Abstract ：

Knowing how much measurement noise is in a signal is critical for evaluating the overall performance of a satellite observation. We developed a triple collocation observation (TCO) algorithm for estimating measurement noise by collocation comparing the local deviations of three satellite data sets. When we evaluated our algorithm with a synthetic data set, the results showed that the algorithm effectively derived measurement noise from satellite signals despite the many intermission signal differences among the satellites. The TCO algorithm produced x003C;6.66x0025; uncertainty in the measurement noise estimates that we derived from the synthetic data set. In addition, to maximally isolate measurement noise from ocean color images, we developed a set of data quality control criteria to apply when identifying synchronous pixel pairs. Using images from the Medium Resolution Spectral Imager II (MERSI II), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, we applied our data quality control criteria and found that the TCO algorithm produced measurement noise consistent with the measured prelaunch or specifications for VIIRS and MERSI II instrument noise. However, the TCO measurement noise was significantly lower than the spaced MODIS noise because MODISx2019;s extended service time likely produced instrument degradation. Overall, MODIS performed better than MERSI II but worse than VIIRS. Furthermore, we found that the residual error in remote sensing reflectance exponentially decreased as the measurement signal-to-noise ratio (MSNR) increased. Because of this exponential relationship, the MSNR should not be lower than 181 to achieve the x003C;5x0025; uncertainty goal of remote sensing reflectance at 443 nm that NASA proposed. Our results suggest that the TCO algorithm is an effective approach for comprehensively estimating and comparing instrument performance.

Keyword ：

Atmospheric measurements Image color analysis instrumental noise Instrument noise Instruments Noise measurement ocean color Oceans remote sensing Satellite broadcasting Sea measurements triple collocation observation (TCO) algorithm

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Abstract ：

Climate change and human activities have been heavily affecting oceanic and inland waters, and it is critical to have a comprehensive understanding of the aquatic optical properties of lakes. Since many key watercolor parameters of Qinghai Lake are not yet available, this paper aims to study the spatial and temporal variations of the water clarity (i.e., Secchi-disk depth, Z(SD)) and suspended particulate matter concentration (CSPM) in Qinghai Lake from 2001 to 2020 using MODIS images. First, the four atmospheric correction models, including the NIR-SWIR, MUMM, POLYMER, and C2RCC were tested. The NIR-SWIR with decent accuracy in all bands was chosen for the experiment. Then, four existing models for Z(SD) and six models for C-SPM were evaluated. Two semi-analytical models proposed by Lee (2015) and Jiang (2021) were selected for Z(SD) (R-2 = 0.74) and C-SPM (R-2 = 0.73), respectively. Finally, the distribution and variation of the Z(SD) and C-SPM were derived over the past 20 years. Overall, the water of Qinghai Lake is quite clear: the monthly mean Z(SD) is 5.34 +/- 1.33 m, and C-SPM is 2.05 +/- 1.22 mg/L. Further analytical results reveal that the Z(SD) and C-SPM are highly correlated, and the relationship can be formulated with Z(SD)=8.072e(-0.212)C(SPM) (R-2 = 0.65). Moreover, turbid water mainly exists along the edge of Qinghai Lake, especially on the northwestern and northeastern shores. The variation in the lakeshore exhibits some irregularity, while the main area of the lake experiences mild water quality deterioration. Statistically, 81.67% of the total area is dominated by constantly increased C-SPM, and the area with decreased C-SPM occupies 4.56%. There has been distinct seasonal water quality deterioration in the non-frozen period (from May to October). The water quality broadly deteriorated from 2001 to 2008. The year 2008 witnessed a sudden distinct improvement, and after that, the water quality experienced an extremely inconspicuous degradation. This study can fill the gap regarding the long-time monitoring of water clarity and total suspended matter in Qinghai Lake and is expected to provide a scientific reference for the protection and management of the lake.

Keyword ：

BEAST Mann-Kendall test MODIS Qinghai Lake remote sensing Secchi disk depth total suspended matter water color

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In this study, we combined ground-based hyperspectral data, unmanned aerial vehicles (UAVs) remotely sensed hyperspectral images, and 1D-CNN algorithms to quantitatively characterize and estimate the Chemical Oxygen Demand (COD) of estuarine urban rivers. The spectral response mechanism of COD is imprecise due to its complex composition; however, we found that hyperspectral remote sensing data could be used for COD monitoring because of the data's rich spectral information. The potential of hyperspectral sensors installed on UAVs to estimate and map the COD of urban rivers has not been thoroughly explored. We used in situ above water hyperspectral data from 498 sites and synchronous water samples in band ratio, SVM, and 1D-CNN algorithms to build retrieval models. We found that the 1D-CNN model performed the best with an R-2 of 0.78 and an RMSE of 5.22 when using the original reflectance data as input. The 1D-CNN model may also have a better ability to identify water samples with abnormally high concentrations. Our results revealed that transferring the ground-based derived 1D-CNN retrieval model for COD to the high-resolution hyperspectral images is a reliable method for determining COD from the images. We concluded that UAV remotely sensed hyperspectral images are valuable for COD concentration monitoring and mapping, critical to urban water quality management decision making.

Keyword ：

1D-CNN Chemical Oxygen Demand (COD) Hyperspectral UAV Urban river

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Abstract ：

Remotely sensed hyperspectral data can support more effective water quality monitoring. Nevertheless, the variability and complexity of urban river water make it hard to retrieve comprehensive water quality characteristics directly, so that most current water quality assessments rely on semiempirical, semianalytical, or bio-optical approaches. In this study, we carried out simultaneous in situ hyperspectral data and water quality measurements. We used the 382 hyperspectral data from urban rivers of Zhongshan City in the Pearl River Delta to test how well the random forest (RF) and one-dimensional convolutional neural networks (1D-CNNs) algorithms retrieved the newly established water quality index (WQI). The RF algorithm also identified essential wavelengths for retrieving the WQI. Our results demonstrate that the RF and ID-CNN algorithms performed well in WQI estimations. The ID-CNN model performed significantly better than the RF model, especially on high WQI samples. Both models were insensitive to smoothing of the hyperspectral data, showing that the noise of the original hyperspectral reflectance data has a limited impact on the algorithms. In addition, when we used the essential wavelength data (mainly located between 580-590 nm and near 722, 751, 821, and 830 nm) as input data, we achieved better retrieval results. The ID-CNN model performed the best with an R-2 of 0.87, RMSE of 0.574, and RPIQ of 3.082 when we used the top tenth percentile of the essential wavelength data. This study demonstrates the potential of the 1D-CNN algorithm for hyperspectral data analysis to retrieve comprehensive water quality.

Keyword ：

In situ hyperspectral one-dimensional convolutional neural network (ID-CNN) random forest (RF) urban river water quality index (WQI)

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Abstract ：

Being able to accurately estimate inherent optical properties (IOPs) at long time scales is key to comprehending the aquatic biological and biogeochemical responses to long-term global climate change. We employed the near-infrared band and combined it with four "common bands" at visible wavelengths (around 443, 490, 551, and 670 nm) to adjust the IOPs data processing system, IDAS(v2). We applied the IDAS(v2) algorithm further to correct for the residual error in images of turbid waters. We evaluated the performance of the IDAS(v2) algorithm using datasets covering a wide range of natural water types from clear open ocean to turbid coastal and inland waters. Due to the water-leaving signals' sensitivity to the optically significant constituents of highly turbid waters, the near-infrared band was very important for retrieving IOPs from those waters. In our analysis, we found that the IDAS(v2) algorithm provided IOPs data with <28.36% uncertainty for oceanic waters and <37.83% uncertainty for inland waters, which was much more effective than what a quasi-analytical algorithm provided. Moreover, the near-infrared band was better at removing the residual error and partial intermission bias in satellite remote sensing reflectance (R-rs) data because of the strong absorption of pure water. We tested the IDAS(v2) algorithm with numerically simulated and satellite observed data of turbid water. After applying IDAS(v2), the IOPs data were accurately determined from R-rs data contaminated by the residual error. Furthermore, the mean intermission difference between Medium Resolution Spectral Imager 2 and Visible Infrared Imaging Radiometer R-rs data at 443 and 551 nm decreased from 8%-25% to 1%-9%. These results suggest that we can accurately estimate IOPs data for natural waters including naturally clear and turbid waters.

Keyword ：

Absorption Adaptive optics Backscatter IDAS inherent optical property Lakes natural turbid water Optical imaging Optical sensors Water

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A new semi-analytical algorithm that retrieves the total concentration of suspended matter (TSM) was derived from the China Eastern Coastal Zone using multiple ocean colour instruments including the Visible Infrared Imaging Radiometer (VIIRS), the Moderate-resolution Imaging Spectroradiometer (MODIS), and the Medium Resolution Spectral Imager II (MERSI II). The algorithm in one model derives TSM concentration (C-TSM) from satellite inherent optical properties products for clear and moderately turbid water. For turbid waters, a second model with a four-band approach was used. The two model algorithms were combined into one algorithm using a linking weighted function to generate a smooth C-TSM for clear and turbid water and for water with intermediate transparency. Compared to eight existing algorithms, our new algorithm performed better with a wide dynamic range for the C-TSM retrievals. The mean absolute percent difference was 28.22% for the CECZ data and the C-TSM varied from 0.5 mg l(-1) to 2435.4 mg l(-1). New algorithm decreased the MAPD by >11% for the C-TSM retrievals compared to the eight existing algorithms. Furthermore, when new algorithm was tested with a synthetic data set that had been contaminated with residual error, it exhibited more residual error tolerance than the other algorithms when retrieving C-TSM, which agreed well with the results provided by a multi-mission consistency analysis. These results indicate that new algorithm could provide accurate and consistent multi-mission C-TSM from turbid coastal water with a widely varying range of C-TSM.

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Abstract ：

Frequent and extensive sun glint is a serious obstacle to real-time monitoring of ocean colour anomalies. We semi-analytically adjust an inherent optical properties (IOPs) data processing system for the open ocean to correct for sun glint (called ‘IDAS-SGC’) and for remote sensing reflectance (R rs) and IOPs retrievals. Tests with synthetic data validated the effectiveness of our algorithm in deriving ocean colour data from severely glint-contaminated images to produce high quality images. Evaluating results from single mission images suggested that our approach provides spatially smooth and consistent ocean colour products from both severe glint and glint-free regions for Visible Infrared Imaging Radiometer Suite and for Medium Resolution Spectral Imager II instruments. Specifically, complete coverage of circulation-caused ocean colour anomalies can be recovered from a single severe sun glint image. Comparing multi-mission images found that the inter-mission consistency for IDAS-SGC R rs in sun glint regions is comparable with the inter-mission consistency in glint-free regions. Furthermore, we evaluate the performance of the Cox-Munk algorithm for sun glint estimation, and we find that our IDAS-SGC algorithm is more effective than the Cox-Munk algorithm in deriving R rs products from severe sun glint regions due to the absence of accurate real-time wind data. Our results suggest that the IDAS-SGC algorithm obtains meaningful ocean colour products from sun glint-contaminated images of the open oceans. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

Keyword ：

Color Data handling Oceanography Remote sensing Spectroscopy Thermography (imaging)

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Chlorophyll-a (Chl-a) is an objective biological indicator, which reflects the nutritional status of coastal waters. However, the turbid coastal waters pose challenges to the application of existing Chl-a remote sensing models of case II waters. Based on the bio-optical models, we analyzed the suppression of coastal total suspended matter (TSM) on the Chl-a optical characteristics and developed an improved model using the imagery from a hyper-spectrometer mounted on an unmanned aerial vehicle (UAV). The new model was applied to estimate the spatiotemporal distribution of Chl-a concentration in coastal waters of Qingdao on 17 December 2018, 22 March 2019, and 20 July 2019. Compared with the previous models, the correlation coefficients (R-2) of Chl-a concentrations retrieved by the new model and in situ measurements were greatly improved, proving that the new model shows a better performance in retrieving coastal Chl-a concentration. On this basis, the spatiotemporal variations of Chl-a in Qingdao coastal waters were analyzed, showing that the spatial variation is mainly related to the TSM concentration, wind waves, and aquaculture, and the temporal variation is mainly influenced by the sea surface temperature (SST), sea surface salinity (SSS), and human activities.

Keyword ：

bio-optical model chlorophyll-a concentration coastal water Qingdao spatiotemporal variation spectral correction UAV-borne hyper-spectrometer

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Multi-sensor remote sensing is a critical part of the surveillance of coastal ocean for hazard management. The world's largest green macroalgae blooms (green tide) in the Yellow Sea since 2007 are caused by the macroalgae of Ulva, which are disposed as biofoulings into sea water when workers recycle seaweed (Porphyra) farming facilities. We traced the development processes of seaweed cultivation in which area since 2000 and the variation in macroalgal blooms since 2007 through multi-sensors (satellite, Unmanned Aerial Vehicle, and ground spectroradiometer) remote sensing in this study. We found that the sudden occurrence of large-scale green tide in 2007 and the increasing trend since that year were caused by the seaweed aquaculture in a specific mode at specific locations. A numerical simulation and satellite observations on the relationship between the timing of recycling seaweed facilities and the volume of green tide suggest that the green tide is manageable. Adoption of multi-sensor, multi-scale, and multi-temporal observations, translocating seaweed farming sites, and changing the cultivation mode are deemed as key tools for controlling the green tide and sustaining the seaweed aqua culture.

Keyword ：

Floating algae Human activity Marine hazard Multi-sensor observation Seaweed cultivation The Yellow Sea

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A scheme to semi-analytically derive waters' Secchi depth (Z(sd)) from remote sensing reflectance (R-rs) considering the effects of the residual errors in satellite R-rs was developed for the China Eastern Coastal Zone (CECZ). This approach was evaluated and compared against three existing algorithms using field measurements. As it was challenging to provide the accurately inherent optical properties data for running the three existing algorithms in the extremely turbid waters, the new developed algorithm worked more effective than the latter. Moreover, with both synthetic and match-up data, the results indicated that the proposed algorithm was able to minimize some residual errors in R-rs, and thus could generate inter-mission consistent Z(sd) results from two ocean color missions. Finally, after application of new model to satellite images, we presented the spatial and temporal variations of Secchi depth and trophic state in the CECZ during 2002-2014. The study led to several findings: Firstly, the Z(sd)-based trophic state index (TSI) in the East China Sea first increased since 2002, and then gradually dropped during 2008-2014. Secondly, more and more waters within 30-35 m and 20-25 m isobaths were deteriorating from oligotrophic to mesotrophic type and from mesotrophic to eutrophic water, respectively, during 2002-2014. Lastly, the TSI increased on average 0.091 and 0.286 m per year respectively in Bohai Sea and Yellow Sea since 2002, and it might only take 14 and 67 years for Bohai Sea and Yellow Sea to deteriorate from mesotrophic to eutrophic water, following their current yearly deterioration rate and trophic trend. These results highlighted the importance to make some strict regulations for protecting the aquatic environment in the CECZ.

Keyword ：

china eastern coastal zone optically complex water remote sensing Secchi depth trophic state index

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