scholarly journals Preliminary Investigation on Phytoplankton Dynamics and Primary Production Models in an Oligotrophic Lake from Remote Sensing Measurements

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5072
Author(s):  
Ilaria Cesana ◽  
Mariano Bresciani ◽  
Sergio Cogliati ◽  
Claudia Giardino ◽  
Remika Gupana ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Preliminary Investigation ◽  
Fluorescence Emission ◽  
Phytoplankton Primary Production ◽  
Chl A ◽  
Production Models ◽  
Solar Noon ◽  
Lake Waters

The aim of this study is to test a series of methods relying on hyperspectral measurements to characterize phytoplankton in clear lake waters. The phytoplankton temporal evolutions were analyzed exploiting remote sensed indices and metrics linked to the amount of light reaching the target (EPAR), the chlorophyll-a concentration ([Chl-a]OC4) and the fluorescence emission proxy. The latter one evaluated by an adapted version of the Fluorescence Line Height algorithm (FFLH). A peculiar trend was observed around the solar noon during the clear sky days. It is characterized by a drop of the FFLH metric and the [Chl-a]OC4 index. In addition to remote sensed parameters, water samples were also collected and analyzed to characterize the water body and to evaluate the in-situ fluorescence (FF) and absorbed light (FA). The relations between the remote sensed quantities and the in-situ values were employed to develop and test several phytoplankton primary production (PP) models. Promising results were achieved replacing the FA by the EPAR or FFLH in the equation evaluating a PP proxy (R2 > 0.65). This study represents a preliminary outcome supporting the PP monitoring in inland waters by means of remote sensing-based indices and fluorescence metrics.

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

Modelling Primary Production in Water Bodies: A Numerical Approach that Allows Vertical Inhomogeneities

1973 ◽  
Vol 30 (10) ◽  
pp. 1469-1473 ◽  
Author(s):  
Everett J. Fee
Keyword(s):  
Primary Production ◽  
Algal Biomass ◽  
Numerical Approach ◽  
Light Responses ◽  
Phytoplankton Primary Production ◽  
Basic Approach ◽  
Mathematical Solution ◽  
Model Predictions ◽  
Vertical Variations

A new model for computing integral daily phytoplankton primary production is described. The model incorporates vertical variations of algal biomass, complex photosynthesis vs. light responses, nonexponential extinction of light vs. depth, and any distribution of surface light over a day. The basic approach is to combine measured relations for photosynthetic rate vs. light, light vs. depth, and light vs. time in an interpolative scheme rather than attempting to fit equations to the data and using the resulting equations to obtain a mathematical solution. The model is general and should have wide applicability. Model predictions agreed well with in situ measurements of production.

scholarly journals A Model-Based Assessment of Canopy-Scale Primary Productivity for the Baltic Sea Benthic Vegetation Using Environmental Variables and Spectral Indices

2021 ◽  
Vol 14 (1) ◽  
pp. 158
Author(s):  
Ele Vahtmäe ◽  
Jonne Kotta ◽  
Laura Argus ◽  
Mihkel Kotta ◽  
Ilmar Kotta ◽  
...  
Keyword(s):  
Primary Production ◽  
Aquatic Vegetation ◽  
Meteorological Data ◽  
Boosted Regression Trees ◽  
Model Description ◽  
Spectral Indices ◽  
Blue Band ◽  
Chl A ◽  
The Baltic

This study investigated the potential to predict primary production in benthic ecosystems using meteorological variables and spectral indices. In situ production experiments were carried out during the vegetation season of 2020, wherein the primary production and spectral reflectance of different communities of submerged aquatic vegetation (SAV) were measured and chlorophyll (Chl a+b) concentration was quantified in the laboratory. The reflectance of SAV was measured both in air and underwater. First, in situ reflectance spectra of each SAV class were used to calculate different spectral indices, and then the indices were correlated with Chl a+b. Indices using red and blue band combinations such as 650/450 and 650/480 nm explained the largest part of variability in Chl a+b for datasets measured in air and underwater. Subsequently, the best-performing indices were used in boosted regression trees (BRT) models, together with meteorological data to predict the community photosynthesis of different SAV classes. The predictive power (R2) of production models were very high, estimated at the range of 0.82-0.87. The variable contributing the most to the model description was SAV class, followed in most cases by the water temperature. Nevertheless, the inclusion of spectral indices significantly improved BRT models, often by over 20%, and surprisingly their contribution mostly exceeded that of photosynthetically active radiation.

scholarly journals Spatio-Temporal Variability of Phytoplankton Primary Production in Baltic Lakes Using Sentinel-3 OLCI Data

2020 ◽  
Vol 12 (15) ◽  
pp. 2415
Author(s):  
Tuuli Soomets ◽  
Kristi Uudeberg ◽  
Kersti Kangro ◽  
Dainis Jakovels ◽  
Agris Brauns ◽  
...  
Keyword(s):  
Primary Production ◽  
Temporal Variability ◽  
Carbon Fixation ◽  
Remote Sensing Data ◽  
Spatial And Temporal Variation ◽  
Spatial And Temporal Variability ◽  
Phytoplankton Primary Production ◽  
Spatio Temporal ◽  
Lake Size

Phytoplankton primary production (PP) in lakes play an important role in the global carbon cycle. However, monitoring the PP in lakes with traditional complicated and costly in situ sampling methods are impossible due to the large number of lakes worldwide (estimated to be 117 million lakes). In this study, bio-optical modelling and remote sensing data (Sentinel-3 Ocean and Land Colour Instrument) was combined to investigate the spatial and temporal variation of PP in four Baltic lakes during 2018. The model used has three input parameters: concentration of chlorophyll-a, the diffuse attenuation coefficient, and incident downwelling irradiance. The largest of our studied lakes, Võrtsjärv (270 km2), had the highest total yearly estimated production (61 Gg C y−1) compared to the smaller lakes Lubans (18 Gg C y−1) and Razna (7 Gg C y−1). However, the most productive was the smallest studied, Lake Burtnieks (40.2 km2); although the total yearly production was 13 Gg C y−1, the daily average areal production was 910 mg C m−2 d−1 in 2018. Even if lake size plays a significant role in the total PP of the lake, the abundance of small and medium-sized lakes would sum up to a significant contribution of carbon fixation. Our method is applicable to larger regions to monitor the spatial and temporal variability of lake PP.

scholarly journals Design and Experiments of a Water Color Remote Sensing-Oriented Unmanned Surface Vehicle

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2183
Author(s):  
Yong Li ◽  
Liqiao Tian ◽  
Wenkai Li ◽  
Jian Li ◽  
Anna Wei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Acquisition ◽  
Obstacle Avoidance ◽  
Ocean Color ◽  
Water Color ◽  
Unmanned Surface Vehicle ◽  
Ocean Color Remote Sensing ◽  
Chl A ◽  
In Situ Observations

Integrated and intelligent in situ observations are important for the remote sensing monitoring of dynamic water environments. To meet the field investigation requirements of ocean color remote sensing, we developed a water color remote sensing-oriented unmanned surface vehicle (WC-USV), which consisted of an unmanned surface vehicle platform with ground control station, data acquisition, and transmission modules. The WC-USV was designed with functions, such as remote controlling, status monitoring, automatic obstacle avoidance, and water and meteorological parameter measurement acquisition, transmission, and processing. The key data acquisition module consisted of four parts: A floating optical buoy (FOBY) for collecting remote sensing reflectance ( R r s ) via the skylight-blocked approach; a water sample autocollection system that can collect 12 1-L bottles for analysis in the laboratory; a water quality measurement system for obtaining water parameters, including Chlorophyll-a (Chl-a), turbidity, and water temperature, among others; and meteorological sensors for measuring wind speed and direction, air pressure, temperature, and humidity. Field experiments were conducted to validate the performance of the WC-USV on 23–28 March 2018 in the Honghu Lake, which is the seventh largest freshwater lake in China. The tests proved the following: (1) The WC-USV performed well in terms of autonomous navigation and obstacle avoidance; (2) the mounted FOBY-derived R r s showed good precision in terms of the quality assurance score (QAS), which was higher than 0.98; (3) the Chl-a and suspended matters (SPM) as ocean color parameters measured by the WC-USV were highly consistent with laboratory analysis results, with determination coefficients (R2) of 0.71 and 0.77, respectively; and (4) meteorological parameters could be continuously and stably measured by WC-USV. Results demonstrated the feasibility and practicability of the WC-USV for automatic in situ observations. The USV provided a new way of thinking for the future development of intelligent automation of the aquatic remote sensing ground verification system. It could be a good option to conduct field investigations for ocean color remote sensing and provide an alternative for highly polluted and/or shallow high-risk waters which large vessels have difficulty reaching.

scholarly journals A Prediction Model of Water In Situ Data Change under the Influence of Environmental Variables in Remote Sensing Validation

2020 ◽  
Vol 13 (1) ◽  
pp. 70
Author(s):  
Futai Xie ◽  
Zui Tao ◽  
Xiang Zhou ◽  
Tingting Lv ◽  
Jin Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Prediction Model ◽  
Environmental Variables ◽  
Time Window ◽  
Generalized Regression Neural Network ◽  
Chl A ◽  
Data Matching ◽  
In Situ Data ◽  
Relative Errors

Validation is an essential process to evaluate the quality of waterbody remote sensing products, and the reliability and effective application of the in situ data of waterbody parameters are an important part of validation. Based on the in situ data of chlorophyll-a (Chl-a), total suspended solids (TSS) and other environmental variables (EVs) measured at the fixed station in Taihu Lake, we attempt to develop a prediction model to determine whether the in situ measurement has enough representativeness for validating waterbody remote sensing products. Key EVs that affect the changes of Chl-a and TSS are firstly identified by using correlation analysis, which participate in modeling as variables. In addition, three multi-parameter modeling approaches are selected to simulate the daily changes of Chl-a and TSS under different EVs configurations. The results indicate that the highest prediction accuracy can be achieved through the generalized regression neural network (GRNN) based model. In the all-valid dataset, the testing absolute average relative errors (AEs) of GRNN-based Chl-a and TSS prediction model are 11.4% and 11.3%, respectively, and in the sunny-day dataset, the testing AEs are 8.6% and 8.2%, respectively. Meanwhile, the application example proves that the prediction model in this paper can be effectively used to screen the in situ data and determine the time window for satellite-ground data matching.

Remotely sensed primary production in the western Ross Sea: results of in situ tuned models

2003 ◽  
Vol 15 (1) ◽  
pp. 77-84 ◽  
Author(s):  
R. BARBINI ◽  
F. COLAO ◽  
R. FANTONI ◽  
L. FIORANI ◽  
A. PALUCCI ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Primary Production ◽  
Ross Sea ◽  
Wide Field ◽  
Production Models ◽  
Wide Field Of View ◽  
And Performance ◽  
The Antarctic ◽  
Global Carbon

The Southern Ocean plays an important role in the global carbon cycle and, as a consequence, in the planetary climate equilibrium. The Ross Sea is one of the more productive regions in the Southern Ocean, due to strong phytoplankton blooms occurring during summer. Satellite remote sensing is a powerful tool for investigating such phenomena, especially if the bio-optical algorithms are tuned with in situ data. In this paper, after having compared the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the ENEA Lidar Fluorosensor (ELF), the SeaWiFS chlorophyll a (Chl a) algorithm is tuned in the Ross Sea by means of the ELF measurements. The Chl a concentrations obtained in this way have been the basis for estimating productivity values and their evolution during summer 1997–98. Three primary production models have been used, providing information on their accuracy and performance in the Antarctic environment. Our investigations suggest that the primary production was lower than usual during the period 3 December 1997–16 January 1998.

scholarly journals A 55-Year Time Series Station for Primary Production in the Adriatic Sea: Data Correction, Extraction of Photosynthesis Parameters and Regime Shifts

2018 ◽  
Vol 10 (9) ◽  
pp. 1460 ◽  
Author(s):  
Žarko Kovač ◽  
Trevor Platt ◽  
Živana Ninčević Gladan ◽  
Mira Morović ◽  
Shubha Sathyendranath ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Water Column ◽  
Seasonal Cycle ◽  
Adriatic Sea ◽  
Remotely Sensed ◽  
Linear Extrapolation ◽  
Daily Production ◽  
Assimilation Number

In 1962, a series of in situ primary production measurements began in the Adriatic Sea, at a station near the island of Vis. To this day, over 55 years of monthly measurements through the photic zone have been accumulated, including close to 3000 production measurements at different depths. The measurements are conducted over a six-hour period around noon, and the average production rate extrapolated linearly over day length to calculate daily production. Here, a non-linear primary production model is used to correct these estimates for potential overestimation of daily production due to linear extrapolation. The assimilation numbers are recovered from the measured production profiles and subsequently used to model production at depth. Using the recovered parameters, the model explained 87% of variability in measured normalized production at depth. The model is then used to calculate daily production at depth, and it is observed to give on average 20% lower daily production at depth than the estimates based on linear extrapolation. Subsequently, water column production is calculated, and here, the model predicted on average 26% lower water column production. With the recovered parameters and the known magnitude of the overestimation, the time-series of water column production is then re-established with the non-linearly-corrected data. During this 55-year period, distinct regimes were observed, which were classified with a regime shift detection method. It is then demonstrated how the recovered parameters can be used in a remote sensing application. A seasonal cycle of the recovered assimilation number is constructed along with the seasonal cycle of remotely-sensed chlorophyll. The two are then used to model the seasonal cycle of water column production. An upper and a lower bound on the seasonal cycle of water column production based on remotely-sensed chlorophyll data are then presented. Measured water column production was found to be well within the range of remotely-sensed estimates. With this work, the utility of in situ measurements as a means of providing information on the assimilation number is presented and its application as a reference for remote sensing models highlighted.

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