Estimation Of Chlorophyll-a Concentration Research Articles

Inversion Method for Chlorophyll-a Concentration in High-Salinity Water Based on Hyperspectral Remote Sensing Data.

As one of the important lakes in the "One Lake and Two Seas" of the Inner Mongolia Autonomous Region, the monitoring of water quality in Lake Daihai has attracted increasing attention, and the concentration of chlorophyll-a directly affects the water quality, making the monitoring of chlorophyll-a concentration in Lake Daihai particularly crucial. Traditional methods of monitoring chlorophyll-a concentration are not only inefficient but also require significant human and material resources. Remote sensing technology has the advantages of wide coverage and short update cycles. For lakes such as Daihai with a high salinity content, salinity is considered a key factor when inverting the concentration of chlorophyll-a. In this study, machine learning models, including model stacking from ensemble learning, a ridge regression model, and a random forest model, were constructed. After comparing the training accuracy of the three models on Zhuhai-1 satellite data, the random forest model, which had the highest accuracy, was selected as the final training model. By comparing the accuracy changes before and after adding salinity factors to the random forest model, a high-precision model for inverting chlorophyll-a concentration in hypersaline lakes was obtained. The research results show that, without considering the salinity factor, the root mean square error (RMSE) of the model was 0.056, and the coefficient of determination (R2) was 0.64, indicating moderate model performance. After adding the salinity factor, the model accuracy significantly improved: the RMSE decreased to 0.047, and the R2 increased to 0.92. This study provides a solid basis for the application of remote sensing technology in hypersaline aquatic environments, confirming the importance of considering salinity when estimating chlorophyll-a concentration in hypersaline waters. This research helps us gain a deeper understanding of the water quality and ecosystem evolution in Daihai Lake.

Optimization of convolutional neural network with dual attention mechanism: Estimation of chlorophyll-a concentration in the Taiwan Strait using MODIS data

Chlorophyll-a (Chl-a) concentration plays a crucial role in monitoring marine phytoplankton, holding significant implications for marine ecosystems and human livelihoods. Remote sensing serves as a valuable method for directly estimating Chl-a concentration and facilitating marine monitoring activities. In order to accurately estimate Chl-a concentration and effectively monitor the marine ecological environment of the Taiwan Strait, this paper introduces a novel neural network model called Convolutional Neural Network with Dual Attention Mechanism Optimization (CNN-CBAM). Additionally, the spectral bands derived from MODIS 500 m imagery are subjected to various processing techniques to generate four distinct sets of feature data. These datasets are subsequently merged with in-situ measurements of Chl-a concentration obtained from buoy stations. Given the limited availability of buoy stations, the temporal continuity of data is leveraged to overcome the spatial distribution constraints. This enables the estimation of Chl-a concentration at various time intervals within the research area of the Taiwan Strait. To evaluate the performance of the model, metrics such as root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 are employed. The results demonstrate that the CNN-CBAM model, utilizing MODIS temporal data, effectively retrieves Chl-a concentration in the research area of the Taiwan Strait with high accuracy. For the CNN-CBAM model, the RMSE is 0.34 μg L−1, MAPE is 22%, and R2 is 0.86. In comparison, the traditional empirical model using the Band Ratio (BR) model has an RMSE of 0.61 μg L−1, MAPE of 57%, and R2 of 0.31. The results indicate that the CNN-CBAM model has improved R2 by 0.55 compared to the traditional BR model. Additionally, the RMSE and MAPE have decreased by 0.27 μg L−1 and 35%, respectively. The superiority of the CNN-CBAM model over the traditional BR model is evident, showcasing a substantial enhancement in the accuracy of Chl-a concentration retrieval. Given the challenges posed by the intricate and ever-changing marine environment, particularly the scarcity of in-situ buoy stations, this study offers a practical solution for monitoring Chl-a concentration. It establishes a framework for leveraging satellite-based multispectral sensors to enable large-scale and multi-temporal remote sensing retrieval of Chl-a concentration in the research area of the Taiwan Strait.

Patterns in the temporal complexity of global chlorophyll concentration

Decades of research have relied on satellite-based estimates of chlorophyll-a concentration to identify oceanographic processes and plan in situ observational campaigns; however, the patterns of intrinsic temporal variation in chlorophyll-a concentration have not been investigated on a global scale. Here we develop a metric to quantify time series complexity (i.e., a measure of the ups and downs of sequential observations) in chlorophyll-a concentration and show that seemingly disparate regions (e.g., Atlantic vs Indian, equatorial vs subtropical) in the global ocean can be inherently similar. These patterns can be linked to the regularity of chlorophyll-a concentration change and the likelihood of anomalous events within the satellite record. Despite distinct spatial changes in decadal chlorophyll-a concentration, changes in time series complexity have been relatively consistent. This work provides different metrics for monitoring the global ocean and suggests that the complexity of chlorophyll-a time series can be independent of its magnitude.

Machine and Deep Learning Regression of Chlorophyll-a Concentrations in Lakes Using PRISMA Satellite Hyperspectral Imagery

The estimation of Chlorophyll-a concentration is crucial for monitoring freshwater ecosystem health, particularly in lakes, as it is closely linked to eutrophication processes. Satellite imagery enables synoptic and frequent evaluations of Chlorophyll-a in water bodies, providing essential insights into spatiotemporal eutrophication dynamics. Frontier applications in water remote sensing support the utilization of machine and deep learning models applied to hyperspectral satellite imagery. This paper presents a comparative analysis of conventional machine and deep learning models—namely, Random Forest Regressor, Support Vector Regressor, Long Short-Term Memory, and Gated Recurrent Unit networks—for estimating Chlorophyll-a concentrations. The analysis is based on data from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral mission, complemented by low-resolution Chlorophyll-a concentration maps. The analysis focuses on three sub-alpine lakes, spanning Northern Italy and Switzerland as testing areas. Through a series of modelling experiments, best-performing model configurations are pinpointed for both Chlorophyll-a concentration estimations and the improvement of spatial resolution in predictions. Support Vector Regressor demonstrated a superior performance in Chlorophyll-a concentration estimations, while Random Forest Regressor emerged as the most effective solution for refining the spatial resolution of predictions.

Monitoring temporal chlorophyll-a using Sentinel-2 imagery in urban retention ponds receiving a biological-chemical treatment

Eutrophic conditions occur when waterbodies accumulate excessive nutrients, which leads to algae growth, depleted dissolved oxygen, and increased turbidity. To improve water quality, a biological-chemical treatment was implemented using floating treatment wetlands (FTWs) and slow-release lanthanum delivered through airlift pumps. FTWs remove nutrients via plant uptake and denitrification while lanthanum binds and precipitates phosphate from the water column. The study sites, Densmore and Wilderness Ridge Ponds, had two FTWs and two lanthanum airlift pumps installed in 2020 and 2021. Our hypothesis was that if the biological-chemical treatment reduced nitrate and phosphate, algae growth would be limited, water clarity would improve, and benthic vegetation would increase. Chlorophyll-a, the photosynthetic pigment found in plants and algae, was analyzed to create a direct correlation to algae content within waterbodies. Chlorophyll-a samples were collected twice a month at strategically selected, georeferenced locations in both ponds between May and October in 2022. Water samples were collected when the European Union's Sentinel-2 multispectral satellite passed over the study sites. To estimate chlorophyll-a concentrations prior to the biological-chemical treatment, a correlation was developed between the collected chlorophyll-a concentrations and Sentinel-2 band algorithms. Sentinel-2 imagery provided a means to analyze how chlorophyll-a fluctuated before and during treatment. We found the novel band algorithm, 3BR1, had the best correlation (R2 = 0.72). Applying this band ratio for Sentinel-2 images from 2019 to 2022, a Tukey's HSD test analyzed monthly differences between years. The FTW and lanthanum treatment reduced chlorophyll-a concentrations in Densmore Pond, but not in Wilderness Ridge Pond. One likely reason was Wilderness Ridge Pond had a complex irrigation system and was continuously replenished with nitrate-rich groundwater. Our results show Sentinel-2 can be used to predict historical chlorophyll-a concentrations by using the novel 3BR1 band algorithm and that FTWs and lanthanum can effectively reduce algal growth.

An application of C2-Net atmospheric corrections for chlorophyll-a estimation in small reservoirs

This study assesses Sentinel 2 remote sensing data for estimating chlorophyll-a concentrations in 32 small reservoirs located in the Spanish region of Extremadura. The atmospheric correction algorithms C2RCC, C2X, and C2XC were used to obtain chlorophyll-a estimations. In-situ measurements of chlorophyll-a were collected from ninety-four water samples in the study area, and their values were used to validate the satellite-derived estimates. The results showed that the C2RCC atmospheric correction algorithm outperformed the other two algorithms, with a Mean Absolute Average Error (MAAE) of 4.79 mg/m3 for non-hypertrophic reservoirs. C2X and C2XC algorithm performed inaccurately, with extreme RMSE and MAAE values. Spearman correlation coefficients are good for the three methods applied. The findings could be of interest to regional water management authorities and researchers in monitoring water quality in small reservoirs.

Machine-learning-estimation of high-spatiotemporal-resolution chlorophyll-a concentration using multi-satellite imagery

Chlorophyll-a concentration for quantifying phytoplankton biomass is commonly used as an indicator for evaluating the trophic level of lakes and water quality. This research aimed to develop a high spatiotemporal-resolution model for the retrieval of chlorophyll-a in inland water. Firstly, the machine learning based models considering Sentinel-2 Multispectral Instrument and Sentinel-3 Ocean and Land Color Instrument (OLCI) images were applied to estimate chlorophyll-a concentrations (R2 = 0.873 and 0.822, respectively). The spatiotemporal fusion was performed to fuse the OLCI and MSI chlorophyll-a images with low temporal resolution but fine spatial-resolution, and with high temporal resolution but coarse spatial-resolution. The random forest was applied to fuse images from two distinct sensors, and to refine the spatial resolution of OLCI estimations to be the same as those of Sentinel-2 MSI. Results showed that the spatiotemporal fusion can estimate dense-temporal 10 m spatial resolution chlorophyll-a concentration in the Tsengwen Reservoir (Root-Mean-Square Error, RMSE = 1.25–1.47 μg L−1). The spatiotemporal fusion model was effectively applied to determine high spatiotemporal-resolution chlorophyll-a measurements in the aquatic system.

A preliminary assessment of Sentinel-3 ocean and land color instrument data for the estimation of chlorophyll-a concentration using bio-optical methods in Annaba Bay and El Kala’s coast (Algerian Basin)

The use of bio-optical methods and high spatial and spectral resolution satellite sensors has enabled the estimation of chlorophyll-a (Chl-a) on a large spatiotemporal scale. However, their application on a regional scale necessitates validation and, in certain cases, calibration, especially when it comes to coastal waters. This study was carried out in the eastern Algerian coastal waters to evaluate the relevance of Sentinel-3 satellite data in Annaba Bay and El Kala’s coast. Sentinel-3 OLCI (Ocean and Land Color Instrument) Level-2 products (OC4Me and Chl-a NN) and the Chl-a generated by applying the C2RCC processor on Level-1B (Chl-a C2RCC) were compared to the measured Chl-a at fixed locations. In addition, nine other Chl-a algorithms have been tested. Finally, six of those later were locally calibrated. The results demonstrate the good performance of the initially available products (Chl-a C2RCC and Chl-a NN), followed by the blue–green algorithms (OC4, OC5, OC6, MedOC4, and ADOC4), whereas the near-infrared (NIR) algorithms (2B-OLCI, 3B-OLCI, and G2B) show underwhelming performance and very poor prediction of the Chl-a concentration. Even though the OC4Me has the best correlation (r = 0.9820), it also has the highest MAE, RMSD, and BIAS (0.628, 1.264, and 0.5696, respectively). The fit of the NIR methods has been significantly refined. Nevertheless, their performance is still inadequate for the estimation of the Chl-a in our region. The performance of the calibrated OCx was improved, with the OC6-AW at the top of the list (r = 0.9881, MAE = 0.08). The latter outperforms the existing ones, which improves the estimation of Chl-a based on the Sentinel-3 OLCI data in our study area.

Development of chlorophyll-a concentration estimation algorithm for turbid productive inland waters in India

Estimation of Chlorophyll-a (Chl-a) concentration from remote-sensing reflectance derived from satellite imagery is highly effective in monitoring the spatial and temporal optical variations in water bodies. In turbid, productive and inland waters, implementation of traditional Chl-a estimation algorithms using the blue and green wavelength bands results in higher error in estimated Chl-a. To minimize the absorption and scattering effects of other water constituents like colored dissolved organic matter and particulate matter, the wavelengths bands in red and Near Infrared (NIR) are generally used for estimating Chl-a concentration in productive waters. Owing to a widely observed optical variability in natural waters, a single universal algorithm is not applicable to all waterbodies, hence, regional tuning of algorithms is necessary for estimating Chl-a in turbid and inland productive waters. Here, a 2-band based algorithm of the form and a 3-band algorithm of form were tuned for Chl-a estimation based on field measurements carried out in three water bodies, Chilika Lake of Orissa, Ujjani Reservoir of Maharashtra, Vallabh Sagar Reservoir of Gujarat, India, wherein the observed Chl-a concentration range is 5–33 mg/m3. Together with measured in situ reflectance spectra and concurrent Chl-a concentrations, tuning of red-NIR algorithms is performed. The optimal wavelengths to estimate Chl-a found by the proximal sensing method are = 691 nm and = 667 nm in case of 2-band red-NIR type algorithm. For 3-band red-NIR algorithm, the optimal wavelengths are = 670 nm, = 696 nm and = 740 nm. The mean absolute percentage error (MAPE) and root-mean-square error (RMSE) values obtained for the estimated Chl-a using the tuned 2-band red-NIR algorithm are 29.9% and 5.35 respectively. Similarly, RMSE and MAPE values for Chl-a concentration estimated using the 3-band tuned red-NIR algorithm are 4.24 and 22.5%, respectively. The tuned 2-band and 3-band algorithms resulted in better Chl-a estimates than the fourteen blue-green and red-NIR algorithms from previous studies. The 2-band and 3-band algorithms were re-calibrated to wavelengths present in the ocean land color imager (OLCI) sensor on Sentinel-3 satellite to monitor these water bodies. Spatio-temporal analysis of OLCI imagery derived Chl-a concentrations over Chilika lake (2019 and 2020) indicated seasonal variations. The tuned 3-band red-NIR algorithm exhibited robust performance in all seasons despite wide variations in colored dissolved organic matter and suspended sediments. The tuned red-NIR algorithms are useful in monitoring inland turbid productive waters with upcoming missions such as Plankton, Aerosol, Cloud Ecosystem (PACE) and Oceansat-3.

Accurate Estimation of Chlorophyll-a Concentration in the Coastal Areas of the Ebro Delta (NW Mediterranean) Using Sentinel-2 and Its Application in the Selection of Areas for Mussel Aquaculture

Multispectral satellite remote sensing imagery, together with appropriate modeling, have been proven to provide chlorophyll-a maps that are useful to evaluate the suitability of coastal areas for carrying out shellfish aquaculture. However, current approaches used for chlorophyll-a estimation in very shallow coastal areas often fail in their accuracy. To overcome this limitation, an algorithm that provides an accurate estimation of chlorophyll-a concentration in the coastal areas of the Ebro delta (North Western Mediterranean) using atmospherically corrected Sentinel 2 (S2) remote sensing reflectances (Rrs) has been calibrated and validated. The derived chlorophyll-a maps created have been used in a dynamic carrying capacity model that covers areas from very rich waters inside the embayment to the more oligotrophic waters in the open sea. The use of carrying capacity models is recommended to evaluate the potential of marine coastal areas for bivalve mollusk aquaculture. In this context, the depletion of chlorophyll-a is an indicator of negative environmental impact and thus a continuous monitoring of chlorophyll-a is key. The proposed methodology allows estimation of chlorophyll-a concentration from Sentinel-2 with an accuracy higher than 70% in most cases. The carrying capacity and the suitability of the external areas of the Ebro delta have been determined. The results show that these areas can hold a significant mussel production. The methodology presented in this study aims to provide a tool to the shellfish aquaculture industry.

Quality control based Chlorophyll-a estimation with two-band and three-band algorithms using Sentinel-2 MSI data in a complex inland lake, China

The estimation of Chlorophyll-a concentration remains challenging for inland waters owing to the optical complexity. Hence, a quality control-based Chlorophyll-a retrieval approach was proposed for the Sentinel-2 multispectral instrument. Two algorithms were selected: 2-band algorithm, which is suitable for extremely turbid waters, and improved 3-band algorithm designed for moderately turbid waters. Two scatter plots between Rrs560/Rrs490 and Rrs412/Rrs443 and between Rrs560/Rrs490 and Rrs560 were generated to determine boundary lines of the quality control test for the two selected algorithms. The quality control test was calibrated using 188 groups of field data collected from Daihai Lake. Results show that the two selected algorithms were considerably improved after the quality control test. The retrieval of Chlorophyll-a concentration was further enhanced by integrating the improved algorithms. The quality control test was also evaluated against twelve Sentinel-2 images. The integrated algorithm was found to be suitable for most areas in Daihai Lake.

Estimation of Chlorophyll-A Concentration with Remotely Sensed Data for the Nine Plateau Lakes in Yunnan Province

The quantitative retrieval of the chlorophyll-a concentration is an important remote sensing method that is used to monitor the nutritional status of water bodies. The high spatial resolution of the Sentinel-2 MSI and its subdivision in the red-edge band highlight the characteristics of water chlorophyll-a, which is an important detection tool for assessing water quality parameters in plateau lakes. In this study, the Nine Plateau Lakes in the Yunnan-Kweichow Plateau of China were selected as the study area. Using Sentinel-2 MSI transit images and in situ measured chlorophyll-a concentration as the data source, the chlorophyll-a concentrations of plateau lakes (CCAPLs) were investigated, and the surface temperatures of plateau lakes (STPLs) were retrieved to verify the hypothesis that the lake surface temperature could increase the chlorophyll-a concentration. By comparing feature importance using a random forest (RF), the Sentinel-2 MSI surface reflectance and in situ data were linearly fitted using four retrieval spectral indices with high feature importance, and the accuracy of the estimated concentration of chlorophyll-a was evaluated by monitoring station data in the same period. Then, Landsat-8 TIRS Band 10 data were used to retrieve the STPL with a single-channel temperature retrieval algorithm and to verify the correlation between the STPL and the CCAPL. The results showed that the retrievals of the CCAPL and the STPL were consistent with the actual situation. The root-mean-square error (RMSE) of the fifteenth normalized difference chlorophyll-a index (NDCI15) was 0.0249. When the CCAPL was greater than 0.05 mg/L and the STPL was within 28–34 °C, there was a positive linear correlation between the CCAPL and the STPL. These results will provide support for the remote sensing monitoring of eutrophication in plateau lakes and will contribute to the scientific and effective management of plateau lakes.

Investigating lake chlorophyll-a responses to the 2019 European double heatwave using satellite remote sensing

Compounded weather events such as sequential heatwaves are likely to increasingly impact freshwater ecosystems in the future. Satellite-derived chlorophyll-a concentration estimates for 36 European lakes during a widespread double heatwave event in the summer of 2019 show that deep and medium depth lakes at higher latitudes displayed a synchronous chlorophyll-a increase with temperature, possibly as the result of an improved light climate resulting from increased stratification. Many deep or northern lakes had a notable response to the heatwaves. Warmer, southern shallow lakes had the most asynchronous response, tending to show a greater response to subsequent low pressure or storm events than to the heatwave itself. Chlorophyll-a peaks typically occurred five days after the peak of the heatwave for shallow lakes. For some shallow lakes, the sequential cycle of several heatwaves and low pressure events was found to punctuate the seasonal pattern of chlorophyll-a. Notably, in several of these nutrient-rich lakes the response to the heatwave was dwarfed by large algal blooms occurring later during the typical cyanobacterial bloom period in early autumn, underlining the importance of timing and phenology in response to heatwaves in addition to depth, latitude and trophic state.

Remotely sensed data for estimating chlorophyll-a concentration in wetlands located in the Limpopo Transboundary River Basin, South Africa

Wetlands in semi-arid regions are highly productive and biologically diverse ecosystems that contribute significantly to livelihood and economic development and play a substantial role in sustaining rural livelihoods. These ecosystems are not only rich in biodiversity, but also predominantly valuable in terms of the services they provide to people, including water security, hydrological regulation, and other services. Chlorophyll-a concentrations and associated dynamics in two tropical wetland systems were estimated in the Makuleke and Nylsvlei wetlands. The Makuleke and Nylsvlei wetlands are in the Limpopo Transboundary River Basin, South Africa. Moderate-resolution Landsat 8 images for September 2018 and June 2019 and in situ field measurements were used to estimate and map chlorophyll-a concentration from the two wetlands. Landsat-derived chlorophyll-a concentrations were validated using field-derived chlorophyll-a measurements. Validation was implemented to assess the consistency of the remotely sensed chlorophyll-a estimates. The relationship between field-measured chlorophyll estimates and Landsat-derived chlorophyll estimates was determined using the coefficient of determination (r-square: R2) and the root mean square error (RMSE). The results showed that the Makuleke wetland had low estimates of Chl-a during September 2018. The variation of Chl-a concentration in Makuleke ranged from 0 to 1.15 μg/L, whereas for Nylsvlei the wetland range varied between 0 and 1.42 μg/L, for the period under study. The spatial characterization of Chl-a concentration varied significantly between the two wetlands, with much of it concentrated along the wetland shorelines. The finding of this study underscores the relevance of remotely detected data in the evaluation and routine monitoring of wetland water quality, a previously challenging task within situ measurements.

Anomalously low diatom fluxes during 2009–2010 at Alfonso Basin, Gulf of California

A long-term information baseline is necessary to identify the seasonal and interannual variability of diatom production driven by environmental dynamics. This motivated our compiling hydrographic data and concurrent diatom fluxes at the Alfonso Basin sub-zone. The diatom data time series spanned from February 2008 to September 2012. These data were analyzed along with primary productivity and chlorophyll-a concentration estimates derived from satellite imagery from 2002 to 2012. Planktonic diatom flux (range: 1.62 × 106–3.55 × 107 valves·m-2d-1) was significantly correlated (r2 = 0.37) with chlorophyll-a concentration. The bimodal production season (December–July) exhibited a winter mixing period influenced by water from the Gulf of California. The diatom flux was dominated by fast-growing surface species such as Chaetoceros spp. and Thalassiosira spp. The second peak in May occurred under the influence of southwest winds concurrent with the entry of tropical surface water and a cyclonic eddy that injected nutrients into the base of the euphotic zone, eliciting a bloom of Pseudo-nitzschia spp. The summer stratification resulted in the deepening of Subtropical Subsurface Water, and the diatom flux included large species of oceanic affinity such as Chaetoceros (Phaeoceros), Rhizosolenia acuminata, and Rh. imbricata. The environment at Alfonso Basin promotes diatom sinking; thus, material from sediment traps accurately reflects the primary production cycle associated with hydrographic processes. Interannual variability was characterized by anomalously low diatom fluxes between August 2009 and July 2010. A significant negative association between diatom flux anomalies and El Niño Modoki Index values suggests that oceanographic changes are related to this event. Additional reductions in diatom fluxes were coeval with SST positive anomalies recorded from July to February 2008–2009 and 2011–2012, which showed a significant relationship with the tropical Pacific Meridional Mode. The temporal trends like the additive signals of interannual (CP-El Niño) and decadal (Pacific Meridional Mode) time scales indicate that climatic disturbances affected the local ecosystem in the basin, reducing the vertical flux of diatoms initially dominated by Pseudo-nitzschia spp., and boosting the presence of oceanic warm-water species and small highly silicified species such as Thalassionema nitzschioides var. parva.

Estimation of Chlorophyll-a Concentration in Lake Taihu from Gaofen-1 Wide-Field-of-View Data through a Machine Learning Trained Algorithm

Estimation of Chlorophyll-a Concentration in Lake Taihu from Gaofen-1 Wide-Field-of-View Data through a Machine Learning Trained Algorithm

Automated high-resolution satellite-derived coastal bathymetry mapping

Accurate and up-to-date maps of coastal bathymetry are fundamental for coastal resource management, commercial and military navigation, and aquaculture, among many applications. Existing methods for bathymetry mapping require intensive and costly field surveys or targeted aerial captures, neither of which are easily or affordably replicated for repeat mapping and change monitoring. Satellite-derived bathymetry (SDB), however, offers the potential to map shallow water bodies repeatedly and efficiently with high spatial and temporal resolution (i.e., daily-weekly at 5 m or better). One challenge to large-scale implementation of SDB lies in the automated derivation of inherent water column properties such that they may be accurately compensated for across a variety of depth and substrate conditions. Here we present an algorithm that leverages WorldView (Maxar/Digital GlobeTM) satellite imagery to map the entire 3700 km2 Florida Keys (USA) island chain at 2-meter resolution without the need for any in-situ data collections. Preprocessing included radiometric calibration, atmospheric correction, automated deglinting, and automated detection of optically deep water, which was then used to estimate chlorophyll-a concentration assuming that the study area is primarily comprised of Case-I water (i.e., those where the optical signal is dominated by water, Chlorophyll-a in phytoplankton, and properties that vary in proportion to Chlorophyll-a concentration). Estimating Chlorophyll-a concentration allowed us to calculate the appropriate tuning coefficients used in a spectral band ratio equation for estimating bathymetry. The entire process was fully automated from ingestion of Level-1B image to bathymetry raster output. Mapping the Florida Keys from Key Largo to Key West required 34 WorldView images and was completed in approximately 27 min for an average processing time of 47 s per image using a single GPU core (i.e., supercomputing resources were not needed). After combining the products (mosaicking) in ArcMap, the wall-to-wall bathymetry map was validated against a LiDAR-derived bathymetry model with over 600,000 points; results show an RMSE of 1.95 m over depths from 0 to 15 m.

Estimation of Chlorophyll-a Concentrations in Small Water Bodies: Comparison of Fused Gaofen-6 and Sentinel-2 Sensors

Chlorophyll-a concentrations in water bodies are one of the most important environmental evaluation indicators in monitoring the water environment. Small water bodies include headwater streams, springs, ditches, flushes, small lakes, and ponds, which represent important freshwater resources. However, the relatively narrow and fragmented nature of small water bodies makes it difficult to monitor chlorophyll-a via medium-resolution remote sensing. In the present study, we first fused Gaofen-6 (a new Chinese satellite) images to obtain 2 m resolution images with 8 bands, which was approved as a good data source for Chlorophyll-a monitoring in small water bodies as Sentinel-2. Further, we compared five semi-empirical and four machine learning models to estimate chlorophyll-a concentrations via simulated reflectance using fused Gaofen-6 and Sentinel-2 spectral response function. The results showed that the extreme gradient boosting tree model (one of the machine learning models) is the most accurate. The mean relative error (MRE) was 9.03%, and the root-mean-square error (RMSE) was 4.5 mg/m3 for the Sentinel-2 sensor, while for the fused Gaofen-6 image, MRE was 6.73%, and RMSE was 3.26 mg/m3. Thus, both fused Gaofen-6 and Sentinel-2 could estimate the chlorophyll-a concentrations in small water bodies. Since the fused Gaofen-6 exhibited a higher spatial resolution and Sentinel-2 exhibited a higher temporal resolution.

Estimating chlorophyll-a concentrations in tropical reservoirs from band-ratio machine learning models

Remote sensing technology has been a popular tool with high potential to measure chlorophyll-a concentration in inland waters. This study proposed the band ratio algorithm to estimate the chlorophyll-a concentration in inland water using a machine learning approach. The blue-green, NIR-red band ratios, and the three-band models were sensitive to chlorophyll-a concentration in various water bodies. Various band ratio variables were used to obtain the optimum model for chlorophyll-a estimation in multiple tropical reservoirs. The spectral reflectance in the tropical reservoirs was derived from Sentinel-2 Level 2A. The corresponded reflectance of band ratios and in-situ chlorophyll-a data (N = 109) were applied to identify the correlation and thus justified its potential as appropriate inputs in the machine learning model. Result shows that the NIR-red band ratios are strongly correlated with chlorophyll-a concentration. These spectral band ratios are appropriate inputs for the machine learning model. The effective multiple band ratios are determined from correlation (r > 0.5 between ratios and concentrations) and collinearity test (variance inflation factor, VIF<3). Result confirms that the multiple band ratio algorithm represents a superior performance in increasing the accuracy of each machine learning model. Overall, the random forest provides a robust and reliable estimation using the three-band NIR-red, and blue-green band ratios (RMSE = 2.963 μg/L, r2 = 0.955).

A novel multi-source data fusion method based on Bayesian inference for accurate estimation of chlorophyll-a concentration over eutrophic lakes

A novel multi-source data fusion method based on Bayesian inference (BIF) was proposed in this study to blend the advantages of in-situ observations and remote sensing estimations for obtaining accurate chlorophyll-a (Chla) concentration in Lake Taihu (China). Two error models (additive and multiplicative) were adopted to construct the likelihood function in BIF; the BIF method was also compared with three commonly used data fusion algorithms, including linear and nonlinear regression data fusion (LRF and NLRF) and cumulative distribution function matching data fusion (CDFF). The results showed the multiplicative error model had small normalized residual errors and was a more suitable choice. The BIF method largely outperformed the data fusion algorithms of CDFF, NLRF and LRF, with the largest correlation coefficients and smallest root mean square error. Moreover, the BIF results can capture the high Chla concentrations in the northwest and the low Chla concentrations in the east of Lake Taihu.