Terrestrial ecosystem primary productivity is a key indicator of ecosystem functions, including, but not limited to, carbon storage, provision of food and fiber, and sustaining biodiversity. However, measuring terrestrial ecosystem primary productivity in the field is extremely laborious and expensive. Optical remote sensing has revolutionized our ability to map terrestrial ecosystem primary productivity over large areas ranging from regions to the entire globe in a repeated, cost-efficient manner. This progress report reviews the theory and practice of mapping terrestrial primary productivity using optical remotely sensed data. Terrestrial ecosystem primary productivity is generally estimated with optical remote sensing via one of the following approaches: (1) empirical estimation from spectral vegetation indices; (2) models that are based on light-use-efficiency (LUE) theory; (3) models that are not based on LUE theory, but the biophysical processes of plant photosynthesis. Among these three, models based on LUE are the primary approach because there is a solid physical basis for the linkage between fraction of absorbed photosynthetically active radiation (fAPAR) and remotely sensed spectral signatures of vegetation. There has been much inconsistency in the literature with regard to the appropriate value for LUE. This issue should be resolved with the ongoing efforts aimed at direct mapping of LUE from remote sensing. At the same time, major efforts have been dedicated to mapping vegetation canopy biochemical composition via imaging spectroscopy for use in process-based models to estimating primary productivity. In so doing, optical remote sensing will continue to play a vital role in global carbon cycle science research.
Photosynthesis, transpiration, and primary productivity: Scaling up from leaves to canopies and regions using process models and remotely sensed data
