Diversity II water quality parameters for 300 lakes worldwide from ENVISAT (2002–2012)

Abstract. The use of ground sampled water quality information for global studies is limited due to practical and financial constraints. Remote sensing is a valuable means to overcome such limitations and to provide synoptic views of ambient water quality at appropriate spatio-temporal scales. In past years several large data processing efforts were initiated to provide corresponding data sources. The Diversity II water quality dataset consists of several monthly, yearly and 9-year averaged water quality parameters for 340 lakes worldwide and is based on data from the full ENVISAT MERIS operation period (2002–2012). Existing retrieval methods and datasets were selected after an extensive algorithm intercomparison exercise using in situ reference measurements for more than 40 lakes representing a wide range of bio-optical conditions. Chlorophyll- a , total suspended matter, turbidity, coloured dissolved organic matter, lake surface water temperature, cyanobacteria and floating vegetation maps, as well as several auxiliary data layers, provide a generically specified data basis that can be used for assessing a variety of locally relevant ecosystem properties and environmental problems. We demonstrate the use of the products by illustrating and discussing remotely sensed evidence of lake-specific processes and prominent regime shifts documented in literature. The Diversity II data are available from https://doi.pangaea.de/10.1594/PANGAEA.871462 , and Python scripts for their analysis and visualization are provided at https://github.com/odermatt/diversity/ .

[1]  S. MacIntyre,et al.  Stratification and horizontal exchange in Lake Victoria, East Africa , 2014 .

[2]  Carsten Brockmann,et al.  An Optical Classification Tool for Global Lake Waters , 2017, Remote. Sens..

[3]  M. Matthews A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters , 2011 .

[4]  M. Rast,et al.  The ESA Medium Resolution Imaging Spectrometer MERIS a review of the instrument and its mission , 1999 .

[5]  Jean-Claude Roger,et al.  Atmospheric correction over land for MERIS , 1999 .

[6]  Carsten Brockmann,et al.  Calvalus: Full-mission EO cal/val, processing and exploitation services , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[7]  C. Verpoorter,et al.  A global inventory of lakes based on high‐resolution satellite imagery , 2014 .

[8]  Frank Fell,et al.  Numerical simulation of the light field in the atmosphere–ocean system using the matrix-operator method , 2001 .

[9]  A. Payne The Ecology of Tropical Lakes and Rivers , 1986 .

[10]  J. Beckers,et al.  Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: application to the Adriatic Sea surface temperature , 2005 .

[11]  Jia Zong,et al.  Algorithm Theoretical Basis , 1999 .

[12]  Stefano Pignatti,et al.  Assessment of the abnormal growth of floating macrophytes in Winam Gulf (Kenya) by using MODIS imagery time series , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[13]  L. Beadle The inland waters of tropical Africa: an introduction to tropical limnology. , 1976 .

[14]  G. Kroenung,et al.  The SRTM Data Finishing Process and Products , 2006 .

[15]  Igor Ogashawara,et al.  Optical types of inland and coastal waters , 2017 .

[16]  P. Döll,et al.  Development and validation of a global database of lakes, reservoirs and wetlands , 2004 .

[17]  A. Meyer,et al.  Conservation: Nicaragua Canal could wreak environmental ruin , 2014, Nature.

[18]  Daniel Odermatt,et al.  Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters , 2015 .

[19]  N. Yoshida,et al.  Spatial distribution of nitrate sources of rivers in the Lake Biwa watershed, Japan: Controlling factors revealed by nitrogen and oxygen isotope values , 2010 .

[20]  D. Mironov Parameterization of Lakes in Numerical Weather Prediction. Description of a Lake Model , 2020 .

[21]  Jennifer P. Cannizzaro,et al.  Estimating chlorophyll a concentrations from remote-sensing reflectance in optically shallow waters , 2006 .

[22]  S. MacIntyre,et al.  Spatial variability of nutrient concentrations, fluxes, and ecosystem metabolism in Nyanza Gulf and Rusinga Channel, Lake Victoria (East Africa) , 2013 .

[23]  T. Schroeder,et al.  Retrieval of atmospheric and oceanic properties from MERIS measurements: A new Case‐2 water processor for BEAM , 2007 .

[24]  M. Schaepman,et al.  Review of constituent retrieval in optically deep and complex waters from satellite imagery , 2012 .

[25]  R. Doerffer,et al.  The MERIS Case 2 water algorithm , 2007 .

[26]  Urs Wegmüller,et al.  Multi-temporal Synthetic Aperture Radar Metrics Applied to Map Open Water Bodies , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  D. Odermatt,et al.  Application of remote sensing for the optimization of in-situ sampling for monitoring of phytoplankton abundance in a large lake. , 2015, The Science of the total environment.

[28]  T. Moore,et al.  An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters. , 2014, Remote sensing of environment.

[29]  T. Itai,et al.  Hypoxia-induced exposure of isaza fish to manganese and arsenic at the bottom of Lake Biwa, Japan: experimental and geochemical verification. , 2012, Environmental science & technology.

[30]  J. Gower,et al.  Interpretation of the 685nm peak in water-leaving radiance spectra in terms of fluorescence, absorption and scattering, and its observation by MERIS , 1999 .

[31]  Petra Philipson,et al.  Assessing the potential of remote sensing-derived water quality data to explain variations in fish assemblages and to support fish status assessments in large lakes , 2016, Hydrobiologia.