Relationships between soil respiration and photosynthesis-related spectral vegetation indices in two cropland ecosystems

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• 作者：Ni Huang^a ; ^b ; Zheng Niu^a ; Yulin Zhan^a ; ^{yulinzhan@irsa.ac.cn} ; Shiguang Xu^a ; ^b ; Michelle C. Tappert^c ; Chaoyang Wu^a ; Wenjiang Huang^d ; Shuai Gao^a ; Xuehui Hou^a ; ^b ; Dewen Cai^a ; ^b
• 关键词：Maize ; Winter wheat ; Remote sensing ; Soil respiration ; Vegetation index
• 刊名：Agricultural and Forest Meteorology
• 出版年：2012
• 期刊代码：2_01681923
• 类别：abs
• 出版时间：15 July, 2012
• 卷：160
• 期：Complete
• 页码：80-89
• 文件大小：901 K

摘要

Empirical evidence for the link between vegetation photosynthesis and soil respiration (R_s) is reported for most ecosystems, but the quantification of photosynthesis parameters seldom use remote sensing data. In an attempt to clarify this issue, we focused on the growing season and examined direct relationships between R_s and photosynthesis-related vegetation indices (VIs) in maize and winter wheat agroecosystems. At seasonal time scale, crop biophysical parameters, such as leaf chlorophyll content (Chl_leaf) and green leaf area index (GLAI), explained most R_s variation in the maize and winter wheat fields. Among the selected VIs, enhanced vegetation index (EVI) and red edge chlorophyll index (CI_{red edge}) showed stronger correlations with Chl_leaf for maize or GLAI for winter wheat than normalized difference vegetation index (NDVI). Moreover, for both fields, the relationship between daily mean R_s and either EVI or CI_{red edge} was consistently stronger than the relationship between daily mean R_s and NDVI. The reason may be attributed to the saturate of NDVI at high vegetation densities and sensitivity of NDVI to background reflectance. Our results demonstrated that simple VIs (i.e. EVI and CI_{red edge}) based entirely on remote-sensing data can provide better correlations with R_s than a number of variables which are more frequently correlated with R_s in field studies for both maize and winter wheat over the growing season. These results will be helpful for the development of future R_s model over a large spatial scale.