Using imaging spectroscopy to detect variation in terrestrial ecosystem productivity across a water‐stressed landscape

Abstract

A central challenge to understanding how climate anomalies, such as drought and heatwaves, impact the terrestrial carbon cycle, is quantification and scaling of spatial and temporal variation in ecosystem gross primary productivity (GPP). Existing empirical and model-based satellite broadband spectra-based products have been shown to miss critical variation in GPP. Here, we evaluate the potential of high spectral resolution (10 nm) shortwave (400-2,500 nm) imagery to better detect spatial and temporal variations in GPP across a range of ecosystems, including forests, grassland-savannas, wetlands, and shrublands in a water-stressed region. Estimates of GPP from eddy covariance observations were compared against airborne hyperspectral imagery, collected across California during the 2013-2014 HyspIRI airborne preparatory campaign. Observations from 19 flux towers across 23 flight campaigns (102 total image-flux tower pairs) showed GPP to be strongly correlated to a suite of spectral wavelengths and band ratios associated with foliar physiology and chemistry. A partial least squares regression (PLSR) modeling approach was then used to predict GPP with higher validation accuracy (adjusted R = 0.71) and low bias (0.04) compared to existing broadband approaches (e.g., adjusted R = 0.68 and bias = -5.71 with the Sims et al. model). Significant wavelengths contributing to the PLSR include those previously shown to coincide with Rubisco (wavelengths 1,680, 1,740, and 2,290 nm) and V (wavelengths 1,680, 1,722, 1,732, 1,760, and 2,300 nm). These results provide strong evidence that advances in satellite spectral resolution offer significant promise for improved satellite-based monitoring of GPP variability across a diverse range of terrestrial ecosystems.

Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression

“…The understanding of the physiological mechanism for correlating reflectance spectra with photosynthetic variables, however, remained unsolved using complex machine learning algorithms (Fu et al, ). Alternatively, vegetation indices (VIs) such as normalized difference VI, photochemical reflectance index, and chlorophyll index have also been used to reveal photosynthetic productivity (Ainsworth et al, ; Drolet et al, ; DuBois et al, ; Gamon et al, ; Muraoka et al, ). However, the potential of these VIs and their best band combinations have seldomly been explored to map photosynthesis at the canopy level.…”

Combining hyperspectral remote sensing and eddy covariance data streams for estimation of vegetation functional traits

“…The evaluation of EF provides a more independent comparison of these fluxes and is closer to the 1:1 line than the original fluxes; but, still deviations from this reference are observed. So far, different inversions of SCOPE have relied either on remote thermal 495 imagery (Bayat et al, 2018), on EC TIR irradiance (this work) or on λE (Dutta et al, 2019) to constrain functional parameters related with transpiration such as the Ball-Berry sensitivity parameter; but no comparison of these constraints has been yet carried out. Some of the advantages and disadvantages of each variable are clear: TIR radiation measured in EC towers from hemispherical diffusors offer high temporal frequency, but the radiometric footprint does not match the EC footprint.…”

“…Further improvements of this model are mSCOPE , which allows representing vertically heterogeneous canopies, and senSCOPE (Pacheco-Labrador et al, 2020), which improves the 105 representation of canopies featuring mixed green and senescent leaves (Pacheco-Labrador et al, 2020). Using satellite imagery, SCOPE has been used to obtain estimates of physiological parameters of vegetation such as V cmax and / or m exploiting reflectance factors (R λ , where λ denotes spectral) and SIF (Zhang et al, 2014), R λ , SIF and EC fluxes (Zhang et al, 2018), R λ and TIR data (Bayat et al, 2018), or R λ and EC fluxes (Dutta et al, 2019). Also, proximal sensing data have been used to constrain SCOPE providing estimates of functional parameters (Pacheco-Labrador et al, 2019;Hu et al, 2018); and 110 more recently, airborne imagery has been used to retrieve V cmax from R λ and SIF (Camino et al, 2019).…”

Tracing Leaf Photosynthetic Parameters Using Hyperspectral Indices in an Alpine Deciduous Forest

“…To date, a large number of indices have been developed to fulfill the needs for monitoring and assessing plant structural and biochemical aspects [26][27][28], and most of the well-known indices reported were developed in multispectral information but with certain adjustments, such that these indices could potentially be used for hyperspectral reflectance. However, even though the use of vegetation indices for a quick assessment of photosynthesis or photosynthetic parameters has been attempted in several previous works [29][30][31][32][33], no consensus has yet been reached [34], and is dramatically behind the indices for structural or biochemical parameters. Furthermore, the few reported indices are generally only applicable to a specific area and specific forest stands depending on the condition for the index developed, or for specific leaf groups [35].…”