def _compute_lst_mono_window(self, **kwargs) -> np.ndarray:
"""
Computes the LST
kwargs:
**emissivity_10 (np.ndarray): Emissivity image obtained for band 10
**brightness_temperature_10 (np.ndarray): Brightness temperature image obtained for band 10
**mask (np.ndarray[bool]): Mask image. Output will have NaN value where mask is True.
Returns:
np.ndarray: Land surface temperature image
"""
required_keywords = [
"brightness_temperature_10", "emissivity_10", "mask"
]
assert_required_keywords_provided(required_keywords, **kwargs)
temperature_band = kwargs["brightness_temperature_10"]
emissivity = kwargs["emissivity_10"]
mask = kwargs["mask"]
if not (mask.shape == temperature_band.shape == emissivity.shape):
raise ValueError("Input images must be of the same size/shape")
land_surface_temp = temperature_band / (1 + ((
(0.0000115 * temperature_band) / 14380) * np.log(emissivity)))
land_surface_temp[mask] = np.nan
return land_surface_temp
def _compute_lst(self, **kwargs) -> np.ndarray:
"""Computes the LST
kwargs:
**brightness_temperature_10 (np.ndarray): Brightness temperature image obtained for band 10
**brightness_temperature_11 (np.ndarray): Brightness temperature image obtained for band 11
**ndvi (np.ndarray): NDVI image
**mask (np.ndarray[bool]): Mask image. Output will have NaN value where mask is True.
Returns:
np.ndarray: Land surface temperature image
"""
required_keywords = [
"brightness_temperature_10",
"brightness_temperature_11",
"ndvi",
"mask",
]
assert_required_keywords_provided(required_keywords, **kwargs)
tb_10 = kwargs["brightness_temperature_10"]
tb_11 = kwargs["brightness_temperature_11"]
ndvi = kwargs["ndvi"]
mask = kwargs["mask"]
pv = fractional_vegetation_cover(ndvi)
lst = ((tb_10 * ((0.5 * pv) + 3.1)) + (tb_11 * ((-0.5 * pv) - 2.1)) -
((5.5 * pv) + 3.1))
lst[mask] = np.nan
return lst
def _compute_lst(self, **kwargs) -> np.ndarray:
"""
kwargs:
**emissivity_10 (np.ndarray): Emissivity image obtained for band 10
**emissivity_11 (np.ndarray): Emissivity image obtained for band 11
**brightness_temperature_10 (np.ndarray): Brightness temperature image obtained for band 10
**brightness_temperature_11 (np.ndarray): Brightness temperature image obtained for band 11
**mask (np.ndarray[bool]): Mask image. Output will have NaN value where mask is True.
Returns:
np.ndarray: Land surface temperature image
"""
required_keywords = [
"emissivity_10",
"emissivity_11",
"brightness_temperature_10",
"brightness_temperature_11",
"mask",
]
assert_required_keywords_provided(required_keywords, **kwargs)
tb_10 = kwargs["brightness_temperature_10"]
tb_11 = kwargs["brightness_temperature_11"]
emm_10 = kwargs["emissivity_10"]
emm_11 = kwargs["emissivity_11"]
mask = kwargs["mask"]
diff_tb = tb_10 - tb_11
diff_e = emm_10 - emm_11
lst = (tb_10 + (1.06 * (diff_tb)) + (0.46 * diff_tb**2) +
(53 * (1 - emm_10)) - (53 * (diff_e)))
lst[mask] = np.nan
return lst
def _compute_lst(self, **kwargs) -> np.ndarray:
"""Computes the LST
kwargs:
**emissivity_10 (np.ndarray): Emissivity image obtained for band 10
**emissivity_11 (np.ndarray): Emissivity image obtained for band 11
**brightness_temperature_10 (np.ndarray): Brightness temperature image obtained for band 10
**brightness_temperature_11 (np.ndarray): Brightness temperature image obtained for band 11
**mask (np.ndarray[bool]): Mask image. Output will have NaN value where mask is True.
Returns:
np.ndarray: Land surface temperature image
"""
required_keywords = [
"emissivity_10",
"emissivity_11",
"brightness_temperature_10",
"brightness_temperature_11",
"mask",
]
assert_required_keywords_provided(required_keywords, **kwargs)
tb_10 = kwargs["brightness_temperature_10"]
tb_11 = kwargs["brightness_temperature_11"]
emm_10 = kwargs["emissivity_10"]
emm_11 = kwargs["emissivity_11"]
mask = kwargs["mask"]
lst = (tb_10 + 3.33 *
(tb_10 - tb_11)) * ((5.5 - emm_10) / 4.5) + (0.75 * tb_11 *
(emm_10 - emm_11))
lst[mask] = np.nan
return lst
def _compute_lst(self, **kwargs) -> np.ndarray:
"""Computes the keyword arguments
kwargs:
**brightness_temperature_10 (np.ndarray): Brightness temperature image obtained for band 10
**brightness_temperature_11 (np.ndarray): Brightness temperature image obtained for band 11
**mask (np.ndarray[bool]): Mask image. Output will have NaN value where mask is True.
Returns:
np.ndarray: Land surface temperature image
"""
required_keywords = [
"brightness_temperature_10",
"brightness_temperature_11",
"mask",
]
assert_required_keywords_provided(required_keywords, **kwargs)
tb_10 = kwargs["brightness_temperature_10"]
tb_11 = kwargs["brightness_temperature_11"]
mask = kwargs["mask"]
lst = (1.035 * tb_10) + (3.046 * (tb_10 - tb_11)) - 10.93
lst[mask] = np.nan
return lst
def _compute_lst(self, **kwargs) -> np.ndarray:
"""Computes the LST
kwargs:
**emissivity_10 (np.ndarray): Emissivity image obtained for band 10
**emissivity_11 (np.ndarray): Emissivity image obtained for band 11
**brightness_temperature_10 (np.ndarray): Brightness temperature image obtained for band 10
**brightness_temperature_11 (np.ndarray): Brightness temperature image obtained for band 11
**mask (np.ndarray[bool]): Mask image. Output will have NaN value where mask is True.
Returns:
np.ndarray: Land surface temperature image
"""
required_keywords = [
"emissivity_10",
"emissivity_11",
"brightness_temperature_10",
"brightness_temperature_11",
"mask",
]
assert_required_keywords_provided(required_keywords, **kwargs)
tb_10 = kwargs["brightness_temperature_10"]
tb_11 = kwargs["brightness_temperature_11"]
emissivity_10 = kwargs["emissivity_10"]
emissivity_11 = kwargs["emissivity_11"]
mask = kwargs["mask"]
mean_e = (emissivity_10 + emissivity_11) / 2
diff_e = emissivity_10 - emissivity_11
diff_tb = tb_10 - tb_11
lst = (tb_10 + (1.387 * diff_tb) + (0.183 * (diff_tb**2)) - 0.268 +
((54.3 - (2.238 * self.cwv)) *
(1 - mean_e)) + ((-129.2 + (16.4 * self.cwv)) * diff_e))
lst[mask] = np.nan
return lst