Empirical models for estimating the suspended sediment concentration in Amazonian white water rivers using Landsat 5/TM

Abstract Suspended sediment yield is a very important environmental indicator within Amazonian fluvial systems, especially for rivers dominated by inorganic particles, referred to as white water rivers. For vast portions of Amazonian rivers, suspended sediment concentration (SSC) is measured infrequently or not at all. However, remote sensing techniques have been used to estimate water quality parameters worldwide, from which data for suspended matter is the most successfully retrieved. This paper presents empirical models for SSC retrieval in Amazonian white water rivers using reflectance data derived from Landsat 5/TM. The models use multiple regression for both the entire dataset (global model, N  = 504) and for five segmented datasets (regional models) defined by general geological features of drainage basins. The models use VNIR bands, band ratios, and the SWIR band 5 as input. For the global model, the adjusted R 2 is 0.76, while the adjusted R 2 values for regional models vary from 0.77 to 0.89, all significant ( p -value

[1]  Michael A. Wulder,et al.  Opening the archive: How free data has enabled the science and monitoring promise of Landsat , 2012 .

[2]  S. King,et al.  Spatial dynamics of overbank sedimentation in floodplain systems , 2008 .

[3]  C. M. Cooper,et al.  Using landsat multispectral scanner data to estimate suspended sediments in Moon Lake, Mississippi , 1987 .

[4]  D. Neil,et al.  A composite DOP approach to excluding bottom reflectance in mapping water parameters of shallow coastal zones from TM imagery , 2004 .

[5]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[6]  P. Antoine,et al.  How does the Nazca Ridge subduction influence the modern Amazonian foreland basin , 2007 .

[7]  Philippe Vauchel,et al.  The integration of field measurements and satellite observations to determine river solid loads in poorly monitored basins , 2012 .

[8]  L. Mertes,et al.  Remote sensing of riverine landscapes , 2002 .

[9]  G. Nanson,et al.  A genetic classification of floodplains , 1992 .

[10]  E. Novo,et al.  Spatial and temporal variability of light attenuation in large rivers of the Amazon , 2013, Hydrobiologia.

[11]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[12]  L. Mertes,et al.  Rates of flood-plain sedimentation on the central Amazon River , 1994 .

[13]  D. Walling,et al.  The spatial variability of overbank sedimentation on river floodplains , 1998 .

[14]  J. R. Jensen.  Remote Sensing of the Environment: An Earth Resource Perspective , 2000 .

[15]  Julia A. Barsi,et al.  The next Landsat satellite: The Landsat Data Continuity Mission , 2012 .

[16]  R. Wetzel.  Limnology: Lake and River Ecosystems , 1975 .

[17]  N. Salmaso,et al.  Preface: phytoplankton responses to human impacts at different scales , 2012, Hydrobiologia.

[18]  Desmond E. Walling,et al.  Landsat-MSS radiance as a measure of suspended sediment in the Lower Yellow River (Hwang Ho)☆ , 1988 .

[19]  Michael H. Kutner.  Applied Linear Statistical Models , 1974 .

[20]  Donald C. Rundquist,et al.  Spectral characterization of suspended sediments generated from two texture classes of clay soil , 1996 .

[21]  M. A. Lodhi,et al.  THE POTENTIAL FOR REMOTE SENSING OF LOESS SOILS SUSPENDED IN SURFACE WATERS 1 , 1997 .

[22]  Arnold G. Dekker,et al.  Analytical algorithms for lake water TSM estimation for retrospective analyses of TM and SPOT sensor data , 2002 .

[23]  P. Kubik,et al.  From source to sink: Preserving the cosmogenic 10Be-derived denudation rate signal of the Bolivian Andes in sediment of the Beni and Mamoré foreland basins , 2009 .

[24]  John L. Dwyer,et al.  Landsat: building a strong future , 2012 .

[25]  Kevin Winter,et al.  Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS , 2010 .

[26]  S. Khorram.  Development of water quality models applicable throughout the entire San Francisco Bay and Delta , 1985 .

[27]  Jean-Loup Guyot,et al.  Increase in suspended sediment discharge of the Amazon River assessed by monitoring network and satellite data , 2009 .

[28]  Jean-Michel Martinez,et al.  Discharge and suspended sediment flux estimated along the mainstream of the Amazon and the Madeira Rivers (from in situ and MODIS Satellite Data) , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[29]  H. Sioli,et al.  The Amazon : limnology and landscape ecology of a mighty tropical river and its basin , 1984 .

[30]  Soo Chin Liew,et al.  Retrieval of suspended sediment concentrations in large turbid rivers using Landsat ETM+: an example from the Yangtze River, China , 2009 .

[31]  P. Curran,et al.  The effect of sediment type on the relationship between reflectance and suspended sediment concentration , 1989 .

[32]  E. Novo,et al.  The Relationship Between Suspended Sediment Concentration and Remotely Sensed Spectral Radiance: A Review , 1988 .

[33]  J. Guyot,et al.  Tectonic control of erosion and sedimentation in the Amazon Basin of Bolivia , 2009 .

[34]  John B. Adams,et al.  Estimating suspended sediment concentrations in surface waters of the Amazon River wetlands from Landsat images , 1993 .

[35]  A. Suddhiprakarn,et al.  Iron oxides in tropical soils on various parent materials , 2007, Clay Minerals.

[36]  B. Markham,et al.  Forty-year calibrated record of earth-reflected radiance from Landsat: A review , 2012 .

[37]  R. Kalliola,et al.  Fluvial Perturbance in the Western Amazon Basin: Regulation by Long-Term Sub-Andean Tectonics , 1987, Science.

[38]  R. Naiman,et al.  Andean Influences on the Biogeochemistry and Ecology of the Amazon River , 2008 .

[39]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[40]  J. L. Barker,et al.  Landsat MSS and TM post-calibration dynamic ranges , 1986 .

[41]  R. Sinha,et al.  Preface to the special volume on tropical rivers , 2005 .

[42]  M. Kishino,et al.  Retrieval of Chlorophyll a, suspended solids, and colored dissolved organic matter in Tokyo Bay using ASTER data , 2005 .

[43]  T. Dunne,et al.  Geomorphic Controls on Andean Denudation Rates , 2006, The Journal of Geology.

[44]  F. Wittmann,et al.  A Classification of Major Naturally-Occurring Amazonian Lowland Wetlands , 2011, Wetlands.

[45]  Menghua Wang.  Remote sensing of the ocean contributions from ultraviolet to near-infrared using the shortwave infrared bands: simulations. , 2007, Applied optics.

[46]  Xixi Lu,et al.  Estimation of suspended sediment concentrations using Terra MODIS: an example from the Lower Yangtze River, China. , 2010, Science of the Total Environment.

[47]  Jean-Michel Martinez,et al.  A study of sediment transport in the Madeira River, Brazil, using MODIS remote-sensing images , 2013 .

[48]  David M. Harper,et al.  Remote sensing of chlorophyll-a as a measure of cyanobacterial biomass in Lake Bogoria, a hypertrophic, saline―alkaline, flamingo lake, using Landsat ETM + , 2013 .

[49]  Robert H. Kennedy,et al.  Reservoir Sedimentation and Water Quality-An Heuristic Model , 1981 .

[50]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .