Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data

NWIPB OpenIR

	Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data
	Huang, Ni ; He, Jin-Sheng ; Niu, Zheng ; Huang, N (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.
	2013-03-01
发表期刊	ECOLOGICAL INDICATORS ; Huang, N; He, JS; Niu, Z.Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data,ECOLOGICAL INDICATORS,2013,26():117-125
摘要	Monitoring soil respiration (R-s) at regional scales using images from operational satellites remains a challenge because of the problem in scaling local R-s to the regional scales. In this study, we estimated the spatial distribution of R-s in the Tibetan alpine grasslands as a product of vegetation index (VI). Three kinds of vegetation indices (VIs), that is, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and modified soil adjusted vegetation index (MSAVI), derived from Landsat Thematic Mapper (TM) and Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product were selected to test our method. Different statistical models were used to analyze the relationships among the three VIs and R-s. The results showed that, based on the remote sensing data from either MODIS or Landsat TM, exponential function was the optimal fit function for describing the relationships among VIs and R-s during the peak growing season of alpine grasslands. Additionally, NDVI consistently showed higher explanation capacity for the spatial variation in R-s than EVI and MSAVI. Thus, we used the exponential function of TM-based NDVI as the R-s predictor model. Since it is difficult to achieve full spatial coverage of the entire study area with Landsat TM images only, we used the MODIS 8-day composite images to obtain the spatial extrapolation of plot-level R-s after converting the NDVI_MODIS into its corresponding NDVI_TM. The performance of the R-s predictor model was validated by comparing it with the field measured R-s using an independent dataset. The TM-calibrated MODIS-estimated R-s was within an accuracy of field measured R-s with R-2 of 0.78 and root mean square error of 1.45 gC m(-2) d(-1). At the peak growing season of alpine grasslands, R-s was generally much higher in the southeastern part of the Tibetan Plateau and gradually decreased toward the northwestern part. Satellite remote sensing demonstrated the potential for the large scale mapping of R-s in this study. (C) 2012 Elsevier Ltd. All rights reserved.; Monitoring soil respiration (R-s) at regional scales using images from operational satellites remains a challenge because of the problem in scaling local R-s to the regional scales. In this study, we estimated the spatial distribution of R-s in the Tibetan alpine grasslands as a product of vegetation index (VI). Three kinds of vegetation indices (VIs), that is, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and modified soil adjusted vegetation index (MSAVI), derived from Landsat Thematic Mapper (TM) and Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product were selected to test our method. Different statistical models were used to analyze the relationships among the three VIs and R-s. The results showed that, based on the remote sensing data from either MODIS or Landsat TM, exponential function was the optimal fit function for describing the relationships among VIs and R-s during the peak growing season of alpine grasslands. Additionally, NDVI consistently showed higher explanation capacity for the spatial variation in R-s than EVI and MSAVI. Thus, we used the exponential function of TM-based NDVI as the R-s predictor model. Since it is difficult to achieve full spatial coverage of the entire study area with Landsat TM images only, we used the MODIS 8-day composite images to obtain the spatial extrapolation of plot-level R-s after converting the NDVI_MODIS into its corresponding NDVI_TM. The performance of the R-s predictor model was validated by comparing it with the field measured R-s using an independent dataset. The TM-calibrated MODIS-estimated R-s was within an accuracy of field measured R-s with R-2 of 0.78 and root mean square error of 1.45 gC m(-2) d(-1). At the peak growing season of alpine grasslands, R-s was generally much higher in the southeastern part of the Tibetan Plateau and gradually decreased toward the northwestern part. Satellite remote sensing demonstrated the potential for the large scale mapping of R-s in this study. (C) 2012 Elsevier Ltd. All rights reserved.
文献类型	期刊论文
条目标识符	http://210.75.249.4/handle/363003/26923
专题	中国科学院西北高原生物研究所
推荐引用方式 GB/T 7714	Huang, Ni,He, Jin-Sheng,Niu, Zheng,et al. Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data[J]. ECOLOGICAL INDICATORS, Huang, N; He, JS; Niu, Z.Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data,ECOLOGICAL INDICATORS,2013,26():117-125,2013.
APA	Huang, Ni,He, Jin-Sheng,Niu, Zheng,&Huang, N .(2013).Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data.ECOLOGICAL INDICATORS.
MLA	Huang, Ni,et al."Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data".ECOLOGICAL INDICATORS (2013).