Serbin_et_al_2019

[JulienLamour]

@serbinsh DO you have the raw data for this one? https://ecosis.org/package/uw-bnl-nasa-hyspiri-airborne-campaign-leaf-and-canopy-spectra-and-trait-data

[serbinsh]

Yes - I will place into the git repo.

Not all of the gasex data has had the area corrects to fit the data properly and thats a level of work that I dont think we will be able to get done

[serbinsh]

@JulienLamour OK, initial stab at starting the process of including the HyspIRI datasets here: https://github.com/TESTgroup-BNL/Global_Vcmax/tree/main/Datasets/Serbin_et_al_2019

However I have some questions about the steps and how I am expected to curate the data? I did check and it seems the new fitted data is close to the estimates I generated before

Also just to confirm the "Ref" fitted data are at 25C?

[serbinsh]

@JulienLamour working on initial fitting of california gasex data and am running into this error that isnt very informative

Error in FUN(X[[i]], ...) : 
  trying to get slot "coef" from an object of a basic class ("NULL") with no slots
In addition: There were 50 or more warnings (use warnings() to see the first 50)
> traceback()
6: FUN(X[[i]], ...)
5: lapply(X = X, FUN = FUN, ...)
4: sapply(result_Ac_Aj, FUN = function(x) c(x[[2]]@coef, BIC = BIC(x[[2]]), 
       Tleaf = x[[3]]["Tleaf"]))
3: t(sapply(result_Ac_Aj, FUN = function(x) c(x[[2]]@coef, BIC = BIC(x[[2]]), 
       Tleaf = x[[3]]["Tleaf"])))
2: as.data.frame(t(sapply(result_Ac_Aj, FUN = function(x) c(x[[2]]@coef, 
       BIC = BIC(x[[2]]), Tleaf = x[[3]]["Tleaf"])))) at fit_Aci.R#47
1: f.fit_Aci(measures = curated_data, param = f.make.param(RdHd = 0, 
       RdS = 0))

It looks like its happening after fitting when looking at best fits. Suggests perhaps a curve doesnt provide any results and thus is missing coef values?

[serbinsh]

Also seeing warnings like this sometimes

Error in optim(par = c(sigma = 4.6183914593709e-05, JmaxRef = 368.253811164414,  : 
  non-finite finite-difference value [1]
In addition: Warning messages:
1: In mean.default(measures$Tair, na.rm = TRUE) :
  argument is not numeric or logical: returning NA
2: In mean.default(measures$VPDleaf, na.rm = TRUE) :
  argument is not numeric or logical: returning NA
3: In mean.default(measures$Tair, na.rm = TRUE) :
  argument is not numeric or logical: returning NA
4: In mean.default(measures$VPDleaf, na.rm = TRUE) :
  argument is not numeric or logical: returning NA
5: In mean.default(measures$Tair, na.rm = TRUE) :
  argument is not numeric or logical: returning NA
6: In mean.default(measures$VPDleaf, na.rm = TRUE) :
  argument is not numeric or logical: returning NA
7: In mean.default(measures$Tair, na.rm = TRUE) :
  argument is not numeric or logical: returning NA
8: In mean.default(measures$VPDleaf, na.rm = TRUE) :
  argument is not numeric or logical: returning NA
9: In dnorm(x = data$A, mean = A_pred$A, sd = (sigma), log = TRUE) :
  NaNs produced
$par

[JulienLamour]

The warning: "In mean.default(measures$Tair, na.rm = TRUE) : argument is not numeric or logical: returning NA" comes from the LeafGasExchange package. When I fit the data I also summarise the info to comply with the ESS standard when we publish the data. So I produce a table with the mean Tair, VPDleaf, Tleaf, etc.. If the Tair is not present in the datafile "measures" you have this warning.

[serbinsh]

@JulienLamour OK so I have a partial start to the HyspIRI data - at least the ag data in the latest push to main in #58

What is left to do is figure out how to keep what I need in the datasets to merge the spectra and traits in the full multi-dataset merge step. I did explore quickly vcmax fits after outlier removal and it looks promising. I wont be able to work on this again for another week but can come back and try to wrap up this first set of data for paper 1 in early july