GEO-CAPE Ocean Color Study:
The influence of cloud coverage space and time scales on the retrieval of ocean
surface reflectance and chlorophyll fluorescence measurements.
John R. Moisan
NASA, Goddard Space Flight Center
Wallops Flight Facility, Wallops Island, VA 23337
Ph: 757-824-1312; Cell: 443-735-0086
e-mail: John.R.Moisan@NASA.gov
Previous Cloud Analysis Results
In the previous year, hourly cloud observations for 2007 of the U.S. east and west
coasts were obtained from the GOES Cloud Sounder Effective Cloud Amount (ECA)
observations from the University of Wisconsin’s Cooperative Institute for Meteorological
Satellite Studies (Li et al, 2009). Effective Cloud Amount is the product of cloud fraction
and emittance. These observations are at a nominal spatial resolution of 12 km. A
comparable cloud product is available using the MODIS instrument at spatial scales of 5
km (Baum and Platnick, 2006). The full time series of 8760 hourly observations was
analyzed to derive the probabilities of acquiring ocean color observations from areas
where the ECA was 0.
The first component of the analysis was to use the daily local noon observations
in order to essentially develop a “baseline” probability (Figure 1) of acquiring ocean
color imagery for a traditional Low Earth Orbiting (LEO) ocean color satellite such as
MODIS/Aqua or SeaWiFS. The next set of analysis focused on understanding how more
frequent opportunities of acquiring an image over the period of a day could improve the
level of probability of getting at least one pixel imaged per day. The results (Figure 2)
show that there is a high probability
over all coastal ocean regions for
getting at least one image per day for
a specific pixel/region. These results
are directly comparable with the
previous LEO results from Figure 1.
By having a satellite in geostationary
orbit the probability of acquiring a
cloud free image once per day is
much higher than of that from a
satellite in LEO.
Figure 1. The probability of obtaining a clear pixel from a local noon GOES observation.
Areas of high likelihood of obtaining clear pixels (such as the Sea of Cortez) have values
close to 1, while chronically cloudy areas such as off the North Pacific coast have low
probability values. The overlap in the image is because there are 2 separate satellite views
employed in the analysis.
Figure 2. The probability of
acquiring at least 1 clear pixel during
the daytime. The high probabilities
are a derived from the fact that there
are multiple times during the daytime
that could be imaged and that cloud
formations (fronts and storms
systems) tend to move through a
region in less than half a day.
Further analysis focused on
estimating the probability of obtaining multiple observations over the period of one day
(6AM to 6PM) period. These probability maps essentially morph from the high
probability image of acquiring at least 1 clear pixel per day to the lower probability
image of obtaining a local noon cloud free pixel. For ocean color applications that
require only one image per day a geostationary imager will provide much more
observations that from that of a LEO satellite. But, for those applications that require
more than one cloud free image per day, such as studies to investigate the diurnal
variability of phytoplankton fluorescence, the probabilities of obtaining the necessary
observations will vary depending on the level of daily observations that are required to
resolve the signals.
Proposed FY10 Work Plan
In the coming year, the proposed work will focus on improving our understanding
of the spatial variability of the clouds in coastal areas. The GOES cloud product is at a
nominal 12 km resolution. There are a number of satellite cloud products that have
higher spatial resolutions (MODIS: 1km; Landsat: 30m), but with much reduced temporal
resolution. The work in the coming year will investigate the spatial scales of clouds
using these finer spatial resolution data sets and attempt to modify the GOES cloud
product to improve the present cloud masking by optimizing it with present ocean color
cloud masks. The goal here is to improve our understanding of the spatial scales of cloud
signals in the scales ranging from 30 m to 12 km. In addition to this, work will progress
towards completion of an instantaneous hourly GOES PAR product that will provide the
necessary time periodicity for supporting GEO-CAPE studies aimed at understanding the
likely diurnal signals from phytoplankton fluorescence.
Task 1: Comparison of GOES ECA data with MODIS cloud mask data:
MODIS produces a 1 km Level-2 cloud mask product (MOD35). The objectives of this
task will be to compare GOES ECA data to its time-adjacent MODIS cloud mask in order
to establish what level of ECA’s compare best with the MODIS cloud mask. The GOES
cloud data set will be expanded to include 2000-2002. This will yield about 1095
individual scenes and allow for comparison of various seasons as well as annual means.
This period of time was chosen because it corresponds with the period of time (19992003) that the Landsat-7 Scan Line Corrector was in operation, a requirement for Task 2
below. The ‘optimized’ ECA value will then be used as an effective cloud mask for the
GOES ECA observations used in the remaining analysis.
Task 2: Characterizing cloud spatial scales using GOES, MODIS and
LANDSAT cloud observations: Landsat-7 Enhanced Thematic Mapper Plus (ETM+)
data are now available for free via the web at landsat.usgs.gov. These observations are at
30 m resolution and are available on a 16-day repeat cycle. This data set was processed
using the Landsat-7 Processing System that determines the percentage of cloud cover by
applying the Automatic Cloud Cover Assessment (ACCA) algorithm (Figure 3). At 30m
resolution, this data set provides 1,111 pixels for each MODIS 1km pixel and 160,000
pixels for each of the GOES 12km pixels and should provide the spatial coverage needed
to characterize the spatial resolution requirements and for understanding the sub-pixel
cloud contamination issues.
The combination of 3 different spatial scale cloud products (30 m, 1 km, and 12
km) will be used to characterize the cloud spatial statistics over a wide range of spatial
scales.
The Landsat-7 cloud product will be used as a “truth” field and
resampled/mapped into coarser resolution grids (100m x 100m; 200m x 200m; 500m x
500m; 1km x 1km, and 12km x 12km). The resulting courser cloud products will be
compared to quantify how increased levels of spatial resolution improve the ability of a
geostationary sensor. Those resampled cloud products that are at the spatial resolution of
the MODIS and GOES cloud products will be compared to the spatial analysis of those.
Figure 3. Example of the 30 m resolution Landsat-7 ACCA cloud product. The visible
channel (A) shows a land area with several small spotted cloud features. The ACCA
algorithm identifies these cloud
features and creates an image
mask (B), where the colors
yellow and orange indicate
clouds, blue indicates pixels
adjacent to clouds, dark green is
for cloud shadow areas, and light
green is clear conditions. (Figure
from Dr. E. Vermote, UMd)
Task 3: Hourly instantaneous GOES surface PAR estimates: The MODIS
PAR product is a course resolution 1 km global area coverage (GAC), daily-averaged
PAR product that is really only meaningful when averaged over multiple days. A similar
PAR product is available for SeaWiFS it may be a better product as it does not saturate
over clouds so easily. However, for both of these, sunglint remains a much bigger
problem for the PAR product. The focus of this task is to use the radiative transfer model
of Gregg and Carder (1990) along with the GOES cloud mask data set to provide
estimates of Photosynthetically Available Radiance (PAR) estimates in support of GEOCAPE studies that will investigate the feasibility of measuring chlorophyll fluorescence
at dawn and dusk periods. This model is already operational and has been easily merged
with the GOES cloud observations to yield hourly estimates of PAR observations during
cloud free conditions (Figure 4). This task will be a collaborative effort with others in
the GEO-CAPE team. Specifically, we will work with Dr. Hu (USF) who is proposing to
develop a sun glint parameterization and with Drs. H. Sosik and T. Moisan who are
independently proposing to investigate the issue of measuring sun-stimulated
phytoplankton fluorescence. Finally, we will implement the Zheng et al. (2009)
algorithm for instantaneous PAR using GOES observations and validate them using local
ground based FLUXNET sites.
Figure 4. January-March 2007 hourly instantaneous cloud free PAR (umE m-2 s-1) for a
GOES coastal ocean pixel adjacent to the Chesapeake Light Tower. The red dots indicate
GOES daytime PAR values, blue indicates nighttime conditions. The black dots indicate
times of MODIS PAR estimates.
References:
Gregg, W. W., and K. L. Carder. A simple spectral solar irradiance model for cloudless
maritime atmospheres. Limnol. Oceanogr. 35, 1657-1675, 1990.
Baum, B. A., and S. Platnick, Introduction to MODIS Cloud Products, In: Earth Science
Satellite Remote Sensing, Qu, J. J., W. Gao, M. Kafatos, R. E. Murphy, and V. V.
Salomonson (eds.), Springer, Berlin, pp. 74-91, 2006.
Li, Z.L., Li, J., Menzel, P., Nelson, J.P., Schmit, T.J., Weisz, E., & Ackerman, S.A.
Forecasting and nowcasting improvement in cloudy regions with high temporal
GOES sounder infrared radiance measurements. Journal of Geophysical Research
- Atmospheres, 114, D09216, 2009.
Wu, D.L., Ackerman, S.A. , Davies, R., Diner, D.J., Garay, M.J., Kahn, B.H., Maddux,
B.C., Moroney, C.M., Stephens, G.L., Veefkind, J.P., & Vaughan, M.A. Vertical
distributions and relationships of cloud occurrence frequency as observed by
MISR, AIRS, MODIS, OMI, CALIPSO, and CloudSat. Geophysical Research
Letters, 36, L09821, 2009.
Zheng, T., S. Liang, and K. Wang, Estimation of incident PAR from GOES imagery,
Journal of Applied Meteorology and Climatology, 47, 853-868, 2008.