raster and oceancolor L2 netcdf data

Mike
I had not really thought about order of operations to be honest.  I just
noticed early on when I was attempting to use raster approach that the data
were not mapped as hoped or orthorectified.  I certainly don't need to
remap before calculating chlor-a on a daily basis as all the bands I need
for a single day are aligned (if not mapped the way I wish).  In the end I
do need the data correctly mapped as I want to do matchups with data
collected with an LRAUV.

I am planning on using locally calibrated coefficients.  I will check out
your package!  I initially wanted to use L3 data but I and a colleague
determined that there was for some reason poor agreement between the L3
data and our in situ matchup data even though at L2 there is good
agreement.  This colleague has typically done the heavy lifting using ENVI,
which I don't have and would rather not learn if what I want to do can be
done in R.

It looks like I can create a raster with vect2rast.SpatialPoints() from the
plotKML package quite easily but the default settings for cell.size lead to
loss of data (I think).  You can set a cell.size but I am not sure if it
works correctly without having multiple values per cell or not.  Or what it
does if you have multiple values per cell.  There is some functionality
that allows you to pick the first, last, the min, the max, or the mean if
there are multiple  values for the same grid cell but I can't get that to
work without Saga GIS.

Cheers and thanks,
Dave

On Wed, Apr 12, 2017 at 8:57 AM, Michael Sumner <mdsumner at gmail.com> wrote:

Glad it's some help, but it sounds like you intend to calculate after
mapping (?) which is definitely not the right way to go. Calculate
chlorophyll and then map, that's how Seadas does it, even though the
remapping is the hard part. And apologies if I misread,  just checking.

I have two algorithms in my roc package on GitHub in case they help
understanding how the calcs get done. Presumably you'll have locally
calibrated parameters for a local algo?

If you want to aggregate into a local map I think it's fair to group-by
directly on L2 pixels coords and then sum into a geographic map, without
worrying about swath-as-image at all. We've road tested doing this but want
the entire southern ocean eventually so it needs a bit of unrelated
preparation for the raw files.

I'd be happy to explore an R solution off list if you're interested. L2 is
surprisingly easy and efficient in R via GDAL.

(This is also a good example for future workflows for the planned stars
package imo.)

Cheers, Mike

On Wed, Apr 12, 2017, 22:35 Warner, David <dmwarner at usgs.gov> wrote:

Thanks Mike!

The goal is to estimate daily chlorophyll via band ratio polynomial
equation for hundreds of days of data (hundreds of data files).  Sounds
like rather than finding a way to orthorectify in R I should learn to batch
reproject using SeaDAS, which does produce a product that is in geotiff
format, is orthorectified, and has readily mappable.  I was trying to avoid
that as the help and documentation available for doing that seems much less
abundant.  One file at a time is easy using the SeaDAS gui.

Thanks very, very much for the other tricks.  Not surprisingly, ggplot2
comes through again with plots that look right!
Cheers,
Dave



On Wed, Apr 12, 2017 at 7:01 AM, Michael Sumner <mdsumner at gmail.com>
wrote:

You can't georeference these data without remapping the data, essentially
treating the pixels as points. They have no natural regular grid form,
except possibly a unique satellite-perspective one. The data are in 2D
array form, but they have explicit "geolocation arrays", i.e. a longitude
and latitude for every cell and not based on a regular mapping.

R does not have tools for this directly from these data, though it can be
treated as a resampling or modelling problem.
You can use raster to get at the values of the locations easily enough,
here's a couple of plotting options in case it's useful:

u <- "https://github.com/dmwarn/Tethys/blob/master/
A2016244185500.L2_LAC_OC.x.nc?raw=true"
f <- basename(f)
download.file(u, f, mode = "wb")

library(raster)
## use raster to treat as raw point data, on long-lat locations
rrs <- raster(f, varname = "geophysical_data/Rrs_412")
longitude <- raster(f, varname = "navigation_data/longitude")
latitude <- raster(f, varname = "navigation_data/latitude")

## plot in grid space, and add the geolocation space as a graticule
plot(rrs)
contour(longitude, add = TRUE)
contour(latitude, add = TRUE)

## raw scaling against rrs values
scl <- function(x) (x - min(x, na.rm = TRUE))/diff(range(x, na.rm = TRUE))
plot(values(longitude), values(latitude), pch = ".", col =
topo.colors(56)[scl(values(rrs)) * 55 + 1])

## ggplot
library(ggplot2)
d <- data.frame(x = values(longitude), y = values(latitude), rrs =
values(rrs))
ggplot(d, aes(x = x, y = y, colour = rrs)) + geom_point(pch = ".")

## might as well discard the missing values (depends on the other vars in
the file)
d <- d[!is.na(d$rrs), ]
ggplot(d, aes(x = x, y = y, colour = rrs)) + geom_point(pch = 19, cex =
0.1)

There are some MODIS and GDAL based packages that might be of use, but I
haven't yet seen any R tool that does this remapping task at scale. (I
believe the MODIS tools and the best warping tools in GDAL use thin-plate
spline techniques).

 Some applications would use the observations as points (i.e. the ocean
colour L3 bins as a daily aggregate from L2) and others use a direct
remapping of the data as an array, for say high-resolution sea ice imagery.

You might not need to do anything particularly complicated though, what's
the goal?

Cheers, Mike.

On Wed, Apr 12, 2017, 20:06 Warner, David <dmwarner at usgs.gov> wrote:

Greetings all

I am trying to develop R code for processing L2 data (netcdf v4 files)
from
the Ocean Biology Processing Group.

The data file I am working with to develop the code is at
https://github.com/dmwarn/Tethys/blob/master/
A2016244185500.L2_LAC_OC.x.nc
and represents primarily Lake Michigan in the United States.  The data
were
extracted from a global dataset by the oceancolor L1 and L2 data server,
not by me.

I have been using the code below to try to get the data into R and ready
for processing but am having problems with dimensions and/or
orthorectification.  The

#extent of the scene obtained using nc_open and ncvar_get
nc <- nc_open('A2016214184500.L2_LAC_OC.x.nc')
lon <- ncvar_get(nc, "navigation_data/longitude")
lat <- ncvar_get(nc, "navigation_data/latitude")
minx <- min(lon)
maxx <- max(lon)
miny <- min(lat)
maxy <- max(lat)

#create extent object
myext <- extent(-90.817, -81.92438, 40.46493, 47.14244)

#create raster
rrs.412 <- raster('A2016214184500.L2_LAC_OC.x.nc', var
="geophysical_data/Rrs_412" ,
                  ext=myext)
rrs.412

rrs.412

class       : RasterLayer
dimensions  : 644, 528, 340032  (nrow, ncol, ncell)
resolution  : 1, 1  (x, y)
extent      : 0.5, 528.5, 0.5, 644.5  (xmin, xmax, ymin, ymax)
coord. ref. : NA
data source : /Users/dmwarner/Documents/MODIS/OC/
A2016214184500.L2_LAC_OC.x.nc
names       : Remote.sensing.reflectance.at.412.nm
zvar        : geophysical_data/Rrs_412

In spite of having tried to assign an extent, the raster extent is in rows
and columns.

Further, plotting the raster reveals that it is flipped on x axis and
somewhat rotated relative to what it should look like.  Even when flipped,
it is still not orthorectified.

How do I get the raster to have the correct extent and also get it
orthorectified?
Thanks,
Dave Warner

--
David Warner
Research Fisheries Biologist
U.S.G.S. Great Lakes Science Center
1451 Green Road
Ann Arbor, MI 48105
734-214-9392 <(734)%20214-9392>

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--
Dr. Michael Sumner
Software and Database Engineer
Australian Antarctic Division
203 Channel Highway
Kingston Tasmania 7050 Australia