Spatio-temporal distribution of internal waves in the Andaman Sea based on satellite remote sensing

2016 ◽  
Author(s):  
Liying Zhou ◽  
Jingsong Yang ◽  
Juan Wang ◽  
Shuangyan He ◽  
Zhiguo He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Internal Waves ◽  
Satellite Remote Sensing ◽  
Temporal Distribution ◽  
Andaman Sea ◽  
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>

scholarly journals On the spatio-temporal representativeness of observations

2017 ◽  
Vol 17 (16) ◽  
pp. 9761-9780 ◽  
Author(s):  
Nick Schutgens ◽  
Svetlana Tsyro ◽  
Edward Gryspeerdt ◽  
Daisuke Goto ◽  
Natalie Weigum ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Measurement Errors ◽  
Remote Sensing Data ◽  
Temporal Sampling ◽  
Representation Error ◽  
Temporal Averaging ◽  
Carbon Mass ◽  
Spatio Temporal

Abstract. The discontinuous spatio-temporal sampling of observations has an impact when using them to construct climatologies or evaluate models. Here we provide estimates of this so-called representation error for a range of timescales and length scales (semi-annually down to sub-daily, 300 to 50 km) and show that even after substantial averaging of data significant representation errors may remain, larger than typical measurement errors. Our study considers a variety of observations: ground-site or in situ remote sensing (PM2. 5, black carbon mass or number concentrations), satellite remote sensing with imagers or lidar (extinction). We show that observational coverage (a measure of how dense the spatio-temporal sampling of the observations is) is not an effective metric to limit representation errors. Different strategies to construct monthly gridded satellite L3 data are assessed and temporal averaging of spatially aggregated observations (super-observations) is found to be the best, although it still allows for significant representation errors. However, temporal collocation of data (possible when observations are compared to model data or other observations), combined with temporal averaging, can be very effective at reducing representation errors. We also show that ground-based and wide-swath imager satellite remote sensing data give rise to similar representation errors, although their observational sampling is different. Finally, emission sources and orography can lead to representation errors that are very hard to reduce, even with substantial temporal averaging.

Satellite remote sensing of biomass burning with optical and thermal sensors

2000 ◽  
Vol 24 (4) ◽  
pp. 543-561 ◽  
Author(s):  
Douglas O. Fuller
Keyword(s):  
Remote Sensing ◽  
Biomass Burning ◽  
Satellite Remote Sensing ◽  
Field Experiments ◽  
Temporal Distribution ◽  
Fire Detection ◽  
Fire Regimes ◽  
Fire Monitoring ◽  
Fire Scar ◽  
In Fire

A major goal in satellite remote sensing of fire is to derive globally accurate measurements of the spatial and temporal distribution of burning. To date, the main sensor employed in fire and fire-scar detection has been the Advanced Very High Resolution Radiometer (AVHRR) on board NOAA polar-orbiting platforms. Other sources supporting fire observation over large areas include the Defense Meteorological Satellite Program -Optical Linescan (DMSP-OLS), the Geostationary Operational Environmental Satellite - 8 (GOES-8) and the Along Track Scanning Radiometer (ATSR). These sources have often been used in conjunction with high spatial-resolution imagery provided by the Landsat Thematic Mapper and SPOT to assess the accuracy of proposed fire and fire-scar retrieval algorithms. Although a range of fire detection algorithms have been proposed based on more than a decade of research on the AVHRR data, it remains to be seen whether variations in land-cover type, surface temperature and fire regimes will permit application of global thresholds of temperature and reflectance. Moreover, the emerging set of satellite sensors with demonstrated utility in fire monitoring indicates substantial possibilities for greater synergy of current and future remote-sensing systems leading to improved monitoring of fire extent and frequency. As a more complete global picture of biomass burning emerges with the launch of new sensors for fire monitoring (e.g., MODIS), this information, combined with detailed data from field experiments, can help provide reliable budgets of trace gases and particulate species that affect global energy balance and climate.

scholarly journals Spatio-temporal distribution and classification of utilization of urban bare lots in low-slope hilly regions

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246746
Author(s):  
Qi Cao ◽  
Manjiang Shi
Keyword(s):  
Remote Sensing ◽  
Temporal Distribution ◽  
Urban Landscapes ◽  
Distribution Characteristics ◽  
Remote Sensing Images ◽  
Landscape Index ◽  
Activity Intensity ◽  
Transportation Service ◽  
Spatio Temporal

Urban bare lots are persistent phenomena in urban landscapes in the course of urbanization. In the present study, we examined the spatio-temporal distribution of urban bare lots in low-slope hilly areas, and to assess the major pathways by which they are generated and later re-transformed for exploitation. We extracted land use and land cover (LULC) change information and analyzed spatio-temporal distribution characteristics of urban bare lots using Landsat TM/OLI series remote sensing images. Subsequently, we proposed an index system for their evaluation and classification, and identified five types of urban bare lots. Urban bare lot quantity and distribution are closely correlated with human activity intensity. Stakeholders should consider the multiple effects of location, topography, landscape index, transportation, service facilities, and urban planning in urban bare lot classification activities for renovation and re-transformation.

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