**4. Challenges and opportunities**

#### **4.1 Uncertainties in satellite remote sensing data**

Although ocean color remote sensing observations enabled advances in our understanding of phytoplankton in the ocean, there are several fundamental limitations in the passive radiometric technique. The major uncertainties of remote sensing pigment estimates are from atmospheric correction errors, as a result of the high signal contribution of components other than the targeted water to radiances measured by ocean color instruments, such as reflection from the ocean surface, surface foam, subsurface bubbles, and atmospheric constituents, including clouds, aerosols, and air molecules. A small error from the correction of these atmospheric contribution results in large errors in the obtained remote sensing reflectance and the associated pigment information ([106] and references therein).

Another challenge with ocean color remote sensing comes from the interferences of the optical properties of retrieved water components, including absorption by phytoplankton pigments, colored dissolved matter, and nonalgal particles, and backscattering by suspended particles. This makes the uncertainties from these properties and the derived geophysical parameters from them hard to reduce. The upcoming PACE mission is designed with expanded spectral range and resolution to address this problem [107].

Finally, clouds and strongly scattering aerosol layers have been significant limitation factors of the availability of satellite ocean color data. On average, about 70% of the Earth's ocean area were covered by clouds on the daily scene obtained from a sensor. For broken cloud or aerosol interfered scenes, the accuracy of ocean color retrievals can be compromised compared to clear sky pixels. In high altitude regions, specifically the polar regions, cloud conditions and low sun angles limited ocean color sampling from late fall through early spring of next year. The lack of sampling for this long period of time makes it impossible for a complete understanding of the biogeochemistry and plankton annual cycles of some of the most productive waters [108].

Other issues are from the limitation of spectral, spatial, and temporal resolutions of the existing satellite sensors: some harmful algal blooms occurring in small lakes and ponds are not able to be detected by satellite sensors with low spatial resolution (~1 km); while the high spatial resolution sensors (e.g., Landsat 8) cannot provide timely coverage of bloom events due to their low temporal resolution.

#### **4.2 More accurate** *in situ* **measurements**

The satellite ocean color remote sensing has been tasked to acquire remote sensing imagery, validate and monitor its accuracy, process the radiometric data into geophysical information using different algorithms, and apply the final products into scientific research. One of the principles of *in situ* datasets for the calibration and validation procedure is estimates of near-surface pigment concentrations [109]. Thus, accurate and complete pigment measurements are important to algorithm development as used with remote sensing of phytoplankton pigments. The application of pigment chemotaxonomy in oceanography will be more firmly established by advances in taxonomy and improved pigment analysis (e.g. greater resolution with advanced HPLC and ultra-high performance liquid chromatography – UPLC), more rapid and secure chemical identification, and further measurement and estimation of in vivo pigment absorption coefficients. With improvement in these techniques, more discoveries in pigment and taxonomic diversity and further understanding of their influences on the biogeochemical cycles of the ocean will be achieved. The current challenging environment from climate change makes this an urgent need [14, 15, 75, 76, 91, 110, 111].

#### **4.3 Active remote sensing: LIDAR**

Compared to passive ocean color remote sensing, lidar shows many advantages, such as operating at night and high latitudes, and can generally penetrate to the subsurface chlorophyll maximum [112, 113]. Airborne lidar is particularly useful for mapping the depth distribution of phytoplankton. The characteristic depth profiles of phytoplankton provide useful information for differentiation of phytoplankton species as described in Moore et al. [114] two different species of harmful Cyanobacteria in Lake Erie, USA can be identified by the differences in their characteristic depth profiles.

Combining the observations from lidar and ocean color sensors, especially the advanced upcoming PACE mission, would enable the achievement of greater synergies. The pairing of an ocean-optimized satellite profiling lidar with a passive ocean color sensor would provide maximized global data coverage, and enable three-dimensional reconstruction of ocean ecosystems, which would further favor the algorithm development, and expand the retrieval of geophysical properties.

### **Acknowledgements**

We thank the National Aeronautics and Space Administration (NASA) for providing the MERIS imagery, and the support from the NASA Advanced Information Systems Technology (AIST) program.

*Remote Sensing of Phytoplankton Pigments DOI: http://dx.doi.org/10.5772/intechopen.95381*
