Status of Standards and Guides Related to the Application of Spatial Methods to Environmental and Hydrologic Problems

2008 ◽  
pp. 122-122-14
Author(s):  
DT Hansen
Keyword(s):  
Spatial Methods

scholarly journals Analysis of the atmospheric macro-physical using spatial methods

2020 ◽  
Vol 1702 ◽  
pp. 012011
Author(s):  
J E Andrades-Grassi ◽  
L Cuesta-Herrera ◽  
H A Torres-Mantilla ◽  
J Y López-Hernández
Keyword(s):  
Spatial Methods

An assessment of spatial methods for merging terrestrial with GGM-derived gravity anomaly data

2021 ◽  
Vol 179 ◽  
pp. 104202
Author(s):  
Joseph O. Odumosu ◽  
Victor C. Nnam ◽  
Ifeanyi J. Nwadialor
Keyword(s):  
Gravity Anomaly ◽  
Spatial Methods

Temporal and spatial methods in ocean optical systems

2020 ◽  
Vol 49 (2) ◽  
pp. 203003
Author(s):  
张雨凡 Zhang Yufan ◽  
徐 敬 Xu Jing
Keyword(s):  
Optical Systems ◽  
Spatial Methods ◽  
Temporal And Spatial

scholarly journals The recent developments in cloud removal approaches of MODIS snow cover product

2019 ◽  
Vol 23 (5) ◽  
pp. 2401-2416 ◽  
Author(s):  
Xinghua Li ◽  
Yinghong Jing ◽  
Huanfeng Shen ◽  
Liangpei Zhang
Keyword(s):  
Snow Cover ◽  
Cloud Cover ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Optical Observations ◽  
Optical Remote Sensing ◽  
Spatial Methods ◽  
Fusion Methods ◽  
Spatio Temporal ◽  
Cloud Removal

Abstract. The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, and the energy balance. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are the most popular datasets used in the community. However, for MODIS, cloud cover results in spatial and temporal discontinuity for long-term snow monitoring. In the last few decades, a large number of cloud removal methods for MODIS snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating cloud-free MODIS snow cover products and to expose the development trends. The methods of generating cloud-free MODIS snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. The spatial methods and temporal methods remove the cloud cover of the snow product based on the spatial patterns and temporal changing correlation of the snowpack, respectively. The spatio-temporal methods utilize the spatial and temporal features of snow jointly. The multi-source fusion methods utilize the complementary information among different sources among optical observations, microwave observations, and station observations.

scholarly journals Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods

2020 ◽  
Vol 12 (5) ◽  
pp. 839 ◽  
Author(s):  
Jiping Cao ◽  
Yumin Chen ◽  
John P. Wilson ◽  
Huangyuan Tan ◽  
Jiaxin Yang ◽  
...  
Keyword(s):  
Spatial Autocorrelation ◽  
Infrared Imaging ◽  
Population Distribution ◽  
Population Data ◽  
Ordinary Least Squares ◽  
Local Economy ◽  
Spatial Error ◽  
Reference Models ◽  
Spatial Methods ◽  
Permanent Resident

Nighttime light (NTL) data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar Orbiting Partnership (NPP) satellite, has been widely used to evaluate gross domestic product (GDP). Nevertheless, due to the monthly VIIRS data fluctuation and missing data (excluded by producers) over high-latitude regions, the suitability of VIIRS data for longitudinal city-level economic estimation needs to be examined. While GDP distribution in China is always accompanied by regional disparity, previous studies have hardly considered the spatial autocorrelation of the GDP distribution when using NTL imagery. Thus, this paper aims to enhance the precision of the longitudinal GDP estimation using spatial methods. The NTL images are used with road networks and permanent resident population data to estimate the 2013, 2015, and 2017 3-year prefecture-level (342 regions) GDP in mainland China, based on eigenvector spatial filtering (ESF) regression (mean R2 = 0.98). The ordinary least squares (OLS) (mean R2 = 0.86) and spatial error model (SEM) (mean pseudo R2 = 0.89) were chosen as reference models. The ESF regression exhibits better performance for root-mean-square error (RMSE), mean absolute relative error (MARE), and Akaike information criterion (AIC) than the reference models and effectively eliminated the spatial autocorrelation in the residuals in all 3 years. The results indicate that the spatial economic disparity, as well as population distribution across China’s prefectures, is decreasing. The ESF regression also demonstrates that the population is crucial to the local economy and that the contribution of urbanization is growing.

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