Algo#

class hgrs.hgrs_kernel.Algo(l1c_obj=None, xcoarsen=20, ycoarsen=20, expon=2)[source]#

static Gamma2sigma(Gamma)#: Function to convert FWHM (Gamma) to standard deviation (sigma)

static super_gaussian(x, amplitude=1.0, mu=0.0, sigma=1.0, expon=10.0)#

Super-Gaussian distribution: super_gaussian(x, amplitude, mu, sigma, expon) =

(amplitude/(sqrt(2*pi)*sigma)) * exp(-abs(x-mu)**expon / (2*sigma**expon))

Parameters:

Returns:

static super_gaussian_fwhm2sigma(fwhm, expon)#: Function to convert FWHM to standard deviation (sigma) of the super-gaussian distribution :param fwhm: :param expon: :return:

get_omnicloudmask(rgnir)#

Apply OmniCloudMAsk for clouds and cloud shadows masking

refs:: Wright, N., Duncan, J. M. A., Callow, J. N., Thompson, S. E., & George, R. J. (2025). Training sensor-agnostic deep learning models for remote sensing: Achieving state-of-the-art cloud and cloud shadow identification with OmniCloudMask. Remote Sensing of Environment, 322, 114694. https://doi.org/10.1016/J.RSE.2025.114694

Parameters:: rgnir – raster xarray object with the red, green and nir bands
Return omnimask:: raster of the retrieved mask

get_pressure(alt, psl)[source]#: Compute the pressure for a given altitude alt : altitude in meters (float or np.array) psl : pressure at sea level in hPa palt : pressure at the given altitude in hPa

water_vapor_correction(raster_name='coarse_masked_raster', variable='Rtoa')[source]#

Parameters:

Returns:

Algo