Remote Sensing Reflectance Values Research Articles

The influence of geometric correction on the accuracy of the extraction of the remote sensing reflectance of water

ABSTRACT In colour remote sensing of water, the effect of geometric correction on the extraction of water reflectance is worth exploring. The spectral data were measured by sampling three polluted water bodies in Zhengzhou City, and the Planet satellite image data of these areas were obtained and divided into two groups (with and without geometric correction). The two groups of images were then subjected to atmospheric correction using the quick atmospheric correction (QUAC) algorithm to obtain the remote-sensing reflectance values of the water bodies. The results showed that, for the two different extraction methods, the original data and geometrically corrected extraction results are approximately equal, and the spectral curves show similar trends. Compared with the measured spectral data, it is found that the two groups of data extracted from remote sensing images are more likely to show large-scale changes between the red band and the near-infrared (NIR) band. It is concluded that geometric correction affects values of water reflectance based on extraction of images, but has less effect on the overall trend of the curve, and the probability of changes of water reflectance extraction in NIR band is higher than in other bands.

Evaluation of the Influence of Aquatic Plants and Lake Bottom on the Remote-Sensing Reflectance of Optically Shallow Waters

Aquatic plants and lake bottoms in optically shallow waters (OSWs) wield great influence on reflectance spectra, resulting in the inapplicability of most existing bio-optical models for water colour remote sensing in lakes. Based on radiative transfer theory and measured spectra from a campaign for Lake Taihu in October 2008, absorption and backscattering coefficients were used to simulate the remote-sensing reflectance, which are considered to be reliable if matched to their measured counterparts. Several cases of measured spectra at different depths, Secchi disk depth transparency, and aquatic plant height and coverage were analyzed thoroughly for spectral properties. The contribution of aquatic plants was evaluated and compared with the measured and simulated remote-sensing reflectance values. This is helpful for removing the influence of aquatic plants and lake bottoms from the spectra and for constructing an improved chlorophyll a retrieval model for OSWs, such as that for Lake Taihu, China.

Long-term analysis of turbidity patterns in Danube Delta coastal area based on MODIS satellite data

Abstract The monitoring of coastal areas is becoming an urgent necessity in the context of increased pressure over these ecosystems due to climate change and human activities. Long term evaluation of specific parameters regarding water quality can now be achieved, thanks to the increased number of archived Earth Observation satellite data, now covering decades. Within this study, 12 years of MODIS information were used to compute surface water turbidity products that were further temporal binned into composite datasets (e.g. monthly, annual). A regional algorithm, based on local in situ measurements, was used in order to inverse remote sensing reflectance values into turbidity units. The interpretation of the final maps revealed important characteristics of the processes that play a major role in the regional turbidity dynamics. Observations were made regarding the relation between surface water turbidity and Danube River's discharge rates, winds, currents and also the bottom sedimentary characteristics of the shelf area. We discuss how different regions are affected by various external factors, depending on their geographical location, and we reinforce the idea that the river solid input is not the only parameter controlling water clarity in the Danube Delta coastal area, resuspension processes playing also an important role.

Smart Information Reconstruction via Time-Space-Spectrum Continuum for Cloud Removal in Satellite Images

Cloud contamination is a big obstacle when processing satellite images retrieved from visible and infrared spectral ranges for application. Although computational techniques including interpolation and substitution have been applied to recover missing information caused by cloud contamination, these algorithms are subject to many limitations. In this paper, a novel smart information reconstruction (SMIR) method is proposed, in order to reconstruct cloud contaminated pixel values from the time-space-spectrum continuum with the aid of a machine learning tool, namely extreme learning machine (ELM). For the purpose of demonstration, the performance of SMIR is evaluated by reconstructing the missing remote sensing reflectance values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite over Lake Nicaragua, where is a very cloudy area year round. For comparison, the traditional backpropagation neural network algorithms will also be implemented to reconstruct the missing values. Experimental results show that the ELM outperforms the BP algorithms by an enhanced machine learning capacity with simulated memory effect embedded in MODIS due to linking the complex time-space-spectrum continuum between cloud-free and cloudy pixels. The ELM-based SMIR practice presents a correlation coefficient of 0.88 with root mean squared error of 7.4E - 04sr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> between simulated and observed reflectance values. Finding suggests that the SMIR method is effective to reconstruct all the missing information providing visually logical and quantitatively assured images for further image processing and interpretation in environmental applications.

An Algorithm for Estimating Suspended Sediment Concentrations in the Coastal Waters of India using Remotely Sensed Reflectance and its Application to Coastal Environments

This study presents an empirical relationship of suspended sediment concentrations (SSCs) inthe coastal waters, which is derived from the conventional methods, to radiometer remote sensing reflectance values (Rrs) and satellite data in coastal waters of Kerala. An algorithm is then developed to utilize both in situ SSCs measured from conventional methods and Rrs values. This algorithm is validated with the SSCs retrieved from Oceansat-2 Ocean Colour Monitor (OCM) data. A significant coefficient of determination (R2=0.62) is observed between the SSCs measured in situ and satellite derived SSCs. Reflectance values from more than two spectral wavelength bands are also employed to test the accuracy of results. Satellite derived SSCs range from 1- 40 mg/L in the coastal waters off Cochin, southwest India. The regional algorithm developed for the study area gives better results than Tassan’s algorithm, and this algorithm can be used in estimation of SSC for coastal waters of western India.

BOMBER: A tool for estimating water quality and bottom properties from remote sensing images

BOMBER (Bio-Optical Model Based tool for Estimating water quality and bottom properties from Remote sensing images) is a software package for simultaneous retrieval of the optical properties of water column and bottom from remotely sensed imagery, which makes use of bio-optical models for optically deep and optically shallow waters. Several menus allow the user to choose the model type, to specify the input and output files, and to set all of the variables involved in the model parameterization and inversion. The optimization technique allows the user to retrieve the maps of chlorophyll concentration, suspended particulate matter concentration, coloured dissolved organic matter absorption and, in case of shallow waters, bottom depth and distributions of up to three different types of substrate, defined by the user according to their albedo. The software requires input image data that must be atmospherically corrected to remote sensing reflectance values. For both deep and shallow water models, a map of the relative error involved in the inversion procedure is also given. The tool was originally intended to estimate water quality in lakes; however thanks to its general design, it can be applied to any other aquatic environments (e.g., coastal zones, estuaries, lagoons) for which remote sensing reflectance values are known. BOMBER is fully programmed in IDL (Interactive Data Language) and uses IDL widgets as graphical user interface. It runs as an add-on tool for the ENVI+IDL image processing software and is available on request.

Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters.

For open ocean and coastal waters, a multiband quasi-analytical algorithm is developed to retrieve absorption and backscattering coefficients, as well as absorption coefficients of phytoplankton pigments and gelbstoff. This algorithm is based on remote-sensing reflectance models derived from the radiative transfer equation, and values of total absorption and backscattering coefficients are analytically calculated from values of remote-sensing reflectance. In the calculation of total absorption coefficient, no spectral models for pigment and gelbstoff absorption coefficients are used. Actually those absorption coefficients are spectrally decomposed from the derived total absorption coefficient in a separate calculation. The algorithm is easy to understand and simple to implement. It can be applied to data from past and current satellite sensors, as well as to data from hyperspectral sensors. There are only limited empirical relationships involved in the algorithm, and they are for less important properties, which implies that the concept and details of the algorithm could be applied to many data for oceanic observations. The algorithm is applied to simulated data and field data, both non-case1, to test its performance, and the results are quite promising. More independent tests with field-measured data are desired to validate and improve this algorithm.