> For the complete documentation index, see [llms.txt](https://coastwatch.gitbook.io/satellite-course/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://coastwatch.gitbook.io/satellite-course/tutorials/python-tutorial/2.-comparison-of-chlorophyll-data-from-different-sensors.md). # 2. Comparison of chlorophyll data from different sensors Different ocean color sensors have been launched since 1997 to provide continuous global ocean color data. Unfortunately, because of differences in sensor design and calibration, chlorophyll-a concentration values don’t match during their periods of overlap, making it challenging to study long-term trends. As an example, we are going to plot time-series of mean chlorophyll a concentration from various sensors from 1997 to 2018 to look at the periods of overlap.\ We are going to download data from Seawifs (1997-2010), MODIS (2002-present) and VIIRS (2012-present) and compare it to the ESA-CCI data which combines all 3 sensors into a homogeneous time-series. This tutorial is also available as a [Jupyter notebook](https://github.com/melhawaii/python-satellite-course/blob/master/OW_tutorial2.ipynb). First, let's load all the packages needed: `import urllib.request` \ `import xarray as xr` \ `import netCDF4 as nc` \ `import pandas as pd` \ `import numpy as np` \ `import pyproj` \ `from matplotlib import pyplot as plt` \ `from matplotlib.colors import LinearSegmentedColormap` \ `np.warnings.filterwarnings('ignore')` The OceanWatch website has a data catalog containing documentation and links to all the datasets available:\ Navigate to the "Ocean Color" tab. From there you can access the different datasets using ERDDAP or THREDDS. ## **Get monthly seawifs data, which starts in 1997.** Go to ERDDAP to find the name of the dataset for monthly SeaWIFS data: `sw_chla_monthly_2018_0` You should always examine the dataset in ERDDAP to check the date range, names of the variables and dataset ID, to make sure your `griddap` calls are correct: Notice also that for this dataset and others, the latitudes are ordered from North to South, which will affect the construction of the download URL. (ie. instead of selecting latitudes 0-40N, you need to request 40-0). Test, test * let's download data for a box around the Hawaiian Islands: `url='`\[`https://oceanwatch.pifsc.noaa.gov/erddap/griddap/sw_chla_monthly_2018_0.nc?chlor_a[(1997-10-16T12:00:00Z):1:(2010-10-16T12:00:00Z)][(25):1:(15)][(198):1:(208)]'`\ `` urllib.request.urlretrieve(url, "sw.nc")` `` * let's use xarray to extract the data from the downloaded file: `sw_ds = xr.open_dataset('sw.nc',decode_cf=False)` `sw_ds.data_vars` `Data variables:` \ `chlor_a (time, latitude, longitude) float32 ...` The downloaded data contains only one variable: chlor\_a. * let's compute the monthly mean over the region and extract the dates corresponding to each month of data: `swAVG=np.mean(sw_ds.chlor_a,axis=(1,2))`\ `swdates=nc.num2date(sw_ds.time,sw_ds.time.units)` `sw_ds.close()` ## Get monthly MODIS data, which starts in 2002. `url2="https://oceanwatch.pifsc.noaa.gov/erddap/griddap/aqua_chla_monthly_2018_0.nc?chlor_a[(2002-07-16T12:00:00Z):1:(2019-12-16T12:00:00Z)][(25):1:(15)][(198):1:(208)]"`\ `urllib.request.urlretrieve(url2, "aq.nc")` `aq_ds = xr.open_dataset('aq.nc',decode_cf=False)`\ `aqAVG=np.mean(aq_ds.chlor_a,axis=(1,2))`\ `aqdates=nc.num2date(aq_ds.time,aq_ds.time.units)` `aq_ds.close()` ## Get monthly VIIRS data, which starts in 2012. `url3='https://oceanwatch.pifsc.noaa.gov/erddap/griddap/noaa_snpp_chla_monthly.nc?chlor_a[(2012-01-02T12:00:00Z):1:(2019-12-01T12:00:00Z)][(25):1:(15)][(198):1:(208)]'` \ `urllib.request.urlretrieve(url3, "snpp.nc")` `snpp_ds = xr.open_dataset('snpp.nc',decode_cf=False)`\ `snppAVG=np.mean(snpp_ds.chlor_a,axis=(1,2))`\ `snppdates=nc.num2date(snpp_ds.time,snpp_ds.time.units)` `snpp_ds.close()` ## Get OC-CCI data (September 1997 to Dec 2019) `url4="https://oceanwatch.pifsc.noaa.gov/erddap/griddap/esa-cci-chla-monthly-v4-2.nc?chlor_a[(1997-09-04):1:(2019-12-01T00:00:00Z)][(25):1:(15)][(198):1:(208)]"`\ `` urllib.request.urlretrieve(url4, "cci.nc")` `` `cci_ds = xr.open_dataset('cci.nc',decode_cf=False)`\ `cciAVG=np.mean(cci_ds.chlor_a,axis=(1,2))`\ `ccidates=nc.num2date(cci_ds.time,cci_ds.time.units)`\ `cci_ds.close()` ## Plot the data `plt.figure(figsize=(12,5)) plt.plot(swdates,swAVG,label='sw',c='red',marker='.',linestyle='-') plt.plot(aqdates,aqAVG,label='aq',c='blue',marker='.',linestyle='-') plt.plot(snppdates,snppAVG,label='snpp',c='green',marker='.',linestyle='-')` \ `plt.ylabel('Chl-a (mg/m^3)')`\ `plt.legend()` ![](/files/-M8qmDXV1Dc67E0DJ1qx) `plt.figure(figsize=(12,5))` \ `plt.plot(ccidates,cciAVG, label='cci',c='black')` \ `plt.scatter(swdates,swAVG,label='sw',c='red')` \ `plt.scatter(aqdates,aqAVG,label='aq',c='blue')` \ `plt.scatter(snppdates,snppAVG,label='snpp',c='green')` \ `plt.ylabel('Chl-a (mg/m^3)')` \ `plt.legend()` ![](/files/-M8qmg9T0vb_tmkCqolo) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://coastwatch.gitbook.io/satellite-course/tutorials/python-tutorial/2.-comparison-of-chlorophyll-data-from-different-sensors.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.