In impact-initiatives-geospatial/surveyGEER: Bringing relevant GEE Data Into Your Assessment

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(surveyGEER)
library(tidyrgee)
library(tidyverse)
library(rgee)
library(sf)

ee_Initialize()

Intro

This is a tutorial on using GRACE and GLDAS data to explore groundwater fluctuations.

https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/

All reported data are anomalies relative to the 2004.0-2009.999 time-mean baseline. Note that this baseline needs to be consistent when comparing GRACE/GRACE-FO data to other anomaly data (e.g., groundwater or sea level).

I thought that there were so many images due to tiling, but it looks like there is one imgae of the entire global every 3 hours. Each image is the whole earth - therefore clipping does not reduce the #

Instead we can group_by year, month

con <- quick_db_con(schema = "public")
adm1<-st_read(con,"syr_adm1")
aoi <- adm1 |> 
  filter(str_detect(adm1_en,"Has")) |> 
  st_bbox() |> 
  st_as_sfc()
aoi_ee <-  rgee::sf_as_ee(aoi)

grace_tws <-  ee$ImageCollection("NASA/GRACE/MASS_GRIDS/MASCON")
grace_tws <- as_tidyee(grace_tws)
gldas <- ee$ImageCollection("NASA/GLDAS/V021/NOAH/G025/T3H")
gldas_aoi <-  gldas$filterBounds(aoi_ee)
gldas_aoi_clip <-  gldas$map(function(img){img$clip(aoi_ee)})
gldas_tidy <-  as_tidyee(gldas)
Map$addLayer(gldas$
               first()$
               select("SoilMoi0_10cm_inst"),
             visParams = list(min=1.99,
                              max= 47.59, 
                              palette=c("blue","red")),
             "adsfdasf")
SoilMoi0_10cm_inst
SoilMoi0_40cm_inst
gldas_monthly<- gldas_tidy |> 
  select("SoilMoi0_10cm_inst"
         # "SoilMoi10_40cm_inst",
         # "SoilMoi40_100cm_inst",
         # "SoilMoi100_200cm_inst",
         # "RootMoist_inst",
         # "SWE_inst") |>
  ) |> 
  group_by(month) |> 
  summarise(stat="mean")

ck <- gldas_monthly$ee_ob$first()
# this should work,but idk
ck$bandNames()$getInfo()


1 kg/m2 = 0.1

sm <- gldas_aoi$select("SoilMoi0_10cm_inst")
sm$size()$getInfo()
gldas$size()$getInfo()
gldas_aoi_clip$size()$getInfo()



sm_2004_2009_mean <- sm$filterDate("2004-01-01","2009-12-31")$mean()
sm_2004_2009_mean$bandNames()$getInfo()

sm_2004_2009_mean_tidy<- as_tidyee(sm) |> 
  filter(year %in% c(2004:2009)) |> 
  summarise(stat="mean") 
sm_2004_2009_mean_tidy$ee_ob$bandNames()$getInfo()

gldas_sm <- gldas$map(function(img) {
    sm_total <- img$expression(
      "float(SoilMoi0_10cm_inst+
      SoilMoi10_40cm_inst+
      SoilMoi40_100cm_inst+
      SoilMoi100_200cm_inst
      )",
      opt_map = list(
        SoilMoi0_10cm_inst = img$select("SoilMoi0_10cm_inst"),
        SoilMoi10_40cm_inst = img$select("SoilMoi10_40cm_inst"),
        SoilMoi40_100cm_inst = img$select("SoilMoi40_100cm_inst"),
        SoilMoi100_200cm_inst = img$select("SoilMoi100_200cm_inst")
      )
    )$rename("sm_total")
    img$addBands(sm_total)$select("sm_total")
})

gldas_sm$first()$bandNames()$getInfo()

gldas_sm_tidy <- as_tidyee(gldas_sm)

sm_jan_baseline <- gldas_sm_tidy |> 
  filter(year %in% c(2004:2009)) |> 
  # group_by(month) |> 
  summarise(stat="mean") |> 
  filter(month==1)

sm_jan_baseline <- gldas_sm_tidy |> 
  filter(year %in% c(2004:2009)) |> 
  summarise(stat="mean") 

sm_jan_baseline$ee_ob$bandNames()$getInfo()


gldas_sm_tidy$ee_ob$filterDate("2004-01-01","2009-12-31")$sum()$bandNames()$getInfo()



#|> 
  select(sm_baseline= "sm_total_mean")


  sm_jan_baseline$ee_ob$bandNames()$getInfo()

sm_jan_baseline$ee_ob$bandNames()$getInfo()

sm_jan_yearly <- gldas_sm_tidy |> 
  filter(year %in% c(2003:2021)) |> 
  group_by(year, month) |> 
  summarise(stat="mean") |> 
  filter(month==1) #|> 
  select(sm_yearly = "sm_total_mean")

sm_jan_yearly$ee_ob$first()$bandNames()$getInfo()
rgee::ee_clean_container(name = "drive")

sm_baseline_yearly <- sm_jan_yearly |> 
  inner_join(sm_jan_baseline,by="month")


sm_baseline_yearly$ee_ob$first()$select("sm_yearly")$bandNames()$getInfo()
sm_baseline_yearly$ee_ob$first()

sm_baseline_yearly$ee_ob$map(function(img){
   sm_anomaly <- img$expression("float(sm_yearly-sm_baseline)",
                                opt_map = list(
                                  sm_yearly=img$select(sm_yearly),
                                  sm_baseline= img$select(sm_baseline)
                                ))$rename("sm_anomaly")
   img$addBands(sm_anomaly)



})

impact-initiatives-geospatial/surveyGEER documentation built on Feb. 4, 2023, 12:13 p.m.