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Exploring CO2 Anomalies in Brazilian Biomes Combining OCO-2 & 3 data: Linkages to Wildfires Patterns

Costa, L.M. et al, Advances in Space Research

Initial Considerations

This is the repository linked to the article, here you will find the code used to process the data from OCO-2 and OCO-3, in R folder you will find the script for downloading the data from the url and some filters and process used.

The first step is to use the download_data.R, than the data_extraction.R and finaly the data_filtering.R


In the following paths:data-raw/OCO-2, data-raw/OCO-3, data/OCO-2 and data/OCO-3, there are files to ensure the creation of that path on github, however you can delete them.

Following is the steps with the already processed data

Good Quality data

Data and functions

source('fun/out.R')
source('fun/mapa.R')
br <- geobr::read_country()
## Loading required namespace: sf

## Using year 2010

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oco2 <- readr::read_rds('processed_data/oco2.rds') 

oco3 <- readr::read_rds('processed_data/oco3.rds')

frp <- read.csv('processed_data/frp.csv') |> 
  dplyr::mutate(
    lon=round(lon,2),
    lat=round(lat,2),
    year=lubridate::year(date)
  ) |> 
  dplyr::select(lon,lat,year,Period,Biome,frp) |> 
  dplyr::filter(
    frp>remove_outlier(frp)[1]&
      frp<remove_outlier(frp)[2]
  )

Coverage

br |> 
  ggplot2::ggplot()+
  ggplot2::geom_sf(fill="white", color="black",
                   size=.15, show.legend = FALSE)+
  ggplot2::geom_point(data = oco2 |> 
                        dplyr::mutate(
                          mes=lubridate::month(date),
                          year=lubridate::year(date)
                        ) |> 
                        dplyr::filter(mes==3 & year == 2020),
                      ggplot2::aes(x=lon,y=lat,color='OCO-2'),
                      alpha=.2)+
  ggplot2::geom_point(data = oco3 |> 
                        dplyr::mutate(
                          mes=lubridate::month(date),
                          year=lubridate::year(date)
                        ) |> 
                        dplyr::filter(mes==3 & year == 2020),
                      ggplot2::aes(x=lon,y=lat,color='OCO-3'),
                      alpha=.2)+
  ggplot2::scale_color_manual(name='Mission',
                              breaks=c('OCO-2','OCO-3'),
                              values=c('OCO-2'='red', 'OCO-3'='dark blue'))+
  ggplot2::theme(axis.text = ggplot2::element_text(color='black'))+
  ggplot2::labs(x='Longitude', y='Latitude')+
  tema_mapa()
## Scale on map varies by more than 10%, scale bar may be inaccurate

Uncertainty

oco2 |>
  dplyr::group_by(date) |>
  dplyr::summarise(uncertanty=mean(uncertanty)) |>
  ggplot2::ggplot(ggplot2::aes(x=date,y=uncertanty))+
  ggplot2::geom_area(ggplot2::aes(color='OCO-2', fill='OCO-2'),alpha=.2)+
  ggplot2::geom_area(data=oco3 |>
                       dplyr::group_by(date) |>
                       dplyr::summarise(uncertanty=mean(uncertanty)),
                     ggplot2::aes(color='OCO-3',fill='OCO-3'),alpha=.2)+
  ggplot2::ylab(expression('Xco'[2]~' Uncertanty (ppm)'))+
  ggplot2::ylim(0,1.5)+
  ggplot2::scale_color_manual(name='Mission',
                              breaks=c('OCO-2','OCO-3'),
                              values=c('OCO-2'='red', 'OCO-3'='dark blue'))+
  ggplot2::scale_fill_manual(name='Mission',
                             breaks=c('OCO-2','OCO-3'),
                             values=c('OCO-2'='red', 'OCO-3'='dark blue'))+
  ggplot2::theme(axis.text = ggplot2::element_text(color='black'))

Anomaly calculation

oco2 <- oco2 |> 
  dplyr::filter(
    xco2 > remove_outlier(xco2)[1]&
      xco2<remove_outlier(xco2)[2]
  )
oco3 <- oco3 |> 
  dplyr::filter(
    xco2 > remove_outlier(xco2)[1]&
      xco2<remove_outlier(xco2)[2]
  )

xco2 <- rbind(oco2,oco3)

xco2 <- xco2 |>  
  dplyr::filter(
    xco2 > remove_outlier(xco2)[1]&
      xco2<remove_outlier(xco2)[2]
  ) |>
  dplyr::group_by(date) |>
  dplyr::mutate(
    Anomaly = xco2-median(xco2)
  )


xco2 <- xco2 |> 
  dplyr::mutate(
    year=lubridate::year(date),
    Biome = dplyr::case_when(
      RASTERVALU==3~'AMZ',
      RASTERVALU==6~'AF',
      RASTERVALU==7~'CERR',
      RASTERVALU==8~'CAAT',
      RASTERVALU==9~'PNT',
      RASTERVALU==10~'PMP'
    ),
    mes=lubridate::month(date),
    Period = dplyr::case_when(
      mes==1~'Wet' ,
      mes==2~ 'Wet',
      mes==3~'Wet' ,
      mes==4~ 'Dry',
      mes==5~ 'Dry',
      mes==6~ 'Dry',
      mes==7~ 'Dry',
      mes==8~ 'Dry',
      mes==9~ 'Dry',
      mes==10~ 'Wet',
      mes==11~ 'Wet',
      mes==12~'Wet'
    )
  ) |> 
  na.omit()

Density Plot

xco2 |>
  dplyr::mutate(
    Season=stringr::str_c(
      lubridate::year(date),Period,sep='-'
    )
  ) |>
  ggplot2::ggplot(ggplot2::aes(x=Anomaly, y=Biome,col=Biome, fill=Biome))+
  ggridges::geom_density_ridges(alpha=.4)+
  ggplot2::facet_wrap(~Season,scales = 'fixed')+
  ggplot2::geom_vline(xintercept = 0, linetype='dashed')+
  ggplot2::scale_color_viridis_d()+
  ggplot2::scale_fill_viridis_d()+
  ggplot2::xlab('Anomaly (ppm)')
## Picking joint bandwidth of 0.0969

## Picking joint bandwidth of 0.128

## Picking joint bandwidth of 0.112

## Picking joint bandwidth of 0.149

Fire relation

df <- xco2 |> 
  dplyr::mutate(
    lon=round(lon,2),
    lat=round(lat,2)
  ) |> 
  dplyr::select(lon,lat,year,Period,Biome, Anomaly) |> 
  dplyr::left_join(frp) |>
  na.omit()
## Adding missing grouping variables: `date`
## Joining with `by = join_by(lon, lat, year, Period, Biome)`

## Warning in dplyr::left_join(dplyr::select(dplyr::mutate(xco2, lon = round(lon, : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 4382 of `x` matches multiple rows in `y`.
## ℹ Row 109586 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

df |>
  dplyr::mutate(
    lon=round(lon,1),
    lat=round(lat,1),
    Season=stringr::str_c(
      year,Period,sep='-'
    )) |>
  dplyr::group_by(lon,lat,Season,Biome) |>
  dplyr::summarise(
    ff=dplyr::n(),
    Anomaly=max(Anomaly)
  ) |>
  dplyr::filter(Biome != 'PMP') |>
  ggplot2::ggplot(ggplot2::aes(x=ff,y=Anomaly,col=Season))+
  ggplot2::geom_point()+
  ggplot2::geom_smooth(se=FALSE,method='lm')+
  ggplot2::facet_wrap(~Biome,scales = 'fixed')+
  ggpubr::stat_cor(method = 'spearman',label.y.npc = 'bottom',label.x =40)+
  #ggpubr::stat_regline_equation(label.y.npc = 'bottom',label.x = 30)+
  ggplot2::xlab(
    'Fire Foci count')+
  ggplot2::ylab('Anomaly (ppm)')+
  ggplot2::scale_color_viridis_d()+
  ggplot2::theme_bw()+
  ggplot2::theme(axis.text = ggplot2::element_text(color='black',size = 12),
                 axis.title = ggplot2::element_text(color='black',size=12),)
## `summarise()` has grouped output by 'lon', 'lat', 'Season'. You can override
## using the `.groups` argument.
## `geom_smooth()` using formula = 'y ~ x'

With Cloud

New data

oco2 <- readr::read_rds('processed_data/oco2_qf_1.rds')
oco3 <- readr::read_rds('processed_data/oco3_qf_1.rds')

Uncertanity

oco2 |>
  dplyr::group_by(date) |>
  dplyr::summarise(uncertanty=mean(uncertanty)) |>
  ggplot2::ggplot(ggplot2::aes(x=date,y=uncertanty))+
  ggplot2::geom_area(ggplot2::aes(color='OCO-2', fill='OCO-2'),alpha=.2)+
  ggplot2::geom_area(data=oco3 |>
                       dplyr::group_by(date) |>
                       dplyr::summarise(uncertanty=mean(uncertanty)),
                     ggplot2::aes(color='OCO-3',fill='OCO-3'),alpha=.2)+
  ggplot2::ylab(expression('Xco'[2]~' Uncertanty (ppm)'))+
  ggplot2::ylim(0,1.5)+
  ggplot2::scale_color_manual(name='Mission',
                              breaks=c('OCO-2','OCO-3'),
                              values=c('OCO-2'='red', 'OCO-3'='dark blue'))+
  ggplot2::scale_fill_manual(name='Mission',
                             breaks=c('OCO-2','OCO-3'),
                             values=c('OCO-2'='red', 'OCO-3'='dark blue'))+
  ggplot2::theme(axis.text = ggplot2::element_text(color='black'))
## Warning: Removed 6 rows containing non-finite values (`stat_align()`).

Anomaly calculation

oco2 <- oco2 |> 
  dplyr::filter(
    xco2 > remove_outlier(xco2)[1]&
      xco2<remove_outlier(xco2)[2]
  )

oco3 <- oco3 |> 
  dplyr::filter(
    xco2 > remove_outlier(xco2)[1]&
      xco2<remove_outlier(xco2)[2]
  )

xco2 <- rbind(oco2,oco3)

xco2 <- xco2 |>  
  dplyr::filter(
    xco2 > remove_outlier(xco2)[1]&
      xco2<remove_outlier(xco2)[2]
  ) |>
  dplyr::group_by(date) |>
  dplyr::mutate(
    Anomaly = xco2-median(xco2)
  )

xco2 <- xco2 |> 
  dplyr::mutate(
    year=lubridate::year(date),
    Biome = dplyr::case_when(
      RASTERVALU==3~'AMZ',
      RASTERVALU==6~'AF',
      RASTERVALU==7~'CERR',
      RASTERVALU==8~'CAAT',
      RASTERVALU==9~'PNT',
      RASTERVALU==10~'PMP'
    ),
    mes=lubridate::month(date),
    Period = dplyr::case_when(
      mes==1~'Wet' ,
      mes==2~ 'Wet',
      mes==3~'Wet' ,
      mes==4~ 'Dry',
      mes==5~ 'Dry',
      mes==6~ 'Dry',
      mes==7~ 'Dry',
      mes==8~ 'Dry',
      mes==9~ 'Dry',
      mes==10~ 'Wet',
      mes==11~ 'Wet',
      mes==12~'Wet'
    )
  ) |> 
  na.omit()

Fire Relation

df <- xco2 |> 
  dplyr::mutate(
    lon=round(lon,2),
    lat=round(lat,2)
  ) |> 
  dplyr::select(lon,lat,year,Period,Biome, Anomaly) |> 
  dplyr::left_join(frp) |>
  na.omit()
## Adding missing grouping variables: `date`
## Joining with `by = join_by(lon, lat, year, Period, Biome)`

## Warning in dplyr::left_join(dplyr::select(dplyr::mutate(xco2, lon = round(lon, : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 799 of `x` matches multiple rows in `y`.
## ℹ Row 109586 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

df |>
  dplyr::mutate(
    lon=round(lon,1),
    lat=round(lat,1),
    Season=stringr::str_c(
      year,Period,sep='-'
    )) |>
  dplyr::group_by(lon,lat,Season,Biome) |>
  dplyr::summarise(
    ff=dplyr::n(),
    Anomaly=max(Anomaly)
  ) |>
  dplyr::filter(Biome != 'PMP') |>
  ggplot2::ggplot(ggplot2::aes(x=ff,y=Anomaly,col=Season))+
  ggplot2::geom_point()+
  ggplot2::geom_smooth(se=FALSE,method='lm')+
  ggplot2::facet_wrap(~Biome,scales = 'fixed')+
  ggpubr::stat_cor(method = 'spearman',label.y.npc = 'bottom',label.x =40)+
  #ggpubr::stat_regline_equation(label.y.npc = 'bottom',label.x = 30)+
  ggplot2::xlab(
    'Fire Foci count')+
  ggplot2::ylab('Anomaly (ppm)')+
  ggplot2::scale_color_viridis_d()+
  ggplot2::theme_bw()+
  ggplot2::theme(axis.text = ggplot2::element_text(color='black',size = 12),
                 axis.title = ggplot2::element_text(color='black',size=12),)
## `summarise()` has grouped output by 'lon', 'lat', 'Season'. You can override
## using the `.groups` argument.
## `geom_smooth()` using formula = 'y ~ x'

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