The impacts of environmental variables on water reflectance measured using a lightweight unmanned aerial vehicle (UAV)-based spectrometer system

Abstract Remote sensing methods to study spatial and temporal changes in water quality using satellite or aerial imagery are limited by the inherently low reflectance signal of water across the visible and near infrared spectrum, as well as environmental variables such as surface scattering effects (sun glint), substrate and aquatic vegetation reflectance, and atmospheric effects. This study exploits the low altitude, high-resolution remote sensing capabilities of unmanned aerial vehicle (UAV) platforms to examine the major environmental variables that affect water reflectance acquisition, without the confounding influence of atmospheric effects typical of higher-altitude platforms. After observation and analysis, we found: (1) multiple water spectra measured at the same location had a standard deviation of 10.4%; (2) water spectra changes associated with increasing altitude from 20 m to 100 m were negligible; (3) the difference between mean reflectance at three off-shore locations in an urban water body reached 29.9%; (4) water bottom visibility increased water reflectance by 20.1% in near shore areas compared to deep water spectra in a clear water lake; (5) emergent plants caused the water spectra to shift towards a shape that is characteristic of vegetation, whereas submerged vegetation showed limited effect on water spectra in the studied lake; (6) cloud and sun glint had major effects and caused water spectra to change abruptly; while glint and shadow effects on spectra may balance each other under certain conditions, the water reflectance can also be unpredictable at times due to wave effects and their effects on lines-of-site to calm water; (7) water spectra collected under a variety of different conditions (e.g. multiple locations, waves) resulted in weaker regression models compared to spectra collected under ideal conditions (e.g. single location, no wave), although the resulting model coefficients were relatively stable. The methods and results from this study contribute to better understanding of water reflectance acquisition using remote sensing, and can be applied in UAV-based water quality assessment or to aid in validation of higher altitude imagery.

[1]  Ning Wang,et al.  Spectral Calibration of Hyperspectral Data Observed From a Hyperspectrometer Loaded on an Unmanned Aerial Vehicle Platform , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  Els Knaeps,et al.  A Wavelet-Enhanced Inversion Method for Water Quality Retrieval From High Spectral Resolution Data for Complex Waters , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[3]  C. Mobley,et al.  Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization. , 1999, Applied optics.

[4]  T. Cui,et al.  A simple two-band semi-analytical model for retrieval of specific absorption coefficients in coastal waters , 2014 .

[5]  D. Barr,et al.  Mean and Variance of Truncated Normal Distributions , 1999 .

[6]  B. Matsushita,et al.  A hybrid algorithm for estimating the chlorophyll-a concentration across different trophic states in Asian inland waters , 2015 .

[7]  C. Mobley,et al.  Estimation of the remote-sensing reflectance from above-surface measurements. , 1999, Applied optics.

[8]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[9]  Lu Wang,et al.  Estimation of chlorophyll-a concentration of different seasons in outdoor ponds using hyperspectral imaging. , 2016, Talanta.

[10]  Nello Cristianini,et al.  Cross‐Validation (K‐Fold Cross‐Validation, Leave‐One‐Out, Jackknife, Bootstrap) , 2014 .

[11]  Chuanmin Hu,et al.  Aquatic color radiometry remote sensing of coastal and inland waters: Challenges and recommendations for future satellite missions , 2015 .

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

[13]  P. Zarco-Tejada,et al.  Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera , 2012 .

[14]  C. Barbosa,et al.  Parametrization and calibration of a quasi-analytical algorithm for tropical eutrophic waters , 2016 .

[15]  L. Han,et al.  Applications of Landsat‐5 TM imagery in assessing and mapping water quality in Reelfoot Lake, Tennessee , 2006 .

[16]  Geoffrey H. Ball,et al.  ISODATA, A NOVEL METHOD OF DATA ANALYSIS AND PATTERN CLASSIFICATION , 1965 .

[17]  Yves Cornet,et al.  Spatio-temporal dynamics of phytoplankton and primary production in Lake Tanganyika using a MODIS based bio-optical time series , 2010 .

[18]  R. Arnone,et al.  A model for the diffuse attenuation coefficient of downwelling irradiance , 2005 .

[19]  C. Mobley.  Light and Water: Radiative Transfer in Natural Waters , 1994 .

[20]  Stephanie C. J. Palmer,et al.  Remote sensing of inland waters: Challenges, progress and future directions , 2015 .

[21]  Gonzalo Pajares,et al.  Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs) , 2015 .

[22]  R. Santer,et al.  Adjacency effects on water surfaces: primary scattering approximation and sensitivity study. , 2000, Applied optics.

[23]  Young-Heon Jo,et al.  Future Retrievals of Water Column Bio-Optical Properties using the Hyperspectral Infrared Imager (HyspIRI) , 2013, Remote. Sens..

[24]  N. Oppelt,et al.  Remote sensing for lake research and monitoring – Recent advances , 2016 .

[25]  M. Bauer,et al.  Factors affecting the measurement of CDOM by remote sensing of optically complex inland waters , 2015 .

[26]  G. Likens,et al.  Spectral reflectance and water quality of Adirondack mountain region lakes , 1989 .

[27]  Anders Knudby,et al.  The Potential for Landsat-Based Bathymetry in Canada , 2016 .

[28]  Bertram Ostendorf,et al.  A review of methods for analysing spatial and temporal patterns in coastal water quality , 2011 .

[29]  David McKee,et al.  Potential impacts of nonalgal materials on water-leaving Sun induced chlorophyll fluorescence signals in coastal waters. , 2007, Applied optics.

[30]  Kaishan Song,et al.  Remote estimation of K-d (PAR) using MODIS and Landsat imagery for turbid inland waters in Northeast China , 2017 .

[31]  Boqiang Qin,et al.  Photochemical degradation of chromophoric-dissolved organic matter exposed to simulated UV-B and natural solar radiation , 2009, Hydrobiologia.

[32]  C. Giardino,et al.  Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission , 2015 .

[33]  M. Bauer,et al.  Airborne hyperspectral remote sensing to assess spatial distribution of water quality characteristics in large rivers: the Mississippi River and its tributaries in Minnesota. , 2013 .

[34]  Andreas Burkart,et al.  Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance , 2015 .

[35]  Anatoly A. Gitelson,et al.  Optical models of mesotrophic and eutrophic water bodies , 1991 .

[36]  Idris Mushrifah,et al.  Water Quality Changes in Chini Lake, Pahang, West Malaysia , 2007, Environmental monitoring and assessment.

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

[38]  C. Binding,et al.  Long term water clarity changes in North America's Great Lakes from multi‐sensor satellite observations , 2015 .

[39]  Jim Aiken,et al.  The atmospheric correction of water colour and the quantitative retrieval of suspended particulate matter in Case II waters: Application to MERIS , 1999 .

[40]  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.

[41]  Charles L. Gallegos,et al.  Calculating optical water quality targets to restore and protect submersed aquatic vegetation: Overcoming problems in partitioning the diffuse attenuation coefficient for photosynthetically active radiation , 2001 .

[42]  Yukio Kosugi,et al.  Development of a Low-Cost, Lightweight Hyperspectral Imaging System Based on a Polygon Mirror and Compact Spectrometers , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[43]  Jean-François Berthon,et al.  Detailed validation of the bidirectional effect in various Case I and Case II waters. , 2012, Optics express.