A pigment ratio index based on remotely sensed reflectance provides the potential for universal gross primary production estimation. (11th May 2021)

• Record Type: Journal Article
• Authors: Wu, W; Epstein, H E; Guo, H; Li, X; Gong, C
• Is Part Of: Environmental research letters. Volume 16:Number 5(2021)
• Publication Date: 2021-05-11
• Subjects: remote sensing -- leaf pigments -- light use efficiency -- canopy photosynthesis -- terrestrial productivity; Environmental sciences -- Periodicals; Human ecology -- Research -- Periodicals; Environmental health -- Periodicals; 333.7
• DOI: 10.1088/1748-9326/abf3dc
• Languages: English
• ISSNs: 1748-9326

Abstract

Abstract: Gross primary production (GPP) estimation usually involves a priori assumptions about biome-specific rules or climate controls, which hampers an objective analysis of driving mechanisms. Observation-based methods that are biome-invariant and globally uniform are thus highly desirable. To facilitate this, a reflectance index representing the ratio of chlorophyll to total pigments ( R chl ) was proposed to consider the variation of energy conversion efficiency driven by different pigment contents in the canopy. Experiments based on simulated reflectance spectra showed that R chl could explain over 83% of chlorophyll ratio dynamics. A model was then developed which approximates GPP as the product of R chl, the normalized difference vegetation index, the near-infrared reflectance, and the photosynthetically active radiation. The model is simple, fast, with definite physical meaning and independent of climatic parameters such as temperature and humidity. Validated with over one hundred thousand field measurements, the model exhibited comparable accuracy to biome- and climate-based GPP models ( r = 0.74 for both types of models), demonstrating satisfactory performance. It also achieved significantly better results compared with a regression model excluding R chl, which emphasizes the important role of R chl . By avoiding circular analyses in mechanism studies on GPP variations, this model may extend our previous understanding of global terrestrial carbon uptake.