Spatiotemporal Monitoring of Soil CO2 Efflux in a Subtropical Forest during the Dry Season Based on Field Observations and Remote Sensing Imagery

doi:10.3390/rs13173481

	Spatiotemporal Monitoring of Soil CO2 Efflux in a Subtropical Forest during the Dry Season Based on Field Observations and Remote Sensing Imagery
	Chen, Tao 1; Xu, Zhenwu 2; Tang, Guoping 1; Chen, Xiaohua 1; Fang, Hong 1; Guo, Hao 3,4; Yuan, Ye 5; Zheng, Guoxiong 5; Jiang, Liangliang 6; Niu, Xiangyu 1
刊名	REMOTE SENSING
	2021-09-01
卷号	13 期号:17 页码:23
关键词	forest soil CO2 flux satellite remote sensing data random forest algorithm subtropical forest ecosystem
DOI	10.3390/rs13173481
通讯作者	Tang, Guoping(tanggp3@mail.sysu.edu.cn)
英文摘要	The CO2 efflux from forest soil (FCO2) is one of the largest components of the global carbon cycle. Accurate estimation of FCO2 can help us better understand the carbon cycle in forested areas and precisely predict future climate change. However, the scarcity of field-measured FCO2 data in the subtropical forested area greatly limits our understanding of FCO2 dynamics at regional and global scales. This study used an automatic cavity ring-down spectrophotometer (CRDS) analyzer to measure FCO2 in a typical subtropical forest of southern China in the dry season. We found that the measured FCO2 at two experimental areas experienced similar temporal trends in the dry season and reached the minima around December, whereas the mean FCO2 differed apparently across the two areas (9.05 vs. 5.03 g C m(-2) day(-1)) during the dry season. Moreover, we found that both abiotic (soil temperature and moisture) and biotic (vegetation productivity) factors are significantly and positively correlated, respectively, with the FCO2 variation during the study period. Furthermore, a machine-learning random forest model (RF model) that incorporates remote sensing data is developed and used to predict the FCO2 pattern in the subtropical forest, and the topographic effects on spatiotemporal patterns of FCO2 were further investigated. The model evaluation indicated that the proposed model illustrated high prediction accuracy for the training and testing dataset. Based on the proposed model, the spatiotemporal patterns of FCO2 in the forested watershed that encloses the two monitoring sites were mapped. Results showed that the spatial distribution of FCO2 is obviously affected by topography: the high FCO2 values mainly occur in relatively high altitudinal areas, in slopes of 10-25 degrees, and in sunny slopes. The results emphasized that future studies should consider topographical effects when simulating FCO2 in subtropical forests. Overall, our study unraveled the spatiotemporal variations of FCO2 and their driving factors in a subtropical forest of southern China in the dry season, and demonstrated that the proposed RF model in combination with remote sensing data can be a useful tool for predicting FCO2 in forested areas, particularly in subtropical and tropical forest ecosystems.
资助项目	National Natural Science Foundation of China[42171025] ; Guangzhou Municipal Scientific Program[42050441]
WOS关键词	DIFFERENT SUCCESSIONAL STAGES ; INTERANNUAL VARIABILITY ; AUTOTROPHIC COMPONENTS ; PHRAGMITES-AUSTRALIS ; VEGETATION TYPES ; GROWING-SEASON ; ATMOSPHERE CO2 ; WATER-STRESS ; RESPIRATION ; MODIS
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	MDPI
WOS记录号	WOS:000694496700001
资助机构	National Natural Science Foundation of China ; Guangzhou Municipal Scientific Program
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/165490]
专题	中国科学院地理科学与资源研究所
通讯作者	Tang, Guoping
作者单位	1.Sun Yat Sen Univ, Sch Geog & Planning, Dept Phys Geog Resources & Environm, Guangzhou 510275, Guangdong, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 3.Qufu Normal Univ, Sch Geog & Tourism, Rizhao 276825, Peoples R China 4.Qufu Normal Univ, Ctr Land Res, Rizhao 276825, Peoples R China 5.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China 6.Chongqing Normal Univ, Coll Geog & Tourism, Chongqing 401331, Peoples R China
推荐引用方式 GB/T 7714	Chen, Tao,Xu, Zhenwu,Tang, Guoping,et al. Spatiotemporal Monitoring of Soil CO2 Efflux in a Subtropical Forest during the Dry Season Based on Field Observations and Remote Sensing Imagery[J]. REMOTE SENSING,2021,13(17):23.
APA	Chen, Tao.,Xu, Zhenwu.,Tang, Guoping.,Chen, Xiaohua.,Fang, Hong.,...&Niu, Xiangyu.(2021).Spatiotemporal Monitoring of Soil CO2 Efflux in a Subtropical Forest during the Dry Season Based on Field Observations and Remote Sensing Imagery.REMOTE SENSING,13(17),23.
MLA	Chen, Tao,et al."Spatiotemporal Monitoring of Soil CO2 Efflux in a Subtropical Forest during the Dry Season Based on Field Observations and Remote Sensing Imagery".REMOTE SENSING 13.17(2021):23.