Skip to content

Commit

Permalink
Merge pull request #56 from Open-ET/et-fraction-grass-source-param
Browse files Browse the repository at this point in the history 
Add support for an ET fraction grass source parameter
  • Loading branch information
@cgmorton
cgmorton authored Dec 5, 2023
2 parents d0a3df4 + 8974241 commit e52861e
Show file tree
Hide file tree
Showing 5 changed files with 267 additions and 121 deletions.
93 changes: 40 additions & 53 deletions openet/ssebop/image.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -51,6 +51,7 @@ def __init__(
dt_min=5,
dt_max=25,
et_fraction_type='alfalfa',
et_fraction_grass_source=None,
reflectance_type='SR',
**kwargs,
):
Expand Down Expand Up @@ -85,7 +86,7 @@ def __init__(
'DAYMET_MEDIAN_V2', 'CIMIS_MEDIAN_V1',
collection ID, or float}, optional
Maximum air temperature source. The default is
'projects/usgs-ssebop/tmax/daymet_v3_median_1980_2018'.
'projects/usgs-ssebop/tmax/daymet_v4_mean_1981_2010'.
elr_flag : bool, str, optional
If True, apply Elevation Lapse Rate (ELR) adjustment
(the default is False).
Expand All @@ -94,10 +95,16 @@ def __init__(
dt_max : float, optional
Maximum allowable dT [K] (the default is 25).
et_fraction_type : {'alfalfa', 'grass'}, optional
ET fraction (the default is 'alfalfa').
ET fraction reference type (the default is 'alfalfa').
If set to "grass" the et_fraction_grass_source must also be set.
et_fraction_grass_source : {'NASA/NLDAS/FORA0125_H002',
'ECMWF/ERA5_LAND/HOURLY'}, float, optional
Reference ET source for alfalfa to grass reference adjustment.
Parameter must be set if et_fraction_type is 'grass'.
The default is currently the NLDAS hourly collection,
but having a default will likely be removed in a future version.
reflectance_type : {'SR', 'TOA'}, optional
Used to set the Tcorr NDVI thresholds
(the default is 'SR').
Used to set the Tcorr NDVI thresholds (the default is 'SR').
kwargs : dict, optional
dt_resample : {'nearest', 'bilinear'}
tcorr_resample : {'nearest', 'bilinear'}
Expand Down Expand Up @@ -194,14 +201,28 @@ def __init__(
raise ValueError(f'elr_flag "{self._elr_flag}" could not be interpreted as bool')

# ET fraction type
# CGM - Should et_fraction_type be set as a kwarg instead?
if et_fraction_type.lower() not in ['alfalfa', 'grass']:
raise ValueError('et_fraction_type must "alfalfa" or "grass"')
self.et_fraction_type = et_fraction_type.lower()
# if 'et_fraction_type' in kwargs.keys():
# self.et_fraction_type = kwargs['et_fraction_type'].lower()
# else:
# self.et_fraction_type = 'alfalfa'

# ET fraction alfalfa to grass reference adjustment
# The NLDAS hourly collection will be used if a source value is not set
if self.et_fraction_type.lower() == 'grass' and not et_fraction_grass_source:
warnings.warn(
'NLDAS is being set as the default ET fraction grass adjustment source. '
'In a future version the parameter will need to be set explicitly as: '
'et_fraction_grass_source="NASA/NLDAS/FORA0125_H002".',
FutureWarning
)
et_fraction_grass_source = 'NASA/NLDAS/FORA0125_H002'
self.et_fraction_grass_source = et_fraction_grass_source
# if self.et_fraction_type.lower() == 'grass' and not et_fraction_grass_source:
# raise ValueError(
# 'et_fraction_grass_source parameter must be set if et_fraction_type==\'grass\''
# )
# # Should the supported source values be checked here instead of in model.py?
# if et_fraction_grass_source not in et_fraction_grass_sources:
# raise ValueError('unsupported et_fraction_grass_source')

self.reflectance_type = reflectance_type
if reflectance_type not in ['SR', 'TOA']:
Expand Down Expand Up @@ -333,50 +354,16 @@ def et_fraction(self):

et_fraction = model.et_fraction(lst=self.lst, tmax=tmax, tcorr=self.tcorr, dt=dt)

# TODO: Add support for setting the conversion source dataset
# TODO: Interpolate "instantaneous" ETo and ETr?
# TODO: Move openet.refetgee import to top?
# TODO: Check if etr/eto is right (I think it is)
if self.et_fraction_type.lower() == 'grass':
import openet.refetgee
nldas_coll = (
ee.ImageCollection('NASA/NLDAS/FORA0125_H002')
.select(['temperature', 'specific_humidity', 'shortwave_radiation', 'wind_u', 'wind_v'])
)

# Interpolating hourly NLDAS to the Landsat scene time
# CGM - The 2 hour window is useful in case an image is missing
# I think EEMETRIC is using a 4 hour window
# CGM - Need to check if the NLDAS images are instantaneous
# or some sort of average of the previous or next hour
time_start = ee.Number(self._time_start)
prev_img = ee.Image(
nldas_coll
.filterDate(time_start.subtract(2 * 60 * 60 * 1000), time_start)
.limit(1, 'system:time_start', False)
.first()
)
next_img = ee.Image(
nldas_coll.filterDate(time_start, time_start.add(2 * 60 * 60 * 1000)).first()
)
prev_time = ee.Number(prev_img.get('system:time_start'))
next_time = ee.Number(next_img.get('system:time_start'))
time_ratio = time_start.subtract(prev_time).divide(next_time.subtract(prev_time))
nldas_img = (
next_img.subtract(prev_img).multiply(time_ratio).add(prev_img)
.set({'system:time_start': self._time_start})
)

# # DEADBEEF - Select NLDAS image before the Landsat scene time
# nldas_img = ee.Image(nldas_coll
# .filterDate(self._date.advance(-1, 'hour'), self._date)
# .first())

et_fraction = (
et_fraction
.multiply(openet.refetgee.Hourly.nldas(nldas_img).etr)
.divide(openet.refetgee.Hourly.nldas(nldas_img).eto)
)
# Convert the ET fraction to a grass reference fraction
if self.et_fraction_type.lower() == 'grass' and self.et_fraction_grass_source:
if utils.is_number(self.et_fraction_grass_source):
et_fraction = et_fraction.multiply(self.et_fraction_grass_source)
else:
et_fraction = model.etf_grass_type_adjust(
etf=et_fraction,
src_coll_id=self.et_fraction_grass_source,
time_start=self._time_start
)

return et_fraction.set(self._properties)\
.set({
Expand Down
93 changes: 82 additions & 11 deletions openet/ssebop/model.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,8 @@

import ee

import openet.refetgee


def et_fraction(lst, tmax, tcorr, dt):
"""SSEBop fraction of reference ET (ETf)
Expand Down Expand Up @@ -31,17 +33,12 @@ def et_fraction(lst, tmax, tcorr, dt):
"""

et_fraction = lst.expression(
etf = lst.expression(
'(lst * (-1) + tmax * tcorr + dt) / dt',
{'tmax': tmax, 'dt': dt, 'lst': lst, 'tcorr': tcorr}
)

return (
et_fraction
.updateMask(et_fraction.lte(2.0))
.clamp(0, 1.0)
.rename(['et_fraction'])
)
return etf.updateMask(etf.lte(2.0)).clamp(0, 1.0).rename(['et_fraction'])


def dt(tmax, tmin, elev, doy, lat=None, rs=None, ea=None):
Expand Down Expand Up @@ -147,9 +144,7 @@ def dt(tmax, tmin, elev, doy, lat=None, rs=None, ea=None):
den = tmax.add(tmin).multiply(0.5).pow(-1).multiply(pair).multiply(3.486 / 1.01)

# Temperature difference [K] (Senay2018 A.5)
dt = rn.divide(den).multiply(110.0 / ((1.013 / 1000) * 86400))

return dt
return rn.divide(den).multiply(110.0 / ((1.013 / 1000) * 86400))


def lapse_adjust(temperature, elev, lapse_threshold=1500):
Expand Down Expand Up @@ -191,7 +186,7 @@ def elr_adjust(temperature, elevation, radius=80):
Returns
-------
ee.Image of adjusted temperature
ee.Image
Notes
-----
Expand Down Expand Up @@ -233,3 +228,79 @@ def elr_adjust(temperature, elevation, radius=80):
tmax_img = tmax_img.where(elr_mask, elr_adjust)

return tmax_img


# TODO: Decide if using the instantaneous is the right/best approach
# We could use the closest hour in time, an average of a few hours
# or just switch to using the raw daily or bias corrected assets
def etf_grass_type_adjust(etf, src_coll_id, time_start):
""""Convert ET fraction from an alfalfa reference to grass reference
Parameters
----------
etf : ee.Image
ET fraction (alfalfa reference).
src_coll_id : str
Hourly meteorology collection ID for computing reference ET.
time_start : int, ee.Number
Image system time start [millis].
Returns
-------
ee.Image
"""
hourly_et_reference_sources = [
'NASA/NLDAS/FORA0125_H002',
'ECMWF/ERA5_LAND/HOURLY',
]
if src_coll_id not in hourly_et_reference_sources:
raise ValueError(f'unsupported hourly ET reference source: {src_coll_id}')
elif not src_coll_id:
raise ValueError('hourly ET reference source not')
else:
src_coll = ee.ImageCollection(src_coll_id)

# Interpolating hourly NLDAS to the Landsat scene time
# CGM - The 2 hour window is useful in case an image is missing
# I think EEMETRIC is using a 4 hour window
# CGM - Need to check if the NLDAS images are instantaneous
# or some sort of average of the previous or next hour
time_start = ee.Number(time_start)
prev_img = ee.Image(
src_coll
.filterDate(time_start.subtract(2 * 60 * 60 * 1000), time_start)
.limit(1, 'system:time_start', False)
.first()
)
next_img = ee.Image(
src_coll.filterDate(time_start, time_start.add(2 * 60 * 60 * 1000)).first()
)
prev_time = ee.Number(prev_img.get('system:time_start'))
next_time = ee.Number(next_img.get('system:time_start'))
time_ratio = time_start.subtract(prev_time).divide(next_time.subtract(prev_time))
interp_img = (
next_img.subtract(prev_img).multiply(time_ratio).add(prev_img)
.set({'system:time_start': time_start})
)

# # DEADBEEF - Select the NLDAS image before the Landsat scene time
# interp_img = ee.Image(
# hourly_coll.filterDate(self._date.advance(-1, 'hour'), self._date).first()
# )

if src_coll_id.upper() == 'NASA/NLDAS/FORA0125_H002':
etf_grass = (
etf
.multiply(openet.refetgee.Hourly.nldas(interp_img).etr)
.divide(openet.refetgee.Hourly.nldas(interp_img).eto)
)
elif src_coll_id.upper() == 'ECMWF/ERA5_LAND/HOURLY':
etf_grass = (
etf
.multiply(openet.refetgee.Hourly.era5_land(interp_img).etr)
.divide(openet.refetgee.Hourly.era5_land(interp_img).eto)
)
# else:

return etf_grass
55 changes: 55 additions & 0 deletions openet/ssebop/tests/test_b_model.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,16 @@
import openet.ssebop.model as model
import openet.ssebop.utils as utils

COLL_ID = 'LANDSAT/LC08/C02/T1_L2'
SCENE_ID = 'LC08_042035_20150713'
# SCENE_DT = datetime.datetime.strptime(SCENE_ID[-8:], '%Y%m%d')
# SCENE_DATE = SCENE_DT.strftime('%Y-%m-%d')
# SCENE_DOY = int(SCENE_DT.strftime('%j'))
# # SCENE_TIME = utils.millis(SCENE_DT)
SCENE_TIME = 1436812419150
SCENE_POINT = (-119.5, 36.0)
TEST_POINT = (-119.44252382373145, 36.04047742246546)


@pytest.mark.parametrize(
# Note: These are made up values
Expand Down Expand Up @@ -187,3 +197,48 @@ def test_Model_elr_adjust(xy, adjusted):
assert output < original
else:
assert output == original


def test_Image_et_reference_source_parameters():
"""Check that the function parameter names and order don't change"""
etf_img = (
ee.Image(f'{COLL_ID}/{SCENE_ID}').select([0]).multiply(0).add(1.0)
.rename(['et_fraction']).set('system:time_start', SCENE_TIME)
)
output = model.etf_grass_type_adjust(
etf=etf_img, src_coll_id='NASA/NLDAS/FORA0125_H002', time_start=SCENE_TIME
)
assert utils.point_image_value(output, SCENE_POINT, scale=100)['et_fraction'] > 1

output = model.etf_grass_type_adjust(etf_img, 'NASA/NLDAS/FORA0125_H002', SCENE_TIME)
assert utils.point_image_value(output, SCENE_POINT, scale=100)['et_fraction'] > 1


@pytest.mark.parametrize(
'src_coll_id, expected',
[
['NASA/NLDAS/FORA0125_H002', 1.23],
['ECMWF/ERA5_LAND/HOURLY', 1.15],
]
)
def test_Model_etf_grass_type_adjust(src_coll_id, expected, tol=0.01):
"""Check alfalfa to grass reference adjustment factor"""
etf_img = (
ee.Image(f'{COLL_ID}/{SCENE_ID}').select([0]).multiply(0).add(1.0)
.rename(['et_fraction']).set('system:time_start', SCENE_TIME)
)
output = model.etf_grass_type_adjust(
etf=etf_img, src_coll_id=src_coll_id, time_start=SCENE_TIME
)
output = utils.point_image_value(output, SCENE_POINT, scale=100)
assert abs(output['et_fraction'] - expected) <= tol


def test_Model_etf_grass_type_adjust_src_coll_id_exception():
"""Function should raise an exception for unsupported src_coll_id values"""
with pytest.raises(ValueError):
utils.getinfo(model.etf_grass_type_adjust(
etf=ee.Image.constant(1), src_coll_id='DEADBEEF', time_start=SCENE_TIME
))


Loading

0 comments on commit e52861e

Please sign in to comment.