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Abstract Originally developed for terrestrial science and applications, the US Geological Survey Landsat surface reflectance (SR) archive spanning ~ 40 yr of observations has been increasingly utilized in large‐scale water‐quality studies. These products, however, have not been rigorously validated using in situ measured reflectance. This letter quantifies and demonstrates the quality of the SR products by harnessing a sizeable global dataset (N = 1100). We found that the Landsat 8/9 SR in the green and red bands marginally meet the targeted accuracy requirements (30%), whereas the uncertainties in the blue and coastal‐aerosol bands ranged from 48% to 110%. We further observed > +25% biases in the visible bands of Landsat 5/7 SR, which can introduce an apparent downward trend when applied in time‐series analyses combined with Landsat 8/9. Users must exercise caution when using this archive for trend analyses, and progress in atmospheric correction is required to foster advanced applications of the Landsat archive for aquatic science.

The Amazon Basin is the largest on the planet, and its aquatic ecosystems affect and are affected by the Earth’s processes. Specifically, Amazon aquatic ecosystems have been subjected to severe anthropogenic impacts due to deforestation, mining, dam construction, and widespread agribusiness expansion. Therefore, the monitoring of these impacts has become crucial for conservation plans and environmental legislation enforcement. However, its continental dimensions, the high variability of Amazonian water mass constituents, and cloud cover frequency impose a challenge for developing accurate satellite algorithms for water quality retrieval such as chlorophyll-a concentration (Chl-a), which is a proxy for the trophic state. This study presents the first application of the hybrid semi-analytical algorithm (HSAA) for Chl-a retrieval using a Sentinel-3 OLCI sensor over five Amazonian floodplain lakes. Inherent and apparent optical properties (IOPs and AOPs), as well as limnological data, were collected at 94 sampling stations during four field campaigns along hydrological years spanning from 2015 to 2017 and used to parameterize the hybrid SAA to retrieve Chl-a in highly turbid Amazonian waters. We implemented a re-parametrizing approach, called the generalized stacked constraints model to the Amazonian waters (GSCMLAFW), and used it to decompose the total absorption αt(λ) into the absorption coefficients of detritus, CDOM, and phytoplankton (αphy(λ)). The estimated GSCMLAFWαphy(λ) achieved errors lower than 24% at the visible bands and 70% at NIR. The performance of HSAA-based Chl-a retrieval was validated with in situ measurements of Chl-a concentration, and then it was compared to literature Chl-a algorithms. The results showed a smaller mean absolute percentage error (MAPE) for HSAA Chl-a retrieval (36.93%) than empirical Rrs models (73.39%) using a 3-band algorithm, which confirms the better performance of the semi-analytical approach. Last, the calibrated HSAA model was used to estimate the Chl-a concentration in OLCI images acquired during 2017 and 2019 field campaigns, and the results demonstrated reasonable errors (MAPE = 57%) and indicated the potential of OLCI bands for Chl-a estimation. Therefore, the outcomes of this study support the advance of semi-analytical models in highly turbid waters and highlight the importance of re-parameterization with GSCM and the applicability of HSAA in Sentinel-3 OLCI data.

The composition of phytoplankton and the concentration of pigments in their cells make their absorption and specific absorption coefficients key parameters for bio-optical modeling. This study investigated whether the multispectral vertical diffuse attenuation coefficient of downward irradiance (Kd) gradients could be a good framework for accessing phytoplankton genera. In situ measurements of remote sensing reflectance (Rrs), obtained in an Amazon Floodplain Lake (Lago Grande do Curuai), were used to invert Kd, focusing on Sentinel-3/Ocean and Land Color Instrument (OLCI) sensor bands. After that, an analysis based on the organization of three-way tables (STATICO) was applied to evaluate the relationships between phytoplankton genera and Kd at different OLCI bands. Our results indicate that phytoplankton genera are organized according to their ability to use light intensity and different spectral ranges of visible light (400 to 700 nm). As the light availability changes seasonally, the structure of phytoplankton changes as well. Some genera, such as Microcystis, are adapted to low light intensity at 550–650 nm, therefore high values of Kd in this range would indicate the dominance of Microcysts. Other genera, such as Aulacoseira, are highly adapted to harvesting blue-green light with higher intensity and probably grow in lakes with lower concentrations of colored dissolved organic matter that highly absorbs blue light (405–498). These findings are an important step to describing phytoplankton communities using orbital data in tropical freshwater floodplains. Furthermore, this approach can be used with biodiversity indexes to access phytoplankton diversity in these environments.

Traditionally, water conditions of coffee areas are monitored by measuring the leaf water potential (ΨW) throughout a pressure pump. However, there is a demand for the development of technologies that can estimate large areas or regions. In this context, the objective of this study was to estimate the ΨW by surface reflectance values and vegetation indices obtained from the Landsat-8/OLI sensor in Minas Gerais-Brazil Several algorithms using OLI bands and vegetation indexes were evaluated and from the correlation analysis, a quadratic algorithm that uses the Normalized Difference Vegetation Index (NDVI) performed better, with a correlation coefficient (R2) of 0.82. Leave-One-Out Cross-Validation (LOOCV) was performed to validate the models and the best results were for NDVI quadratic algorithm, presenting a Mean Absolute Percentage Error (MAPE) of 27.09% and an R2 of 0.85. Subsequently, the NDVI quadratic algorithm was applied to Landsat-8 images, aiming to spatialize the ΨW estimated in a representative area of regional coffee planting between September 2014 to July 2015. From the proposed algorithm, it was possible to estimate ΨW from Landsat-8/OLI imagery, contributing to drought monitoring in the coffee area leading to cost reduction to the producers.

Due to increasing algae bloom occurrence and water degradation on a global scale, there is a demand for water quality monitoring systems based on remote sensing imagery. This paper describes the scientific, theoretical, and methodological background for creating a cloud-computing interface on Google Earth Engine (GEE) which allows end-users to access algae bloom related products with high spatial (30 m) and temporal (~5 day) resolution. The proposed methodology uses Sentinel-2 images corrected for atmospheric and sun-glint effects to generate an image collection of the Normalized Difference Chlorophyll-a Index (NDCI) for the entire time-series. NDCI is used to estimate both Chl-a concentration, based on a non-linear fitting model, and Trophic State Index (TSI), based on a tree-decision model classification into five classes. Once the Chl-a and TSI algorithms had been calibrated and validated they were implemented in GEE as an Earth Engine App, entitled Algae Bloom Monitoring Application (AlgaeMAp). AlgaeMAp is the first online platform built within the GEE platform that offers high spatial resolution of water quality parameters. The App benefits from the huge processing capability of GEE that allows any user with internet access to easily extract detailed spatial (30 m) and long temporal Chl-a and TSI information (from August 2015 and with images every 5 days) throughout the most important reservoirs in the State of São Paulo/Brazil. The application will be adapted to extend to other relevant areas in Latin America.

The diffuse attenuation coefficient of downwelling irradiance (Kd) is an essential parameter for inland waters research by remotely sensing the water transparency. Lately, Kd semi-analytical algorithms substituted the empirical algorithms widely employed. The purpose of this research was to reparametrize a semi-analytical algorithm to estimate Kd and then apply it to a Sentinel-2 MSI time-series (2017–2019) for the Três Marias reservoir, Brazil. The results for the Kd semi-analytical reparametrization achieved good accuracies, reaching mean absolute percentage errors (MAPE) for bands B2, B3 and B4 (492, 560 and 665 nm), lower than 21% when derived from in-situ remote sensing reflectance (Rrs), while for MSI Data, a derived MAPE of 12% and 38% for B2 and B3, respectively. After the application of the algorithm to Sentinel-2 images time-series, seasonal patterns were observed in the results, showing high Kd values at 492 nm during the rainy periods, mainly in the tributary mouths, possibly due to an increase in the surface runoff and inflows and outflow rates in the reservoir watershed.