Outgoing Near‐Infrared Radiation From Vegetation Scales With Canopy Photosynthesis Across a Spectrum of Function, Structure, Physiological Capacity, and Weather

Abstract

We test the relationship between canopy photosynthesis and reflected near‐infrared radiation from vegetation across a range of functional (photosynthetic pathway and capacity) and structural conditions (leaf area index, fraction of green and dead leaves, canopy height, reproductive stage, and leaf angle inclination), weather conditions, and years using a network of field sites from across central California. We based our analysis on direct measurements of canopy photosynthesis, with eddy covariance, and measurements of reflected near‐infrared and red radiation from vegetation, with light‐emitting diode sensors. And we interpreted the observed relationships between photosynthesis and reflected near‐infrared radiation using simulations based on the multilayer, biophysical model, CanVeg. Measurements of reflected near‐infrared radiation were highly correlated with measurements of canopy photosynthesis on half‐hourly, daily, seasonal, annual, and decadal time scales across the wide range of function and structure and weather conditions. Slopes of the regression between canopy photosynthesis and reflected near‐infrared radiation were greatest for the fertilized and irrigated C4 corn crop, intermediate for the C3 tules on nutrient‐rich organic soil and nitrogen fixing alfalfa, and least for the native annual grasslands and oak savanna on nutrient‐poor, mineral soils. Reflected near‐infrared radiation from vegetation has several advantages over other remotely sensed vegetation indices that are used to infer canopy photosynthesis; it does not saturate at high leaf area indices, it is insensitive to the presence of dead legacy vegetation, the sensors are inexpensive, and the reflectance signal is strong. Hence, information on reflected near‐infrared radiation from vegetation may have utility in monitoring carbon assimilation in carbon sequestration projects or on microsatellites orbiting Earth for precision agriculture applications.

On the inter‐ and intra‐annual variability of ecosystem evapotranspiration and water use efficiency of an oak savanna and annual grassland subjected to booms and busts in rainfall

“…The annual grassland also experienced a wide range in the duration of its physiologically active period. The day of year that the grassland became senescent ranged between day of year 129 and 172 (Baldocchi et al., 2020). We examined the correlation between the sums of annual evapotranspiration with the day of year photosynthesis ceased and found it explained only 15% of its inter‐annual variation.…”

NIRvP: a robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales

“…Differences between NIRVP and NIRV regarding their correlation to SIF and GPP are expected to become more important when using data from upcoming geostationary satellite missions such as GeoCarb, TEMPO and Sentinel-4 2 . As canopy-level PAR cannot be directly observed from airplanes or satellites, either a simple approach that approximates PAR via the downwelling NIR radiance 22,37,40 , or more complex methods involving atmospheric radiative transfer modelling 38,55 or machine learning 56 can be used. The radiance-based approach has previously been shown to have comparable performance with direct PAR observations at the site level 22 .…”

“…Since one of the major goals of SIF research is to improve remote sensing based GPP estimation 1,7 , it is important to also compare the performance of SIF and NIRVP for this purpose. Several recent studies demonstrated the promise of using NIRV and NIRVP to estimate GPP across multiple spatial scales 33,[37][38][39] .…”

On the Covariation of Chlorophyll Fluorescence and Photosynthesis Across Scales

“…When interpreting spaceborne SIF observations, it is important to consider that SIF likely mirrors changes in carbon assimilation as a function of APAR chl and to a lesser degree tracking photosynthesis at the physiological level for most ecosystems. This is particularly true for crops, or annual species, where changes in APAR chl often mirror GPP (Baldocchi et al, 2020; Dechant et al, 2020); however, in structurally invariant evergreen forests with little to no understory, a seasonal increase in sustained NPQ will reduce ΦF, permitting the tracking of photosynthetic downregulation with SIF (Magney, Bowling, et al, 2019).…”