll a and CDOM, that are
critically important for understanding lake metabolism and carbon cycling. A third
variable, SD, probably is the most widely measured lake water quality parameter
because its simplicity and low cost facilitates use by citizen monitoring programs.
SD also is important because it is related directly to water quality as perceived by
lake users and to trophic conditions and chlorophyll levels. TSS and turbidity round
out the common water quality variables amenable to measurement by ORS.
2.2.2 Potential Variables with Improved Spectral Characteristic
Sensors
As noted above, a few other variables could become important in applications of
ORS to regional-scale measurements of inland lake water quality when sensors with
improved spectral characteristics and adequate spatial resolution become available.
These include SS min , K d , and specific plant pigments indicative of various classes of
algae (e.g., see [10]), such as phycocyanin for cyanobacteria. Identification and
measurement of the abundance of submerged and emergent aquatic plants also can
be achieved using ORS [20–22], but details of this topic are beyond our scope.
2.2.3 Non-optical Variables Sometimes Correlated with Variables
Having Optical Properties
Many examples can be found in the remote sensing literature that claim the ability
to measure water quality variables that do not directly affect light reflectance or are
present in natural waters at such low concentrations that they do not affect reflectance signals measured by satellite sensors. Examples include mercury, bacteria
(e.g., Escherichia coli) [23], and total phosphorus (TP) [24, 25]. In all cases, the
reported relationships involve empirical regression equations. Despite the fact that
Remote Sensing for Regional Lake Water Quality Assessment: Capabilities and. . .
117