Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures

Parrens, Marie; Bitar, Ahmad Al; Frappart, Frédéric; Papa, Fabrice; Calmant, Stéphane; Crétaux, Jean-François; Wigneron, Jean‐Pierre; Kerr, Yann H.

doi:10.3390/w9050350

Cited by 43 publications

(46 citation statements)

References 65 publications

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“…Both the timing and the duration of the low and the high flow periods were well observed in the SMOS time series. The impact of vegetation cover appeared to be reduced by using the low frequency data and was in agreement with other results in the literature [50,56]. ( Figure 11; Amazon River upstream from Parintins).…”

Section: Validation Of Smos Gauging Resultssupporting

confidence: 91%

“…The performance of inundation detection in the tropics is dependent on the capability to penetrate dense vegetation to receive microwave emission from the ground beneath the canopy. With smaller vegetation attenuation effects, lower frequency microwave radiation is less influenced by forest canopy in these regions [49,50]. Thereby, we expected that SMOS brightness temperature results might exhibit a better performance in monitoring river flow conditions or detecting flood events in tropical regions.…”

Section: Smos Data Processingmentioning

confidence: 99%

“…Vittucci et al [49] uses SMOS data as input to hydrological forecasting but not for systematic river flow monitoring. Parrens et al [50] map water surface fraction over the Amazon basin by SMOS in a spatial context but not on a pixel-based single-observation methodology. We now compare the performance of low and high frequency passive microwave data to observe changing flow conditions over rivers in the tropical climate on a systematic basis and to provide physical insights into the behavior of different passive microwave sensors in the selected study areas.…”

Section: Introductionmentioning

confidence: 99%

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L-Band Passive Microwave Data from SMOS for River Gauging Observations in Tropical Climates

2019

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The Global Flood Detection Systems (GFDS) currently operated at the European Commission’s Joint Research Centre (JRC) is a satellite-based observation system that provides daily stream flow measurements of global rivers. The system was initially established using NASA Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) Ka-band passive microwave satellite data. Since its initiation in 2006, the methodology and the GFDS database have been further adapted for data acquired by the Tropical Rainfall Measuring Mission (TRMM) GOES Precipitation Index (GPI), the AMSR2 sensor onboard the Global Change Observation Mission – Water satellite (GCOM-W1), and the Global Precipitation Measurement (GPM) GPM Microwave Imager (GMI) sensor. This paper extends the same flow monitoring methodology to low frequency (L-band) passive microwave observations obtained by the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) sensor that was launched in 2009. A primary focus is tropical climate regions with dense rainforest vegetation (the Amazon, the Orinoco, and the Congo basins) where high-frequency microwave observations from GFDS reveal a significant influence of vegetation cover and atmospheric humidity. In contrast, SMOS passive microwave signatures at the much lower L-band frequency exhibit deeper penetration through the dense vegetation and minimal atmospheric effects, enabling more robust river stage retrievals in these regions. The SMOS satellite river gauging observations are for 2010–2018 and are compared to single-sensor GFDS data over several river sites. To reduce noise, different filtering techniques were tested to select the one most suitable for analysis of the L-band time series information. In-situ water level (stage) measurements from the French Observation Service SO Hybam database were used for validation to further evaluate the performance of the SMOS data series. In addition to GFDS data, water stage information from Jason-2 and Jason-3 altimetry was compared to the microwave results. Correlation of SMOS gauging time series with in-situ stage data revealed a good agreement (r = 0.8–0.94) during the analyzed period of 2010–2018. Moderate correlation was found with both high frequency GFDS data series and altimetry data series. With lower vegetation attenuation, SMOS signatures exhibited a robust linear relationship with river stage without seasonal bias from the complex hysteresis effects that appeared in the Ka-band observations, apparently due to different attenuation impacts through dense forests at different seasonal vegetation stages.

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Section: Validation Of Smos Gauging Resultssupporting

confidence: 91%

Section: Smos Data Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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L-Band Passive Microwave Data from SMOS for River Gauging Observations in Tropical Climates

2019

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“…Water retention in wetlands leads to lower and delayed runoff peaks, higher base flows and evapotranspiration, which directly influence climate (Bierkens and van den Hurk, 2007;Lin et al, 2016). Wetlands also serve to purify pollution from natural and human sources, thus maintaining clean and sustainable water for ecosystems (Billen and Garnier, 1999;Dhote and Dixit, 2009;Curie et al, 2011;Passy et al, 2012). Despite their widely recognized importance, no consensus exists on wetland definitions and their respective areal extents among the reviewed literature (Table 1).…”

Section: Introductionmentioning

confidence: 99%

Multi-source global wetland maps combining surface water imagery and groundwater constraints

Tootchi

Jost

Ducharne

2019

Earth Syst. Sci. Data

157

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Abstract. Many maps of open water and wetlands have been developed based on three main methods: (i) compiling national and regional wetland surveys, (ii) identifying inundated areas via satellite imagery and (iii) delineating wetlands as shallow water table areas based on groundwater modeling. However, the resulting global wetland extents vary from 3 % to 21 % of the land surface area because of inconsistencies in wetland definitions and limitations in observation or modeling systems. To reconcile these differences, we propose composite wetland (CW) maps, combining two classes of wetlands: (1) regularly flooded wetlands (RFWs) obtained by overlapping selected open-water and inundation datasets; and (2) groundwater-driven wetlands (GDWs) derived from groundwater modeling (either direct or simplified using several variants of the topographic index). Wetlands are statically defined as areas with persistent near-saturated soil surfaces because of regular flooding or shallow groundwater, disregarding most human alterations (potential wetlands). Seven CW maps were generated at 15 arcsec resolution (ca. 500 m at the Equator) using geographic information system (GIS) tools and by combining one RFW and different GDW maps. To validate this approach, these CW maps were compared with existing wetland datasets at the global and regional scales. The spatial patterns were decently captured, but the wetland extents were difficult to assess compared to the dispersion of the validation datasets. Compared with the only regional dataset encompassing both GDWs and RFWs, over France, the CW maps performed well and better than all other considered global wetland datasets. Two CW maps, showing the best overall match with the available evaluation datasets, were eventually selected. These maps provided global wetland extents of 27.5 and 29 million km2, i.e., 21.1 % and 21.6 % of the global land area, which are among the highest values in the literature and are in line with recent estimates also recognizing the contribution of GDWs. This wetland class covers 15 % of the global land area compared with 9.7 % for RFW (with an overlap of ca. 3.4 %), including wetlands under canopy and/or cloud cover, leading to high wetland densities in the tropics and small scattered wetlands that cover less than 5 % of land but are highly important for hydrological and ecological functioning in temperate to arid areas. By distinguishing the RFWs and GDWs based globally on uniform principles, the proposed dataset might be useful for large-scale land surface modeling (hydrological, ecological and biogeochemical modeling) and environmental planning. The dataset consisting of the two selected CW maps and the contributing GDW and RFW maps is available from PANGAEA at https://doi.org/10.1594/PANGAEA.892657 (Tootchi et al., 2018).

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“…Method A: O'Neill and Chan approach: SMAP approach The Tb measured in each pixel is a surface-weighted sum of Tb water and Tb land (Parrens et al 2017;O'Neill and Chan 2012). Equation 1is used to correct the brightness temperature by removing the damping effect of water bodies from the pixels.…”

Section: Data Processingmentioning

confidence: 99%

New Approaches for Removing the Effect of Water Damping on SMAP Freeze/Thaw Mapping

Touati

Ratsimbazafy

Ludwig

et al. 2019

Canadian Journal of Remote Sensing

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The Northern Quebec landscape is typically covered by numerous lakes. The lowest emissivity of water bodies dominates the brightness temperature (Tb) data measured over this region. Thus, it is necessary to eliminate the effect of water bodies from Tb measurements. The primary objective of this study is to develop two approaches to correct the Soil Moisture Active Passive brightness temperature (SMAP L1C), collected between January and December of 2016, for the damping effect of water bodies within each 36 km by 36 km pixel. The first algorithm normalizes Tb with the intercept of its linear regression versus the water fraction of each pixel. A second algorithm used Tb regression with water fraction by vegetation classes for each scene. Surface soil temperature and moisture measured near Umiujaq were used to validate Tb correction. The proposed Tb corrections resolve the divergence observed with SMAP standard correction when the water fraction is higher than 20%. Corrected brightness temperature is then tested for mapping the soil freeze/thaw state using the Normalized Polarization Ratio. Agreements of up to 90% (ascending orbit) and 79% (descending orbit) were reached for the proposed approach versus 64% and 50% for the existing approach. R ESUM E Le paysage du Nord du Qu ebec comprend plusieurs lacs. La temp erature de brillance (Tb) mesur ee est domin ee par la plus basse emissivit e des surfaces d'eau. Ainsi, il est n ecessaire d' eliminer cet artifact. L'objectif principal de cette etude est de proposer deux nouvelles approches pour corriger la temp erature de brillance des produits SMAP L1C de l'effet att enuant des masses d'eau pours chacun des pixels de 36 km par 36 km d'images acquises entre janvier et d ecembre 2016. Le premier algorithme normalise les valeurs de Tb avec l'ordonn ee a l'origine de sa r egression lin eaire par rapport a la fraction de l'eau dans chacun des pixels. Le second algorithme normalise par rapport a la fraction de l'eau par classe de couverture v eg etale. Pour valider les valeurs corrig ees, nous avons utilis e des donn ees de temp erature et d'humidit e du sol de sondes install ees pr es d'Umiujaq. La correction propos ee de Tb r esout les divergences observ ees avec la correction SMAP de r ef erence lorsque la proportion d'eau au sein d'un pixel d epasse 20%. Le gel/non gel du sol a ensuite et e cartographi e en utilisant le Rapport de polarisation normalis e. Une concordance jusqu' a 90% (orbite ascendant) et 79% (orbite descendant) a et e obtenue par rapport a 64% et 50% pour l'approche de r ef erence.

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Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures

Cited by 43 publications

References 65 publications

L-Band Passive Microwave Data from SMOS for River Gauging Observations in Tropical Climates

L-Band Passive Microwave Data from SMOS for River Gauging Observations in Tropical Climates

Multi-source global wetland maps combining surface water imagery and groundwater constraints

New Approaches for Removing the Effect of Water Damping on SMAP Freeze/Thaw Mapping

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