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In this study, the spectral responses to drought stress of different parts in the leaf of oil palm seedlings named base, middle, and tip were investigated. The ability to detect stress even before symptoms emerge requires knowledge of which part of oil palm leaves responds more quickly to the stress. The analysis of the reflectance spectra in region 650–1050 nm was conducted on respective sections of the leaves of the oil palm seedlings. The analysis revealed that the stress affects the tip of the leaf, followed by the middle and then the base. It was noticed that the greatest loss of water and chlorophyll content was at the tip of the leaf. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used for clustering, while support vector machine (SVM) and linear discriminant analysis (LDA) were applied for categorization purposes. The outcomes of the PCA and HCA showed that the separation between the samples was based on the day and stress levels at respective sections of the leaf. With this, the possession of distinct morphological and physiological features by each part of the leaf can be concluded. From the PCA loadings, it was found that the regions 699–756 nm, 833–877 nm, and 933–958 nm showed noticeable bands and can be used to distinguish between the oil palm seedlings under stress. In addition, LDA and SVM classifiers demonstrated that the prediction accuracy of the stress level in oil palm seedlings was not influenced by the location in the leaf where the spectra were acquired. [ABSTRACT FROM AUTHOR]

Retrieving chlorophyll-a concentration (Chla) from remote-sensing data is especially challenging in coastal regions because of the presence of other co-dominant optically significant constituents (OSCs). In this study, we characterize bio-optical variability at a fixed coastal time series station in the South Brazil Bight (SBB), the ANTARES-Ubatuba site (São Paulo, Brazil) from radiometric measurements and water sample analyses, and evaluate the performance of ocean colour (OC) algorithms for Chla retrieval. In situ Chla and the spectral absorption of phytoplankton (aph), detritus, and coloured dissolved organic matter (CDOM), as well as the above-water remote-sensing reflectance (Rrs) were obtained from 2004 to 2019 at a quasi-monthly sampling frequency. Chla exhibited high variability, with a standard deviation twice as large as the mean (1.0 ± 1.83 mg m−3), and values ranging from 0.18 mg m−3 up to 18.09 mg m−3 during episodic bloom events. CDOM was the dominant OSC contributing to the absorption coefficient at 443 nm all-year round, suggesting the influence of continental sources, even though the station is located at the external limit of the inner shelf (40 m). Two optical water types were defined: one composed mainly of 'blue' waters with lower concentrations of the OSCs and another of 'green' waters with higher concentrations of OSCs. Despite the CDOM spectral dominance, the empirical OCx performed reasonably well with a positive bias for both in situ (17–25%) and satellite (25–31%) Chla retrievals. The performance was slightly better using the 3-band OC3 algorithm by selecting the best band ratio with lower influence of CDM (CDOM + detritus) absorption (Rrs (490/555)). The main sources of uncertainty were caused by higher CDM proportions and phytoplankton-specific absorption, yielding positive biases on the OC3 retrievals. The results suggest that standard satellite products of Chla can be used (with some caution) to monitor and study the dynamics of the phytoplankton biomass within the region, knowing the expected uncertainties. [ABSTRACT FROM AUTHOR]

Accurate estimation of the near-surface chlorophyll-a (Chl-a) concentration in turbid estuaries via remote sensing is an ongoing challenge. Recently, with the successful launch of the third satellite of the China Ocean Colour Satellite Series, HY-1 C, equipped with the Coastal Zone Imager (CZI) with a 3-day revisit period and 50-m resolution in four bands, this study constructed a Chl-a inversion model via symbolic regression (SR) in the Pearl River Estuary (PRE) considering 60 in situ samples to explore the current ocean colour monitoring potential of the CZI. Compared to five state-of-the-art algorithms, the SR model attained a lower mean absolute percentage error (MAPE, 35.51%), mean absolute error (MAE, 1.56) and root-mean-square error (RMSE, 0.24). Additionally, the Chl-a spatial distributions determined with the SR model also better coincided with those determined with synchronous satellite products. The above results indicate that the SR model performed better than the five state-of-the-art models and could be effectively applied to turbid PRE waters. This research may provide a reference regarding the practicality and feasibility of the CZI in water environment monitoring in coastal and estuarial waters at a high temporal-spatial resolution. [ABSTRACT FROM AUTHOR]

The influence of mesoscale dynamics on variability of phytoplankton biomass in terms of chlorophyll-a (chl-a) concentration was studied in the coastal waters of the South Eastern Arabian Sea (SEAS) using long-term satellite data. Satellite-derived chl-a, sea level anomaly, sea surface temperature, and sea surface wind data for the period 1998–2016 were compiled from various sources and analysed to investigate the chl-a variability associated with coastal upwelling and mesoscale eddies. The Empirical Orthogonal Function and Morlet wavelet analyses were performed to estimate the quantitative variability and the result showed strong seasonal and interannual modulation in chl-a concentration and associated environmental variables. The Okubo–Weiss criterion was applied for the identification of mesoscale eddies. The results indicated the presence of cyclonic (cold core) eddies during the summer monsoon season (June–September). The wind-induced upwelling and the cyclonic eddies were most intense during the summer monsoon season, causing higher values of chl-a compared with other season. It is revealed that the variability of chl-a, which might be attributed to seasonal and interannual differences in the surface and sub-surface nutrients, is caused either by coastal upwelling or cyclonic eddies. In particular, the wind-induced upwelling strongly controls the spatial and temporal variability of chl-a compared with mesoscale eddies along the SEAS. The regression model we adopted points out the dominant role played by the wind and its forcing bring about variability in chl-a. The occurrence of extreme climatic events such as El Niño, La Niña and Indian Ocean Dipole (IOD) was noticed during the study period and particularly taken into account to understand the interannual fluctuations in chl-a and associated environmental variables. The relative variability in chl-a concentration was prominent during strong El Niño, La Niña, and IOD. We have attempted to determine the relationship between chl-a with coastal upwelling and mesoscale eddies, the overall importance of such physical forcings, and their influence on bio-production in the SEAS. [ABSTRACT FROM AUTHOR]

The Guanting Reservoir supplied drinking water to Beijing until 1997, following which the water quality of the reservoir deteriorated. The chlorophyll-a concentration (Cchl-a) of water is an important indicator of eutrophication. Therefore, changes in the Cchl-a of the Guanting Reservoir should be monitored and analysed. For more than 30 years, the monitoring of Cchl-a in inland waterbodies has only been possible using the Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM), and Landsat 8 Operational Land Imager (OLI). However, there are data consistency problems in monitoring Cchl-a using these sensors. To address this issue, this study utilized inter-sensor normalization models of the different sensors and a unified Cchl-a estimation model for Landsat series data with a long time span. After inter-sensor normalization, the mean relative error (MRE) of remote sensing reflectance (Rrs) between OLI and TM/ETM+ was corrected to 1–5%. The unified model of Cchl-a estimation employed the normalized ratio index of blue RB and near-infrared RNIR remote sensing reflectance: (RNIR – RB): (RNIR + RB). The MRE for estimating Cchl-a was 25.7% and the root-mean-square error (RMSE) was 5.65 mg m–3. The Cchl-a of the Guanting Reservoir was then estimated for years between 1985 and 2019. During this time, Cchl-a had a distinct seasonal distribution trend; the annual changes go through four stages, and each period shows different change characteristics. This study is the first to systematically understand the history of chlorophyll-a changes in the Guanting Reservoir over the past 30 years and provides references data for its eutrophication process and management. [ABSTRACT FROM AUTHOR]

The importance of providing multi satellite-based chlorophyll-a in an ocean model for accurate representation of penetrative shortwave radiation is demonstrated in this study using Modular Ocean Model. It questions the traditional usage of chlorophyll-a climatology in models. The model sensitivity experiments Chl_IA (with interannually varying multi satellite-based chlorophyll-a) and Chl_Clim (with chlorophyll-a climatology) during 1998–2016 reveals the importance of using the interannual chlorophyll. The maximum difference between the two chlorophyll-a products is found during the peak phase of strong El Niño (January-March 1998 and 2016) in the Niño1 + 2 region, allowing 5–11 W m−2 more shortwave radiation to penetrate below 25 m in Chl_IA, effectively warming the subsurface temperature and cooling the sea surface temperature (SST) by ~0.4°C in the Niño1 + 2 region. The reduction in SST bias (warm) by 0.5°C, deepening of mixed layer and isothermal layer, enhancement of upwelling, reduction of upper ocean stability with realistic distribution of upper ocean heat content are some of the major improvements in Chl_IA. The interannual chlorophyll-a variability in fact rectifies the warm (cold) bias in surface (subsurface) temperature commonly found in ocean models especially in the eastern Pacific region during the peak phase of strong El Niño. [ABSTRACT FROM AUTHOR]

In this study, bio-optical parameters were derived from hyperspectral data of optically complex waters using spectrum matching technique (SMT). Models for inherent optical properties (IOPs) of the water column were tuned using in-situ dataset from the study area (i.e., Chilika lagoon). Constructed IOPs were used to simulate remote sensing reflectance ( R r s ) spectra at 60 wavelengths (equally spaced between 400 and 700 nm) using radiative transfer solution provided by Hydrolight-Ecolight software (HE53). Results of the simulation were stored as a R r s – IOP look up table (LUT). To check the accuracy, in-situ measured R r s were compared with those from the LUT. Retrieved values of two bio-optical parameters i.e., chlorophyll-a concentration (Chl-a) and coloured dissolved organic matter (CDOM)+Detritus absorption coefficient at 440 nm ( a d g (440)) were compared with corresponding in-situ measurements to get good statistical match. Coefficient of determination ( R 2 ) and root mean squared error (RMSE) were 0.80 and 2.66 mg m − 3 respectively for Chl-a, whereas 0.77 and 0.23 m − 1 respectively for a d g (440). These parameters were also retrieved using two commonly used semi-analytical inversion algorithms (SAA)- (a) Linear matrix inversion (LMI) and (b) Garver-Siegal Maritorena (GSM). Both the SAA showed poor performance. R 2 for Chl-a from GSM and LMI were 0.13 and 0.41, respectively, with RMSE of 6.85 mg m − 3 and 4.82 mg m − 3 respectively. For a d g (440) , the value of R 2 from GSM and LMI were 0.87 and 0.71, respectively, but with a high RMSE of 0.91 m − 1 and 0.81 m − 1 respectively. SMT was applied to airborne hyperspectral AVIRIS-NG (Airborne Visible/Infrared Imaging Spectrometer Next Generation) dataset of Chilika lake to derive pixel-wise chlorophyll-a concentration and the magnitude of CDOM+Detritus absorption coefficient at 440 nm ( a d g (440)). Spatial variability of these parameters in its different domains (i.e. Northern-, Central- and Southern-region of the lake) have been addressed. [ABSTRACT FROM AUTHOR]

In colour remote sensing of water, the effect of geometric correction on the extraction of water reflectance is worth exploring. The spectral data were measured by sampling three polluted water bodies in Zhengzhou City, and the Planet satellite image data of these areas were obtained and divided into two groups (with and without geometric correction). The two groups of images were then subjected to atmospheric correction using the quick atmospheric correction (QUAC) algorithm to obtain the remote-sensing reflectance values of the water bodies. The results showed that, for the two different extraction methods, the original data and geometrically corrected extraction results are approximately equal, and the spectral curves show similar trends. Compared with the measured spectral data, it is found that the two groups of data extracted from remote sensing images are more likely to show large-scale changes between the red band and the near-infrared (NIR) band. It is concluded that geometric correction affects values of water reflectance based on extraction of images, but has less effect on the overall trend of the curve, and the probability of changes of water reflectance extraction in NIR band is higher than in other bands. [ABSTRACT FROM AUTHOR]

Two decades (1997–2017) of blended satellite-retrieved phytoplankton chlorophyll a concentration (Chl-a) data were used to assess the interannual variation and recent trends of phytoplankton biomass in Arctic and subarctic ocean (ASAO) regions. Satellite and reanalysis data for geophysical variables were used to assess the probable environmental factors responsible for the observed Chl-a changes. Aggregate Chl-a in the entire ASAO increased steadily from 1997 and then began to decline in the early 2010s. This decline reflects Chl-a declines in most ASAO regions, but the responsible factors and the year in which the trend reversal occurred varied greatly from region to region. In open ocean regions, large-scale climate variability might underlie the Chl-a decline, whereas, in semi-enclosed coastal waters and embayments, the underlying factors might be local or regional (including land–ocean interactions). Therefore, although sea surface temperatures in the ASAO continued to increase steadily, the decline in aggregate Chl-a in the ASAO is unlikely to be connected to global warming. [ABSTRACT FROM AUTHOR]

Previous studies in the US Virgin Islands (USVI) have shown that land-based sources of pollution associated with watershed development coupled with climate is causing coral reef degradation. A good indicator that can be used to assess stress on these environments is the water quality. Conventional assessment methods based on in-situ measurements are timely and costly. Satellite remote sensing techniques offer better spatial coverage and temporal resolution to accurately characterize the dynamic nature of Water Quality Parameters (WQPs, chlorophyll-a), total suspended sediments (TSM), and coloured-dissolved organic matter). However, the interference of multiple optically active WQPs that characterize the water column as well as reflectance from the bottom substrates poses a challenge especially in shallow coastal environments such as the USVI. The optical properties of USVI waters are a function of multiple WQPs with chlorophyll-a values ranging from 0.10 to 2.35 μg L−1 and TSM values from 8.97 to 15.7 mg L−1. In this study, field data were combined with NASA's Landsat Operational Land Imager (OLI) sensor data to specifically model chlorophyll-a variability in the USVI waters based on spectral indices approach. Landsat OLI spectral-based model produced promising results for characterizing the variability of chlorophyll-a in the USVI (coefficient of determination (R2) = 0.70; root-mean-square error (RMSE) = 0.042 μg L−1). Model developed in this study will significantly improve near-real time and long-term water quality monitoring in USVI and this will potentially help to provide insight to factors contributing to coral reef decline. [ABSTRACT FROM AUTHOR]

Remote sensing plays an important role in the monitoring of inland waters. Recently, Cubesats have become an alternative data source for studies that require high spatiotemporal resolutions. However, the radiometric quality of those data was never evaluated for inland water studies. This letter presents the experimental results to assess the quality of the Remote Sensing Reflectance (Rrs) from commercial PlanetScope (PS) CubeSat constellation. The radiometric assessment was performed using in situ Rrs measured at 37 field stations on distinct lakes across Lower Amazon Floodplain. In turbid lakes, PS bands presented Rrs accuracy of 46% for all VNIR bands (coefficient of determination (R2) = 0.56), while in clear water lakes the results were not satisfactory (R2 = 0.08). Furthermore, results showed a spectral dependence in the quality of Rrs retrievals for the turbid lakes, with sounder metrics in visible bands (red band: R2 = 0.83). Finally, there is a high correlation between water quality parameters (sediments concentration and Secchi Depth) and PS Rrs at the red band (R2 > 0.74). Although PS images are mostly designed for land applications, the results suggest that the radiometric quality is sufficient for the monitoring of fine-scale mixing processes between river waters and floodplain lakes. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (chl-a) serves as an indicator of productivity in surface water. Estimating chl-a concentration is pivotal for monitoring and subsequent conservation of surface water quality. Artificial neural network (ANN) based models were validated and tested for their efficacy against various regression models to determine the chl-a concentration in the Upper Ganga river. Landsat-8 Operational Land Imager (OLI) surface reflectance (SR) imagery for May and October along with in-situ data over a period of 2 years (2016–2017) was used to develop and validated models. Regression model performance was acceptable with a coefficient of determination (R2) of 0.57, 0.63, 0.66 and 0.68 for linear, exponential, logarithmic and power model, respectively. However, there was a significant improvement in the efficacy of chl-a determination using ANN model performance having a root mean square error (RMSE) of 1.52 µg l–1 and R2 = 0.97 in comparison to the best-performing regression model (power) with RMSE = 9.86 µg l–1 and R2 = 0.68. ANN exhibited comparatively more precise spatial and seasonal variability with mean absolute error (MAE) of 1.26 µg l–1 as compared to the best regression model (power) MAE = 7.98 µg l–1 suggesting the applicability of ANN for large-scale spatial and temporal monitoring river stretches using Landsat-8 OLI SR images. [ABSTRACT FROM AUTHOR]

Monitoring the riverine output of Suspended Particulate Matter (SPM) distribution in marine embayment is a crucial factor for the water quality of neighbouring coastal regions. This study presents satellite-derived SPM calculations against in-situ measurements in the continental shelf of North-East Aegean surrounding the transboundary Evros river mouth. Surface SPM, Inherent Optical Properties (IOPs) and remote sensing reflectance (Rrs) data were collected in a field campaign during low river discharge period (June 2016). The relationship between the optical backscattering coefficient (bbp) and the in-situ SPM concentrations was investigated. Subsequently, an empirical single band model was applied for estimating SPM concentrations by using the Landsat-8 Operational Land Imager (L8/OLI) red band and the model was then locally tuned within the study area. Furthermore, a multi-band SPM-retrieval algorithm was developed using the in-situ surface reflectance Rrs for calibration and it was validated using the Leave-One-Out Cross Validation technique (LOOCV). The relationship between in-situ SPM and backscattering coefficient values showed good proportionality, thus, nominating the predominance of terrestrial mineral particles. Validation against field measurements indicated that the SPM concentrations derived from the newly-developed multi-band algorithm had an improved significance correlation (96%), compared to both the single band model (not-tuned) (coefficient of determination, R2 = 0.82) and its locally tuned version (R2 = 0.83). Most importantly, the generated multi-band model apart from exhibiting the best performance (R2 = 0.93), it revealed high SPM spots which were not detected by the locally tuned single band model, indicating additional processes originating from river outflows, coastal erosion and subaqueous thermal springs in the area. In contrast, the locally tuned single band model overestimated SPM values in offshore waters, where low concentrations are encountered under the influence of the clear Black Sea Water (BSW). [ABSTRACT FROM AUTHOR]

In this study, a harmful algal bloom (HAB) index, commonly known as a red tide index (RI), was developed and tested usingin situmeasurements in the shallow Arabian Gulf where periodical occurrence of red tide has been reported. The index is based on a combination of ratio and difference of bands centred at 443, 531, and 547 nm. The index was applied to Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua imagery and compared with other proxies of HABs such as chlorophyll-a(chl-a) concentrations and fluorescence line height (FLH). Validation of the proposed index produced a positive correlation between the index and chlorophyll-aconcentration with a determination coefficient (R2) of 0.61. Seasonality analysis of RI indicated that HABs in the Gulf region occur mainly in fall and winter. The inter-annual variations of RI showed similar patterns with the conventional chl-aproduct. This new index can provide insights into the dynamics of red tides and guidance for mitigation efforts, and assist in establishing an effective and timely forecasting and warning system for red tide. [ABSTRACT FROM PUBLISHER]

This study presents a different approach to the classification of Mesoscale Oceanic Structures (MOS) present in the Northwest African area, based on their location. The main improvement stems from the partition of this area in four large zones perfectly differentiated by their morphological characteristics, with attention to seafloor topography and coastal relief. This decomposition makes it easier to recognize structures under adverse conditions, basically the presence of clouds partly hiding them. This is observed particularly well in upwellings, which are usually very large structures with a different morphology and genesis in each zone. This approach not only improves the classification of the upwellings, but also makes it possible to analyse changes in the MOS over time, thereby improving the prediction of its morphological evolution. To identify and label the MOS classified in the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Aqua-MODIS (Moderate Resolution Imaging Spectroradiometer) chlorophyll-aand temperature images, we used a tool specifically designed by our group for this purpose and which has again shown its validity in this new proposal. [ABSTRACT FROM PUBLISHER]