Since the inception of ocean color satellite observation systems, remote sensing has been based on measurements of the radiance emerging at the top of the atmosphere. Ocean color remote sensing utilizes the intensity and spectral variation of visible light scattered upward from beneath the ocean surface to derive concentrations of biogeochemical constituents and inherent optical properties (the absorption and back-scattering coefficients of the seawater) within the ocean surface layer. Passive ocean color space-borne observations began in the late 1970s with the launch of the CZCS space mission. An uninterrupted record of global ocean color data has been sustained since 1997. These passive observations have enabled a global view of the distribution of optically-active marine particles (phytoplankton, total suspended matter and colored dissolved organic matter). However, these measurements are limited to clear sky, day-light, high Sun elevation angles, ice-free oceans and are exponentially weighted toward the ocean surface. Moreover, the processing of the ocean color images requires the knowledge of the atmospheric components (gases, air molecules and aerosols), which contributes to ninety percent to the total signal measured by the remote sensor. At last, as passive ocean color measurements are unable to resolve phytoplankton vertical structure, it can be a primary source of error in global phytoplankton biomass and net primary production estimates. This means that it becomes highly necessary to use complementary remote sensing techniques for getting a 3-D view of the ocean color.
Among existing remote sensing techniques, Lidar is of high interest. This “laser radar” technique has been used for a wide range of oceanic applications from ship, airplanes or satellites (Churnside, 2014) along open and coastal waters for the past thirty years. Lidar can overcome some limitations of passive ocean color remote sensing as it enables to observe during nighttime and to retrieve bio-optical and biogeochemical parameters over the water column up to around fifty meters as it has up to three times a better penetration into the seawater than passive sensors. Despite the oceanic applications of lidar, this active remote sensing technique has not received significant attention from the ocean color remote sensing community. Several reasons can explain this: cost and size of the instrument, lack of sampling swath, few wavelengths, lack of dedicated space-borne oceanic profiling lidar. However, this technique has regained interest from the ocean community in the past years. New studies (Behrenfeld et al., 2013; Dionisi et al., 2020; Lu et al., 2014, 2020). used the signal from space-borne lidar measurements (CALIOP and ATLAS instruments on-board CALIPSO and IceSat-2 satellites, respectively) to estimate the oceanic particulate backscatter coefficient. The signal from those satellite lidar sensors can provide realistic estimates of this parameter over the globe (Bisson et al., 2020; Lacour et al., 2020). This helps studying polar phytoplankton biomass (Behrenfeld et al., 2017) and daily vertical migrations of ocean animals (Behrenfeld et al., 2019). However, those space-borne instruments were not designed for oceanic applications, thus obviously they suffer several limitations when used for such observations. For example, the CALIOP has a coarse vertical resolution, preventing to get any information over the water column. On the contrary, ATLAS, with a better vertical resolution than CALIOP, has been proved to quantify the vertical distribution of the optical and biogeochemical properties on the first tens of meters of the water column from space (Lu et al., 2020).
Both CALIOP and ATLAS are elastic backscatter lidar. The lidar signal return is basically proportional to volume backscattering coefficient (which is the sum of the backward scattering from water molecules, βW, and suspended particles βP) and the attenuation coefficient of the lidar signal, KL. The parameter βP is of interest, as it is linked to the hemispherical particulate back-scattering parameter, bbp, important in ocean color (as it depends on the size, type and composition of the optically-active marine particles). Unfortunately, this technique cannot separate the backscattered signal from attenuation, so the retrievals of the particulate backscattering coefficient reported in previous publications (Behrenfeld et al., 2013; Dionisi et al., 2020; Churnside et al., 2013; Lu et al., 2014) require either assuming a predictable relationship between backscatter and attenuation or combining lidar and passive ocean color data. The former can introduce significant errors when applied at the local scale.
We propose to tackle two issues: 1) Validation of CALIOP and ICESat-2 retrievals including inter-comparison of their retrievals and 2) Analysis of the limitations of the current space-borne lidars.
As mentioned by Jamet et al. (2019), there is a major need to develop a lidar ocean community and a strong need for an optimized space-borne lidar that will enable significant advances in ocean science. The main purposes of gathering such an international team are 1) to advocate for a future space-borne satellite with ocean capabilities and 2) to promote the use of the current space-borne lidar by proposing evaluation and improvements of current data processing algorithms.
To achieve those goals, our work will be divided in four Working Packages (WP):
WP1: State-of-the art on the space-borne oceanic lidar data processing algorithms (P. Chen; D. Dionisi; Y. Hu; D. Liu; X. Lu)
We will perform an in-depth review on the algorithms that dealt with the estimation of the attenuated backscatter parameter from the lidar signal. These algorithms will be divided into different categories depending on their hypotheses. We can already list the algorithms published in Behrenfeld et al. (2013), Dionisi et al. (2020), Lu et al. (2014) for CALIOP and Lu et al. (2020) for IceSat-2. The WP will produce one peer-reviewed publication.
WP2: Dataset (P. Di Girolamo; C., Jamet; I., Stachlewska; O. Zawadzka-Mańko):
We will gather two types of datasets: 1) In-situ measurements: the available in-situ measurements obtained during sea campaigns. We will, at least, use two datasets: A compilation of bio-optical data over global waters from Valente et al. (2018) and the Bio-Argo profiling floats (Claustre et al., 2020); 2) Satellite data: the ocean optical properties are routinely estimated using standard passive space-borne images (MODIS-Aqua, VIIRS, OLCI) for the past twenty years provided freely by the space agencies. Lidar data from CALIOP/CALIPSO and ATLAS/ICESat-2 over the in-situ dataset will be collected (other regions of interest could be also inspected such as the polar regions).
WP3: Evaluation of oceanic lidar algorithms (P. Chen; D. Dionisi; C. Jamet; D., Liu; I., Stachlewska)
For the validation, we will use the datasets described in Task 2 with different protocols for comparing the in-situ measurements and the space-borne images to the lidar retrievals through a match-up exercise (i.e. co-location in space and time of the space-borne lidar overpasses with in situ measurements in space and time). We will also compare the protocols developed in Bisson et al. (2020) and in Lacour et al. (2020) to evaluate the impact on the statistics of the comparison schemes. The goal of this WP is to provide in-depth analysis and guidance on each algorithm (advantages, limitations, …) to the ocean color community. The WP will produce one peer-reviewed publication.
WP4: Scientific roadmap for future space-borne lidar (all members)
Based on the results of the WP3, we will propose a scientific roadmap for improving the lidar data processing algorithms and for proposing a future space-borne oceanic profiling lidar. We will involve the ocean color community for the development of the scientific roadmap. The WP will produce one peer-reviewed publication.