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Water colour remote sensing is a valuable tool for assessing bio-optical and biogeochemical parameters across the vast extent of the Amazon River Continuum (ARC). However, accurate retrieval depends on selecting the best atmospheric correction (AC). Four AC processors (Acolite, Polymer, C2RCC, OC-SMART) were evaluated against in situ remote sensing reflectance (Rrs) measurements. K-means classification identified four optical water types (OWTs) that are affected by the ARC. Two OWTs showed seasonal differences in the Lower Amazon River, influenced by the increase in suspended sediment concentration with river discharge. The other OWTs in the Amazon River Plume are dominated by phytoplankton or by a mixture of optically significant constituents. The Quality Water Index Polynomial method used to assess the quality of in situ and orbital Rrs had a high failure rate when the Apparent Visible Wavelength was >580 nm for in situ Rrs. OC-SMART Rrs products showed better spectral quality compared to Rrs derived from other AC processors evaluated in this study. These results improve our understanding of remotely sensing very turbid waters, such as those in the Amazon River Continuum. 

BackgroundSchistosomiasis, a chronic parasitic disease, remains a public health issue in tropical and subtropical regions, especially in low and moderate-income countries lacking assured access to safe water and proper sanitation. A national prevalence survey carried out by the Brazilian Ministry of Health from 2011 to 2015 found a decrease in human infection rates to 1%, with 19 out of 26 states still classified as endemic areas. There is a risk of schistosomiasis reemerging as a public health concern in low-endemic regions. This study proposes an integrated landscape-based approach to aid surveillance and control strategies for schistosomiasis in low-endemic areas. Methodology/Principal findingsIn the Middle Paranapanema river basin, specific landscapes linked to schistosomiasis were identified using a comprehensive methodology. This approach merged remote sensing, environmental, socioeconomic, epidemiological, and malacological data. A team of experts identified ten distinct landscape categories associated with varying levels of schistosomiasis transmission potential. These categories were used to train a supervised classification machine learning algorithm, resulting in a 92.5% overall accuracy and a 6.5% classification error. Evaluation revealed that 74.6% of collected snails from water collections in five key municipalities within the basin belonged to landscape types with higher potential forS. mansoniinfection. Landscape connectivity metrics were also analysed. Conclusions/SignificanceThis study highlights the role of integrated landscape-based analyses in informing strategies for eliminating schistosomiasis. The methodology has produced new schistosomiasis risk maps covering the entire basin. The region’s low endemicity can be partly explained by the limited connectivity among grouped landscape-units more prone to triggering schistosomiasis transmission. Nevertheless, changes in social, economic, and environmental landscapes, especially those linked to the rising pace of incomplete urbanization processes in the region, have the potential to increase risk of schistosomiasis transmission. This study will help target interventions to bring the region closer to schistosomiasis elimination. 

Optical water types (OWTs) were identified from an in situ dataset of concomitant biogeochemical and optical parameters acquired in the Amazon River and its tributaries, in the Lower Amazon region, at different hydrological conditions from 2014 to 2017. A seasonal bio-optical characterization was performed. The k-means classification was applied to the in situ normalized reflectance spectra (rn(λ)), allowing the identification of four OWTs. An optical index method was also applied to the rn(λ) defining the thresholds of the OWTs. Next, level-3 Sentinel-3 Ocean and Land Color Instrument images representative of the seasonal discharge conditions were classified using the identified in situ OWTs as reference. The differences between Amazon River and clearwater tributary OWTs were dependent on the hydrological dynamics of the Amazon River, also showing a strong seasonal variability. Each OWT was associated with a specific bio-optical and biogeochemical environment assessed from the corresponding absorption coefficient values of colored dissolved organic matter (aCDOM) and particulate matter (ap), chlorophyll-a and suspended particulate matter (SPM) concentrations, and aCDOM/ap ratio. The rising water season presented a unique OWT with high SPM concentration and high relative contribution of ap to total absorption compared to the other OWTs. This bio-optical characterization of Lower Amazon River waters represents a first step for developing remote sensing inversion models adjusted to the optical complexity of this region. 

Abstract. A global in situ data set for validation of ocean colour productsfrom the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented.This version of the compilation, starting in 1997, now extends to 2021,which is important for the validation of the most recent satellite opticalsensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprisesin situ observations of the following variables: spectral remote-sensingreflectance, concentration of chlorophyll-a, spectral inherent opticalproperties, spectral diffuse attenuation coefficient, and total suspendedmatter. Data were obtained from multi-project archives acquired via openinternet services or from individual projects acquired directly from dataproviders. Methodologies were implemented for homogenization, qualitycontrol, and merging of all data. Minimal changes were made on the originaldata, other than conversion to a standard format, elimination of some points,after quality control and averaging of observations that were close in timeand space. The result is a merged table available in text format. Overall,the size of the data set grew with 148 432 rows, with each row representing aunique station in space and time (cf. 136 250 rows in previous version;Valente et al., 2019). Observations of remote-sensing reflectance increasedto 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There wasalso a near tenfold increase in chlorophyll data since 2016. Metadata ofeach in situ measurement (original source, cruise or experiment, principalinvestigator) are included in the final table. By making the metadataavailable, provenance is better documented and it is also possible toanalyse each set of data separately. The compiled data are available athttps://doi.org/10.1594/PANGAEA.941318 (Valente et al., 2022). 

Abstract. A global compilation of in situ data is useful to evaluate thequality of ocean-colour satellite data records. Here we describe the datacompiled for the validation of the ocean-colour products from the ESA OceanColour Climate Change Initiative (OC-CCI). The data were acquired fromseveral sources (including, inter alia, MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD,MERMAID, AMT, ICES, HOT and GeP&CO) and span the period from 1997 to 2018.Observations of the following variables were compiled: spectralremote-sensing reflectances, concentrations of chlorophyll a, spectralinherent optical properties, spectral diffuse attenuation coefficients andtotal suspended matter. The data were from multi-project archives acquiredvia open internet services or from individual projects, acquired directlyfrom data providers. Methodologies were implemented for homogenization,quality control and merging of all data. No changes were made to theoriginal data, other than averaging of observations that were close in timeand space, elimination of some points after quality control and conversionto a standard format. The final result is a merged table designed forvalidation of satellite-derived ocean-colour products and available in textformat. Metadata of each in situ measurement (original source, cruise orexperiment, principal investigator) was propagated throughout the work andmade available in the final table. By making the metadata available,provenance is better documented, and it is also possible to analyse each setof data separately. This paper also describes the changes that were made tothe compilation in relation to the previous version (Valente et al., 2016).The compiled data are available athttps://doi.org/10.1594/PANGAEA.898188 (Valente et al., 2019).