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.

Multiangle Implementation of Atmospheric Correction (MAIAC) is a new Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm that combines time series approach and image processing to derive surface reflectance and atmosphere products, such as aerosol optical depth (AOD) and columnar water vapor (CWV). The quality assessment of MAIAC AOD at 1 km resolution is still lacking across South America. In the present study, critical assessment of MAIAC AOD550 was performed using ground‐truth data from 19 Aerosol Robotic Network (AERONET) sites over South America. Additionally, we validated the MAIAC CWV retrievals using the same AERONET sites. In general, MAIAC AOD Terra/Aqua retrievals show high agreement with ground‐based measurements, with a correlation coefficient (R) close to unity (RTerra:0.956 and RAqua: 0.949). MAIAC accuracy depends on the surface properties and comparisons revealed high confidence retrievals over cropland, forest, savanna, and grassland covers, where more than 2/3 (~66%) of retrievals are within the expected error (EE = ±(0.05 + 0.05 × AOD)) and R exceeding 0.86. However, AOD retrievals over bright surfaces show lower correlation than those over vegetated areas. Both MAIAC Terra and Aqua retrievals are similarly comparable to AERONET AOD over the MODIS lifetime (small bias offset ~0.006). Additionally, MAIAC CWV presents quantitative information with R ~ 0.97 and more than 70% of retrievals within error (±15%). Nonetheless, the time series validation shows an upward bias trend in CWV Terra retrievals and systematic negative bias for CWV Aqua. These results contribute to a comprehensive evaluation of MAIAC AOD retrievals as a new atmospheric product for future aerosol studies over South America.

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The quantitative assessment of cloud cover and atmospheric constituents improves our ability to exploit the climate feedback into the Amazon basin. In the 21st century, three droughts have already occurred in the Amazonia (e.g. 2005, 2010, 2015), inducing regional changes in the seasonal patterns of atmospheric constituents. In addition to climate, the atmospheric dynamic and attenuation properties are long-term challenges for satellite-based remote sensing of this ecosystem: high cloudiness, abundant water vapor content and biomass burning season. Therefore, while climatology analysis supports the understanding of atmospheric variability and trends, it also offers valuable insights for remote sensing applications. In this study, we evaluate the seasonal and interannual variability of cloud cover and atmospheric constituents (aerosol loading, water vapor and ozone content) over the Amazon basin, with focus on both climate analysis and remote sensing implications. We take the advantage of new atmosphere daily products at 1 km resolution derived from Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm developed for Moderate Resolution Imaging Spectroradiometer (MODIS) data. An intercomparison of Aerosol Robotic Network (AERONET) and MAIAC aerosol optical depth (AOD) and columnar water vapor (CWV) showed quantitative information with a correlation coefficient higher than 0.81. Our results show distinct regional patterns of cloud cover across the Amazon basin: northwestern region presets a persistent cloud cover (>80%) throughout the year, while low cloud cover (0-20%) occurs in the southern Amazon during the dry season. The cloud-free period in the southern Amazon is followed by an increase in the atmospheric burden due to fire emissions. Our results reveal that AOD records are changing in terms of area and intensity. During the 2005 and 2010 droughts, the positive AOD anomalies (δ > 0.1) occurred over 39.03% (240.3 million ha) and 27.14% (165.99 million ha) of total basin in the SON season, respectively. In contrast, the recent 2015 drought occurred towards the end of year (October through December) and these anomalies were observed over 23.72% (145 million ha) affecting areas in the central and eastern Amazon-unlike previous droughts. The water vapor presents high concentration values (4.0-5.0 g cm−2) in the wet season (DJF), while we observed a strong spatial gradient from northwestern to southeastern of the basin during the dry season. In addition, we also found a positive trend of water vapor content (∼0.3 g/cm2) between 2000 and 2015. The total ozone typically varies between 220 and 270 DU, and it has a seasonal change of ∼25-35 DU from wet season to dry season caused by large emissions of ozone precursors and long-range transport. Finally, while this study contributes to climatological analysis of atmospheric constituents, the remote sensing users can also understand the regional constraints caused by atmospheric attenuation, such as high aerosol loading and cloud obstacles for surface obs...

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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)(2014)(2015)(2016)(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 2,073 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 (~1,035.5 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.

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The water vapor is a relevant greenhouse gas in the Earth's climate system, and satellite products become one of the most effective way to characterize and monitor the columnar water vapor (CWV) content at global scale. Recently, a new product (MCD19) was released as part of MODIS (Moderate Resolution Imaging Spectroradiometer) Collection 6 (C6). This operational product from Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm includes a high 1 km resolution CWV retrievals. This study presents the first global validation of MAIAC C6 CWV obtained from MODIS MCD19A2 product. This evaluation was performed using Aerosol Robotic Network (AERONET) observations at 265 sites (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). Overall, the results show a good agreement between MAIAC/AERONET CWV retrievals, with correlation coefficient higher than 0.95 and RMS error lower than 0.250 cm. The binned error analysis revealed an underestimation (~10%) of Aqua CWV retrievals with negative bias for CWV higher than 3.0 cm. In contrast, Terra CWV retrievals show a slope of regression close to unity and a low mean bias of 0.075 cm. While the accuracy is relatively similar between 1.0 and 5.0 cm for both sensor products, Terra dataset is more reliable for applications in humid tropical areas (>5.0 cm). The expected error was defined as ±15%, with >68% of retrievals falling within this envelope. However, the accuracy is regionally dependent, and lower error should be expected in some regions, such as South America and Oceania. Since MODIS instruments have exceeded their design lifetime, time series analysis was also presented for both sensor products. The temporal analysis revealed a systematic offset of global average between Terra and Aqua CWV records. We also found an upward trend (~0.2 cm/decade) in Terra CWV retrievals, while Aqua CWV retrievals remain stable over time. The sensor degradation influences the ability to detect climate signals, and this study indicates the need for revisiting calibration of the MODIS bands 17-19, mainly for Terra instrument, to assure the quality of the MODIS water vapor product. Finally, this study presents a comprehensive validation analysis of MAIAC CWV over land, raising the understanding of its overall quality.The water vapor is a relevant greenhouse gas in the Earth's climate system, and satellite products become one of the most effective way to characterize and monitor the columnar water vapor (CWV) content at global scale. Recently, a new product (MCD19) was released as part of MODIS (Moderate Resolution Imaging Spectroradiometer) Collection 6 (C6). This operational product from Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm includes a high 1 km resolution CWV retrievals. This study presents the first global validation of MAIAC C6 CWV obtained from MODIS MCD19A2 product. This evaluation was performed using Aerosol Robotic Network (AERONET) observations at 265 sites (2000)(2001)(2002)(2003)(...