Temporal and spatial variability of SST and LST concentrations in the Korea Sea using empirical orthogonal function (EOF) analysis of remote sensing data

2010 ◽  
Author(s):  
Suk Yoon ◽  
Joo-Hyung Ryu ◽  
Yu-Hwan Ahn ◽  
Hong Sik Min ◽  
Joong-Sun Won
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Empirical Orthogonal Function ◽  
Remote Sensing Data ◽  
Eof Analysis ◽  
Sensing Data ◽  
Orthogonal Function ◽  
Temporal And Spatial Variability ◽  
Temporal And Spatial

Effect of Slope Mesorelief on the Spatial Variability of Soil Properties and Vegetation Index Based on Remote Sensing Data

2019 ◽  
Vol 55 (9) ◽  
pp. 1329-1337
Author(s):  
N. V. Gopp ◽  
T. V. Nechaeva ◽  
O. A. Savenkov ◽  
N. V. Smirnova ◽  
V. V. Smirnov
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Soil Properties ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Spatial pattern evaluation of a calibrated national hydrological model – a remote sensing based diagnostic approach

2017 ◽  
Author(s):  
Gorka Mendiguren ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Hydrological Model ◽  
Remote Sensing Data ◽  
Driving Mechanism ◽  
Distributed Hydrological Model ◽  
Eof Analysis ◽  
Sensing Data ◽  
Spatio Temporal

Abstract. Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two source energy balance model (TSEB) driven mainly by satellite remote sensing data. The main hypothesis of the study is that while both approaches are essentially estimates, the spatial patterns of the remote sensing based approach are explicitly driven by observed land surface temperature and therefore represent the most direct spatial pattern information of ET; enabling its use for distributed hydrological model evaluation. Ideally the hydrological model simulation and remote sensing based approach should present similar spatial patterns and driving mechanism of ET. However, the spatial comparison showed that the differences are significant and indicating insufficient spatial pattern performance of the hydrological model. The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in 6 domains that are calibrated independently from each other, as it is often the case for large scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of Leaf Area Index (LAI), root depth (RD) and Crop coefficient (Kc) for each land cover type. This zonal approach of model parametrization ignores the spatio-temporal complexity of the natural system. To overcome this limitation, the study features a modified version of the DK-Model in which LAI, RD, and KC are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatio-temporal variability and spatial consistency between the 6 domains. The effects of these changes are analyzed by using the empirical orthogonal functions (EOF) analysis to evaluate spatial patterns. The EOF-analysis shows that including remote sensing derived LAI, RD and KC in the distributed hydrological model adds spatial features found in the spatial pattern of remote sensing based ET.

Temporal and Spatial Variability and Trend Investigation of Total Ozone Column over Iraq Employing Remote Sensing Data: 1979-2012

2015 ◽  
Vol 53 ◽  
pp. 1-18
Author(s):  
Ali M. Al-Salihi ◽  
Zehraa M. Hassan
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Total Ozone ◽  
Positive Trend ◽  
Climate Change Research ◽  
Total Ozone Column ◽  
Temporal And Spatial Variability ◽  
Temporal And Spatial ◽  
Ozone Column

The objective of this paper is to analyze the temporal and spatial variability of the total ozone column (TOC) distributions and trends over Iraq, during the last 30 years (1979–2012) using remote sensing-derived TOC data. Due to shortage of ground-based TOC measurements. TOC data derived from the Total Ozone Mapping Spectrometer (TOMS) for the period 1979–2004 and Ozone Monitoring Instrument (OMI) for the period 2005–2012 with spatial resolution (1o×1o) were used in present study. The spatial, long-term, monthly variations of TOC over Iraq were analysed. For the spatial variability, the latitudinal variability has a large range between (45 to 55) DU in winter and spring whereas during summer and autumn months ranged between (6 to 10) DU. Also represents an annual cycle with maximum in March and minimum in October. In contrast, the longitudinal variability is not significant. The long-term variability represented a notable decline for the period 1979–2012. The ozone negative trend was observed significantly during 1979–2004, for all months with trend ranged between (− 0.3 to 2) DU/year whereas the ozone positive trend was appear clearly during 2005–2007, for all months (0.1 to 2.3) DU/year ,except February and September which presented negative trends. The results can provide comprehensive descriptions of the TOC variations in Iraq and benefit climate change research in this region.

Temporal and spatial variation in phytoplankton biomass based on remote sensing data in the East China Sea

2017 ◽  
Vol 37 (23) ◽  
Author(s):  
党晓岩 DANG Xiaoyan ◽  
伍玉梅 WU Yumei ◽  
樊伟 FAN Wei ◽  
纪世建 JI Shijian ◽  
杨胜龙 YANG Shenglong
Keyword(s):  
Remote Sensing ◽  
Spatial Variation ◽  
East China Sea ◽  
Phytoplankton Biomass ◽  
Remote Sensing Data ◽  
East China ◽  
The East China Sea ◽  
Temporal And Spatial Variation ◽  
Sensing Data ◽  
Temporal And Spatial
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