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Comparison of Continuous In-Situ CO2 Measurements with Co-Located Column-Averaged XCO2 TCCON/Satellite Observations and CarbonTracker Model Over the Zugspitze Region
Open AccessArticle

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

1
Zhejiang Climate Center, Hangzhou 310017, China
2
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
3
The Third Engineering of Surveying and Mapping Academy in Sichuan Province, Chengdu 610500, China
4
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA
5
Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN 47907-2051, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 576; https://doi.org/10.3390/rs12030576 (registering DOI)
Received: 20 December 2019 / Revised: 6 February 2020 / Accepted: 6 February 2020 / Published: 9 February 2020
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases and Air Pollution)
Column-averaged dry air mole fraction of atmospheric CO2 (XCO2), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO2 concentrations which are related to carbon uptake and emissions. In order to construct long-term spatio-temporal continuous XCO2 from multiple satellites with different temporal and spatial periods of observations, we developed a precision-weighted spatio-temporal kriging method for integrating and mapping multi-satellite observed XCO2. The approach integrated XCO2 from different sensors considering differences in vertical sensitivity, overpass time, the field of view, repeat cycle and measurement precision. We produced globally mapped XCO2 (GM-XCO2) with spatial/temporal resolution of 1 × 1 degree every eight days from 2003 to 2016 with corresponding data precision and interpolation uncertainty in each grid. The predicted GM-XCO2 precision improved in most grids compared with conventional spatio-temporal kriging results, especially during the satellites overlapping period (0.3–0.5 ppm). The method showed good reliability with R2 of 0.97 from cross-validation. GM-XCO2 showed good accuracy with a standard deviation of bias from total carbon column observing network (TCCON) measurements of 1.05 ppm. This method has potential applications for integrating and mapping XCO2 or other similar datasets observed from multiple satellite sensors. The resulting GM-XCO2 product may be also used in different carbon cycle research applications with different precision requirements. View Full-Text
Keywords: XCO2; multi-satellites; precision weighting; spatio-temporal kriging; mapping XCO2; multi-satellites; precision weighting; spatio-temporal kriging; mapping
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MDPI and ACS Style

He, Z.; Lei, L.; Zhang, Y.; Sheng, M.; Wu, C.; Li, L.; Zeng, Z.-C.; Welp, L.R. Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method. Remote Sens. 2020, 12, 576.

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