The spatial and temporal correlation analysis between MODIS NDVI and SWAT predicted soil moisture during forest NDVI increasing and decreasing periods

<p>Climate change has led to increased droughts and floods over mainland Australia, resulting in water scarcity, excessive surplus and socioeconomic losses. Therefore, it is of great significance to comprehensively evaluate droughts and floods from the meteorological and hydrological perspective. Firstly, we determine the Standard Precipitation and Evapotranspiration Index (SPEI) by correlation analysis to represent the meteorological conditions. To characterize the hydrological conditions, we calculate the hydrological drought indices including Standard Runoff Index (SRI), Soil Moisture Deficit Index (SMDI), and Total Storage Deficit Index (TSDI), using the runoff and soil moisture data from the Global Land Data Assimilation System (GLDAS) and the Terrestrial Water Storage Change (TWSC) data from Gravity Recovery And Climate Experiment (GRACE) respectively. Results show that the most severe hydrological drought over mainland Australia during the study period occurred from May 2006 to Jan. 2009 with the drought severity of -58.28 (cm months) and the most severe flood from Jun. 2010 to Jan. 2013 is with the severity of 151.36 (cm months). The comprehensive analysis of both meteorological and hydrological drought indices shows that both meteorological and hydrological drought indices can effectively detect the droughts and floods over mainland Australia. Moreover, the meteorological drought and flood are of higher frequency, while hydrological drought and flood have a relatively longer duration. Based on the cross-correlation analysis, we find that the SPEI can firstly reflect the droughts or floods over mainland Australia, and then the SRI, SMDI and TSDI reflect with the time lags of one, three and six months respectively. Furthermore, we calculate the frequency of drought and flood at the basin scale and find that SPEI and SMDI are equally sensitive to drought and flood, while TSDI is more sensitive to flood than drought. This study reveals the relationship between meteorological and hydrological conditions in mainland Australia in the last two decades and highlights its intensifying extreme climate conditions under the circumstances of the increasing temperature and complex changing precipitation.</p>

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Parametric Channel Prediction for Narrowband Mobile MIMO Systems Using Spatio-Temporal Correlation Analysis

2013 IEEE 78th Vehicular Technology Conference (VTC Fall) ◽

10.1109/vtcfall.2013.6692341 ◽

2013 ◽

Cited By ~ 12

Author(s):

Ramoni O. Adeogun ◽

Paul D. Teal ◽

Pawel A. Dmochowski

Keyword(s):

Correlation Analysis ◽

Mimo Systems ◽

Temporal Correlation ◽

Channel Prediction ◽

Spatio Temporal

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

Remote Sensing ◽

10.3390/rs12223804 ◽

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.

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Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data

Geophysical Research Letters ◽

10.1029/2008gl035772 ◽

2008 ◽

Vol 35 (22) ◽

Cited By ~ 109

Author(s):

Yingxin Gu ◽

Eric Hunt ◽

Brian Wardlow ◽

Jeffrey B. Basara ◽

Jesslyn F. Brown ◽

...

Keyword(s):

Soil Moisture ◽

Drought Monitoring ◽

Modis Ndvi ◽

Soil Moisture Data ◽

Oklahoma Mesonet

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Hydrological Evaluation of Satellite Soil Moisture Data in Two Basins of Different Climate and Vegetation Density Conditions

Advances in Meteorology ◽

10.1155/2017/1086456 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 6

Author(s):

Lu Zhuo ◽

Dawei Han

Keyword(s):

Soil Moisture ◽

Real Time ◽

Hydrological Model ◽

Flood Forecasting ◽

Spatial Difference ◽

Vegetation Density ◽

Modis Ndvi ◽

Basin Scale ◽

Microwave Sensors ◽

Validation Tool

Accurate soil moisture information is very important for real-time flood forecasting. Although satellite soil moisture observations are useful information, their validations are generally hindered by the large spatial difference with the point-based measurements, and hence they cannot be directly applied in hydrological modelling. This study adopts a widely applied operational hydrological model Xinanjiang (XAJ) as a hydrological validation tool. Two widely used microwave sensors (SMOS and AMSR-E) are evaluated, over two basins (French Broad and Pontiac) with different climate types and vegetation covers. The results demonstrate SMOS outperforms AMSR-E in the Pontiac basin (cropland), while both products perform poorly in the French Broad basin (forest). The MODIS NDVI thresholds of 0.81 and 0.64 (for cropland and forest basins, resp.) are very effective in dividing soil moisture datasets into “denser” and “thinner” vegetation periods. As a result, in the cropland, the statistical performance is further improved for both satellites (i.e., improved to NSE = 0.74, RMSE = 0.0059 m and NSE = 0.58, RMSE = 0.0066 m for SMOS and AMER-E, resp.). The overall assessment suggests that SMOS is of reasonable quality in estimating basin-scale soil moisture at moderate-vegetated areas, and NDVI is a useful indicator for further improving the performance.

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