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An author of the paper was omitted in the original version (Ted Conroy, University of Waikato, New Zealand). This has been corrected in the pdf and HTML versions of the paper, and the associated metadata.

Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of best-available multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large (N = 905) database of colocated in situ radiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance ( $R_{{rs}})$ spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of < 6 nm. Results show that the multilayer perceptron (MLP) neural network applied to HICO spectral configurations (median errors < 65%) outperforms other ML models. This model is subsequently applied to $R_{{rs}}$ spectra resampled to the band configuration of existing satellite instruments and of the one proposed for the next Landsat sensor. These results confirm that employing MLP models to estimate PC from hyperspectral data delivers tangible improvements compared with retrievals from multispectral data and benchmark algorithms (with median errors between ~73% and 126%) and shows promise for developing a globally applicable cyanobacteria measurement approach.

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

For agricultural applications, identification of non-photosynthetic above-ground vegetation is of great interest as it contributes to assess harvest practices, detecting crop residues or drought events, as well as to better predict the carbon, water and nutrients uptake. While the mapping of green Leaf Area Index (LAI) is well established, current operational retrieval models are not calibrated for LAI estimation over senescent, brown vegetation. This not only leads to an underestimation of LAI when crops are ripening, but is also a missed monitoring opportunity. The high spatial and temporal resolution of Sentinel-2 (S2) satellites constellation offers the possibility to estimate brown LAI (LAIB) next to green LAI (LAIG). By using LAI ground measurements from multiple campaigns associated with airborne or satellite spectra, Gaussian processes regression (GPR) models were developed for both LAIG and LAIB, providing alongside associated uncertainty estimates, which allows to mask out unreliable LAI retrievals with higher uncertainties. A processing chain was implemented to apply both models to S2 images, generating a multiband LAI product at 20 m spatial resolution. The models were adequately validated with in-situ data from various European study sites (LAIG: R2 = 0.7, RMSE = 0.67 m2/m2; LAIB: R2 = 0.62, RMSE = 0.43 m2/m2). Thanks to the S2 bands in the red edge (B5: 705 nm and B6: 740 nm) and in the shortwave infrared (B12: 2190 nm) a distinction between LAIG and LAIB can be achieved. To demonstrate the capability of LAIB to identify when crops start senescing, S2 time series were processed over multiple European study sites and seasonal maps were produced, which show the onset of crop senescence after the green vegetation peak. Particularly, the LAIB product permits the detection of harvest (i.e., sudden drop in LAIB) and the determination of crop residues (i.e., remaining LAIB), although a better spectral sampling in the shortwave infrared would have been desirable to disentangle brown LAI from soil variability and its perturbing effects. Finally, a single total LAI product was created by merging LAIG and LAIB estimates, and then compared to the LAI derived from S2 L2B biophysical processor integrated in SNAP. The spatiotemporal analysis results confirmed the improvement of the proposed descriptors with respect to the standard SNAP LAI product accounting only for photosynthetically active green vegetation.

Remote sensing is an appropriate tool for water management. It allows the study of some of the main sources of pollution, such as cyanobacterial harmful algal blooms. These species are increasing due to eutrophication and the adverse effects of climate change. This leads to water quality loss, which has a major impact on the environment, including human water supplies, which consequently require more expensive purification processes. The application of satellite remote sensing images as bio-optical tools is an effective way to monitor and control phycocyanin concentrations, which indicate the presence of cyanobacteria. For this study, 90 geo-referenced phycocyanin measurements were performed in situ, using a Turner C3 Submersible Fluorometer and a laboratory spectrofluorometer, both calibrated with phycocyanin standard, in water bodies of the Iberian Peninsula. These samples were synchronized with Sentinel-2 satellite orbit. The images were processed using Sentinel Application Program software and corrected with the Case 2 Regional Coast color-extended atmospheric correction tool. To produce algorithms that would help to obtain the phycocyanin concentration from the reflectance measured by the multispectral instrument sensor of the satellite, the following band combinations were tested, among others: band 665 nm, band 705 nm, and band 740 nm. The samples were equally divided: half were used for the algorithm’s calibration, and the other half for its validation. With the best adjustment, the algorithm was made more robust and accurate through a recalculation, obtaining a determination coefficient of 0.7, a Root Mean Square Error of 8.1 µg L−1, and a Relative Root Mean Square Error of 19%. In several reservoirs, we observed alarming phycocyanin concentrations that may trigger many environmental health problems, as established by the World Health Organization. Remote sensing provides a rapid monitoring method for the temporal and spatial distribution of these cyanobacteria blooms to ensure good preventive management and control, in order to improve the environmental quality of inland waters.

Inland waters are very sensitive ecosystems that are mainly affected by pressures and impacts within their watersheds. One of water’s dominant constituents is the suspended particulate matter that affects the optical properties of water bodies and can be detected from remote sensors. It is important to know their composition since the ecological role they play in water bodies depends on whether they are mostly organic compounds (phytoplankton, decomposition of plant matter, etc.) or inorganic compounds (silt, clay, etc.). Nowadays, the European Space Agency Sentinel-2 mission has outstanding characteristics for measuring inland waters’ biophysical variables. This work developed algorithms that can estimate the total concentration of suspended matter (TSM), differentiating organic from inorganic fractions, through the combined use of Sentinel-2 images with an extensive database obtained from reservoirs, lakes and marshes within eastern zones of the Iberian Peninsula. For this, information from 121 georeferenced samples collected throughout 40 field campaigns over a 4-year period was used. All possible two-band combinations were obtained and correlated with the biophysical variables by fitting linear regression between the field data and bands combination. The results determined that only using bands 705 or 783 lead to the obtaining the amount of total suspended matter and their organic and inorganic fractions, with errors of 10.3%, 14.8% and 12.2%, respectively. Therefore, remote sensing provides information about total suspended matter dynamics and characteristics as well as its spatial and temporal variation, which would help to study its causes.

We analyze how boards' reputational concerns influence executive compensation and the use of hidden pay. Independent boards reduce disclosed pay to signal their independence, but are more likely than manager-friendly boards to use hidden pay or to distort incentive contracts. Stronger reputational pressures lead to lower disclosed pay, weaker managerial incentives, and higher hidden pay, whereas greater transparency of executive compensation has the opposite effects. Although reputational concerns can induce boards to choose compensation contracts more favorable to shareholders, we show that there is a threshold beyond which stronger reputational concerns harm shareholders. Similarly, excessive pay transparency can harm shareholders. Pablo Ruiz-Verdú acknowledges the financial support of Spain’s Ministry of Science, Innovation and Universities, the State Research Agency, and FEDER (through grant PGC2018-097187-B-I00), Madrid’s Autonomous Community(through grant EARLYFIN-CM, #S2015/HUM-3353), and Spain’s Ministry of Economy and Competitiveness (through grant ECO2015-69615-R).

Freshwater quality maintenance is essential for human use and ecological functions. To ensure this objective, governments establish programs for a continuous monitoring of the inland waters state. This could be possible with Sentinel-2 (S2) and Sentinel-3 (S3), two remote sensing satellites of the European Space Agency, equipped with spectral optical sensors. To determine optimal water quality algorithms applicable to their spectral bands, 36 algorithms were tested for different key variables (chlorophyll a (Chl_a), colored dissolved organic matter (CDOM), colored dissolved organic matter (TSS), phycocyanin (PC) and Secchi disk depth (SDD)). A database of 296 water-leaving reflectance spectra were used, as well as concomitant water quality measurements of Mediterranean reservoirs and lakes of Spain. Two equal data sets were used for calibration and validation. The best algorithms were recalculated using all database and used the following band relations: SDD, R560/R700, CDOM, R665/R490, PC, R705/R665 for S2 and R620, R665, R709 and R779 for S3, using a semi-analytical algorithm, R700 for TSS &lt, 20 mg/L and R783/R492 (S2) or R779/R510 (S3) for TSS &gt, 20 mg/L, and for Chl_a, the maximum (R443, R492)/R560 for Chl_a &lt, 5 mg/m3 and R700/R665 for Chl_a &gt, 5 mg/m3. A preliminary test with a satellite image in a well-known reservoir showed results consistent with the expected ranges and spatial patterns of the variables.

Mar Menor is a Mediterranean Coastal lagoon with high environmental and social value, but has suffered important episodes of contamination in recent years due to heavy rains, sediment dragging and polluting substances mainly from agriculture as well as the entry of mining waste, causing an increase in eutrophication. Water quality variables such as chlorophyll-a concentration [Chl-a] and turbidity, can be studied through its optical properties by remote sensing techniques. In this work, a methodology is proposed for monitoring [Chl-a] and the turbidity of the Mar Menor using Sentinel-2 images. For this purpose, an extensive database of both variables was used consisting of data taken on different dates between 2016 and 2019 at 12 points of Mar Menor. The images were atmospherically corrected using Case 2 Regional Coast Color Processor (C2RCC) version for turbid waters (C2X) to obtain the water surface reflectance. Then several arithmetic relations between database and reflectance bands used in the bibliography for [Chl-a] and turbidity were analyzed. Comparing the results of each one of these relations with the in situ data, decided that the best index for [Chl-a] estimation is the relation (R560 + R705)/ (R560 + R665) with an RMSE = 2.6 mg/m3 and a NRMSE = 9.1 % and for turbidity R705*R705/R490 with an RMSE = 1.5 NTU and a NRMSE= 10.9 %. Finally, by applying these relationships on different dates, thematic maps of [Chl-a] and turbidity of Mar Menor were obtained. One of these images was some days after September 2019 torrential rains, in which a considerable [Chl-a] and turbidity increase was observed