scholarly journals A Novel Scheme for Merging Active and Passive Satellite Soil Moisture Retrievals Based on Maximizing the Signal to Noise Ratio

2020 ◽  
Vol 12 (22) ◽  
pp. 3804
Author(s):  
B. G. Mousa ◽  
Hong Shu ◽  
Mohamed Freeshah ◽  
Aqil Tariq
Keyword(s):  
Soil Moisture ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Correlation Coefficients ◽  
Temporal Correlation ◽  
Global Scale ◽  
Signal To Noise ◽  
Significance Levels ◽  
Land Data Assimilation ◽  
Land Data Assimilation System

In this research, we developed and evaluated a new scheme for merging soil moisture (SM) retrievals from both passive and active microwave satellite estimates, based on maximized signal-to-noise ratios, in order to produce improved SM products using least-squares theory. The fractional mean-squared-error (fMSE) derived from the triple collocation method (TCM) was used for this purpose. The proposed scheme was applied by using a threshold between signal and noise at fMSE equal to 0.5 to maintain the high-quality SM observations. In the regions where TCM is unreliable, we propose four scenarios based on the determinations of correlations between all three SM products of TCM at significance levels (i.e., p-values). The proposed scheme was applied to combine SM retrievals from Soil Moisture Active Passive (SMAP), Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) to produce SMAP+ASCAT and AMSR2+ASCAT SM datasets at a global scale for the period from June 2015 to December 2017. The merged SM dataset performance was assessed against SM data from ground measurements of international soil moisture network (ISMN), Global Land Data Assimilation System-Noah (GLDAS-Noah) and ERA5. The results show that the two merged SM datasets showed significant improvement over their parent products in the high average temporal correlation coefficients (R) and the lowest root mean squared difference (RMSE), compared with in-situ measurements over different networks of ISMN. Moreover, these datasets outperformed their parent products over different land cover types in most regions of the world, with a high overall average temporal R and the lowest overall average RMSE value with GLDAS and ERA5. In addition, the suggested scenarios improved SM performance in the regions with unreliable TCMs.

scholarly journals Sub-seasonal variability of surface soil moisture over eastern China

2020 ◽  
Vol 55 (11-12) ◽  
pp. 3527-3541
Author(s):  
Yang Zhou ◽  
Xuan Dong ◽  
Haishan Chen ◽  
Lu Cao ◽  
Qing Shao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Eastern China ◽  
Correlation Coefficients ◽  
Surface Soil Moisture ◽  
Spatial Aspect ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Main Factor

Abstract Various surface soil moisture (SM) data from station observations, the Soil Moisture Active Passive (SMAP) mission, three reanalyses (ERA-Interim, CFSR, and NCEP RII), and the Global Land Data Assimilation System (GLDAS) are used to explore the sub-seasonal variations of SM (SSV-SM) over eastern China. Based on the correlation with SM of SMAP, reanalyses, and GLDAS, it is found that the variations of SM observed by Liuhe and Chunan stations can generally represent the SM variations over eastern China. The correlation coefficients between the SMAP and station SM are around 0.7. The SMAP product can well capture the time variation of SM over eastern China. The spectral analysis suggests that periodic variations of SM are mainly and significantly over the 10–30-day period over eastern China in all the data. The significant spectra over the 10–30-day period basically occur during the rainy season over eastern China. For the spatial aspect of SSV-SM, precipitation is the main factor causing the spatial distribution of SSV-SM over eastern China. However, the spectra of the station precipitation are not consistent with those of the station SM, and there is less coherence between the precipitation and SM over the periods during which SM has significant spectra. This indicates that SSV-SM is also affected by other factors.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

scholarly journals Signal Intensity of Stem Diameter Variation for the Diagnosis of Drip Irrigation Water Deficit in Grapevine

Horticulturae ◽  
2021 ◽  
Vol 7 (6) ◽  
pp. 154
Author(s):  
Chen Ru ◽  
Xiaotao Hu ◽  
Wene Wang ◽  
Hui Ran ◽  
Tianyuan Song ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Signal Intensity ◽  
Signal To Noise Ratio ◽  
Water Status ◽  
Stem Diameter ◽  
Vegetative Stage ◽  
Signal To Noise ◽  
Diameter Variation ◽  
Expansion Stage ◽  
Noise Ratio

Precise irrigation management of grapevines in greenhouses requires a reliable method to easily quantify and monitor the grapevine water status to enable effective manipulation of the water stress of the plants. This study describes a study on stem diameter variations of grapevine planted in a greenhouse in the semi-arid area of Northwest China. In order to determine the applicability of signal intensity of stem diameter variation to evaluate the water status of grapevine and soil. The results showed that the relative variation curve of the grapevine stem diameter from the vegetative stage to the fruit expansion stage showed an overall increasing trend. The correlations of MDS (maximum daily shrinkage) and DI (daily increase) with meteorological factors were significant (p < 0.05), and the correlations with SWP, RWC and soil moisture were weak. Although MDS and DI can diagnose grapevine water status in time, SIMDS and SIDI have the advantages of sensitivity and signal intensity compared with other indicators. Compared with MDS and DI, the R2 values of the regression equations of SIMDS and SIDI with SWP and RWC were high, and the correlation reached a very significant level (p < 0.01). Thus, SIMDS and SIDI are more suitable for the diagnosis of grapevine water status. The SIMDS peaked at the fruit expansion stage, reaching 0.957–1.384. The signal-to-noise ratio of SIDI was higher than that of MDS across the three treatments at the vegetative stage. The value and signal-to-noise ratio of SIDI at the flowering stage were similar to those of SIMDS, while the correlation between SIDI and the soil moisture content was higher than that of SIMDS. It can be concluded that that SIDI is suitable as an indicator of water status of grapevine and soil during the vegetative and flowering stages. In addition, the signal-to-noise ratio of SIMDS during the fruit expansion and mature stages was significantly higher than that of SIDI. Therefore, SIMDS is suitable as an indicator of the moisture status of grapevine and soil during the fruit expansion and mature stages. In general, SIMDS and SIDI were very good predictors of the plant water status during the growth stage and their continuous recording offers the promising possibility of their use in automatic irrigation scheduling in grapevine.

Detection of Rarefaction of Capillaries and Avascular Region in Nailfold Capillary Images

2018 ◽  
pp. 1940-1954
Author(s):  
Suma K. V. ◽  
Bheemsain Rao
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Capillary Density ◽  
Signal To Noise ◽  
Median Filters ◽  
Squared Error ◽  
Connected Component Labeling ◽  
Labeling Algorithm ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Reduction in the capillary density in the nailfold region is frequently observed in patients suffering from Hypertension (Feng J, 2010). Loss of capillaries results in avascular regions which have been well characterized in many diseases (Mariusz, 2009). Nailfold capillary images need to be pre-processed so that noise can be removed, background can be separated and the useful parameters may be computed using image processing algorithms. Smoothing filters such as Gaussian, Median and Adaptive Median filters are compared using Mean Squared Error and Peak Signal-to-Noise Ratio. Otsu's thresholding is employed for segmentation. Connected Component Labeling algorithm is applied to calculate the number of capillaries per mm. This capillary density is used to identify rarefaction of capillaries and also the severity of rarefaction. Avascular region is detected by determining the distance between the peaks of the capillaries using Euclidian distance. Detection of rarefaction of capillaries and avascular regions can be used as a diagnostic tool for Hypertension and various other diseases.

Detection of Rarefaction of Capillaries and Avascular Region in Nailfold Capillary Images

2016 ◽  
Vol 5 (2) ◽  
pp. 73-86
Author(s):  
Suma K. V. ◽  
Bheemsain Rao
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Capillary Density ◽  
Connected Component ◽  
Signal To Noise ◽  
Median Filters ◽  
Squared Error ◽  
Labeling Algorithm ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Reduction in the capillary density in the nailfold region is frequently observed in patients suffering from Hypertension (Feng J, 2010). Loss of capillaries results in avascular regions which have been well characterized in many diseases (Mariusz, 2009). Nailfold capillary images need to be pre-processed so that noise can be removed, background can be separated and the useful parameters may be computed using image processing algorithms. Smoothing filters such as Gaussian, Median and Adaptive Median filters are compared using Mean Squared Error and Peak Signal-to-Noise Ratio. Otsu's thresholding is employed for segmentation. Connected Component Labeling algorithm is applied to calculate the number of capillaries per mm. This capillary density is used to identify rarefaction of capillaries and also the severity of rarefaction. Avascular region is detected by determining the distance between the peaks of the capillaries using Euclidian distance. Detection of rarefaction of capillaries and avascular regions can be used as a diagnostic tool for Hypertension and various other diseases.

Perception of Movement and Correlation in Stroboscopically Presented Noise Patterns

Perception ◽  
1985 ◽  
Vol 14 (2) ◽  
pp. 209-224 ◽  
Author(s):  
Andrea J van Doorn ◽  
Jan J Koenderink ◽  
Wim A van de Grind
Keyword(s):  
Visual Field ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Temporal Correlation ◽  
Motion Aftereffect ◽  
Luminance Contrast ◽  
Signal To Noise ◽  
Field Evidence ◽  
Spatiotemporal Integration ◽  
Moving Pattern

The detection of spatiotemporal correlation in visual displays has been studied with stroboscopically presented random-noise patterns and with a signal-to-noise ratio paradigm in which the moving pattern was masked with spatiotemporal white noise. These methods reveal the ability of the visual system to detect correlation of spatiotemporal structures, rather than luminance contrast. The effects of stroboscopic rate, exposure duration, target size, and the extent of discrete spatial shifts were studied in both the central and the peripheral visual field. Evidence for orientation-selective and speed-selective mechanisms was found, as well as for extensive spatiotemporal integration. Bounds on parameters of spatial and temporal correlation and integration were obtained. The results are similar to those reported earlier, and also extend them. Their relation to results obtained through other paradigms (eg the motion aftereffect) is explored.

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