Retrieval and Evaluation of Chlorophyll-A Spatiotemporal Variability Using GF-1 Imagery: Case Study of Qinzhou Bay, China

Chlorophyll-a (Chl-a) concentration is a measure of phytoplankton biomass, and has been used to identify ‘red tide’ events. However, nearshore waters are optically complex, making the accurate determination of the chlorophyll-a concentration challenging. Therefore, in this study, a typical area affected by the Phaeocystis ‘red tide’ bloom, Qinzhou Bay, was selected as the study area. Based on the Gaofen-1 remote sensing satellite image and water quality monitoring data, the sensitive bands and band combinations of the nearshore Chl-a concentration of Qinzhou Bay were screened, and a Qinzhou Bay Chl-a retrieval model was constructed through stepwise regression analysis. The main conclusions of this work are as follows: (1) The Chl-a concentration retrieval regression model based on 1/B4 (near-infrared band (NIR)) has the best accuracy (R2 = 0.67, root-mean-square-error = 0.70 μg/L, and mean absolute percentage error = 0.23) for the remote sensing of Chl-a concentration in Qinzhou Bay. (2) The spatiotemporal distribution of Chl-a in Qinzhou Bay is varied, with lower concentrations (0.50 μg/L) observed near the shore and higher concentrations (6.70 μg/L) observed offshore, with a gradual decreasing trend over time (−0.8).

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Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning

Remote Sensing ◽

10.3390/rs13040576 ◽

2021 ◽

Vol 13 (4) ◽

pp. 576

Author(s):

Hua Su ◽

Xuemei Lu ◽

Zuoqi Chen ◽

Hongsheng Zhang ◽

Wenfang Lu ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Time Series ◽

Chlorophyll A ◽

Gradient Boosting ◽

Percentage Error ◽

Neural Net ◽

Spectral Indices ◽

Chl A ◽

Light Gradient

Chlorophyll-a (chl-a) is an important parameter of water quality and its concentration can be directly retrieved from satellite observations. The Ocean and Land Color Instrument (OLCI), a new-generation water-color sensor onboard Sentinel-3A and Sentinel-3B, is an excellent tool for marine environmental monitoring. In this study, we introduce a new machine learning model, Light Gradient Boosting Machine (LightGBM), for estimating time-series chl-a concentration in Fujian’s coastal waters using multitemporal OLCI data and in situ data. We applied the Case 2 Regional CoastColour (C2RCC) processor to obtain OLCI band reflectance and constructed four spectral indices based on OLCI feature bands as supplementary input features. We also used root-mean-square error (RMSE), mean absolute error (MAE), median absolute percentage error (MAPE), and R2 as performance indicators. The results indicate that the addition of spectral indices can easily improve the prediction accuracy of the model, and normalized fluorescence height index (NFHI) has the best performance, with an RMSE of 0.38 µg/L, MAE of 0.22 µg/L, MAPE of 28.33%, and R2 of 0.785. Moreover, we used the well-known band ratio and three-band methods for chl-a estimation validation, and another two OLCI chl-a products were adopted for comparison (OC4Me chl-a and Inverse Modelling Technique (IMT) Neural Net chl-a). The results confirmed that the LightGBM model outperforms the traditional methods and OLCI chl-a products. This study provides an effective remote sensing technique for coastal chl-a concentration estimation and promotes the advantage of OLCI data in ocean color remote sensing.

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The Use of Sentinel-2 for Chlorophyll-a Spatial Dynamics Assessment: A Comparative Study on Different Lakes in Northern Germany

Remote Sensing ◽

10.3390/rs13081542 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1542

Author(s):

Igor Ogashawara ◽

Christine Kiel ◽

Andreas Jechow ◽

Katrin Kohnert ◽

Thomas Ruhtz ◽

...

Keyword(s):

Remote Sensing ◽

Chlorophyll A ◽

Spectral Band ◽

Satellite Image ◽

Atmospheric Correction ◽

Spatial Dynamics ◽

Eutrophic Lakes ◽

Chl A ◽

Spatial Coverage ◽

Sentinel 2

Eutrophication of inland waters is an environmental issue that is becoming more common with climatic variability. Monitoring of this aquatic problem is commonly based on the chlorophyll-a concentration monitored by routine sampling with limited temporal and spatial coverage. Remote sensing data can be used to improve monitoring, especially after the launch of the MultiSpectral Instrument (MSI) on Sentinel-2. In this study, we compared the estimation of chlorophyll-a (chl-a) from different bio-optical algorithms using hyperspectral proximal remote sensing measurements, from simulated MSI responses and from an MSI image. For the satellite image, we also compare different atmospheric corrections routines before the comparison of different bio-optical algorithms. We used in situ data collected in 2019 from 97 sampling points across 19 different lakes. The atmospheric correction assessment showed that the performances of the routines varied for each spectral band. Therefore, we selected C2X, which performed best for bands 4 (root mean square error—RMSE = 0.003), 5 (RMSE = 0.004) and 6 (RMSE = 0.002), which are usually used for the estimation of chl-a. Considering all samples from the 19 lakes, the best performing chl-a algorithm and calibration achieved a RMSE of 16.97 mg/m3. When we consider only one lake chain composed of meso-to-eutrophic lakes, the performance improved (RMSE: 10.97 mg/m3). This shows that for the studied meso-to-eutrophic waters, we can reliably estimate chl-a concentration, whereas for oligotrophic waters, further research is needed. The assessment of chl-a from space allows us to assess spatial dynamics of the environment, which can be important for the management of water resources. However, to have an accurate product, similar optical water types are important for the overall performance of the bio-optical algorithm.

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Spatiotemporal Variability of Surface Phytoplankton Carbon and Carbon-to-Chlorophyll a Ratio in the South China Sea Based on Satellite Data

Remote Sensing ◽

10.3390/rs13010030 ◽

2020 ◽

Vol 13 (1) ◽

pp. 30

Author(s):

Wenlong Xu ◽

Guifen Wang ◽

Long Jiang ◽

Xuhua Cheng ◽

Wen Zhou ◽

...

Keyword(s):

South China Sea ◽

Chlorophyll A ◽

South China ◽

Satellite Data ◽

Phytoplankton Biomass ◽

The South China Sea ◽

Spatiotemporal Variability ◽

The South ◽

Chl A ◽

China Sea

The spatiotemporal variability of phytoplankton biomass has been widely studied because of its importance in biogeochemical cycles. Chlorophyll a (Chl-a)—an essential pigment present in photoautotrophic organisms—is widely used as an indicator for oceanic phytoplankton biomass because it could be easily measured with calibrated optical sensors. However, the intracellular Chl-a content varies with light, nutrient levels, and temperature and could misrepresent phytoplankton biomass. In this study, we estimated the concentration of phytoplankton carbon—a more suitable indicator for phytoplankton biomass—using a regionally adjusted bio-optical algorithm with satellite data in the South China Sea (SCS). Phytoplankton carbon and the carbon-to-Chl-a ratio (θ) exhibited considerable variability spatially and seasonally. Generally, phytoplankton carbon in the northern SCS was higher than that in the western and central parts. The regional monthly mean phytoplankton carbon in the northern SCS showed a prominent peak during December and January. A similar pattern was shown in the central part of SCS, but its peak was weaker. Besides the winter peak, the western part of SCS had a secondary maximum of phytoplankton carbon during summer. θ exhibited significant seasonal variability in the northern SCS, but a relatively weak seasonal change in the western and central parts. θ had a peak in September and a trough in January in the northern and central parts of SCS, whereas in the western SCS the minimum and maximum θ was found in August and during October–April of the following year, respectively. Overall, θ ranged from 26.06 to 123.99 in the SCS, which implies that the carbon content could vary up to four times given a specific Chl-a value. The variations in θ were found to be related to changing phytoplankton community composition, as well as dynamic phytoplankton physiological activities in response to environmental influences; which also exhibit much spatial differences in the SCS. Our results imply that the spatiotemporal variability of θ should be considered, rather than simply used a single value when converting Chl-a to phytoplankton carbon biomass in the SCS, especially, when verifying the simulation results of biogeochemical models.

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

Sensors ◽

10.3390/s18082699 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2699 ◽

Cited By ~ 2

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.

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Remote sensing of chlorophyll-a as a measure of red tide in Tokyo Bay using hotspot analysis

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2015.09.002 ◽

2015 ◽

Vol 2 ◽

pp. 11-25 ◽

Cited By ~ 11

Author(s):

Ali P. Yunus ◽

Jie Dou ◽

N. Sravanthi

Keyword(s):

Remote Sensing ◽

Chlorophyll A ◽

Red Tide ◽

Tokyo Bay ◽

Hotspot Analysis

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Global distribution and variability of subsurface chlorophyll a concentration

10.5194/os-2021-88 ◽

2021 ◽

Author(s):

Sayaka Yasunaka ◽

Tsuneo Ono ◽

Kosei Sasaoka ◽

Kanako Sato

Keyword(s):

Chlorophyll A ◽

Mixed Layer ◽

Net Primary Production ◽

Thermal Structure ◽

Mixed Layer Depth ◽

Subsurface Layer ◽

Spatiotemporal Variability ◽

Shortwave Radiation ◽

Concentration Data ◽

Chl A

Abstract. Chlorophyll a (Chl-a) often retains its maximum concentration not at the surface but in the subsurface layer. The depth of the Chl-a maximum primarily depends on the balance between light penetration from the surface and nutrient supply from the deep ocean. However, a global map of subsurface Chl-a concentrations based on observations has not been presented yet. In this study, we integrate Chl-a concentration data not only from recent biogeochemical floats but also from historical ship-based and other observations, and present global maps of subsurface Chl-a concentration with related variables. The subsurface Chl-a maximum deeper than the mixed layer depth was stably observed in the subtropics and tropics (30° S to 30° N), only in summer in midlatitudes (30–40° N/S), and rarely at 45–60° S of the Southern Ocean and in the northern North Atlantic (north of 45° N). The depths of the subsurface Chl-a maxima are deeper than those of the euphotic layer in the subtropics and shallower in the tropics and midlatitudes. In the subtropics, seasonal oxygen increases below the mixed layer implied substantial biological new production, which corresponds to 10 % of the net primary production there. During El Niño, the subsurface Chl-a concentration in the equatorial Pacific is higher in the middle to the east and lower in the west than that during La Niña, which is opposite that on the surface. The spatiotemporal variability of the Chl-a concentration described here would be suggestive results not only for the biogeochemical cycle in the ocean but also for the thermal structure and the dynamics of the ocean via the absorption of shortwave radiation.

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Temporal and Spatial Variations of Chlorophyll a Concentration and Eutrophication Assessment (1987–2018) of Donghu Lake in Wuhan Using Landsat Images

Water ◽

10.3390/w12082192 ◽

2020 ◽

Vol 12 (8) ◽

pp. 2192

Author(s):

Xujie Yang ◽

Yan Jiang ◽

Xuwei Deng ◽

Ying Zheng ◽

Zhiying Yue

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Chlorophyll A ◽

Dynamic Monitoring ◽

Water Quality Parameter ◽

Landsat Images ◽

Chl A ◽

Donghu Lake ◽

Cubic Model

Chlorophyll a (Chl-a) concentration, which reflects the biomass and primary productivity of phytoplankton in water, is an important water quality parameter to assess the eutrophication status of water. The band combinations shown in the images of Donghu Lake (Wuhan City, China) captured by Landsat satellites from 1987 to 2018 were analyzed. The (B4 − B3)/(B4 + B3) [(Green − Red)/(Green + Red)] band combination was employed to construct linear, power, exponential, logarithmic and cubic polynomial models based on Chl-a values in Donghu Lake in April 2016. The correlation coefficient (R2), the relative error (RE) and the root mean square error (RMSE) of the cubic model were 0.859, 9.175% and 11.194 μg/L, respectively and those of the validation model were 0.831, 6.509% and 19.846μg/L, respectively. Remote sensing images from 1987 to 2018 were applied to the model and the spatial distribution of Chl-a concentrations in spring and autumn of these years was obtained. At the same time, the eutrophication status of Donghu Lake was monitored and evaluated based on the comprehensive trophic level index (TLI). The results showed that the TLI (∑) of Donghu Lake in April 2016 was 63.49 and the historical data on Chl-a concentration showed that Donghu Lake had been eutrophic. The distribution of Chl-a concentration in Donghu Lake was affected by factors such as construction of bridges and dams, commercial activities and enclosure culture in the lake. The overall distribution of Chl-a concentration in each sub-lake was higher than that in the main lake region and Chl-a concentration was highest in summer, followed by spring, autumn and winter. Based on the data of three long-term (2005–2018) monitoring points in Donghu Lake, the matching patterns between meteorological data and Chl-a concentration were analyzed. It revealed that the Chl-a concentration was relatively high in warmer years or rainy years. The long-term measured data also verified the accuracy of the cubic model for Chl-a concentration. The R2, RE and RMSE of the validation model were 0.641, 2.518% and 22.606 μg/L, respectively, which indicated that it was feasible to use Landsat images to retrieve long-term Chl-a concentrations. Based on longitudinal remote sensing data from 1987 to 2018, long-term and large-scale dynamic monitoring of Chl-a concentrations in Donghu Lake was carried out in this study, providing reference and guidance for lake water quality management in the future.

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Improved MODIS-Aqua Chlorophyll-a Retrievals in the Turbid Semi-Enclosed Ariake Bay, Japan

Remote Sensing ◽

10.3390/rs10091335 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1335 ◽

Cited By ~ 7

Author(s):

Meng Meng Yang ◽

Joji Ishizaka ◽

Joaquim I. Goes ◽

Helga do R. Gomes ◽

Elígio de Raús Maúre ◽

...

Keyword(s):

Chlorophyll A ◽

Red Tide ◽

Atmospheric Correction ◽

Ariake Bay ◽

Band Ratio ◽

Simple Method ◽

Chl A ◽

Switching Algorithm ◽

Economic Significance

The accurate retrieval of chlorophyll-a concentration (Chl-a) from ocean color satellite data is extremely challenging in turbid, optically complex coastal waters. Ariake Bay in Japan is a turbid semi-enclosed bay of great socio-economic significance, but it suffers from serious water quality problems, particularly due to red tide events. Chl-a derived from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on satellite Aqua in Ariake Bay was investigated, and it was determined that the causes of the errors were from inaccurate atmospheric correction and inappropriate in-water algorithms. To improve the accuracy of MODIS remote sensing reflectance (Rrs) in the blue and green bands, a simple method was adopted using in situ Rrs data. This method assumes that the error in MODIS Rrs(547) is small, and MODIS Rrs(412) can be estimated from MODIS Rrs(547) using a linear relation between in situ Rrs(412) and Rrs(547). We also showed that the standard MODIS Chl-a algorithm, OC3M, underestimated Chl-a, which was mostly due to water column turbidity. A new empirical switching algorithm was generated based on the relationship between in situ Chl-a and the blue-to-green band ratio, max(Rrs(443), Rrs(448)/Rrs(547), which was the same as the OC3M algorithm. The criterion of Rrs(667) of 0.005 sr−1 was used to evaluate the extent of turbidity for the switching algorithm. The results showed that the switching algorithm performed better than OC3M, and the root mean square error (RMSE) of estimated Chl-a decreased from 0.414 to 0.326. The RMSE for MODIS Chl-a using the recalculated Rrs and the switching algorithm was 0.287, which was a significant improvement from the RMSE of 0.610, which was obtained using standard MODIS Chl-a. Finally, the accuracy of our method was tested with an independent dataset collected by the local Fisheries Research Institute, and the results revealed that the switching algorithm with the recalculated Rrs reduced the RMSE of MODIS Chl-a from 0.412 of the standard to 0.335.

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Determination of Drought Threshold for Agricultural Land-use using Satellite Image Analysis Techniques

10.5194/egusphere-egu2020-12421 ◽

2020 ◽

Author(s):

Jieun Kim ◽

Jaehyung Yu ◽

Sang Kee Seo ◽

Jin-Hee Baek ◽

Byung Chil Jeon

Keyword(s):

Remote Sensing ◽

Land Use ◽

Near Infrared ◽

Agricultural Land ◽

Irrigation Management ◽

Satellite Image ◽

Agricultural Drought ◽

Agricultural Land Use ◽

Landsat 8 ◽

Eastern Area

<p>The climate change causes major problems in natural disasters such as storms and droughts and has significant impacts on agricultural activities. Especially, global warming changed crops cultivated causing changes in agricultural land-use, and droughts along with land-use change accompanied serious problems in irrigation management. Moreover, it is very problematic to detect drought impacted areas with field survey and it burdens irrigation management. In South Korea, drought in 2012 occurred in western area while 2015 drought occurred in eastern area. The drought cycle in Korea is irregular but the drought frequency has shown an increasing pattern. Remote sensing approaches has been used as a solution to detect drought areas in agricultural land-use and many approaches has been introduced for drought monitoring. This study introduces remote sensing approaches to detect agricultural drought by calculation of local threshold associated with agricultural land-use. We used Landsat-8 satellite images for drought and non-drought years, and Vegetation Health Index(VHI) was calculated using red, near-infrared, and thermal-infrared bands. The comparative analysis of VHI values for the same agricultural land-use between drought year and non-drought year derived the threshold values for each type of land-use. The results showed very effective detection of drought impacted areas showing distinctive differences in VHI value distributions between drought and non-drought years.</p>

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A Deep Multitask Semisupervised Learning Approach for Chlorophyll-a Retrieval from Remote Sensing Images

Remote Sensing ◽

10.3390/rs14010018 ◽

2021 ◽

Vol 14 (1) ◽

pp. 18

Author(s):

Melike Ilteralp ◽

Sema Ariman ◽

Erchan Aptoula

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Image Analysis ◽

Chlorophyll A ◽

Water Quality Assessment ◽

Remote Sensing Image ◽

Semisupervised Learning ◽

Main Task ◽

Chl A ◽

Multitemporal Remote Sensing

This article addresses the scarcity of labeled data in multitemporal remote sensing image analysis, and especially in the context of Chlorophyll-a (Chl-a) estimation for inland water quality assessment. We propose a multitask CNN architecture that can exploit unlabeled satellite imagery and that can be generalized to other multitemporal remote sensing image analysis contexts where the target parameter exhibits seasonal fluctuations. Specifically, Chl-a estimation is set as the main task, and an unlabeled sample’s month classification is set as an auxiliary network task. The proposed approach is validated with multitemporal/spectral Sentinel-2 images of Lake Balik in Turkey using in situ measurements acquired during 2017–2019. We show that harnessing unlabeled data through multitask learning improves water quality estimation performance.

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