Remote sensing of terrestrial gross primary productivity: a review of advances in theoretical foundation, key parameters and methods

作者：Wenquan Zhu, Zhiying Xie, Cenliang Zhao, Zhoutao Zheng, Qiao Kun, Dailiang Peng, Yongshuo H. Fu · 发表于：GIScience & Remote Sensing · 年份：2024 · DOI：10.1080/15481603.2024.2318846 · 被引用次数：53 · 研究领域：Remote Sensing and Land Use、Remote Sensing in Agriculture、Environmental and Agricultural Sciences

Accurately estimating gross primary productivity (GPP), the largest carbon flux in terrestrial ecosystems, is crucial for advancing our understanding of global carbon cycle and predicting climate feedbacks. The advancements in remote sensing (RS) have facilitated the development of GPP estimation models at regional and global scales in recent decades. This article systemically reviews the development of RS-based GPP estimation in three main aspects: theoretical foundation, key parameters and methods. Regarding the theoretical foundation, RS generally excels in representing key characteristics during the light transmission process of photosynthesis. However, it exhibits a relatively weaker ability to describe the carbon reaction process, severely limiting the in-depth understanding of the mechanisms of RS-based GPP estimation. Concerning key parameters, the definition of traditional parameters, such as leaf area index (LAI), photosynthetically active radiation (PAR), and fraction of absorbing PAR, has been detailed in the development of RS (e.g. LAI is divided into sunlit LAI and shaded LAI). However, their accuracy still needs improvement. Additionally, researchers have developed effective parameters (e.g. photochemical reflectance index, sun-induced chlorophyll fluorescence, and the maximum carboxylation rate) that possess increased capability to represent and interpret the carbon reaction process of photosynthesis. Regarding estimation methods, although the four main catego...