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Chlorophyll-a (Chl-a) is one of the optically active constituents in waters, and its concentration is frequently utilized as a proxy for lake trophic levels. However, generating a large-scale, long-term, and consistent data record of Chl-a in lakes from satellite images has been a challenging undertaking due to the limitations of conventional algorithms in monitoring inland waters spanning various optical properties. Here, we develop a practical deep neural network (DNN) model to generate a long-term Chl-a series (2012−2021) in 217 large lakes (> 50 km2) across China from the Visible Infrared Imaging Radiometer Suite (VIIRS) imagery. The assessment showed that the NOAA operational VIIRS remote sensing reflectance (R rs (λ)) products were reliable over 28 of China's examined lakes (N = 340, bias = −12%, mean absolute percentage error [MAPE] = 38%), particularly at bands ranging from the green to near-infrared domain. The DNN model performed satisfactorily on Chl-a retrievals (bias = 5%, MAPE = 32%) in 79 lakes over three orders of magnitude (0.1–300 μg L−1) spanning clear/deep to turbid/shallow waters, with significant improvements compared with the existing algorithms and other machine learning algorithms. The algorithm was applied to VIIRS images to produce a data record of spatial and temporal variations in Chl-a for China's large lakes over the past decade. The VIIRS-derived data record showed that China's lakes have an average Chl-a of 9.5 μg L−1 and are to 45.5% eutrophic. The results revealed a spatial trend of lower Chl-a in the western deep lakes than that in the eastern shallow lakes. In addition, we observed a significant increase in Chl-a in the lakes of the China East Plain but a decreasing trend of Chl-a in the Tibetan Plateau. This study highlights the feasibility of a machine learning approach based on synchronous matchups to derive Chl-a data in various lakes from satellite images. Our results provide a comprehensive understanding of overall changes to the optical conditions of China's lakes and enable scientists to elucidate the roles of climate and human activities in regulating lake productivity. • The performance of VIIRS-derived R rs was evaluated in clear and turbid lakes. • A robust deep-learning algorithm was developed for Chl-a retrievals in lakes. • The algorithm showed capability of integrating VIIRS and OLCI for Chl-a retrievals. • Chl-a variations in 217 large Chinese lakes were mapped from VIIRS images. • Chl-a increased in East China Plain but decreased in the Tibetan Plateau since 2012. [ABSTRACT FROM AUTHOR]

Ocean color measurements taken near cloud boundaries suffer from cloud adjacency effects (AEs). As a result, ~ 50% of the cloud-free ocean data are flagged as low quality. Quantitative assessment of such effects, as well as the methodology required to minimize, or correct for them, is rarely available. The goal of this study is to quantify such effects on top-of-atmosphere (TOA) radiance and ocean color data products for MODIS/Terra, MODIS/Aqua, and SeaWiFS measurements. The AEs estimation was based on statistics and an objective method applied to carefully selected clear-water scenes (the number of cloud patches was > 100,000 for each instrument) where ocean properties are relatively homogeneous, over both the North Atlantic and South Pacific. The AEs were quantified as the relative difference between the near-cloud pixels and pixels at least 20 km away from any cloud. Results show that the AEs on TOA radiance share similar patterns among the three missions. Specifically, the AEs decrease sharply as distance increases from cloud edges, and the AEs increase monotonically with increasing wavelengths because they were evaluated in relative rather than absolute terms. However, while discernable memory effects (MEs) are observed on cloud-adjacency pixels of both MODIS missions, they are insignificant on the SeaWiFS mission, and are found in measurements along the scan direction downstream of the clouds, representing > 15% of the total AEs in TOA radiance. The AEs on the retrieved remote sensing reflectance (R rs ) data products are different among the three missions possibly due to their differences in vicarious calibration and uncertainties in atmospheric correction, leading to different patterns in the chlorophyll-a (Chl-a) and normalized Florescence Line Height (nFLH) data products. Large AEs (> 50%) are observed in nFLH of both MODIS/Terra and MODIS/Aqua, likely due to the opposite AEs on R rs between 667 and 678 nm. Finally, when the OCI Chl-a algorithm is used, the current MODIS stray-light masking window (7 × 5) used to mask the AE-contaminated pixels may be relaxed to 3 × 3 without sacrificing data quality, leading to > 40% of the previously masked low-quality data being recovered for clear waters. [ABSTRACT FROM AUTHOR]

Abstract: Today the water quality of many inland and coastal waters is compromised by cultural eutrophication in consequence of increased human agricultural and industrial activities. Remote sensing is widely applied to monitor the trophic state of these waters. This study investigates the performance of near infrared-red models for the remote estimation of chlorophyll-a concentrations in turbid productive waters and evaluates several near infrared-red models developed within the last 34years. Three models were calibrated for a dataset with chlorophyll-a concentrations from 0 to 100mgm−3 and validated for independent and statistically different datasets with chlorophyll-a concentrations from 0 to 100mgm−3 and 0 to 25mgm−3 for the spectral bands of the MEdium Resolution Imaging Spectrometer (MERIS) and MODerate resolution Imaging Spectroradiometer (MODIS). The MERIS two-band model estimated chlorophyll-a concentrations slightly more accurately than the more complex models, with mean absolute errors of 2.3mgm−3 for chlorophyll-a concentrations from 0 to 100mgm−3 and 1.2mgm−3 for chlorophyll-a concentrations from 0 to 25mgm−3. Comparable results from several near infrared-red models with different levels of complexity, calibrated for inland and coastal waters around the world, indicate a high potential for the development of a simple universally applicable near infrared-red algorithm. [Copyright &y& Elsevier]

Abstract: Satellite-derived sea surface temperature (SST) has been providing high-resolution information of the oceanic front. However, in summer, increasing surface heating effects make SSTs uniform and the SST front disappears. The purpose of the present study is to examine applicability of satellite-derived surface chlorophyll-a (Chl-a) for detection of the Kuroshio front in the ocean south of Japan during the summer season. The Kuroshio surface front is formed between the Kuroshio water (KW) and the coastal water (CW) in the study area. Investigating accumulated in situ observations of SST and Chl-a, it is shown that the difference of Chl-a between KW and CW is increased in summer though that of SST becomes small. Using 76 pairs of cloud-free satellite-derived SST and Chl-a images with 0.01° spatial resolution, their seasonal variations are investigated through two-dimensional (2-D) histograms of Chl-a and SST. In summer, SST ranges from 25 to 30 °C and Chl-a ranges 0.03 to 0.4 mg/m3. Two peaks in the summer 2-D histogram correspond to wider KW and CW areas with rather uniform Chl-a and SST distributions. The peak values are extracted from the 2-D histograms and used to determine a threshold value dividing the KW and CW surface Chl-a during summer. It is found that the Chl-a concentration of 0.17 mg/m3 divides the extracted peaks into two groups, i.e., CW and KW except for a few peaks. The proposed Kuroshio-front detection method using the Chl-a images and the threshold value is examined using the satellite images and in situ data. The threshold contour superimposed in the summer Chl-a image distinctly separates the two representative waters in the ocean south of Japan. The front positions determined by the satellite-derived Chl-a, in situ SST, and in situ surface salinity transects along observation lines crossing the Kuroshio front agree with each other. Applying a traditional Kuroshio front index of temperature at 200 m depth, the capability of new Chl-a threshold in dividing two waters around the near-front area is quantitatively examined. Producing 74 match-ups of the satellite-derived Chl-a and 200-m temperature in the frontal zone, the dividing capability is evaluated quantitatively using the traditional Kuroshio-axis index. The detection capability of the proposed method is 82%, which can be considered high enough for practical applications. [Copyright &y& Elsevier]

The Gulf of Tonkin is a semi-closed gulf northwest of the South China Sea, experiencing reversal seasonal monsoon. Previous studies of water conditions have been conducted in the western waters of the gulf, but very few studies of the Chlorophyll-a (Chl-a) distribution have been carried out for the entire gulf. The present study investigates seasonal and spatial distributions of Chl-a and water conditions in the Gulf of Tonkin by analyzing Sea-viewing Wide Field-of-View Scanner (SeaWiFS) derived Chlorophyll-a (Chl-a), in situ measurements, sea surface temperatures (SST), and other oceanographic data obtained in 1999 and 2000. The results show seasonality of Chl-a and SST variations in the Gulf of Tonkin, and reveal phytoplankton blooming events in the center part of the gulf during the northeast monsoon season. In summer, Chl-a concentrations were relatively low (<0.3 mg m-3) and distributed uniformly throughout most of the area, with a belt of higher Chl-a concentrations along the coast, particularly the coast of Qiongzhou Peninsula; in winter, Chl-a concentration increased (0.5 mg m-3) in the entire gulf, and phytoplankton blooms offshore-ward from the northeast coast to the center of the gulf, while Chl-a concentrations reached high levels (0.8–1 mg m-3) in the center of the blooms. One peak of Chl-a concentrations was observed during the northeast monsoon season in the year. SST were high (27–29 °C) and distributed uniformly in summer, but lower with a large gradient from northeast (17 °C) to southwest (25 °C) in winter, while strong northeast winds (8–10 m/s) were parallel to the east coast of the gulf. Comparison of Chl-a values shows that SeaWiFS derived Chl-a concentrations match well with in situ measurements in most parts of the gulf in May 1999, but SeaWiFS derived Chl-a are higher than in situ data in river mouth waters. The seasonal variation of Chl-a concentrations and SST distribution were associated with the seasonally reversing monsoon; the winter phytoplankton blooms were related to vertical mixing and upwelling nutrients drawn by the northeast wind. [Copyright &y& Elsevier]