Multi-Sensor Improved Lake Surface Temperature Year 1: Review Lake SST
Erik Tavis Crosman(1)
(1) University of Utah Department of Atmospheric Sciences Salt Lake City Utah,
Email: erik.crosman@utah.edu
ABSTRACT
The first year’s MISST2 task (1.3) of reviewing current lake temperature algorithms and other sources of
error associated with satellite-derived lake water surface temperature (WST) has been mostly completed.
The results for the review also aided in the development of improved land masking, QC, bias-correction,
quality-control, temporal compositing, spatial hole filling and spatial smoothing techniques have been
presented in a paper recently accepted for publication in the Journal of Atmospheric and Oceanic
Technology. Next year’s MISST2 task will be to validate existing and improved NAVO-implemented lake
temperature products.
1.
Lake Temperature Algorithm Review (1980-2012) and New Techniques Suggested
Over 50 papers relating to remote sensing of WST were reviewed and are listed in the References section.
Figure 1a-c. Summarizes the observed RMSE and Bias between in situ and satellite-derived WST for a
number of WST studies. The observed biases between lake WST’s and in situ measurements range from +/2 ⁰C, while the observed RMSE between lake WST’s and in situ measurements have been observed to be
as high as 1.9 ⁰C. In general, the bias and RMSE decrease for larger lakes (Figure 1a-b), although there are
a lack of data for small lakes. For instrument platform, the mean biases (~0.45) and RMSE (~1.7) associated
with the AVHRR instrument are higher than MODIS (bias ~ 0.1; RMSE ~ 0.7) or the AATSR (bias ~-0.05;
RMSE~0.5). In addition, night biases and RMSE are reported to be significantly lower than during daytime,
supporting other findings that suggest using only nighttime satellite retrievals over lakes (Fig. 1c-d). Studies
of satellite-derived lake temperature have historically overwhelmingly used split-window algorithms. These
algorithms used either generic SST coefficients or derived coefficients via regressions with in situ buoy data.
However, a lack of in situ data (lake buoy temperature and atmospheric profiles) over many lakes results in
using split-window coefficients developed for the oceans and that are inappropriate for lakes in many cases.
Figure 1. Observed biases (a, c) and RMSE (b, d) reported in lake SST studies between 1980-2013 as
a function of lake area (a-b) and satellite platform (c-d).
Recent work by Hulley et al. 2011 and McCallum and Merchant 2012 have used radiative transfer models
using ECMWF or NCEP reanalyses to estimate atmospheric profiles over various lakes to estimate the
atmospheric variability observed in different lake locations, resulting in improved lake WST. Hulley et al.
2011 developed an Inland Water-body Surface Temperature (IWbST) split-window algorithm while McCallum
and Merchant developed a sophisticated Optimal Estimation techniques to provide lake surface wather
temperature.
These recent developments have resulted in RMSE of 0.1-0.4 K which is significantly
improved over the average RMSE of most previous lake temperature studies shown in Figure 1.
In addition to the need for improved split-window techniques and situ data for developing and tuning better
algorithms, review of lake SST literature indicated persistent problems in the quality of the cloud masks, land
masks, temporal compositing techniques and QC parameters available over lakes. Table 1. Summarizes
many of these issues and possible solutions discussed in the literature. The author also aided in the
development of improved land masking, QC, bias-correction, quality-control, temporal compositing, spatial
hole filling and spatial smoothing techniques have been presented in the following paper recently accepted
for publication in the Journal of Atmospheric and Oceanic Technology.
Techniques for Using MODIS Data to Remotely Sense Lake Water Surface Temperature. J.A. Grim, J.C.
Knievel, and E.T. Crosman, Journal of Atmospheric and Oceanic Technology (accepted)
Topic
Satellite-derived
Problems
Cloud Mask
Manual inspection to remove
retrievals used for many lakes
cloud-contaminated
SST-designed cloud masks not appropriate. Cloud
masks and QC flags specifically designed for
lakes needed.
Land
Near-shoreline land contamination of lake LWST a
common problem. Current shoreline databases
insufficient for fluctuating lakes and reservoirs.
Removal of all pixels within 1-2 pixel width of
shoreline or more complex sub-pixel estimations.
Satellite visible reflectance-based time-varying
shoreline database.
Biases of standard split-window algorithms highly
variable between lakes even within the same region.
Lack of in situ data to derive regression coefficients and
a lack of atmospheric temperature and moisture profiles
for input into transfer model calculations.
More
research
comparing
the
relative
improvement of a full radiative transfer model to
regional split-window algorithms for lakes.
Increased in situ lake temperature monitoring.
Possible use of atmospheric reanalysis data in
radiative transfer model.
Geolocation
and Orthorectification
For some satellites, geolocation errors can be 1-2 pixels
widths, resulting in problems in shoreline placement. In
addition, for lakes more than several hundred meters
above sea level orthorectification will also be needed to
correctly place satellite image.
Verify that satellite data has been geolocated or
orthorectified if possible.
Sampling
Frequency
In many mid-latitude locales clouds preclude regular
satellite observation. Lake temperature, particularly in
shallower waters, changes rapidly.
Use a multi-sensor approach to increase sampling
frequency. Quantification of mean annual cloud
cover as function of lake would help to estimate
reliability of satellite-derived data.
Air-water
Interactions
and Diurnal
Effects
Large air-water temperature differences reduce
accuracy of split-window algorithms and also rapidly
cool or warm shallow regions of lake, contributing error
to a priori lake temperature based on earlier satellite
image. Large skin vs. bulk water temperatures are
observed.
Develop satellite spatial climatological LWST
maps to use as first guess field if certain time has
passed. Develop relationships relating skin and
bulk lake temperature for biological applications.
Time
Satellite
Pass
Many studies found large diurnal variations in LWST.
Thus a single-sensor or single time approach may have
biases associated with time of day.
Incorporate satellite retrievals during spectrum of
times during both day and night to sample diurnal
and inter-diurnal variations in LWST.
Processing
and QC
Many higher-level SST products produced over lakes
are at too coarse of resolution (4 km) to be of use in
smaller lakes or near shoreline.
Reprocess higher-level SST product at native
satellite pixel resolution.
Validation
Outside of a few lakes, in situ data to rigorously validate
satellite-derived lake temperature is lacking
Expand current in situ lake monitoring
observations and number of validation (task for
next year)
Lake State
High salinity decreases the surface emissivity and low
water clarity increases the diurnal warming
Derive monthly maps of current salinity and water
clarity to incorporate into sophisticated algorithms.
Mask
Retrieval
Algorithm
of
Lake
Surface
Temperature
Suggested Solutions
Table 1: Summary of the challenges, and reported problems in the literature of lake SST retrievals
2.
Plans for Next Year
Work for the next year will involve validating existing and improved NAVO-implemented lake temperature
products. At the GHRSST meeting the appropriate lake SST products to be validated will be chosen as well
as the selection of appropriate validation of in situ data.
3.
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