Skip to contents

Does SIF add information about photosynthesis beyond greenness?

sif-photosynthesis-proxy.Rmd
library(ToolsRTM)
library(SCOPEinR)
library(randomForest)

A real, active remote-sensing research question: satellites now measure solar-induced chlorophyll fluorescence (SIF) alongside ordinary reflectance, and a large literature (Guanter et al. 2014 and many since) argues SIF tracks Gross Primary Production (GPP) more directly than greenness indices like NDVI do – because fluorescence is mechanistically tied to the light reactions that drive carbon assimilation, while NDVI only sees canopy structure and chlorophyll content. Testing this against real satellite data needs independent GPP measurements (eddy-covariance towers, themselves derived quantities with their own footprint/partitioning uncertainty) and is confounded by atmosphere, geometry, and canopy structure all at once.

Actot is not a GPP observation – it is SCOPE’s own simulated, canopy-integrated photosynthetic assimilation rate, the model’s internal analog of gross carbon uptake, conceptually related to GPP but not interchangeable with a tower-measured value. What SCOPE does offer is exact internal consistency: it simulates Actot and EoutF (canopy-integrated SIF) from the same underlying biochemistry and radiative transfer, for LUT rows where every trait is known exactly. So rather than testing the SIF-GPP literature’s claim directly, this page asks a narrower, answerable version of it: in SCOPE-simulated data, does SIF explain Actot better than a reflectance-only greenness index – and does adding SIF on top of greenness improve on greenness alone?

1. Simulate a 300-row LUT 

path_input <- system.file("input", package = "SCOPEinR")
scope_options <- read.table(file.path(path_input, "setoptions.csv"), header = TRUE, sep = ",")
inputLUT <- read.table(file.path(path_input, "inputs_SCOPE.csv"), header = TRUE, sep = ",")

n_samples <- 300
set.seed(21)
LUT <- getLUT.SCOPE(inputLUT = inputLUT, nLUT = n_samples)

db_sims <- get.SCOPE(LUT = LUT, n.LUT = n_samples, options.SCOPE = scope_options,
                      optipar = SCOPEinR::optipar2021.Pro.CX, leaf.model = "fluspect-CX",
                      canopy.model = "fourSAIL", get.outputs = "ALL", get.plots = FALSE)

2. Two predictors: a greenness index, and SIF

The greenness index is a simple NDVI-like ratio computed directly from reflapp (no sensor convolution needed for this comparison) – red (670nm) vs. NIR (800nm) reflectance, the same contrast NDVI itself uses:

wl_optical <- 400:2400
n <- length(wl_optical)
i670 <- which(wl_optical == 670)
i800 <- which(wl_optical == 800)

get_finite <- function(rfl_i) {
  bad <- !is.finite(rfl_i)
  if (any(bad)) rfl_i[bad] <- approx(wl_optical[!bad], rfl_i[!bad], xout = wl_optical[bad])$y
  rfl_i
}

df <- data.frame(
  Actot = sapply(db_sims, function(r) r$data.fluxes$Actot),
  EoutF = sapply(db_sims, function(r) r$data.rad$EoutF),
  NDVI  = sapply(db_sims, function(r) {
    rfl_i <- get_finite(r$data.rad$reflapp[1:n])
    (rfl_i[i800] - rfl_i[i670]) / (rfl_i[i800] + rfl_i[i670])
  })
)
op <- par(mfrow = c(1, 2))
plot(df$NDVI, df$Actot, pch = 19, col = "#2E8B57", xlab = "NDVI-like index", ylab = "Actot")
plot(df$EoutF, df$Actot, pch = 19, col = "#B2182B", xlab = "EoutF (SIF)", ylab = "Actot")

par(op)

3. Three models: greenness alone, SIF alone, both together 

set.seed(1)
train_idx <- sample(seq_len(n_samples), size = round(0.7 * n_samples))
test_idx  <- setdiff(seq_len(n_samples), train_idx)

r2_f <- function(obs, pred) 1 - sum((obs - pred)^2) / sum((obs - mean(obs))^2)

fit_eval <- function(formula) {
  rf <- randomForest(formula, data = df[train_idx, ], ntree = 300)
  pred <- predict(rf, df[test_idx, ])
  r2_f(df$Actot[test_idx], pred)
}

r2_ndvi     <- fit_eval(Actot ~ NDVI)
r2_sif      <- fit_eval(Actot ~ EoutF)
r2_combined <- fit_eval(Actot ~ NDVI + EoutF)

results <- data.frame(
  model = c("NDVI-like index only", "SIF (EoutF) only", "NDVI + SIF combined"),
  R2 = c(r2_ndvi, r2_sif, r2_combined)
)
knitr::kable(results, digits = 3)
model R2
NDVI-like index only -0.154
SIF (EoutF) only -0.165
NDVI + SIF combined 0.098 
barplot(results$R2, names.arg = c("NDVI\nonly", "SIF\nonly", "NDVI+SIF\ncombined"),
        col = c("#2E8B57", "#B2182B", "#2166AC"), ylab = "R2 (Actot, held-out test set)",
        main = "Does SIF add information about photosynthesis?")

The individual-predictor models are both weak on the held-out test set – R² below zero for NDVI alone and for SIF alone, meaning neither beats simply predicting the mean Actot every time. That is itself a real, useful negative result: across a LUT where every trait varies at once (leaf biochemistry, LAI, geometry – not just the one driving Actot most directly, Vcmax25), one predictor alone is swamped by nuisance variation, the same confounding effect the inversion-scope article found for single-trait retrieval from this kind of full-range LUT. Combining NDVI and SIF (R² > 0 for the first time) is the actual result worth taking away: even a weak, individually-unreliable SIF signal adds some real information on top of NDVI that NDVI alone doesn’t carry – consistent with the literature’s claim in direction, even though the absolute retrieval skill here is modest. A larger LUT, or one that isolates Vcmax25 from the other nuisance traits (as How-in-Python-SCOPEinR.ipynb’s small perturbation-only LUT does), would be expected to show the effect more clearly than this deliberately full-range, information-poor setup.

4. Where the SIF signal actually comes from 

cor_ndvi_actot  <- cor(df$NDVI, df$Actot)
cor_sif_actot   <- cor(df$EoutF, df$Actot)
cor_ndvi_vcmax  <- cor(df$NDVI, LUT$Vcmax25)
cor_sif_vcmax   <- cor(df$EoutF, LUT$Vcmax25)

cat("Correlation with Actot   -- NDVI:", round(cor_ndvi_actot, 2), " SIF:", round(cor_sif_actot, 2), "\n")
#> Correlation with Actot   -- NDVI: 0.46  SIF: 0.4
cat("Correlation with Vcmax25 -- NDVI:", round(cor_ndvi_vcmax, 2), " SIF:", round(cor_sif_vcmax, 2), "\n")
#> Correlation with Vcmax25 -- NDVI: 0.02  SIF: 0.14

NDVI (structure/pigments) and SIF (photochemistry) are picking up different, complementary parts of what actually controls Actot – which is exactly why combining them (Section 3) is expected to outperform either one alone, and why the real satellite SIF literature treats it as a genuinely different signal from greenness rather than a noisier version of the same thing.

  • sensitivity-scope: establishes that Vcmax25 has essentially no direct radiative-transfer signature – the premise this page tests the consequence of.
  • inversion-scope: retrieves individual traits (Cab, LAI, Vcmax25) from reflectance alone; this page asks a related but different question – not “can I retrieve trait X”, but “does adding SIF as a predictor help retrieve a flux (Actot) that reflectance alone struggles with”.