Retrieving Total and Inorganic Suspended Sediments in Amazon Floodplain Lakes: A Multisensor Approach

Maciel, Daniel Andrade; Novo, Evlyn Márcia Leão de Moraes; Carvalho, Lino Augusto Sander de; Barbosa, Cláudio Clemente Faria; Júnior, Rogério Flores; Lobo, Felipe de Lucia

doi:10.3390/rs11151744

Cited by 34 publications

(27 citation statements)

References 120 publications

(259 reference statements)

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“…It is noteworthy that R 2 values are not high, which could be explained by the time differences between in situ data and satellite overpass as well as by the low variability in R rs values for each band. When the bands were analyzed separately (Table 7), it was noticeable that glint correction improves the accuracy for NIR MSI bands (reduction of up to 20% in MAPE values, expected for B05), corroborating with previous studies [79]. MAPE values for glint correction using MSI B11 were lower than 29% for all bands except for B08.…”

Section: Atmospheric Correction Evaluation and Oha Image Validationsupporting

confidence: 87%

“…When the bands were analyzed separately (Table 7), it was noticeable that glint correction improves the accuracy for NIR MSI/Sentinel-2 band (reduction of up to 20% in MAPE values, expected for B05), corroborating with previous studies [79]. MAPE values for glint correction using MSI/Sentinel-2 B11 were lower than 29% for all bands except for B08.…”

Section: Atmospheric Correction Evaluation and Oha Image Validationsupporting

confidence: 86%

“…In comparison, Rrs without glint correction presented MAPE values for B06, B07, and B08 higher than 50%. Several authors have already demonstrated that errors in atmospheric correction increase in longer wavelengths [79,82]. It is noteworthy that R² values are not high, which could be explained by the time differences between in situ data and satellite overpass as well as by the low variability in Rrs values for each band.…”

Section: Atmospheric Correction Evaluation and Oha Image Validationmentioning

confidence: 94%

“…A modified version of Py6S [77] developed at the Instrumentation Laboratory for Aquatic Systems (LabISA-http://www. dpi.inpe.br/labisa/index_en.html) was used to apply 6S [78], which is a physical-based atmospheric correction algorithm; its good performance over tropical inland aquatic ecosystems for R rs retrieval was demonstrated by some authors [76,79]. Then, the surface reflectance image was divided by π to convert it to the atmospherically corrected remote-sensing reflectance (R rs_atmcorr ).…”

Section: Hybrid Algorithm Application On Msi/sentinel-2 Imagementioning

confidence: 99%

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Hybrid Chlorophyll-a Algorithm for Assessing Trophic States of a Tropical Brazilian Reservoir Based on MSI/Sentinel-2 Data

et al. 2019

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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.

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Section: Atmospheric Correction Evaluation and Oha Image Validationsupporting

confidence: 87%

Section: Atmospheric Correction Evaluation and Oha Image Validationsupporting

confidence: 86%

Section: Atmospheric Correction Evaluation and Oha Image Validationmentioning

confidence: 94%

Section: Hybrid Algorithm Application On Msi/sentinel-2 Imagementioning

confidence: 99%

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Hybrid Chlorophyll-a Algorithm for Assessing Trophic States of a Tropical Brazilian Reservoir Based on MSI/Sentinel-2 Data

et al. 2019

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“…L8SR uses an internal algorithm to provide the user with a product with atmospheric correction. The correction used in L8SR is based on the 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum-Vector Version) [19,20] and several authors demonstrated the accuracy of such correction for different targets worldwide, such as vegetation [21,22] and water [23].…”

Section: Plos Onementioning

confidence: 99%

Leaf water potential of coffee estimated by landsat-8 images

et al. 2020

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Traditionally, water conditions of coffee areas are monitored by measuring the leaf water potential (Ψ W) 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 Ψ W 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 (R 2) 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 R 2 of 0.85. Subsequently, the NDVI quadratic algorithm was applied to Landsat-8 images, aiming to spatialize the Ψ W estimated in a representative area of regional coffee planting between September 2014 to July 2015. From the proposed algorithm, it was possible to estimate Ψ W from Landsat-8/OLI imagery, contributing to drought monitoring in the coffee area leading to cost reduction to the producers.

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Amazon hydrology from space: scientific advances and future challenges

Fassoni‐Andrade

Fleischmann

Papa³

et al. 2021

Preprint

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As the largest river basin on Earth, the Amazon is of major importance to the world's climate and water resources. Over the past decades, advances in satellite-based remote sensing (RS) have brought our understanding of its terrestrial water cycle and the associated hydrological processes to a new era. Here, we review major studies and the various techniques using satellite RS in the Amazon. We show how RS played a major role in supporting new research and key findings regarding the Amazon water cycle, and how the region became a laboratory for groundbreaking investigations of new satellite retrievals and analyses. At the basin-scale, the understanding of several hydrological processes was only possible with the advent of RS observations, such as the characterization of "rainfall hotspots" in the Andes-Amazon transition, evapotranspiration rates, and variations of surface waters and groundwater storage. These results strongly contribute to the recent advances of hydrological models and to our new understanding of the Amazon water budget and aquatic environments. In the context of upcoming hydrology-oriented satellite missions, which will offer the opportunity for new synergies and new observations with finer space-time resolution, this review aims to guide future research agenda toward integrated monitoring and understanding of the Amazon water from space. Integrated multidisciplinary studies, fostered by international collaborations, set up future directions to tackle the great challenges the Amazon is currently facing, from climate change to increased anthropogenic pressure. Plain Language SummaryThe Amazon basin is the largest river basin in the world, characterized by complex hydrological processes that connect high rates of precipitation, extensive floodplains, dense tropical forests, complex topography, and large variations in freshwater storage and discharge. It plays a key role in the water, energy, and carbon cycles and interacts with the global climate system. Earth observations have played a major role in supporting research in Amazon hydrology, and the characterization of several hydrological processes was only possible with the help of remote sensing data. The basin is now facing great risk under current climate change and increased anthropogenic pressure and the resulting environmental alterations require a better understanding of the overall basin's water cycle across scales. We review the strengths and limitations of observations from satellites in the context of the current and upcoming hydrology-oriented satellite missions, and we make recommendations for improving satellite observations of the Amazon basin water cycle, along with an interdisciplinary and stepwise approach to guide research for the next decades.

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Retrieving Total and Inorganic Suspended Sediments in Amazon Floodplain Lakes: A Multisensor Approach

Cited by 34 publications

References 120 publications

Hybrid Chlorophyll-a Algorithm for Assessing Trophic States of a Tropical Brazilian Reservoir Based on MSI/Sentinel-2 Data

Hybrid Chlorophyll-a Algorithm for Assessing Trophic States of a Tropical Brazilian Reservoir Based on MSI/Sentinel-2 Data

Leaf water potential of coffee estimated by landsat-8 images

Amazon hydrology from space: scientific advances and future challenges

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