Spatio-temporal approach to moving window block kriging of satellite data

2016 ◽  
Author(s):  
Anonymous
Keyword(s):  
Satellite Data ◽  
Moving Window ◽  
Block Kriging ◽  
Spatio Temporal

scholarly journals Spatio-temporal approach to moving window block kriging of satellite data v1.0

2017 ◽  
Vol 10 (2) ◽  
pp. 709-720 ◽  
Author(s):  
Jovan M. Tadić ◽  
Xuemei Qiu ◽  
Scot Miller ◽  
Anna M. Michalak
Keyword(s):  
Global Scale ◽  
Variable Density ◽  
Satellite Observations ◽  
Moving Window ◽  
Block Kriging ◽  
Temporal Correlations ◽  
Environmental Properties ◽  
Level 3 ◽  
Spatio Temporal ◽  
The Difference

Abstract. Numerous existing satellites observe physical or environmental properties of the Earth system. Many of these satellites provide global-scale observations, but these observations are often sparse and noisy. By contrast, contiguous, global maps are often most useful to the scientific community (i.e., Level 3 products). We develop a spatio-temporal moving window block kriging method to create contiguous maps from sparse and/or noisy satellite observations. This approach exhibits several advantages over existing methods: (1) it allows for flexibility in setting the spatial resolution of the Level 3 map, (2) it is applicable to observations with variable density, (3) it produces a rigorous uncertainty estimate, (4) it exploits both spatial and temporal correlations in the data, and (5) it facilitates estimation in real time. Moreover, this approach only requires the assumption that the observable quantity exhibits spatial and temporal correlations that are inferable from the data. We test this method by creating Level 3 products from satellite observations of CO2 (XCO2) from the Greenhouse Gases Observing Satellite (GOSAT), CH4 (XCH4) from the Infrared Atmospheric Sounding Interferometer (IASI) and solar-induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2). We evaluate and analyze the difference in performance of spatio-temporal vs. recently developed spatial kriging methods.

scholarly journals Spatio-temporal approach to moving window block kriging of satellite data

2016 ◽  
Author(s):  
Jovan M. Tadić ◽  
Xuemei Qiu ◽  
Scot Miller ◽  
Anna M. Michalak
Keyword(s):  
Global Scale ◽  
Variable Density ◽  
Satellite Observations ◽  
Moving Window ◽  
Block Kriging ◽  
Temporal Correlations ◽  
Environmental Properties ◽  
Level 3 ◽  
Spatio Temporal ◽  
The Difference

Abstract. Numerous existing satellites observe physical or environmental properties of the Earth system. Many of these satellites provide global-scale observations, but these observations are often sparse and noisy. By contrast, contiguous, global maps are often most useful to the scientific community (i.e., level 3 products). We develop a spatiotemporal moving window block kriging method to create contiguous maps from sparse and/or noisy satellite observations. This approach exhibits several advantages over existing methods: 1) it allows for flexibility in setting the spatial resolution of the level 3 map, 2) it is applicable to observations with variable density, 3) it produces a rigorous uncertainty estimate, 4) it exploits both spatial and temporal correlations in the data, and 5) it facilitates estimation in real time. Moreover, this approach only requires a limited number of assumptions – that the observable quantity exhibits spatial and temporal correlations that are inferable from the data. We test this method by creating Level 3 products from satellite observations of CO2 (XCO2) from GOSAT, CH4 (XCH4) from IASI and solar-induced chlorophyll fluorescence (SIF) from GOME-2. We evaluate and analyze the difference in performance of spatio-temporal vs. recently developed spatial kriging methods.

scholarly journals SHAHED: A MapReduce-based system for querying and visualizing spatio-temporal satellite data

2015 ◽  
Author(s):  
Ahmed Eldawy ◽  
Mohamed F. Mokbel ◽  
Saif Alharthi ◽  
Abdulhadi Alzaidy ◽  
Kareem Tarek ◽  
...  
Keyword(s):  
Satellite Data ◽  
Spatio Temporal

scholarly journals Spatio-Temporal Groundwater Drought Monitoring Using Multi-Satellite Data Based on an Artificial Neural Network

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1953 ◽  
Author(s):  
Seo ◽  
Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Satellite Data ◽  
Severity Index ◽  
Drought Severity ◽  
Human Society ◽  
Groundwater Storage ◽  
Groundwater Drought ◽  
Artificial Neural ◽  
Spatio Temporal

Drought is a complex phenomenon caused by lack of precipitation that affects water resources and human society. Groundwater drought is difficult to assess due to its complexity and the lack of spatio-temporal groundwater observations. In this study, we present an approach to evaluate groundwater drought based on relatively high spatial resolution groundwater storage change data. We developed an artificial neural network (ANN) that employed satellite data (Gravity Recovery and Climate Experiment (GRACE) and Tropical Rainfall Measuring Mission (TRMM)) as well as Global Land Data Assimilation System (GLDAS) models. The Standardized Groundwater Level Index (SGI) was calculated by normalizing ANN-predicted groundwater storage changes from 2003 to 2015 across South Korea. The ANN-predicted 25 km groundwater storage changes correlated well with both the in situ and the water balance equation (WBE)-estimated groundwater storage changes, with mean correlation coefficients of 0.87 and 0.64, respectively. The Standardized Precipitation–Evapotranspiration Index (SPEI), having an accumulation time of 1–6 months, and the Palmer Drought Severity Index (PDSI) were used to validate the SGI. The results showed that the SGI had a pattern similar to that of SPEI-1 and SPEI-2 (1- and 2-month accumulation periods, respectively), and PDSI. However, the SGI performance fluctuated slightly due to its relatively short study period (13 years) as compared to SPEI and PDSI (more than 30 years). The SGI, which was developed using a new approach in this study, captured the characteristics of groundwater drought, thus presenting a framework for the assessment of these characteristics.

scholarly journals Estimating Spatial and Temporal Trends in Environmental Indices Based on Satellite Data: A Two-Step Approach

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 361 ◽  
Author(s):  
Brigitte Colin ◽  
Kerrie Mengersen
Keyword(s):  
Satellite Data ◽  
Temporal Trends ◽  
Regression Tree ◽  
Spatial And Temporal Patterns ◽  
Boosted Regression Tree ◽  
Environmental Indices ◽  
Study Region ◽  
Explanatory Variables ◽  
Green Vegetation ◽  
Spatio Temporal

This paper presents a method for employing satellite data to evaluate spatial and temporal patterns in environmental indices of interest. In the first step, linear regression coefficients are extracted for each area in the image. These coefficients are then employed as a response variable in a boosted regression tree with geographic coordinates as explanatory variables. Here, a two-step approach is described in the context of a substantive case study comprising 30 years of satellite derived fractional green vegetation cover for a large region in Queensland, Australia. In addition to analysis of the entire image and timeframe, separate analyses are undertaken over decades and over sub-regions of the study region. The results demonstrate both the utility of the approach and insights into spatio-temporal trends in green vegetation for this site. These findings support the feasibility of using the proposed two-step approach and geographic coordinates in the analysis of satellite derived indices over space and time.

Revealing snow cover dynamics in the Hindu Kush Himalaya over the past decades

2020 ◽  
Author(s):  
Kathrin Naegeli ◽  
Carlo Marin ◽  
Valentina Premier ◽  
Gabriele Schwaizer ◽  
Martin Stengel ◽  
...  
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Satellite Data ◽  
Weather Forecast ◽  
Short Wave ◽  
Climate Modelling ◽  
Snow Cover Area ◽  
Hindu Kush ◽  
Spatio Temporal ◽  
Area Percentage

<p>Knowledge about the snow cover distribution is of high importance for climate studies, weather forecast, hydrological investigations, irrigation or tourism, respectively. The Hindu Kush Himalayan (HKH) region covers almost 3.5 million km<sup>2</sup> and extends over eight different countries. The region is known as ‘water tower’ as it contains the largest volume of ice and snow outside of the polar ice sheets and it is the source of Asia’s largest rivers. These rivers provide ecosystem services, the basis for livelihoods and most importantly living water for drinking, irrigation, energy production and industry for two billion people, a fourth of the world’s population, living in the mountains and downstream.</p><p>The spatio-temporal variability of snow cover in the HKH is high and studies reported average snow-covered area percentage of 10–18%, with greater variability in winter (21–42%) than in summer (2–4%). However, no study systematically investigated snow cover metrics, such as snow cover area percentage (SCA), snow cover duration (SCD) or snow cover onset (SCOD) and melt-out day (SCMD), for the entire region so far. Here, we thus present unique in-sights of regional and sub-regional snow cover dynamics for the HKH based on almost four decades, an exceptionally long and in view of the climate modelling community valuable timeseries, of satellite data obtained within the ESA CCI+ Snow project.</p><p>Our results are based on Advanced Very High Resolution Radiometer (AVHRR) data, collected onboard the polar orbiting satellites NOAA-7 to -19, providing daily, global imagery at a spatial resolution of 5 km. Calibrated and geocoded reflectance data and a consistent cloud mask pre-processed and provided by the ESA Cloud_cci project as global 0.05° composites are used. The retrieval of snow extent considers the high reflectance of snow in the visible spectra and the low reflectance values in the short-wave infrared expressed in the Normalized Difference Snow Index (NDSI). Additional thresholds related to topography and land cover are included to derive the fractional snow cover of every pixel. A temporal gap-filling was applied to mitigate the influence of clouds. Reference snow maps from high-resolution optical satellite data as well as in-situ station data were used to validate the time series.</p>

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