N. Mueller, et al. (2015) observed presence of water from space that is mapped for the surface water of Australian Landsat imagery from 25 years [11]. The WOfS (Water Observations from Space) product provided a nationally consistent tool for understanding surface water across Australia. The maps which are being generated using WOfS on Australian floodplains had provided a source of information. A program established by Australian government across Australia which helps in accessing the improved flood information. They used an algorithm which was used for the water detection and was based on the principle of decision tree classifier, and logistic regression. The development of this analysis can be applied in a systematically manner through decades of data which is a combination of standard grid arranged surface reflectance data and large high-speed storage attached to supercomputer processors [1][11].
Klemas, V. and Pieterse, A. (2015) used remote sensing to Map and Monitor Water Resources in Arid and Semiarid Regions. An overview has been discussed for managing various water resources and monitoring drought in various arid and semiarid regions using the satellite and airborne remote sensing techniques. The basic objective of their research was internally mapping of arid regions and to prepare them on a global scale to get the condition of soil degradation level and deserted areas. To identify this, powerful computer programs were developed. Various techniques like MCDM were used to delineate potential ground zones[11]. The results of detection, mapping, and monitoring of surface water and vegetation in arid and semiarid regions using Airborne and satellite remote sensors. This research also explains the various modeling techniques to determine the intensity and extent of the flooding, forecasting the vulnerability to the flooding area, and accessing the damage caused by floods [2][11].
Madhavana, S., et al. (2016) focused on the dust detecting schemes by using multi-sensor measurements, legacy sensors like Terra (T) and Aqua(A), MODIS (MODerate-resolution Imaging Spectroradiometer) which is fused with OMI (Ozone Monitoring Instrument). They extended the previous work by adding channels: cloud top channel of 13.1 μm along with the water vapor channel of 7.1 μm [11]. Using OMI-based aerosol type, dust pixels were easily identified. In this research, firstly an approach was developed to address all the drawbacks of traditionally used metric of AOT to qualify the large dust particles. In dust monitoring, the improvement of particles exploited with the help of MODIS band 29 used in the cloud detection. After that, the author presented the quantitative and qualitative validation. This research also describes the extent of thermal infrared based dust particles with the help of wavelengths. It leads to the high sensitivity to the Saharan dust which results in the enhancement of the dust radiometry [3][11].
Arvinda, C. S., et al. (2016) analyzed flood assessment using MODIS satellite images under unsupervised techniques like mean shift and SOM for water image pixel identification and its extraction. They determined algorithm performance based on various parameters such as ROC. The SOM algorithm performed well in all cases before, during and after flood images. The ROC performance evaluation parameters were used for identifying non-flooded and flooded regions. It helps in recording the flood database. The ROC parameters were applied for the algorithm and validated against the ground truth image. In the end, SOM method performance was the best in the extraction of flooded regions as compared to mean shift method [4][11].
Baeye, M. et al. (2016) research results used the concept of ocean color satellite imagery in turbid water and detected the shipwrecks. They demonstrated a method that wrecks generate SPM (Suspended Particulate Matter) concentration signals which further can be detected by using high-resolution ocean color satellite data like Landsat-8 [11]. The Surface SPM plumes extended downstream for up to 4km from the wrecks, with the concentration ranging from 15 mg/l to 95 mg/l. The ratio between the pump and the background SPM concentration was around 1.4. Slack tidal phases were used to create fluffy mud deposits near the seabed. The score pits which are developed around the wrecks act as the sinks in which the fine-grained suspended particles deposited at slacks. For the suspended particles, the scour pits act as the sources when bottom current increases after the slacks. During the maximum current and immediately after that, SPM plumps were developed before reaching the maximum flood current. [5][11].
Gierach, M. M., et al. (2014) detected plumes of wastewater diversion in Southern California. Their study examined the impact on the quality of water in the SCB on the basis of detection of capabilities of multi-sensor satellite data to detect the 2006 HTP (Hyperion Treatment Plant) and 2012 OCSD (Orange County Sanitation District) wastewater diversions [11]. Data included ocean color from MODIS-Aqua, sea surface roughness, and SST from ASTER-Terra and SAR instrument on Radarsat-1 Board. The result from this was applied to the recent HTP diversion, which occurred from 21 September to November 2015 in response to the observation made during 2006 HTP diversion inspection. It is also observed in their study that SST values CHL-a values used must have the difference of at least 1 mg m-3 and 0.5°C than the adjacent values of waters for the wastewater plumes and their impact which is biophysical in nature was detectable using the satellite. The wastewater plume which can be determined in wind speeds, SAR (Synthetic aperture radar) imagery must have a range between ~3-8 m s-1. Multiple satellite sensors are also beneficial to monitor the changing environmental response to the wastewater plumes. It can also help in the future wastewater diversions in the coastal areas [6][11].
Ovakoglo, G., et al. (2016) used MODIS images for the detailed Lake Morphometric. They presented a methodology for easy updating the bathymetry of a lake with large level fluctuations in water level using high temporal resolution satellite image. It seems possible to produce the lake DDM (Digital depth model), which uses a time series of shorelines digitized near-infrared band of MODIS images. The validation of the produced DDM uses the GNSS measurements which produced the differences in the accuracy of the methodology. Two versions of the DDM were being produced to access the seasonal water fluctuation influence. Their evaluation was small and attributed to seasonal water ponds and vegetation as well as natural sedimentation process. This methodology was applied to the Lake Kerkini in Greece in order to produce the updated DDM. The application of this was limited to the exposed part of the lake bottom; this methodology seems to be useful to cover the parts of the lake that were too shallow to survey by boat. This research helps in updating the DDM of lakes and the reservoirs with inter and intra-annual fluctuations of the water level with the help of the satellite remote sensing. It was necessary to receive the frequent results of the morphometric mapping and the high sediment loads to define the effective life of the reservoirs and its storage capacity which is waste in nature for power generation, irrigation, domestic power supply, and flood control [7][11].
Rokni, K. et al. (2015) gave a new approach for surface water change detection. They introduced a new approach based on Integration of image fusion and image classification techniques for surface water change detection. The effectiveness of the proposed method helps in detecting the changes of the lake surfaces as compared to the basic changes in the detection methods such as image differencing, post classification comparison and principle components analysis. The result shows the high performance by using the Gram Schmidt ANN and Gram Schmidt SVM approaches which provide a very high accuracy results. This approach has the advantage of producing a high-resolution multispectral image, provided the reliable results, highlighting the changed area in the fused image. In the end, the suggested approach has been proven to be effective in detecting the water surface changes of Lake Urmia, Iran. The result demonstrated that the Lake Urmia lost one-third of its surface area in 2000-2010 [8][11].
Gupta, R., & Panchal, P. (2015) concentrated on daytime cloud detection algorithm using Discrete Wavelet Transformation (DWT) and double threshold values by pixel to pixel processing. The realization of data on various satellite images of NOAA, VIRR and MODIS datasets with double threshold values, applied in the visible region of electromagnetic spectrum. The radiation in the microwave region is low and maximum at the visible region. Therefore, in this paper, computation was done in the visible region. DWT is STFT (Short Time Fourier Transformation). In this paper, images were categorized into different ecological surfaces such that water, soil, vegetation, and snow. The proposed method can differentiate the cloud from clear regions. Limitation of proposed method is that it faces some difficulties to differentiate between thick cloud and snow/Ice. The results of the proposed method were compared with the results of HSV and RGB models. The comparative results of the research show that the proposed method works better as compared to the other methods on various data sets with different ecological surfaces [9][11].
Sarp, G., & Ozcelik, M (2016) covered changes in Burdur Lake from years 1987-2011. This research is used in the satellite image interpretation and GIS to analyze and detect the spatial changes and quantify the water area changes of the Lake Burdur. Satellite images were used to extract the information of the lake water area change which was more fast and accurate as compared to the other observation methods. The approach used in this research was based on the spectral water indexing and SVM-classification. In this research, the spatial and spectral performance of each of the classifier was compared with the help of Root Mean Square Error (RMSE) and the Structural Similarity Index (SSIM). Overall SVM which is being followed by MNDWI, NDWI and AWEI provides the best results among the best techniques being used. It was also being observed that the spatiotemporal changes in the lake which were being applied on the proposed method shows the intense decrease in the surface area in years from 1987 to 2011 and also 1887 to 2000. In years 1987 to 2000, the lake had lost around one-fifth of the surface area and in years from 1987 to 2011, the Lake lost one tenth of surface area as compared to the year 2000. The results indicated the success of the MNDWI and SVM based surface water changes detection which helps in identifying the changes between the specified time intervals [10][11].