Global mapping of greenhouse gases retrieved from GOSAT Level 2 products by using a kriging method

Abstract

Because a synoptic overview facilitates understanding of the temporal and spatial changes in the global distribution of greenhouse gases, we developed a statistical spatial estimation method using kriging. Level 3 (L3) data products for the Greenhouse Gases Observing Satellite (GOSAT) Thermal And Near infrared Sensor for Carbon Observation (TANSO) Fourier Transform Spectrometer (FTS) Short Wave Infrared (SWIR) were generated from column-averaged, dry-air mole fractions of carbon dioxide (XCO 2 ) and methane (XCH 4 ) TANSO-FTS SWIR Level 2 (L2) products using this method. Although there have been some reports on the use of kriging for analysing GOSAT products, the kriging method used in this research was specifically adapted to the statistical characteristics of GOSAT L2 products. In the context of using data for atmospheric research, spatially interpolated data (GOSAT L3 products) cannot be more accurate than modelsimulated global distributions of gas concentrations (GOSAT Level 4B (L4B) products), which are generated using an atmospheric tracer transport model. However, the L3 product takes much less time to generate than the L4B. It would take about a year to produce the L4B after generation of an L2 product. The great advantage of the L3 product is that it gives a comprehensive and reasonable monthly global distribution of gas concentrations with little delay. The L3 product using the kriging method can be generated on a monthly basis by estimating global semi-variogram curves from the L2 products for each month and interpolating spatially within a region with a radius of 1000 km from existing L2 data locations. The main purpose of this paper is to describe the methodology and characteristics of kriging used to generate the GOSAT L3 product, not for strictly scientific use of the estimated values, but for a reasonable global map of gas concentrations derived statistically from the sparsely observed L2 products within a short time frame. The characteristics of this method are compared to XCO 2 products simulated with an atmospheric tracer transport model. The results show that the method proposed in this study is of practical use for generating L3 products from L2 products.

Spectral remote sensing for onshore seepage characterization: A critical overview

“…Generally, where the emission of such seeps occur as concentrated point sources, the escaping methane can form discernible gas-plume in the atmosphere and thus constitute detectable target. Current orbital gas remote sensing instruments like the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and the Greenhouse Gas Observing Satellite (GOSAT) have the capability to measure methane and other trace gasses but only in continental scales (Watanabe et al, 2015). The delineation of point source plumes at local-scale, however, is a niche that is filled with high spatial (GSD<20 m) and spectral resolution hyperspectral imaging instruments.…”

Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method

“…It improved the data precision compared with XCO 2 from conventional spatio-temporal kriging, especially for data in the satellites overlapping period. As a result, GM-XCO 2 could help us understand the temporal and spatial changes in the global distribution of CO 2 [17]. Because GM-XCO 2 is from instantaneous satellite observations, it could capture detailed and abnormal XCO 2 change which could be related to local carbon uptake and emission [10,55].…”

“…A network of surface CO 2 monitoring station observations has been organized into the popular GLOBALVIEW-CO 2 product and provides in situ measurements but is limited by station sparseness and the inherent spatial inhomogeneity of the surface atmosphere. Model simulations can provide continuous maps of CO 2 using estimated surface fluxes and atmospheric mixing transport in addition to the previously noted sparse validation stations [17]. Satellite observations of atmospheric CO 2 have the advantage of global coverage and high measurement density and can complement the surface network to advance our understandings of the carbon cycle and its changes [18,19].…”

“…As a result, there is an opportunity to generate a long time series of global XCO 2 dataset starting from 2003 using XCO 2 retrievals from these satellites with careful integration and gap-filling.Gap-filling satellite-observed XCO 2 has been investigated in several studies from different perspectives [31][32][33]. Geostatistical approaches, especially kriging, were widely used for GOSAT observed XCO 2 Level 3 product production [17,34,35]. Spatial-only geostatistical methods do not take into account the temporal correlation structure of CO 2 data [19], which may provide extra information.…”

Geostatistical Analysis of CH4 Columns over Monsoon Asia Using Five Years of GOSAT Observations

“…Moreover, we compare the prediction accuracy between spatio-temporal and spatial-only method based on the cross-validation statistics. The spatial-only method implemented here is similar to that for generating monthly GOSAT Level 3 data [13]. The whole XCH 4 dataset is firstly grouped into monthly datasets (five years = 60 months), and then the variogram for each month is calculated and modeled by the exponential variogram model.…”