Potential of Mie-Fluorescence-Raman Lidar to Profile Chlorophyll a Concentration in Inland Waters.

Vertical distribution of phytoplankton is crucial for assessing the trophic status and primary production in inland waters. However, there is sparse information about phytoplankton vertical distribution due to the lack of sufficient measurements. Here, we report, to the best of our knowledge, the first Mie-fluorescence-Raman lidar (MFRL) measurements of continuous chlorophyll a (Chl-a) profiles as well as their parametrization in inland water. The lidar-measured Chl-a during several experiments showed good agreement with the in situ data. A case study verified that MFRL had the potential to profile the Chl-a concentration. The results revealed that the maintenance of subsurface chlorophyll maxima (SCM) was influenced by light and nutrient inputs. Furthermore, inspired by the observations from MFRL, an SCM model built upon surface Chl-a concentration and euphotic layer depth was proposed with root mean square relative difference of 16.5% compared to MFRL observations, providing the possibility to map 3D Chl-a distribution in aquatic ecosystems by integrated active-passive remote sensing technology. Profiling and modeling Chl-a concentration with MFRL are expected to be of paramount importance for monitoring inland water ecosystems and environments.

Global divergent trends of algal blooms detected by satellite during 1982-2018.

Algal blooms (ABs) in inland lakes have caused adverse ecological effects, and health impairment of animals and humans. We used archived Landsat images to examine ABs in lakes (>1 km2 ) around the globe over a 37-year time span (1982-2018). Out of the 176032 lakes with area >1 km2 detected globally, 863 were impacted by ABs, 708 had sufficiently long records to define a trend, and 66% exhibited increasing trends in frequency ratio (FRQR, ratio of the number of ABs events observed in a year in a given lake to the number of available Landsat images for that lake) or area ratio (AR, ratio of annual maximum area covered by ABs observed in a lake to the surface area of that lake), while 34% showed a decreasing trend. Across North America, an intensification of ABs severity was observed for FRQR (p < .01) and AR (p < .01) before 1999, followed by a decrease in ABs FRQR (p < .01) and AR (p < .05) after the 2000s. The strongest intensification of ABs was observed in Asia, followed by South America, Africa, and Europe. No clear trend was detected for the Oceania. Across climatic zones, the contributions of anthropogenic factors to ABs intensification (16.5% for fertilizer, 19.4% for gross domestic product, and 18.7% for population) were slightly stronger than climatic drivers (10.1% for temperature, 11.7% for wind speed, 16.8% for pressure, and for 11.6% for rainfall). Collectively, these divergent trends indicate that consideration of anthropogenic factors as well as climate change should be at the forefront of management policies aimed at reducing the severity and frequency of ABs in inland waters.

Remote sensing of CDOM and DOC in alpine lakes across the Qinghai-Tibet Plateau using Sentinel-2A imagery data.

As important components of dissolved organic matter (DOM) in an aquatic environment, colored DOM (CDOM) and dissolved organic carbon (DOC) play an essential role in the carbon cycle of an inland aquatic system. Traditionally, CDOM and DOC in inland waters have been primarily determined using in situ observations and laboratory measurements. Most of past lake investigations on CDOM and DOC focused on easily accessible regions and covered a small fraction of lakes worldwide. To our knowledge, little is known about lakes in less accessible areas like the Qinghai-Tibet Plateau (QTP). To address this challenge, optical satellite remote sensing might be useful for capturing a synoptic view of CDOM and DOC with high frequency at large scales, complementing in situ sampling methods for inland waters. In this study, 216 samples collected from 36 lakes across the QTP (2014-2017) were examined to determine the relationships between CDOM absorption coefficient at 350 nm (a350) and Sentinel-2A Multi Spectral Instrument (MSI) imagery reflectance data. A strong positive linear correlation with a350 was observed with B4/B2 (R2 = 0.78, p < 0.01) and with B4/B3 (R2 = 0.62). A multi-step regression model was established for estimating a350 with B4/B2 and B4/B3 as input variables (R2 = 0.81, p < 0.01). A scattered CDOM-DOC relationship was revealed (R2 = 0.34, p < 0.05) using a pooled dataset. By dividing the inland waters into four separate groups in accordance with their salinity gradients, we were able to develop much stronger relationships (R2 > 0.8, p < 0.01) for CDOM-DOC. Significant differences between fresh and saline waters were demonstrated using satellite-derived CDOM and DOC, where high CDOM (0.86 ± 0.67 m-1) and low DOC (3.76 ± 4.92 mg L-1) concentrations were observed for freshwaters, while inverse trends of CDOM (0.53 ± 0.72 m-1) and DOC (15.76 ± 17.07 mg L-1) were demonstrated for saline lakes in the Tibetan Plateau. This study confirmed that satellite optical imagery can be used for the monitoring of CDOM and DOC of the lakes of the Tibetan Plateau, which are sensitive to a changing climate and are infrequently investigated due to the harsh environment and poor accessibility. Moreover, it highlighted the importance of combining salinity and remote sensing data in the process of estimating lake DOC.

Validation and Comparison of Water Quality Products in Baltic Lakes Using Sentinel-2 MSI and Sentinel-3 OLCI Data.

Inland waters, including lakes, are one of the key points of the carbon cycle. Using remote sensing data in lake monitoring has advantages in both temporal and spatial coverage over traditional in-situ methods that are time consuming and expensive. In this study, we compared two sensors on different Copernicus satellites: Multispectral Instrument (MSI) on Sentinel-2 and Ocean and Land Color Instrument (OLCI) on Sentinel-3 to validate several processors and methods to derive water quality products with best performing atmospheric correction processor applied. For validation we used in-situ data from 49 sampling points across four different lakes, collected during 2018. Level-2 optical water quality products, such as chlorophyll-a and the total suspended matter concentrations, water transparency, and the absorption coefficient of the colored dissolved organic matter were compared against in-situ data. Along with the water quality products, the optical water types were obtained, because in lakes one-method-to-all approach is not working well due to the optical complexity of the inland waters. The dynamics of the optical water types of the two sensors were generally in agreement. In most cases, the band ratio algorithms for both sensors with optical water type guidance gave the best results. The best algorithms to obtain the Level-2 water quality products were different for MSI and OLCI. MSI always outperformed OLCI, with R2 0.84-0.97 for different water quality products. Deriving the water quality parameters with optical water type classification should be the first step in estimating the ecological status of the lakes with remote sensing.

Satellite data is revealing long time changes in the world largest lakes.

Lakes are a crucial source of drinking water, provide ecological services from fisheries and aquaculture to tourism and are also a critical part of the global carbon cycle. Therefore, it is important to understand how lakes are changing over time. The ESA Ocean Colour Climate Change Initiative (OC-CCI) database allows to study changes in the largest lakes over 1997-2023 period. The Caspian Sea and ten next largest lakes were under investigation. Changes in the phytoplankton biomass (Chl-a), the concentration of particulate matter (bbp(555)), the colored dissolved organic matter, CDOM (adg(412)), and the light diffuse attenuation coefficient in water (Kd(490)) were analyzed. Both increasing and decreasing trends (or no significant trend at all) of studied parameters were observed in these lakes over the study period. In some of the Laurentian Great Lakes the changes in CDOM over the study period were found to be in accordance with the lake water level changes i.e. with the inflow from the catchment. There was difference between the trends of Chl-a and bbp(555) in lakes Michigan and Huron indicating that there may have been shift in phytoplankton community that took place around 2005. The study demonstrated that remote sensing products, like the ones created by ESA OC-CCI, are valuable tools to study behavior of large lakes ecosystems over time.

Predicting lake dissolved organic carbon at a global scale.

The pool of dissolved organic carbon (DOC), is one of the main regulators of the ecology and biogeochemistry of inland water ecosystems, and an important loss term in the carbon budgets of land ecosystems. We used a novel machine learning technique and global databases to test if and how different environmental factors contribute to the variability of in situ DOC concentrations in lakes. In order to estimate DOC in lakes globally we predicted DOC in each lake with a surface area larger than 0.1 km2. Catchment properties and meteorological and hydrological features explained most of the variability of the lake DOC concentration, whereas lake morphometry played only a marginal role. The predicted average of the global DOC concentration in lake water was 3.88 mg L-1. The global predicted pool of DOC in lake water was 729 Tg from which 421 Tg was the share of the Caspian Sea. The results provide global-scale evidence for ecological, climate and carbon cycle models of lake ecosystems and related future prognoses.

Predicting the cover and richness of intertidal macroalgae in remote areas: a case study in the Antarctic Peninsula.

Antarctica is an iconic region for scientific explorations as it is remote and a critical component of the global climate system. Recent climate change causes a dramatic retreat of ice in Antarctica with associated impacts to its coastal ecosystem. These anthropogenic impacts have a potential to increase habitat availability for Antarctic intertidal assemblages. Assessing the extent and ecological consequences of these changes requires us to develop accurate biotic baselines and quantitative predictive tools. In this study, we demonstrated that satellite-based remote sensing, when used jointly with in situ ground-truthing and machine learning algorithms, provides a powerful tool to predict the cover and richness of intertidal macroalgae. The salient finding was that the Sentinel-based remote sensing described a significant proportion of variability in the cover and richness of Antarctic macroalgae. The highest performing models were for macroalgal richness and the cover of green algae as opposed to the model of brown and red algal cover. When expanding the geographical range of the ground-truthing, even involving only a few sample points, it becomes possible to potentially map other Antarctic intertidal macroalgal habitats and monitor their dynamics. This is a significant milestone as logistical constraints are an integral part of the Antarctic expeditions. The method has also a potential in other remote coastal areas where extensive in situ mapping is not feasible.

Contrasting seasonality in optical-biogeochemical properties of the Baltic Sea.

Optical-biogeochemical relationships of particulate and dissolved organic matter are presented in support of remote sensing of the Baltic Sea pelagic. This system exhibits strong seasonality in phytoplankton community composition and wide gradients of chromophoric dissolved organic matter (CDOM), properties which are poorly handled by existing remote sensing algorithms. Absorption and scattering properties of particulate matter reflected the seasonality in biological (phytoplankton succession) and physical (thermal stratification) processes. Inherent optical properties showed much wider variability when normalized to the chlorophyll-a concentration compared to normalization to either total suspended matter dry weight or particulate organic carbon. The particle population had the largest optical variability in summer and was dominated by organic matter in both seasons. The geographic variability of CDOM and relationships with dissolved organic carbon (DOC) are also presented. CDOM dominated light absorption at blue wavelengths, contributing 81% (median) of the absorption by all water constituents at 400 nm and 63% at 442 nm. Consequentially, 90% of water-leaving radiance at 412 nm originated from a layer (z90) no deeper than approximately 1.0 m. With water increasingly attenuating light at longer wavelengths, a green peak in light penetration and reflectance is always present in these waters, with z90 up to 3.0-3.5 m depth, whereas z90 only exceeds 5 m at biomass < 5 mg Chla m-3. High absorption combined with a weakly scattering particle population (despite median phytoplankton biomass of 14.1 and 4.3 mg Chla m-3 in spring and summer samples, respectively), characterize this sea as a dark water body for which dedicated or exceptionally robust remote sensing techniques are required. Seasonal and regional optical-biogeochemical models, data distributions, and an extensive set of simulated remote-sensing reflectance spectra for testing of remote sensing algorithms are provided as supplementary data.

Dissolved organic carbon and its potential predictors in eutrophic lakes.

Understanding of the true role of lakes in the global carbon cycle requires reliable estimates of dissolved organic carbon (DOC) and there is a strong need to develop remote sensing methods for mapping lake carbon content at larger regional and global scales. Part of DOC is optically inactive. Therefore, lake DOC content cannot be mapped directly. The objectives of the current study were to estimate the relationships of DOC and other water and environmental variables in order to find the best proxy for remote sensing mapping of lake DOC. The Boosted Regression Trees approach was used to clarify in which relative proportions different water and environmental variables determine DOC. In a studied large and shallow eutrophic lake the concentrations of DOC and coloured dissolved organic matter (CDOM) were rather high while the seasonal and interannual variability of DOC concentrations was small. The relationships between DOC and other water and environmental variables varied seasonally and interannually and it was challenging to find proxies for describing seasonal cycle of DOC. Chlorophyll a (Chl a), total suspended matter and Secchi depth were correlated with DOC and therefore are possible proxies for remote sensing of seasonal changes of DOC in ice free period, while for long term interannual changes transparency-related variables are relevant as DOC proxies. CDOM did not appear to be a good predictor of the seasonality of DOC concentration in Lake Võrtsjärv since the CDOM-DOC coupling varied seasonally. However, combining the data from Võrtsjärv with the published data from six other eutrophic lakes in the world showed that CDOM was the most powerful predictor of DOC and can be used in remote sensing of DOC concentrations in eutrophic lakes.

Predicting species cover of marine macrophyte and invertebrate species combining hyperspectral remote sensing, machine learning and regression techniques.

In order to understand biotic patterns and their changes in nature there is an obvious need for high-quality seamless measurements of such patterns. If remote sensing methods have been applied with reasonable success in terrestrial environment, their use in aquatic ecosystems still remained challenging. In the present study we combined hyperspectral remote sensing and boosted regression tree modelling (BTR), an ensemble method for statistical techniques and machine learning, in order to test their applicability in predicting macrophyte and invertebrate species cover in the optically complex seawater of the Baltic Sea. The BRT technique combined with remote sensing and traditional spatial modelling succeeded in identifying, constructing and testing functionality of abiotic environmental predictors on the coverage of benthic macrophyte and invertebrate species. Our models easily predicted a large quantity of macrophyte and invertebrate species cover and recaptured multitude of interactions between environment and biota indicating a strong potential of the method in the modelling of aquatic species in the large variety of ecosystems.

Relating remotely sensed optical variability to marine benthic biodiversity.

Biodiversity is important in maintaining ecosystem viability, and the availability of adequate biodiversity data is a prerequisite for the sustainable management of natural resources. As such, there is a clear need to map biodiversity at high spatial resolutions across large areas. Airborne and spaceborne optical remote sensing is a potential tool to provide such biodiversity data. The spectral variation hypothesis (SVH) predicts a positive correlation between spectral variability (SV) of a remotely sensed image and biodiversity. The SVH has only been tested on a few terrestrial plant communities. Our study is the first attempt to apply the SVH in the marine environment using hyperspectral imagery recorded by Compact Airborne Spectrographic Imager (CASI). All coverage-based diversity measures of benthic macrophytes and invertebrates showed low but statistically significant positive correlations with SV whereas the relationship between biomass-based diversity measures and SV were weak or lacking. The observed relationships did not vary with spatial scale. SV had the highest independent effect among predictor variables in the statistical models of coverage-derived total benthic species richness and Shannon index. Thus, the relevance of SVH in marine benthic habitats was proved and this forms a prerequisite for the future use of SV in benthic biodiversity assessments.