Get evaporative stress index (ESI) data

Get evaporative stress index (ESI) from SERVIR Global via ClimateSERV API Client. ESI is available every four (or twelve) weeks from 2001 to present. The dataset may contain cloudy data which is returned as NAs. ClimateSERV works with geojson of type 'Polygon'. The input object is then transformed into polygons with a small buffer area around the point.

Usage

get_esi(object, dates, operation = 5, period = 1, ...)

# S3 method for default
get_esi(object, dates, operation = 5, period = 1, ...)

# S3 method for sf
get_esi(object, dates, operation = 5, period = 1, as.sf = FALSE, ...)

# S3 method for geojson
get_esi(object, dates, operation = 5, period = 1, as.geojson = FALSE, ...)

Arguments

object: input, an object of class data.frame (or any other object that can be coerced to data.frame), SpatVector, SpatRaster, SpatExtent, sf or geojson
dates: a character of start and end dates in that order in the format "YYYY-MM-DD"
operation: optional, an integer that represents which type of statistical operation to perform on the dataset
period: an integer value for the period of ESI data, four weeks period = 1, twelve weeks = 2
...: additional arguments passed to terra or sf methods See details
as.sf: logical, returns an object of class sf
as.geojson: logical, returns an object of class geojson

Value

A data frame of ESI data:

id: the index for the rows in object
dates: the dates from which ESI was requested
lon: the longitude as provided in object
lat: the latitude as provided in object
esi: the ESI value

Details

operation: supported operations are:

operation		value
max	=	0
min	=	1
median	=	2
sum	=	4
average	=	5 (default value)

dist: numeric, buffer distance for each object coordinate

nQuadSegs: integer, number of segments per buffer quadrant

Note

get_esi may return some warning messages given by sf, please look sf documentation for possible issues.

Examples

if (FALSE) { # interactive()

lonlat <- data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

dates <- c("2017-12-15","2018-06-20")

# by default the function set a very small buffer around the points
# which can return NAs due to cloudiness in ESI data

dt <- get_esi(lonlat, dates = dates)

# the argument dist passed through sf increase the buffer area

dt <- get_esi(lonlat, dates = dates, dist = 0.1)
}