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

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

library(surveyGEER)
library(tidyverse)
library(sf)
library(leaflet)
con <- quick_db_con(schema = "public")
adm2<- st_read(con,"ssd_adm2")
adm1<- st_read(con,"ssd_adm1")

aoi <- adm2 |> 
  filter(adm2_en %in%c("Leer","Mayendit")) |> 
  select(adm2_en) |> 
  rename(geometry="geom")

soil_moisture_src <- "NASA_USDA/HSL/SMAP10KM_soil_moisture"
soil_moisture <- ee$ImageCollection(soil_moisture_src)

sm_tidy<- as_tidyee(soil_moisture)
sm_tidy$vrt$time_start |> max()

sm_july_mean<-sm_tidy |> 
  filter(date>="2022-07-20",date<="2022-08-02") |> 
  summarise(stat="mean")

moisture_palette <- RColorBrewer::brewer.pal("PuBuGn",n = 5)
leaflet(aoi) |>
  addTiles() |> 
  addPolygons()
ssm_viz <-  list(min=0 , 
                 max= 25.39,
                 palette=moisture_palette,
                 bands="ssm_mean")
smp_viz <-  list(min=0 , 
                 max= 1,
                 palette=moisture_palette,
                 bands="smp_mean")

ssma_viz <-  list(min=-4 , 
                 max= 4,
                 palette=moisture_palette,
                 bands="ssma_mean")
susm_viz <-  list(min=0 , 
                 max= 274.6,
                 palette=moisture_palette,
                 bands="susm_mean")
library(tidyverse)
aoi_ee <- sf_as_ee(aoi)

Map$centerObject(aoi_ee, 8)
Map$addLayer(sm_july_mean$ee_ob,ssm_viz,"ssm")+
  Map$addLayer(aoi_ee)
Map$addLayer(sm_july_mean$ee_ob,smp_viz,"smp_viz")+
  Map$addLayer(aoi_ee)
Map$addLayer(sm_july_mean$ee_ob,ssma_viz,"ssma_viz")+
  Map$addLayer(aoi_ee)

Map$addLayer(sm_july_mean$ee_ob,susm_viz,"susm_viz")+
  Map$addLayer(aoi_ee)

sm_tidy$ee_ob$first()$projection()$nominalScale()$getInfo()
chirps_link <- "UCSB-CHG/CHIRPS/DAILY"
chirps_ic<- rgee::ee$ImageCollection(chirps_link)$filterDate("2022-04-01","2022-08-02")

chirps_ic$first()$projection()$nominalScale()$getInfo()
chirps_tidy <-  chirps_ic |> as_tidyee()

aoi_rain_time_series <- chirps_tidy |> 
  ee_extract_tidy(aoi,scale=5500)

aoi_time_series <- sm_tidy |> 
  filter(date>="2022-04-01",date<="2022-08-02") |> 
  ee_extract_tidy(aoi,scale = 12782)

leer_cumulative_rain |> print(n=91)
leer_cumulative_rain <- aoi_rain_time_series |> group_by(adm2_en) |> 
  mutate(cum_rain= cumsum(value)) |> 
  filter(adm2_en=="Leer") |> 
  ungroup()
mayendit_cumulative_rain <- aoi_rain_time_series |> group_by(adm2_en) |> 
  mutate(cum_rain= cumsum(value)) |> 
  filter(adm2_en=="Mayendit") |> 
  ungroup()
aoi_time_series |> 
  filter(parameter=="smp") |> 
  ggplot(aes(x=date, y=value,color=adm2_en))+
  geom_line()#+
  # geom_line(data= leer_cumulative_rain,
  #           aes(x=date,y=cum_rain/100),
  #           lwd=1,color="#fdae61",alpha=0.75)+
  # geom_line(data= mayendit_cumulative_rain,
  #           aes(x=date,y=cum_rain/100),
  #           lwd=1,color="#fdae61",alpha=0.75)+
  #  scale_y_continuous(
  #   sec.axis= sec_axis(trans = ~.x*100,name = "AWD Cases")
  # )


aoi_time_series |> 
  filter(parameter=="ssm") |> 
  ggplot(aes(x=date, y=value,color=adm2_en))+
  geom_line()

aoi_rain_time_series |> 
  ggplot(aes(x=date, y= value))+
  geom_line()
ee_chirps_spi

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