Remote estimation of crop gross primary production with Landsat data

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• 作者：Anatoly A. Gitelson^a ; ^{agitelson2@unl.edu} ; Yi Peng^a ; Jeffery G. Masek^b ; Donald C. Rundquist^a ; Shashi Verma^a ; Andrew Suyker^a ; John M. Baker^c ; Jerry L. Hatfield^d ; Tilden Meyers^e
• 关键词：Gross primary production ; Landsat ; Chlorophyll content ; Vegetation index ; Potential incident photosynthetically active radiation
• 刊名：Remote Sensing of Environment
• 出版年：2012
• 期刊代码：79_00344257
• 类别：ear
• 出版时间：June, 2012
• 卷：121
• 期：Complete
• 页码：404-414
• 文件大小：1329 K

摘要

An accurate and synoptic quantification of gross primary production (GPP) in crops is essential for studies of carbon budgets at regional and global scales. In this study, we tested a model, relating crop GPP to a product of total canopy chlorophyll (Chl) content and potential incident photosynthetically active radiation (PAR_potential). The approach is based on remotely sensed data; specifically, vegetation indices (VI) that are proxies for total Chl content and PAR_potential, which is incident PAR under a condition of minimal atmospheric aerosol loading. Using VI retrieved from surface reflectance Landsat data, we found that the model is capable of accurately estimating GPP in maize, with coefficient of variation (CV) below 23%, and in soybean with CV below 30%. The algorithms established and calibrated over three Mead, Nebraska AmeriFlux sites were able to estimate maize and soybean GPP at tower flux sites in Minnesota, Iowa and Illinois with acceptable accuracy.