Spatial and temporal distribution of carbon dioxide gas using GOSAT data over IRAN

Abstract

CO concentration (XCO) shows the spatial and temporal variation in Iran. The major purpose of this investigation is the assessment of the spatial distribution of carbon dioxide concentration in the different seasons of 2013 based on the Thermal And Near Infrared Sensor for Carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) level 2 GOSAT data by implementing the ordinary kriging (OK) method. In this study, the Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) data from the MODerate resolution Imaging Spectroradiometer (MODIS), and metrological parameters (temperature and precipitation) were used for the analysis of the spatial distribution of CO over Iran in 2013. The spatial distribution maps of XCO show the highest concentration of this gas in the south and south-east and the lowest concentration in the north and north-west. These results indicate that the concentration of carbon dioxide decreased with the increase of LST and temperature and a decrease of NDVI and humidity in the study area. Therefore, the existence of vegetation has an effective role in capturing carbon from the atmosphere by photosynthesis phenomena, and sustainable land management can be effective for carbon absorption from the atmosphere and mitigation of climate change in arid and semi-arid regions.

Spatiotemporal Analysis of Land Use/Land Cover and Its Effects on Surface Urban Heat Island Using Landsat Data: A Case Study of Metropolitan City Tehran (1988–2018)

“…The city of Tehran is the capital of Iran and has one of the oldest histories of civilization [10,44]. The city has two major types of physiology: It is mountainous in the northern part and has a desert in the southern part [10,45,46,56]. As this city has a history of human attraction to its landscape, in the course of time, the city has experienced explosive impervious development in the form of residential, commercial, industrial, transport network, parking lots, etc., at the cost of transformation of LU/LC classes like VL, FL, and OL.…”

Deriving Full‐Coverage and Fine‐Scale XCO2 Across China Based on OCO‐2 Satellite Retrievals and CarbonTracker Output

“…Geostatistical and regression‐based methods have been employed to interpolate satellite‐retrieved XCO 2 data (Table S2 in Supporting Information ). The geostatistical methods used for XCO 2 interpolation include ordinary kriging, spatiotemporal kriging, and fixed rank kriging (Bhattacharjee et al., 2020; Falahatkar et al., 2017; Zammit‐Mangion et al., 2018). Compared to ordinary kriging, spatiotemporal kriging extends the neighboring observations from a spatial context to a spatiotemporal context, which improves the spatiotemporal continuity but at a much higher computing cost.…”

Using OCO-2 Satellite Data for Investigating the Variability of Atmospheric CO2 Concentration in Relationship with Precipitation, Relative Humidity, and Vegetation over Oman

“…At the same time, precipitation, water vapor content [7], and consequently, vegetation cover have a direct effect [8] on the monthly CO 2 concentration fluctuations. example, Falahatkar et al investigated the interaction of land surface temperature and Normalized Difference Vegetation Index with GOSAT XCO 2 data for one year over Iran [27], while Fu et al tracked the fossil fuel consumption in urban areas and revealed the impacts of urban vegetation on seasonal variation of XCO 2 using OCO-2 observations [28]. In addition, Siabi et al assessed the CO 2 distribution over Iran using XCO 2 data from OCO2 during the growing season [29].…”