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Theia / Hydroweb

Hydroweb Product User Manual

Reference : THEIA-MU-42-0282-CNES

Issue: 1. 0

Date: 2016,Jan.15


THEIA-MU-42-0282-CNES V1.0 2016,Jan.15 i.1

Proprietary information: no part of this document may be reproduced divulged or used in any form without prior permission from CLS. FO

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Chronology Issues:

Issue: Date: Reason for change: Author

1.0 15/01/2016 initial version V. Rosmorduc

People involved in this issue:

Written by (*):

V. Rosmorduc

Checked by (*):

[Checker]

Approved by (*):

[Approver]

Application authorized by (*):

Pacholczyk 2016, Jan. 15

*In the opposite box: Last and First name of the person + company if different from CLS

Index Sheet:

Context:

Keywords: [Mots clés ]

Hyperlink:

Distribution:

Company Means of distribution Names

CLS Notification




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List of tables and figures

List of tables:

Aucune entrée de table d'illustration n'a été trouvée.

List of figures:

Figure 1: Altimetry principle (over ocean) .................................................................... 3

List of items to be confirmed or to be defined

Lists of TBC:

Aucune entrée de table des matières n'a été trouvée.

Lists of TBD:

Aucune entrée de table des matières n'a été trouvée.

Applicable documents

AD 1 Plan d’assurance produit de CLS CLS-ED-NT-03-394

Reference documents

RD 1 Manuel du processus Documentation CLS-DOC




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List of Contents

1. Purpose ...................................................................................... 1

2. Introduction ................................................................................ 1

3. Satellite Radar Altimetry ................................................................. 2

3.1. Vocabulary ............................................................................ 3

3.2. Computing surface height .......................................................... 3

4. Processing ................................................................................... 4

4.1. Lake products ........................................................................ 4

4.1.1. Input data ............................................................................................. 4

4.1.2. Method ................................................................................................. 4

4.1.2.1. First step: extraction of input data ......................................................... 5

4.1.2.2. Second step: lake level computation and filtering ....................................... 5

4.1.2.3. Third step: validation and volume computation, graph plotting and output data .. 5

4.2. River products ........................................................................ 5

4.2.1. Input data ............................................................................................. 6

4.2.2. Method ................................................................................................. 7

4.2.2.1. First step: extraction and editing of input data ........................................... 7

4.2.2.2. Second step: data selection and filtering .................................................. 7

4.2.2.3. Third step: validation and output data ..................................................... 7

5. Product description ....................................................................... 7

5.1. Description ............................................................................ 7

5.2. Acknowledgments ................................................................... 8

6. Data format ................................................................................. 8

6.1. Nomenclature ........................................................................ 8

6.1.1. Lake products ........................................................................................ 8

6.1.2. River products ........................................................................................ 8

6.2. Content ................................................................................ 9

6.2.1. Lake products ........................................................................................ 9

6.2.1.1. First line with descriptive metadata ........................................................ 9

6.2.1.2. Header ............................................................................................ 9

6.2.1.3. Data .............................................................................................. 10

6.2.2. River products ...................................................................................... 10

6.2.2.1. First line with descriptive metadata ....................................................... 10

6.2.2.2. Header ........................................................................................... 10

6.2.2.3. Data .............................................................................................. 11

7. How to download a product ............................................................ 11




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7.1. Registration .......................................................................... 11

7.2. Access service ....................................................................... 11

7.3. Using Json scripts to automate data retrieval ................................ 11

8. References ................................................................................. 11

8.1. Lakes .................................................................................. 11

8.2. Rivers ................................................................................. 12

Appendix A - List of acronyms ............................................................ 13


THEIA-MU-42-0282-CNES V1.0 2016,Jan.15 1


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1. Purpose

This document presents the information needed by users for the products provided in the frame of the Hydroweb project.

This document is organized as follows:

- Chapter 4; processing: input data and method applied.

- Chapter 5; the product description, with the different files provided, the nomenclature

- Chapter 6; the file format

- Chapter 7; how to download products.

- Chapter 8; bibliographical references

2. Introduction

Terrestrial waters represent less than 1% of the total amount of water on Earth. However, they have crucial impact on terrestrial life and human needs, and play a major role in climate variability. Excluding the ice caps, fresh water on land is stored in various reservoirs: snow pack, glaciers, aquifers and other geological formations, root zone (upper few meters of the soil), and surface waters (rivers, lakes, man-made reservoirs, wetlands and inundated areas). Land waters are continuously exchanged with atmosphere and oceans through vertical and horizontal mass fluxes (evaporation, transpiration of the vegetation, surface and underground runoff). Although improved description of the terrestrial branch of the global water cycle is now recognized as being of major importance for climate research as well as for inventory and management of water resources, the global distribution and spatio-temporal variations of continental waters are still poorly known because routine in situ observations are not available globally. So far, global estimates of spatio-temporal change of land water storage essentially rely on hydrological models, either coupled with atmosphere/ocean global circulation models and/or forced by observations.

Concerning surface waters, in-situ gauging networks have been installed for several decades at least in some hydrographic basins. In situ measurements provide time series of water levels and discharge rates, which are used for studies of regional climate variability as well as for socio-economic applications (e.g., water resources allocation, navigation, land use, hydroelectric energy, flood hazards). Gauging stations, however, are scarce or even absent in parts of large river basins due to geographical, political or economic limitations. Moreover, since the beginning of the 1990s, numerous in-situ networks have declined or stopped working, because of political and economic factors.

Recently, remote sensing techniques have been used to monitor components of the water balance of large river basins on time scales ranging from months to decades. Among these, two are particularly promising: satellite altimetry for systematic monitoring of water levels of large rivers, lakes and floodplains and the new space GRACE gravity mission for measurement of spatio-temporal variations of land water storage. Other remote sensing techniques, such as Synthetic Aperture Radar (SAR) Interferometry and passive and active microwave observations also offer important information on land surface waters, such as changing areal extent of large wetlands. By complementing in situ observations and hydrological modelling, space observations have the potential to improve significantly our understanding of hydrological processes at work in large river basins and their influence on climate variability, geodynamics and socio-economic life. Unprecedented information can be expected by combining models and surface observations with observations from space, which offer global geographical coverage, good spatio-temporal sampling, continuous monitoring with time, and capability of measuring water mass change occurring at or below the surface.




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3. Satellite Radar Altimetry

Radar altimetry from space consists of vertical range measurements between the satellite and water level. Difference between the satellite altitude above a reference surface (usually a conventional ellipsoid), determined through precise orbit computation, and satellite-water surface distance, provides measurements of water level above the reference. Placed onto a repeat orbit, the altimeter satellite overflies a given region at regular time intervals (called the orbital cycle), during which a complete coverage of the Earth is performed.

Water level measurement by satellite altimetry has been developed and optimized for open oceans. Nevertheless, the technique is now applied to obtain water levels of inland seas, lakes, rivers, floodplains and wetlands. Several satellite altimetry missions have been launched since the early 1990s : ERS-1 (1991-1996), Topex/Poseidon (1992-2006), ERS-2 (1995-2005), GFO (2000-2008), Jason-1 (2001-2012), Envisat (2002-2012), Jason-2 (2008-), Cryosat (2010-), HY-2A (2011-), Saral (2013-). ERS-1, ERS-2, Envisat and Saral have a 35-day temporal resolution (duration of the orbital cycle) and 80 km inter-track spacing at the equator. Topex/Poseidon, Jason-1 and Jason-2 have a 10-day orbital cycle and 350 km equatorial inter-track spacing. GFO has a 17-day orbital cycle and 170 km equatorial intertrack spacing. The combined global altimetry data set has more than 20 year-long history and is intended to be continuously updated in the coming decade. Combining altimetry data from several in-orbit altimetry missions increases the spatio-temporal resolution of the sensed hydrological variables.

Topex/Poseidon (Jason-1 and Jason-2) passes over the Amazon, with pass numbers. Note that in between those passes, no measurements are made by this particular

satellites




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3.1. Vocabulary

‘Orbit’ is one revolution around the Earth by the satellite.

A satellite ‘Pass’ is half a revolution of the Earth by the satellite from one extreme latitude to the opposite extreme latitude.

‘Repeat Cycle’ is the time period that elapses until the satellite flies over the same location again.

‘Range’ is the satellite-to-surface pseudo distance given from the 2 way travel time of the radar pulse from the satellite to the reflecting water body

‘Retracking’ is the algorithm which computes the altimetric parameters (range, backscatter coefficient...) from the radar echoes

‘Altitude’ is considered as the height over the reference ellipsoid (orthometric height).

3.2. Computing surface height

Figure 1: Altimetry principle (over ocean)

For all satellites, the following operation is done:

‘Surface Height = altitude – corrected_range’

With:

Corrected_range = range + Wet_tropo + Dry_ tropo + iono + polar tide + solid earth tide




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Note that for Saral, ionospheric correction is not taken into account due to the frequency used for this mission (Ka-band), which is not sensitive to ionosphere electron content. To measure the level variations, the following computation is done:

‘Surface Height Anomaly = Surface Height – geoid’

The geoid value considered here is extracted from a mean profile file.

‘Mean Profile’ is an average over several years of the surface heights with respect to geoid, computed following the satellite’s repetitive tracks.

4. Processing

4.1. Lake products

Altimetry missions used are repetitive, i.e. the satellite overflow the same point at a given time interval (10, 17 or 35 days depending on the satellite). The satellite does not deviate from more than +/- 1 km across its track.

A given lake can be overflown by several satellites, with potentially several passes.

The water level and volume time series is operationally updated less than 1.5 working days after the availability of the input altimetry data, for some lakes. Other lakes are also monitored on a research mode basis.

4.1.1. Input data

Past lake levels are based on merged Topex/Poseidon, Jason-1, Envisat and GFO data provided by ESA, NASA and CNES data centers. Updates include Jason-2 and Saral provided by CNES data center.

- Altimetry GDR and IGDR data (IGDR for near-real time processing), with the included corrections.

- Mean along-track profile over the lakes - Location information, including missions and track number for all the processed lakes - Hypsometric curves built using satellite imagery

4.1.2. Method

The altimeter range measurements used for lakes consist of 1 Hz IGDR and GDR data. All classical corrections (orbit, ionospheric and tropospheric corrections, polar and solid Earth tides and sea state bias) are applied. Depending on the size of the lake, the satellite data may be averaged over very long distances. It is thus necessary to correct for the slope of the geoid (or equivalently, the mean lake level). Because the reference geoid provided with the altimetry measurements (e.g., EGM96 for T/P data) may not be accurate enough, we have computed a mean lake level, averaging over time the altimetry measurements themselves. The water levels are further referred to this ‘mean lake level’. If different satellites cover the same lake, the lake level is computed in a 3-step process. Each satellite data are processed independently. Potential radar instrument biases between different satellites are removed using T/P data as reference. Then lake levels from the different satellites are merged on a monthly basis (recall that the orbital cycles vary from 10 days for T/P and Jason, to 17 days for GFO and 35 days for ERS and Envisat). We generally observe an increased precision of lake levels when multi-satellite processing is applied.




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4.1.2.1. First step: extraction of input data

Input data (either IGDR or GDR) are read,

using lake files (names, pass numbers, satellite names, bias to apply, retracking model used, pass segments over the lakes) the data over each given lakes are extracted.

For Jason-2 and Saral, (I)GDR provides high-resolution (resp. 20 and 40Hz) data as well as 1-Hz averaged data. Dates and corrections used with those high-resolution data are the 1-Hz averaged ones, while altimetric range and altitude are the high resolution ones. For the biggest lakes, the data used are preferably the 1-Hz data with the ocean retracking range; for the small lakes, the high-resolution data and the ice1 retracking are used.

An editing is then applied using the correction values and some flags, to remove erroneous measurements.

4.1.2.2. Second step: lake level computation and filtering

Once the relevant data are extracted and edited, mean profiles over the given passes are selected, to compute the surface height (see 3.2 Computing surface ). The mean profile is subtracted from the surface height computed in step 1.

Standard deviation and median values are computed. If a value is over 1.5 times the median, it is removed. Standard deviation and median values are re-computed, and measurements are removed when over twice the standard deviation. Four such iterations are done to remove abnormal points (“outliers”)

4.1.2.3. Third step: validation and volume computation, graph plotting and output data

The final step is the validation and computation of lake volumes, and the output formatting.

Hypsometric files for each lake are read. Using those with the surface height previously computed from altimetry, surfaces are retrieved, and also volumes variations compared with the estimates at the beginning of the lake time series.

The final operation is the formatting of the data and the graph plotting.

NB. for some lakes only the height will be computed.

4.2. River products

Radar echoes over land surfaces are hampered by interfering reflections due to water, vegetation and topography. As a consequence, waveforms (e.g., the power distribution of the radar echo within the range window) may not have the simple broad peaked shape seen over ocean surfaces, but can be complex, multi-peaked, preventing from precise determination of the altimetric height. If the surface is flat, problems may arise from interference between the vegetation canopy and water from wetlands, floodplains, tributaries and main river. In other cases, elevated topography sometimes prevents the altimeter to lock on the water surface, leading to less valid data than over flat areas. The time series available in Hydroweb are constructed using Jason-2 and SaralGDRs. The basic data used for rivers are the 20 or 40 Hz data (“high rate” data).

To construct river water level time series, we need to define virtual stations corresponding to the intersection of the satellite track with the river. For that purpose, we select for each cycle a rectangular “window” taking into account all available along track high rate altimetry data over the river area. The coordinate of the virtual station is defined as the barycenter of the selected data




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within the “window”. After rigourous data editing, all available high rate data of a given cycle are geographically averaged. At least two high rate data are needed for averaging otherwise no mean height is provided. Scattering of high rate data with respect to the mean height defines the uncertainty associated with the mean height.

Definition of a virtual station (T/P mission)

The water level and volume time series is operationally updated less than 1.5 working days after the availability of the input altimetry data, for some virtual stations on rivers. Other virtual stations are monitored on a research mode basis.

4.2.1. Input data

Past river levels are based on Topex/Poseidon,and Envisat data provided by ESA, NASA and CNES data centers. Updates include Jason-2 and Saral provided by CNES data center.

- Altimetry GDR and IGDR data (IGDR for near-real time processing), with the included corrections.

- A file with all the track numbers to process for each virtual station.




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4.2.2. Method

4.2.2.1. First step: extraction and editing of input data

Input data (either IGDR or GDR) are read,

using virtual station rectangle files, the necessary data (date, location, reference surface, corrections) over each given virtual station are extracted.

An editing is then applied using the correction values and some flags, to remove erroneous measurements. This editing depends on the mission.

For all missions high-rate (resp.10, 20 and 40Hz) data are used. Dates and corrections used with those high-resolution data are the 1-Hz averaged ones, interpolated at the dates of the high rate data, while altimetric range and satellite altitude are the high rate ones. Only the repetitive missions are used (e.g. not the Envisat or Jason-1 data when the satellite was put on a drifting orbit)

4.2.2.2. Second step: data selection and filtering

First step provides a rough selection of the data (using a rectangle over the virtual station). Then within this rectangle, a polygon is used for a finer selection, in order to remove data which would not be over water or data for cycles when the pass is too far from the nominal track. These polygons are extracted from a simplified version by Legos of WBD (SRTM Water Body Data) file, available on NASA web site. An algorithm is then applied to decide if a measurement is (or is not) within the polygon.

4.2.2.3. Third step: validation and output data

The third and final step is to compute the mean, validate and produce the output data. The mean water level processing includes

Outlier rejection (i.e. removing erroneous data), using criteria based on the difference with mean+rms

a yearly filtering,

computation of the average & standard deviation after filtering

WGS84 ellipsoid correction: some altimetry satellites are not referenced with respect to the same ellipsoid. This step is meant to use the WGS84 ellipsoid for all Hydroweb virtual station water level.

5. Product description

5.1. Description

Dataset Name Production frequency

Temporal resolution File format

Lake level, surface & volume variation time series (operational)

every day, depending on the tracks

depending on the altimetry mission coverage

csv with metadata and header as comment (one file per lake)




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River level time series (operational)

every day, depending on the tracks

depending on the altimetry mission coverage

csv with metadata and header as comment (one file per virtual station)

Lake level, surface & volume variation time series (research)

once and for all depending on the altimetry mission coverage

csv with metadata and header as comment (one file per lake)

River level time series (research) once and for all depending on the altimetry mission coverage

csv with metadata and header as comment (one file per virtual station)

5.2. Acknowledgments

When using those products, please cite:

“The altimeter products were produced by Hydroweb and distributed by Theia, with support from Cnes and Legos”

6. Data format

Data are available in csv format with a header (.txt files)

Note that some fields can be empty.

6.1. Nomenclature

6.1.1. Lake products

File are named:

L_.txt

Where

is the name of the lake, in English or French

6.1.2. River products

File are named:

R_____.txt

Where :

is a three character code name (from the name in English)

is a three character code name (it can be identical to the basin code name) ; local names are prefered wherever possible (a choice might be done if the river is shared by several different countries)

short name of the satellite mission (3 characters), among:

Code mission full name

TPX Topex/Poseidon

ENV Envisat




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JA2 Jason-2

SRL Saral/Altika

ER1 ERS1

ER2 ERS2

: track (half orbit) number; depends on the mission

: number of the virtual station along the given track, on 2 digits

6.2. Content

Files include:

- a first line with descriptive metadata - a header in a comment format (series of lines with #) - the data themselves

6.2.1. Lake products

6.2.1.1. First line with descriptive metadata

lake=;country=&;basin=;lat=;lon=;date=;first_date=;last_date=;type=(operational or research);diff=public

where :

is the name of the lake (full name), in local language wherever possible (a choice might be done if the lake shore several different countries)

are the names (full name) of the countries where the lake is located (several names possible, separated by &)

is the hydrological basin name (full name), in English

latitude of the lake

longitude of the lake

Date is the processing date when the data have been computed

first_date is the date of the first available measurement

last_date is the date of the last available measurement

type=(operational or research) operational data are produced daily. At a given location, data will be updated if measurements are available (track of a satellite over the area), not updated if no measurement over the area or all measurements filtered; research data are provided by Legos.

6.2.1.2. Header

The header includes a number of useful information:

- Geographical information on the lake (size, depth...)

- Missions and track number of each mission with measurements over this lake




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- Corrections applied

Reference surface used, with citation

- First date of measurement in the following format: yr month day hours minutes

- Last date of measurement in the following format: yr month day hours minutes

- The data formatting, with column number, content and unit or format if relevant, separated by a “,” if on the same line # (1): data1 name (unit), (2): data2 name = format, (3) data3 name # (4)....

- Source and credits information, citation

All lines begins with a #

6.2.1.3. Data

Data are provided in csv format, separated by a “;”. Some fields can be empty

Each line corresponds to a different date/measurement.

6.2.2. River products

6.2.2.1. First line with descriptive metadata

station=;river=;basin=;lat=;lon=;ref=;ref_value=;date=;type=(operational or research);diff=public

where :

is built like the file name (see above): ____

is the name of the river (full name), in local language wherever possible (a choice might be done if the river flows through several different countries)

is the hydrological basin name (full name)

latitude of the virtual station

longitude of the virtual station

is the code name of the reference geoid (e.g. EGM2008)

is the value of the reference geoid at the virtual station location

Date is the processing date when the data have been computed

first_date is the date of the first available measurement

last_date is the date of the last available measurement

type=(operational or research) operational data are produced daily. At a given location, data will be updated if measurements are available (track of a satellite over the area), not updated if no measurement over the area ; research data are provided by Legos.

6.2.2.2. Header

The header includes a number of useful information:




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- Source and credits information

- Corrections applied

- Reference geoid used

- Bias between WGS84 ellipsoid and the different altimetry missions

- Distance to the sea (can be tbd, to be defined)

- The data formatting, with column number, content and unit or format if relevant, separated by a “,” if on the same line # (1): data1 name (unit), (2): data2 name = format, (3) data3 name # (4)....

All lines begins with a #

6.2.2.3. Data

Data are provided in csv format, separated by a “;”. Some fields can be empty

Each line corresponds to a different date/measurement.

7. How to download a product

7.1. Registration

To access the data, you must register. A link is provided top right of the website (sign up/s’inscrire).

Registration is common to all Theia products.

7.2. Access service

The web site http://hydroweb.theia-land.fr/ is the point of access for all Hydroweb data.

7.3. Using Json scripts to automate data retrieval

This option is under development.

8. References

8.1. Lakes

SOLS: A Lake database to monitor in Near Real Time water level and storage variations from remote sensing data, J. Adv. Space Res. Vol 47, 9, 1497-1507, doi:10.1016/j.asr.2011.01.004, 2011 Crétaux J-F , W. Jelinski , S. Calmant , A. Kouraev , V. Vuglinski , M. Bergé Nguyen , M-C. Gennero, F. Nino, R. Abarca Del Rio , A. Cazenave , P. Maisongrande

Lake studies from satellite altimetry, C R Geoscience, Vol 338, 14-15, 1098-1112, doi: 10.1016/J.cre.2006.08.002, 2006 Crétaux J-F and C. Birkett

http://hydroweb.theia-land.fr/




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8.2. Rivers

EGM2008 Citation: The development and evaluation of the Earth Gravitational Model 2008 (EGM2008) - Nikolaos K. Pavlis, Simon A. Holmes, Steve C. Kenyon, John K. Factor; Journal of Geophysical Research: Solid Earth (1978-2012) Volume 117, Issue B4, April 2012

Frappart F., Calmant S., Cauhopé M., Seyler F., Cazenave A. (2006). Preliminary results of ENVISAT RA-2 derived water levels validation over the Amazon basin, Remote Sensing of Environment, 100(2), 252-264, doi:10.1016/j.rse.2005.10.027.

Santos da Silva J., S. Calmant, O. Rotuono Filho, F. Seyler, G. Cochonneau, E. Roux, J. W. Mansour, Water Levels in the Amazon basin derived from the ERS-2 and ENVISAT Radar Altimetry Missions, Remote Sensing of the Environment, doi:10.1016/j.rse.2010.04.020, 2010

Citation for data on specific watersheds:

Congo Basin:

Becker, M., J. Santos da Silva, S. Calmant, V. Robinet, L. Linguet et F. Seyler, Water level fluctuations in the Congo Basin Derived from ENVISAT satellite altimetry, Remote Sensing, 6, doi:10.3390/rs60x000x, 2014

Gange basin

Pandrey, R. K., J-F Crétaux, M Bergé-Nguyen, V. Mani Tiwari, V. Drolon, F. Papa and S. Calmant, Water level estimation by remote sensing for 2008 Flooding of the Kosi River, IJRS, 35:2, 424-440, doi:10.1080/01431161.2013.870678, 2014

Papa, F., F. Frappart, Y. Malbeteau, M. Shamsudduha, V. Venugopal, M. Sekhar, G. Ramillien, C. Prigent, P. Aires, R. K. Pandey, S. Bala, S. Calmant, Satellite-derived Surface and Sub-surface Storage in the Ganges-Brahmaputra River Basin, Journal of Hydrology, regional studies, doi:10,1016/j.ejrh,2015,03,004 2014.

Orinoco :

Frappart, F., F. Papa, Y. Malbeteau, J-G Leon, G. Ramillien, C. Prigent, L. Seoane, F. Seyler, S. Calmant, Surface freshwater storage variations in the Orinoco floodplains using multi-satellite observations, Remote Sensing, 7,89-110, doi:10,3390/rs70100089, 2015.

Other basins :

Santos da Silva, J., F. Seyler, S. Calmant, O. Correa Rotunno Filho, G. Cochonneau et W.J. Mansur, Water level dynamics of Amazon wetlands at the watershed scale by satellite altimetry, Int. J. Of Remote Sensing, 33:11, 3323-3353, doi 10.1080/01431161.2010.531914, 2012

Citation for empirical mean altimetric biases over rivers :

Calmant, S., J. Santos da Silva, D. Medeiros Moreira, F. Seyler, C.K. Shum, J-F. Crétaux, G. Gabalda, Detection of Envisat RA2 / ICE-1 retracked Radar Altimetry Bias Over the Amazon Basin Rivers using GPS, Advances in Space Research, doi: 10.1016/j.asr.2012.07.033, 2012.

Seyler, F., S. Calmant, J. Santos da Silva, D. Medeiros Moreira, F. Mercier, CK Shum, From TOPEX/Poseidon to Jason-2/OSTM in the Amazon basin, Advances in Space Research, doi: 10.1016/j.asr.2012.11.002, 2012




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Appendix A - List of acronyms

TBC To be confirmed

TBD To be defined

AD Applicable Document

RD Reference Document

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