Satellite Remote Sensing For Spatio-Temporal Changes Analysis Of Urban Surface Biogeophysical Parameters

2010 ◽  
Author(s):  
Maria Zoran ◽  
Angelos Angelopoulos ◽  
Takis Fildisis
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Temporal Changes ◽  
Urban Surface ◽  
Spatio Temporal

Bayesian Spatio-temporal Geostatistics-based Method for Multiple Satellite Products Fusion and Downscaling

2020 ◽  
Author(s):  
Yanchen Bo
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Satellite Remote Sensing ◽  
Hydrological Cycle ◽  
Microwave Remote Sensing ◽  
Earth Surface ◽  
Spatio Temporal ◽  
High Level

<p>High-level satellite remote sensing products of Earth surface play an irreplaceable role in global climate change, hydrological cycle modeling and water resources management, environment monitoring and assessment. Earth surface high-level remote sensing products released by NASA, ESA and other agencies are routinely derived from any single remote sensor. Due to the cloud contamination and limitations of retrieval algorithms, the remote sensing products derived from single remote senor are suspected to the incompleteness, low accuracy and less consistency in space and time. Some land surface remote sensing products, such as soil moisture products derived from passive microwave remote sensing data have too coarse spatial resolution to be applied at local scale. Fusion and downscaling is an effective way of improving the quality of satellite remote sensing products.</p><p>We developed a Bayesian spatio-temporal geostatistics-based framework for multiple remote sensing products fusion and downscaling. Compared to the existing methods, the presented method has 2 major advantages. The first is that the method was developed in the Bayesian paradigm, so the uncertainties of the multiple remote sensing products being fused or downscaled could be quantified and explicitly expressed in the fusion and downscaling algorithms. The second advantage is that the spatio-temporal autocorrelation is exploited in the fusion approach so that more complete products could be produced by geostatistical estimation.</p><p>This method has been applied to the fusion of multiple satellite AOD products, multiple satellite SST products, multiple satellite LST products and downscaling of 25 km spatial resolution soil moisture products. The results were evaluated in both spatio-temporal completeness and accuracy.</p>

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