INLAND WATER QUALITY MONITORING IN AUSTRALIA Tim J. Malthus 1 , Erin L. Hestir 1 , Arnold Dekker 1 , Janet Anstee 1 , Hannelie Botha 1 , Nagur Cherukuru 1 , Vittorio Brando 1 , Lesley Clementsen 2 , Rod Oliver 1 , Zygmunt Lorenz 1 1. Division of Land and Water, Commonwealth Scientific and Industrial Research Organization, Australia (tim.malthus@csiro.au) 2. Division of Marine and Atmospheric Research, Commonwealth Scientific and Industrial Research Organization, Australia ABSTRACT Consistent and accurate information on inland water quality over wider areas of the Australian continent are required to assess current condition and trends in response to key environmental and climatic impacts. Optical remote sensing offers a method to objectively assess this over multiple spatial scales provided retrieval algorithms are accurate. Here, we present the results of initial research aimed at exploring the optical variability in Australian inland waters and of linear matrix inversion algorithms applied to both in situ reflectance spectra and high resolution satellite data to retrieve water inland water quality parameters. In situ sampling reveals a high degree of optical variability both within and between lakes across the regions sampled with regional patterns evident; sub-tropical and tropical lakes exhibited greater optical complexity than deep lakes in mid- latitude regions. Clustering analysis indicated the presence of 8 different optical water types in the water bodies measured. The ability of the linear matrix inversion algorithm to map water quality, tested on in situ reflectance and WorldView2 image datasets, showed relative accuracy when parameter sets were sufficient to achieve algorithm closure. Improved algorithm parameterization will be required to account for the high degree in spatial and temporal optical variability observed in Australian inland waters. Index Terms— Inland water quality monitoring, optical variability, linear matrix inversion, Australia, remote sensing, cyanobacteria. 1. INTRODUCTION Australia’s inland water quality is ranked among the worst of the developed countries and it is getting poorer [1, 2]. Similar to the challenges facing many countries, existing data are scarce and declining, have poor geographic and temporal coverage, and may be of questionable accuracy [3]. More consistent and more accurate information on inland water quality over wider areas of the continent are required such that current conditions can be assessed and changes in response to other impacts such as changes in land use, fires, flooding and climate change investigated [4, 5]. Such assessments are needed across a range of scales, from continuous in situ measurements to satellite remote sensing for synoptic supra-regional investigations. Approaches at different scales are complementary; assessment at each scale supports the other, but traditional grab-sampling approaches require repeated travel often to remote areas and costly laboratory analyses. Optical remote sensing offers a method to objectively assess inland water quality over multiple spatial scales. However, while ocean water colour remote sensing is relatively mature, both empirical and semi-analytical algorithms for inland water quality may suffer from several limitations: retrieval of information is often on only a single water quality constituent, specific sensor applicability, the requirement for ongoing coincident in situ data for parameterization, and limited transferability across different inland water optical types, time and concentration ranges [6, 7]. The dearth of globally representative in situ water quality data, particularly specific inherent optical properties (SIOPs), prevents understanding the range of variability in inland water types necessary for the development of algorithms for widespread application. Understanding of the spectral shape, variability and relative contributions of phytoplankton, non-algal particles and dissolved matter to total scattering, backscattering and absorption in the water column is required if we are to achieve this goal [8]. Collation, standardization, and meta-analyses of existing data are needed to understand variability and to inform priority areas for future sampling efforts.    ,((( ,*$566 