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In 2023, an intense drought impacted the Amazon basin triggered by climate change and a strong El Niño event, with the Negro River reaching its lowest water level in 120 years. However, the spatiotemporal open water extent (OWE) during this drought remains unclear. This study comprehensively evaluates OWE variability in the central Amazon using Sentinel-1 synthetic aperture radar (SAR) data since 2017. Monthly OWE masks were generated through an empirical threshold classification with accuracy >95%. Overall, the central Amazon experienced a reduction of ∼8% in OWE in the 2023 dry season months (November and December) when compared to monthly-average. However, reductions of up to 80% in OWE were observed in several specific lakes. Our analysis underscores the unprecedented severity of the 2023/2024 drought on rivers and floodplains. Utilizing SAR remote sensing technologies, this study emphasizes the urgent need for proactive conservation measures to safeguard the Amazon’s ecological integrity amid escalating environmental challenges. Monthly water masks from January/2017 to September/2024 are available here: https://doi.org/10.5281/zenodo.12751783 .

Nanosatellites and CubeSats were first developed for educational purposes. However, their low cost and short development cycle made nanosatellite constellations an affordable option for observing the Earth by remote sensing, increasing the frequency of high-resolution imagery, which is fundamental for studying and monitoring dynamic processes. In this sense, although still incipient, nanosatellite applications and proposed Earth observation missions are steadily growing in number and scientific fields. There are several initiatives from universities, space agencies and private companies to launch new nanosatellite missions. These initiatives are actively investigating new technologies to improve image quality and studying ways to increase acquisition frequency through the launch of larger constellations. So far, the private sector is leading the development of new missions, with proposals ranging from 12 to more than one thousand nanosatellite constellations. Furthermore, new nanosatellite missions have been proposed to tackle specific applications, such as natural disasters, or to test improvements on nanosatellite spatial, temporal and radiometric resolution. The unprecedented combination of high spatial and temporal resolution from nanosatellite constellations associated with improvement efforts in sensor quality is promising and may represent a trend to replace the era of large satellites for smaller and cheaper nanosatellites. This article first reports on the development and new nanosatellite missions of space agencies, universities and private companies. Then a systematic review of published articles using the most successful private constellation (PlanetScope and Doves) is presented and the principal papers are discussed.

Nowadays, the monitoring of water is essential for the sustainability and better management of water resources. The use of remote sensing data is important, since it allows evaluation of dynamic problems in aquatic systems, such as the eutrophication of bodies of water and suspended sediment. The aim of this study was to estimate chlorophyll-a concentrations in a reservoir of the semi-arid region of Brazil using simulated orbital-sensor data, as an aid in the management of water resources. The study area corresponded to the Orós reservoir, in the State of Ceará, Brazil. Water samples for analysis of the chlorophyll-a and measurements of the spectral radiance of the aquatic system were collected from 20 points. The radiance was measured by spectroradiometer. The data were collected in June and August of 2011. The model using three bands of the MERIS sensor (7, 9 and 10) presented an R2 of 0.84. For the two-band model (7 and 9), the value of R2 was 0.85. The waters of the Orós reservoir were all classified as eutrophic. The main optically active component in modelling the shape of the spectra was chlorophyll-a. The models showed a mean absolute error (MAE) of 3.45 and 3.61 μg L-1 for the three- and two-band models respectively. The models displayed high coefficients of determination, i.e. the simulations show the feasibility of estimating chlorophyll-a concentration from the data of the MERIS orbital sensor. = O monitoramento da água nos dias atuais é essencial para a sustentabilidade e para a melhor gestão dos recursos hídricos. O uso de dados de sensoriamento remoto é relevante, uma vez que permite a avaliação de problemas dinâmicos em sistemas aquáticos, tais como a eutrofização de corpos de água e dos sedimentos em suspensão. Objetivou-se estimar as concentrações de clorofila-a em reservatório da região semiárida do Brasil, usando dados simulados de sensor orbital, como suporte à gestão dos recursos hídricos. A área de estudo corresponde ao açude Orós, no estado do Ceará. As amostras de água para análise da clorofila-a e as medições da radiância espectral do sistema aquático foram coletadas em 20 pontos. As medidas de radiância foram realizadas utilizando um espectrorradiômetro. Os dados foram coletados nos meses de junho e agosto de 2011. O modelo usando três bandas (7, 9 e 10) do sensor MERIS apresentou R2 igual a 0,84. Para o modelo de duas bandas (7 e 9), o valor de R2 foi de 0,85. As águas do reservatório Orós foram todas classificadas como eutróficas. O principal componente opticamente ativo modelando a forma dos espectros foi clorofila-a. Os modelos apresentaram um erro médio absoluto (EMA) de 3,45 e 3,61 μg L-1, respectivamente, para os modelos de três e de duas bandas. Os modelos apresentaram coeficientes de determinação altos, ou seja, as simulações mostram a viabilidade de se estimar as concentrações de clorofila-a a partir dos dados do sensor orbital MERIS.

This work proposes a trophic state index based on the remote sensing retrieval of chlorophyll-α concentration. For that, in situ Bidirectional Reflectance Factor (BRF) data acquired in the Ibitinga reservoir were resampled to match Landsat/TM spectral simulated bands (TM_sim bands) and used to run linear correlation with concurrent measurements of chlorophyll-α concentration. Monte Carlo simulation was then applied to select the most suitable model relating chlorophyll-α concentration and simulated TM/Landsat reflectance. TM4_sim/TM3_sim ratio provided the best model with a R2 value of 0.78. The model was then inverted to create a look-up-table (LUT) relating TM4_sim/TM3_sim ratio intervals to chlorophyll-α concentration trophic state classes covering the entire range measured in the reservoir. Atmospheric corrected Landsat TM images converted to surface reflectance were then used to generate a TM4/TM3 ratio image. The ratio image frequency distribution encompassed the range of TM4_sim/TM3_sim ratio indicating agreement between in situ and satellite data and supporting the use of satellite data to map chlorophyll- concentration trophic state distribution in the reservoir. Based on that, the LUT was applied to a Landsat/TM ratio image to map the spatial distribution of chlorophyll- trophic state classes in Ibitinga reservoir. Despite the stochastic selection of TM4_sim/TM3_sim ratio as the best input variable for modeling the chlorophyll-α concentration, it has a physical basis: high concentration of phytoplankton increases the reflectance in the near-infrared (TM4) and decreases the reflectance in the red (TM3). The band ratio, therefore, enhances the relationship between chlorophyll- concentration and remotely sensed reflectance.

Nowadays, the monitoring of water is essential for the sustainability and better management of water resources. The use of remote sensing data is important, since it allows evaluation of dynamic problems in aquatic systems, such as the eutrophication of bodies of water and suspended sediment. The aim of this study was to estimate chlorophyll-a concentrations in a reservoir of the semi-arid region of Brazil using simulated orbital-sensor data, as an aid in the management of water resources. The study area corresponded to the Orós reservoir, in the State of Ceará, Brazil. Water samples for analysis of the chlorophyll-a and measurements of the spectral radiance of the aquatic system were collected from 20 points. The radiance was measured by spectroradiometer. The data were collected in June and August of 2011. The model using three bands of the MERIS sensor (7, 9 and 10) presented an R2 of 0.84. For the two-band model (7 and 9), the value of R2 was 0.85. The waters of the Orós reservoir were all classified as eutrophic. The main optically active component in modelling the shape of the spectra was chlorophyll-a. The models showed a mean absolute error (MAE) of 3.45 and 3.61 μg L-1 for the three- and two-band models respectively. The models displayed high coefficients of determination, i.e. the simulations show the feasibility of estimating chlorophyll-a concentration from the data of the MERIS orbital sensor.

Water composition undergoes complex spatial and temporal variations throughout the central Amazon floodplain. This study analyzed the spectral mixtures of the optically active substances (OASs) in water with spaceborne hyperspectral images. The test site was located upstream the confluence of Amazon (white water) and Tapajós (clear-water) rivers, where two Hyperion images were acquired from the Earth Observing One (EO-1) satellite. The first image was acquired on September 16, 2001, during the falling water period of the Amazon River. The second image was acquired on June 23, 2005, at the end of the high water period. The images were pre-processed to remove stripes of anomalous pixels, convert radiance-calibrated data to surface reflectance, mask land, clouds and macrophytes targets, and spectral subset the data within the range of 457-885nm. A sequential procedure with the techniques Minimum Noise Fraction (MNF), Pixel Purity Index (PPI) and n-dimensional visualization of the MNF feature space was employed to select end-members from both images. A single set of end-members was gathered to represent the following spectrally unique OASs: clear-water; dissolved organic matter; suspended sediments; and phytoplankton. The Linear Spectral Unmixing algorithm was applied to each Hyperion image in order to map the spatial distribution of these constituents, in terms of sub-pixel fractional abundances. Results showed three patterns of changes in the water quality from high to falling flood periods: decrease of suspended inorganic matter concentration in the Amazon River; increase of suspended inorganic matter and phytoplankton concentrations in varzea lakes; and increase of phytoplankton concentration in the Tapajós River.

Water composition undergoes complex spatial and temporal variations throughout the central Amazon floodplain. This study analyzed the spectral mixtures of the optically active substances (OASs) in water with spaceborne hyperspectral images. The test site was located upstream the confluence of Amazon (white water) and Tapajós (clear-water) rivers, where two Hyperion images were acquired from the Earth Observing One (EO-1) satellite. The first image was acquired on September 16, 2001, during the falling water period of the Amazon River. The second image was acquired on June 23, 2005, at the end of the high water period. The images were pre-processed to remove stripes of anomalous pixels, convert radiance-calibrated data to surface reflectance, mask land, clouds and macrophytes targets, and spectral subset the data within the range of 457-885nm. A sequential procedure with the techniques Minimum Noise Fraction (MNF), Pixel Purity Index (PPI) and n-dimensional visualization of the MNF feature space was employed to select end-members from both images. A single set of end-members was gathered to represent the following spectrally unique OASs: clear-water; dissolved organic matter; suspended sediments; and phytoplankton. The Linear Spectral Unmixing algorithm was applied to each Hyperion image in order to map the spatial distribution of these constituents, in terms of sub-pixel fractional abundances. Results showed three patterns of changes in the water quality from high to falling flood periods: decrease of suspended inorganic matter concentration in the Amazon River; increase of suspended inorganic matter and phytoplankton concentrations in varzea lakes; and increase of phytoplankton concentration in the Tapajós River.