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Land use/land cover change (LUCC) combined with gold mining activity (GMA) have reportedly affected the water quality of Amazonian rivers by intensifying surface soil erosion and increasing rivers’ sediment concentration (SC). However, the role of LUCC on river siltation in comparison to that caused by GMA has not been assessed, and predictions of SC in the rivers accounting for future LUCC are scarce. This study applied a sediment modeling approach on Crepori Basin, located at eastern Amazon Basin, to simulate the impacts of past and future LUCC on SC in the river, comparing their impacts to those caused by GMA. Between 1973 and 2012, the expansion of deforested areas in the region had increased sheet erosion-driven SC in the river, especially during the high-water season, by up to 73.3%. LUCC projections for 2050 suggest future increases in sheet erosion-driven SC during the high-water season by more than three times of that caused by the land cover scenario of 1998–2012. Comparison between SC driven by sheet erosion to that caused by GMA has also shown that during the high-water and the low-water seasons, respectively, only about 14% and 6% of the total SC in the Crepori River resulted from laminar soil erosion, with the remaining proportion resulting from GMA.

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.

Sobradinho reservoir has been suffering a severe water loss caused by multi-year drought in the Northeast Brazil. This reservoir contributes to the socio-economic development of the semi-arid region, and the monitoring of water shortage is crucial for people living in this climate-vulnerable region. In this study, we evaluate the surface water change and turbidity variability of Sobradinho reservoir during recent drought years (2013–2017). A time-series dataset was created using 109 Landsat-8 OLI images for mapping the water extent in the reservoir. A non-linear regression between measured turbidity and surface reflectance (red band) was developed and applied for turbidity retrievals. Additionally, we performed a long-term precipitation analysis (17-year) to assess the rainfall deficit over the catchment area. Our results show that the annual precipitation regimes are below the long-term average during 2012–2017 period, except 2013. We also found that negative anomalies occur during 26 out of 36 months between 2014 and 2016, mostly in the rainy season. Since the rainfall regimes and river discharges are the major drivers for water recharge, these drought years have a critical impact on the reservoir level. According to our results, the water surface receded about 2073 km2 (out of total 3303 km2) during September 2017; this represents a reduction of 62.8% in the total water extent. The surface water change is spatially distinct across the reservoir. For instance, the upper section of the reservoir was almost totally dried during September 2017, and the water coverage was ~8% (91.25 km2 out of 1128 km2). Although other sections had a relatively low water change (reduction of ~40%), the losses are significant in terms of area (~1035.5 km2). The receding of water extent affects the people living near to the reservoir, and local communities are more distant from water (up to 13 km). We also observed that the turbidity is seasonally dependent, and water clarity presents a strong variability between rainy and dry seasons. In general, the turbidity levels vary from clear water (0–20 NTU) during the dry season to turbid condition (>50 NTU) during the rainy season. A lack of access to clean and safe drinking water in some periods might be harmful to humans, livestock and domestic animals. Finally, this research contributes to the assessment of drought-related impacts in the Sobradinho, the largest reservoir in the Northeast Brazil. The water shortage is a recurring concern in the semi-arid region, and the remote sensing techniques provide spatially explicit information to enhance the livelihood resilience during drought years.

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.

Land cover changes (LCC) affect the water balance (WB), changing surface runoff (SurfQ), evapotranspiration (ET), groundwater (GW) regimes, and streamflow (Q). The Tapaj&oacute, s Basin (southeastern Amazon) has experienced LCC over the last 40 years, with increasing LCC rates projected for the near future. Several studies have addressed the effects of climate changes on the region&rsquo, s hydrology, but few have explored the effects of LCC on its hydrological regime. In this study, the Soil and Water Assessment Tool (SWAT) was applied to model the LCC effects on the hydrology of the Upper Crepori River Basin (medium Tapaj&oacute, s Basin), using historical and projected LCC based on conservation policies (GOV_2050) and on the &ldquo, Business as Usual&rdquo, trend (BAU_2050). LCC that occurred from 1973 to 2012, increased Q by 2.5%, without noticeably altering the average annual WB. The future GOV_2050 and BAU_2050 scenarios increased SurfQ by 238.87% and 300.90%, and Q by 2.53% and 2.97%, respectively, and reduced GW by 4.00% and 5.21%, and ET by 2.07% and 2.43%, respectively. Results suggest that the increase in deforestation will intensify floods and low-flow events, and that the conservation policies considered in the GOV_2050 scenario may still compromise the region&rsquo, s hydrology at a comparable level to that of the BAU_2050.

Although mining plays an important role for the economy of the Amazon, little is known about its attributes such as area, type, scale, and current status as well as socio/environmental impacts. Therefore, we first propose a low time-consuming and high detection accuracy method for mapping the current mining areas within 13 regions of the Brazilian Amazon using Sentinel-2 images. Then, integrating the maps in a GIS (Geography Information System) environment, mining attributes for each region were further assessed with the aid of the DNPM (National Department for Mineral Production) database. Detection of the mining area was conducted in five main steps. (a) MSI (MultiSpectral Instrument)/Sentinel-2A (S2A) image selection; (b) definition of land-use classes and training samples; (c) supervised classification; (d) vector editing for quality control; and (e) validation with high-resolution RapidEye images (Kappa = 0.70). Mining areas derived from validated S2A classification totals 1084.7 km2 in the regions analyzed. Small-scale mining comprises up to 64% of total mining area detected comprises mostly gold (617.8 km2), followed by tin mining (73.0 km2). The remaining 36% is comprised by industrial mining such as iron (47.8), copper (55.5) and manganese (8.9 km2) in Carajás, bauxite in Trombetas (78.4) and Rio Capim (48.5 km2). Given recent events of mining impacts, the large extension of mining areas detected raises a concern regarding its socio-environmental impacts for the Amazonian ecosystems and for local communities.

Uncertainties in the estimates of water constituents are among the main issues concerning the orbital remote sensing of inland waters. Those uncertainties result from sensor design, atmosphere correction, model equations, and in situ conditions (cloud cover, lake size/shape, and adjacency effects). In the Amazon floodplain lakes, such uncertainties are amplified due to their seasonal dynamic. Therefore, it is imperative to understand the suitability of a sensor to cope with them and assess their impact on the algorithms for the retrieval of constituents. The objective of this paper is to assess the impact of the SNR on the Chl-a and TSS algorithms in four lakes located at Mamirauá Sustainable Development Reserve (Amazonia, Brazil). Two data sets were simulated (noisy and noiseless spectra) based on in situ measurements and on sensor design (MSI/Sentinel-2, OLCI/Sentinel-3, and OLI/Landsat 8). The dataset was tested using three and four algorithms for TSS and Chl-a, respectively. The results showed that the impact of the SNR on each algorithm displayed similar patterns for both constituents. For additive and single band algorithms, the error amplitude is constant for the entire concentration range. However, for multiplicative algorithms, the error changes according to the model equation and the Rrs magnitude. Lastly, for the exponential algorithm, the retrieval amplitude is higher for a low concentration. The OLCI sensor has the best retrieval performance (error of up to 2 μg/L for Chl-a and 3 mg/L for TSS). For MSI, the error of the additive and single band algorithms for TSS and Chl-a are low (up to 5 mg/L and 1 μg/L, respectively); but for the multiplicative algorithm, the errors were above 10 μg/L. The OLI simulation resulted in errors below 3 mg/L for TSS. However, the number and position of OLI bands restrict Chl-a retrieval. Sensor and algorithm selection need a comprehensive analysis of key factors such as sensor design, in situ conditions, water brightness (Rrs), and model equations before being applied for inland water studies.

Using remote sensing for monitoring trophic states of inland waters relies on the calibration of chlorophyll-a (chl-a) bio-optical algorithms. One of the main limiting factors of calibrating those algorithms is that they cannot accurately cope with the wide chl-a concentration ranges in optically complex waters subject to different trophic states. Thus, this study proposes an optical hybrid chl-a algorithm (OHA), which is a combined framework of algorithms for specific chl-a concentration ranges. The study area is Ibitinga Reservoir characterized by high spatiotemporal variability of chl-a concentrations (3–1000 mg/m3). We took the following steps to address this issue: (1) we defined optical classes of specific chl-a concentration ranges using Spectral Angle Mapper (SAM); (2) we calibrated/validated chl-a bio-optical algorithms for each trophic class using simulated Sentinel-2 MSI (Multispectral Instrument) bands; (3) and we applied a decision tree classifier in MSI/Sentinel-2 image to detect the optical classes and to switch to the suitable algorithm for the given class. The results showed that three optical classes represent different ranges of chl-a concentration: class 1 varies 2.89–22.83 mg/m3, class 2 varies 19.51–87.63 mg/m3, and class 3 varies 75.89–938.97 mg/m3. The best algorithms for trophic classes 1, 2, and 3 are the 3-band (R2 = 0.78; MAPE - Mean Absolute Percentage Error = 34.36%), slope (R2 = 0.93; MAPE = 23.35%), and 2-band (R2 = 0.98; MAPE = 20.12%), respectively. The decision tree classifier showed an accuracy of 95% for detecting SAM’s optical trophic classes. The overall performance of OHA was satisfactory (R2 = 0.98; MAPE = 26.33%) using in situ data but reduced in the Sentinel-2 image (R2 = 0.42; MAPE = 28.32%) due to the temporal gap between matchups and the variability in reservoir hydrodynamics. In summary, OHA proved to be a viable method for estimating chl-a concentration in Ibitinga Reservoir and the extension of this framework allowed a more precise chl-a estimate in eutrophic inland waters.

An innovative remote sensing approach that combines land-use change and water quality information is proposed in order to investigate if Artisanal and Small-scale Gold Mining (ASGM) area extension is associated with water siltation in the Tapajos River Basin (Brazil), containing the largest small-scale gold mining district in the world. Taking advantage of a 40-year period of the multi-satellite imagery archive, the objective of this paper is to build a normalized time-series in order to evaluate the influence of temporal mining expansion on the water siltation data (TSS, Total Suspended Solids concentration) derived from previous research. The methodological approach was set to deliver a full characterization of the ASGM expansion from its initial stages in the early 1970s to the present. First, based on IRS/LISSIII images acquired in 2012, the historical Landsat image database (1973–2001) was corrected for radiometric and atmospheric effects using dark vegetation as reference to create a normalized time-series. Next, a complete update of the mining areas distribution in 2012 derived from the TerraClass Project (an official land-use classification for the Brazilian Amazon) was conducted having IRS/LISSIII as the base map with the support of auxiliary data and vector editing. Once the ASGM in 2012 was quantified (261.7 km2) and validated with photos, a reverse classification of ASGM in 2001 (171.7 km2), 1993 (166.3 km2), 1984 (47.5 km2), and 1973 (15.4 km2) with the use of Landsat archives was applied. This procedure relies on the assumption that ASGM changes in the land cover are severe and remain detectable from satellite sensors for decades. The mining expansion area over time was then combined with the (TSS) data retrieved from the same atmospherically corrected satellite imagery based on the literature. In terms of gold mining expansion and water siltation effects, four main periods of ASGM activities were identified in the study area: (i) 1958–1977, first occurrence of mining activities and low water impacts; (ii) 1978–1993, introduction of low-budget mechanization associated with very high gold prices resulting in large mining area expansion and high water siltation levels; (iii) 1994–2003, general recession of ASGM activities and exhaustion of easy-access gold deposits, resulting in decreased TSS; (iv) 2004 to present, intensification of ASGM encouraged by high gold prices, resulting in an increase of TSS.

Artisanal and Small-scale Gold Mining (ASGM) within the Amazon region has created several environmental impacts, such as mercury contamination and changes in water quality due to increased siltation. This paper describes the effects of water siltation on the underwater light environment of rivers under different levels of gold mining activities in the Tapajós River Basin. Furthermore, it investigates possible impacts on the phytoplankton community. Two field campaigns were conducted in the Tapajós River Basin, during high water level and during low water level seasons, to measure Inherent and Apparent Optical Properties (IOPs, AOPs), including scattering (b) and absorption (a) coefficients and biogeochemical data (sediment content, pigments, and phytoplankton quantification). The biogeochemical data was separated into five classes according to the concentration of total suspended solids (TSS) ranging from 1.8 mg·L−1 to 113.6 mg·L−1. The in-water light environment varied among those classes due to a wide range of concentrations of inorganic TSS originated from different levels of mining activities. For tributaries with low or no influence of mining tailings (TSS up to 6.8 mg·L−1), waters are relatively more absorbent with b:a ratio of 0.8 at 440 nm and b660 magnitude of 2.1 m−1. With increased TSS loadings from mining operations (TSS over 100 mg·L−1), the scattering process prevails over absorption (b:a ratio of 10.0 at 440 nm), and b660 increases to 20.8 m−1. Non-impacted tributaries presented a critical depth for phytoplankton productivity of up to 6.0 m with available light evenly distributed throughout the spectra. Whereas for greatly impacted waters, attenuation of light was faster, reducing the critical depth to about 1.7 m, with most of the available light comprising of red wavelengths. Overall, a dominance of diatoms was observed for the upstream rivers, whereas cyanobacteria prevailed in the low section of the Tapajós River. The results suggest that the spatial and temporal distribution of phytoplankton in the Tapajós River Basin is not only a function of light availability, but rather depends on the interplay of factors, including flood pulse, water velocity, nutrient availability, and seasonal variation of incoming irradiance. Ongoing research indicates that the effects of mining tailings on the aquatic environment, described here, are occurring in several rivers within the Amazon River Basin.

Satellite data provide the only viable means for extensive monitoring of remote and large freshwater systems, such as the Amazon floodplain lakes. However, an accurate atmospheric correction is required to retrieve water constituents based on surface water reflectance ( R W ). In this paper, we assessed three atmospheric correction methods (Second Simulation of a Satellite Signal in the Solar Spectrum (6SV), ACOLITE and Sen2Cor) applied to an image acquired by the MultiSpectral Instrument (MSI) on-board of the European Space Agency’s Sentinel-2A platform using concurrent in-situ measurements over four Amazon floodplain lakes in Brazil. In addition, we evaluated the correction of forest adjacency effects based on the linear spectral unmixing model, and performed a temporal evaluation of atmospheric constituents from Multi-Angle Implementation of Atmospheric Correction (MAIAC) products. The validation of MAIAC aerosol optical depth (AOD) indicated satisfactory retrievals over the Amazon region, with a correlation coefficient (R) of ~0.7 and 0.85 for Terra and Aqua products, respectively. The seasonal distribution of the cloud cover and AOD revealed a contrast between the first and second half of the year in the study area. Furthermore, simulation of top-of-atmosphere (TOA) reflectance showed a critical contribution of atmospheric effects (>50%) to all spectral bands, especially the deep blue (92%–96%) and blue (84%–92%) bands. The atmospheric correction results of the visible bands illustrate the limitation of the methods over dark lakes ( R W < 1%), and better match of the R W shape compared with in-situ measurements over turbid lakes, although the accuracy varied depending on the spectral bands and methods. Particularly above 705 nm, R W was highly affected by Amazon forest adjacency, and the proposed adjacency effect correction minimized the spectral distortions in R W (RMSE < 0.006). Finally, an extensive validation of the methods is required for distinct inland water types and atmospheric conditions.