Shallow Water Bathymetry from Multispectral Satellite Images: Extensions of Lyzenga's Method for Improving Accuracy

Determination of the Best Methodology for Bathymetry Mapping Using Spot 6 Imagery: A Study of 12 Empirical Algorithms

“…Even the other eleven algorithms is based on physical and statistical principles, but still includes several assumptions that are often unrealistic and also not effective or appropriate statistical analysis, details as follows. MLR algorithm assumed that water quality and atmospheric condition is uniform, and the number of bottom types is less than a number of used bands are unrealistic for much shallow water environment (Kanno et al 2011). RF algorithm used in this study is run on auto-tuning mode, however, to get the best result of random forest algorithm, it is necessary to do an optimization on the hyper-parameters (Manessa et al 2016a).…”

“…Even Mishra et al (2003) in the publication shows that the LRSPO algorithm works well (RMSE = 2,711 m and R 2 = 0.92) but in this study, this algorithm could not produce a good accuracy (RMSE = 1.37 -2.16 and R 2 = 0.14 -0.21). KNW algorithm only focuses on non-uniform of surface and atmospheric condition (Kanno et al 2011). SMP algorithm only including the elements of the bottom-type-dependent to nails the premise that bottom radiance is discrete (Kanno et al 2011).…”

“…Remote sensing has been suggested as an alternative tool for mapping the bathymetry especially for shallow water environment (Lyzenga 1978;Kanno et al 2011). In this study, satellite derives bathymetry (here and after as SDB) term was used to define the remote sensing method for bathymetry extraction.…”

“…Our research focuses on the finding the best empirical SDB algorithm for SPOT 6 multispectral data. Twelve empirical SDB algorithm was intensionally chosen: 1) Ratio transform (henceforth named "RT") by Stumpt et al (2003); 2) Multiple linear regression (henceforth named "MLR") by Lyzenga et al (2006); 3) Multiple non-linear regression (henceforth named "RF") by Manessa et al (2016a); 4) Second-order polynomial of ratio transform (henceforth named "SPR") by Mishra et al (2006); 5) Principle component (henceforth named "PC") by Van Hengel and Spitzer (1991); four extension of Lyzenga's SDB algorithm by Kanno et al (2011): 6) Multiple linear regression using relaxing uniformity assumption on water and atmosphere (henceforth named "KNW"); 7) Semiparametric regression using depthindependent variables (henceforth named "SMP"); 8) semiparametric regression using spatial coordinates (henceforth named "STR"); 9) Semiparametric regression using depth-independent variables and spatial coordinates (henceforth named "TNP"), and three statistic new statistical approach of Mohamed et al (2017): 10) Bagging Fitting Ensemble (henceforth named "BAG"); 11) Least Squares Boosting Fitting Ensemble (henceforth named "LSB"); and 12) support vector regression (henceforth named "SVR").…”

Validation of shallow-water reflectance model for remote sensing of water depth and bottom type by radiative transfer simulation

“…As a low-cost complementary technique for mapping water depth and bottom type in these areas, various passive remote sensing methods applicable to multi-spectral satellite imagery in the visible region have been proposed. [1][2][3][4][5][6][7][8][9][10][11][12][13][14] Among these methods, that documented by Lyzenga et al 1 is one of the most widely used methods for water depth mapping and that proposed by Lyzenga 2 is one of the most widely used methods for bottom type mapping. Both of these methods, as well as many others, [3][4][5][6] are based on the following shallow-water reflectance model (for each visible band) proposed by Lyzenga 3 [Eq.…”