A Quantitative Comparison of Total Suspended Sediment Algorithms: A Case Study of the Last Decade for MODIS and Landsat-Based Sensors

نویسندگان

  • Passang Dorji
  • Peter Fearns
چکیده

A quantitative comparative study was performed to assess the relative applicability of Total Suspended Solids (TSS) models published in the last decade for the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat-based sensors. The quantitative comparison was performed using a suite of statistical tests and HydroLight simulated data for waters ranging from clear open ocean case-1 to turbid coastal case-2 waters. The quantitative comparison shows that there are clearly some high performing TSS models that can potentially be applied in mapping TSS concentration for regions of uncertain water type. The highest performing TSS models tested were robust enough to retrieve TSS from different water types with Mean Absolute Relative Errors (MARE) of 69.96%–481.82% for HydroLight simulated data. The models were also compared in regional waters of northern Western Australia where the highest performing TSS models yielded a MARE in the range of 43.11%–102.59%. The range of Smallest Relative Error (SRE) and Largest Relative Error (LRE) between the highest and the lowest performing TSS models spanned three orders of magnitude, suggesting users must be cautious in selecting appropriate models for unknown water types.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Applying Artificial Neural Network Algorithms to Estimate Suspended Sediment Load (Case Study: Kasilian Catchment, Iran)

Estimate of sediment load is required in a wide spectrum of water resources engineering problems. The nonlinear nature of suspended sediment load series necessitates the utilization of nonlinear methods to simulate the suspended sediment load. In this study Artificial Neural Networks (ANNs) are employed to estimate daily suspended sediment load. Two different ANN algorithms, Multi Layer Perce...

متن کامل

Comparison of Ant Colony, Elite Ant system and Maximum – Minimum Ant system Algorithms for Optimizing Coefficients of Sediment Rating Curve (Case Study: Sistan River)

By far, different models for determining the relationship between the flow rate  and amount of precipitation have been developed. many models are based on regression models with limited assumptions. one of the most common methods for estimating  sediment of rivers is sediment rating curve. for better estimation of the amount of sediment based of sediment curve  rating equation, it is possible t...

متن کامل

Remote Sensing of Tidal Situation by Monitoring Changes in Suspended Sediment Concentration in Surface Waters

Collecting information on suspended sediments concentration (SSC) in coastal waters and estuaries is vital for proper management of coastal environments. Traditionally, SSC used to be measured by time consuming and costly point measurements. This method allows the accurate measurement of SSC only for a point in space and time. Remote sensing from air-borne and space-borne sensors have proved to...

متن کامل

Investigation of Temporal Phenomena of Sediment Rating Curve and comparison of it with the Some Statistical Methods for Estimating Suspended Sediment Load (Case study: Gamasiab Watershed)

The variable and complex nature of the sediment load of rivers has led that the estimation of sediment entering the reservoirs and the production of long term sediment, for determining the lifetime of the structures encounter with the problem. Application of sediment rating curves is one of the most common methods for estimating the suspended sediment load of rivers. Regardless of the accuracy ...

متن کامل

Optimization of sediment rating curve coefficients using evolutionary algorithms and unsupervised artificial neural network

Sediment rating curve (SRC) is a conventional and a common regression model in estimating suspended sediment load (SSL) of flow discharge. However, in most cases the data log-transformation in SRC models causing a bias which underestimates SSL prediction. In this study, using the daily stream flow and suspended sediment load data from Shalman hydrometric station on Shalmanroud River, Guilan Pro...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Remote Sensing

دوره 8  شماره 

صفحات  -

تاریخ انتشار 2016