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利用 OCO-2 数据在生长季节研究 XCO 的空间分布。

Spatial distribution of XCO using OCO-2 data in growing seasons.

机构信息

Department of Environmental Sciences, Faculty of Natural Resources, Tarbiat Modares University, Noor, Mazandaran, Iran.

Department of Environmental Sciences, Faculty of Natural Resources, Tarbiat Modares University, Noor, Mazandaran, 64414356, Iran.

出版信息

J Environ Manage. 2019 Aug 15;244:110-118. doi: 10.1016/j.jenvman.2019.05.049. Epub 2019 May 18.

Abstract

The purpose of this research is to assess the spatial distribution of CO concentration during the growing seasons (April to September) in 2015 over Iran. The XCO data belonging to orbiting carbon observatory-2 (OCO-2) and eight environmental variables data consist of normalized difference vegetation index (NDVI), net primary productivity (NPP), land surface temperature (LST), leaf area index (LAI), air temperature, wind speed, wind direction, and national land cover map were modeled by multi-layer perceptron (MLP). The values of R and RMSE indices show the good performance of the multi-layer perceptron model for monthly models. Based on sensitivity analysis results, land cover and wind direction had the most important role in the spatial distribution of XCO. Also, the results revealed that the maximum values of XCO observed in the east, south east, and desert areas in central of Iran due to the lack of vegetation cover, lack of local wind current, and high temperature. The western, northwestern and northern regions of Iran have the minimum amounts of XCO because of existing valuable ecosystem such as Hyrcanian and Zagrous forests, rangeland, air currents, and low temperature. The findings of this study indicated that the manageable factors such as land cover and vegetation cover play very important roles in the spatial distribution of CO and finding carbon dioxide source and sink at national scale. Therefore, policymakers and managers by the logical management of these resources are able to control or even reduce the concentration of carbon dioxide in different areas.

摘要

本研究旨在评估 2015 年生长季(4 月至 9 月)期间伊朗上空 CO 浓度的空间分布。轨道碳观测站-2(OCO-2)的 XCO 数据和 8 种环境变量数据(归一化差异植被指数(NDVI)、净初级生产力(NPP)、地表温度(LST)、叶面积指数(LAI)、空气温度、风速、风向和国家土地覆盖图)通过多层感知器(MLP)进行建模。R 和 RMSE 指数的值表明,多层感知器模型对于月度模型具有良好的性能。基于敏感性分析结果,土地覆盖和风向对 XCO 的空间分布具有最重要的作用。此外,研究结果表明,由于植被覆盖不足、缺乏局部风流和高温,伊朗中部的东部、东南部和沙漠地区观察到的 XCO 值最高。由于存在有价值的生态系统,如 Hyrcanian 和 Zagrous 森林、牧场、气流和低温,伊朗的西部、西北部和北部地区的 XCO 值最低。本研究的结果表明,可管理的因素,如土地覆盖和植被覆盖,在 CO 的空间分布中起着非常重要的作用,并在国家范围内寻找二氧化碳的源和汇。因此,政策制定者和管理者通过对这些资源的合理管理,能够控制甚至减少不同地区二氧化碳的浓度。

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