{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 2: Forward modeling of RT model combination (surface + canopy part)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Narrative (Examples of different model combination (surface + canopy part))\n", "\n", "This notebook demonstrates model combinations of\n", "\n", "1. **Dubois95 + SSRT**\n", "2. **Oh04 + WaterCloud**\n", "\n", "A short overview of the required input parameters can be found [here](https://sense-community-sar-scattering-model.readthedocs.io/en/dev/03_Usage.html#required-input-paramter)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Requirements\n", "\n", "- Installation of SenSE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Required imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from sense.util import f2lam\n", "from sense.model import RTModel\n", "from sense.soil import Soil\n", "from sense.canopy import OneLayer\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Dubois95+SSRT for different incidence angles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1 Input parameters for the RT model combination" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# array with different incidence angles in radians\n", "theta_deg = np.arange(0.,70.)\n", "theta = np.deg2rad(theta_deg) # incidence angle [radians]\n", "\n", "# soil model parameters\n", "f = 13. # frequency [GHz]\n", "lam = f2lam(f) # wavelength [m]\n", "s = 0.15/100. # surface roughness [m]\n", "eps = 15. - 4.0j # dielectric constant\n", "\n", "# canopy parameters for short alfalfa\n", "d = 0.17 # vegetation height [m]\n", "tau = 2.5 # optical depths\n", "ke = tau/d # extinction coefficient [m⁻¹]\n", "omega = 0.27 # scattering albedo\n", "ks=omega*ke" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 Forward modeling of RT model combination Dubois95 and SSRT (rayleigh scattering)\n", "**First model run** with model parameters for **small alfalfa**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/sense/surface/dubois95.py:42: RuntimeWarning: divide by zero encountered in divide\n", " b = 10**-2.35 * (np.cos(self.theta) ** 3) / (np.sin(self.theta) ** 3)\n", "/tmp/sense/surface/dubois95.py:45: RuntimeWarning: invalid value encountered in multiply\n", " return b * c * d\n", "/tmp/sense/surface/dubois95.py:35: RuntimeWarning: divide by zero encountered in divide\n", " a = 10**-2.75 * (np.cos(self.theta) ** 1.5) / (np.sin(self.theta) ** 5)\n", "/tmp/sense/surface/dubois95.py:38: RuntimeWarning: invalid value encountered in multiply\n", " return a * c * d\n" ] } ], "source": [ "# Define models and polarization to be used\n", "models = {'surface' : 'Dubois95', 'canopy' : 'turbid_rayleigh'} # For SSRT two scattering models are implemented 'turbid_rayleigh' and 'turbid_isotropic'\n", "# models = {'surface' : 'Oh92', 'canopy' : 'turbid_isotropic'} # alternative with different surface and canopy models\n", "pol='vv' # target polarization\n", "\n", "# Soil model initialization based on previously defined input parameters\n", "S = Soil(f=f, s=s, eps=eps)\n", "\n", "# Canopy model initialization based on previously defined input parameters\n", "C = OneLayer(ke_h=ke, ke_v=ke, d=d, ks_v=ks, ks_h=ks, canopy=models['canopy'])\n", "\n", "# Combined Model initialization\n", "RT = RTModel(theta=theta, models=models, surface=S, canopy=C, freq=f)\n", "\n", "# Run RT model\n", "RT.sigma0()\n", "back_short = RT.stot[pol]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Second model run** with model parameters (canopy) for **tall alfalfa**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# redefine canopy parameters for tall alfalfa\n", "d = 0.55 # vegetation height [m]\n", "tau = 0.45 # optical depths\n", "ke = tau/d # extinction coefficient [m⁻¹]\n", "omega = 0.175 # scattering albedo\n", "ks=omega*ke\n", "\n", "# initialize and run of the model combination\n", "S = Soil(f=f, s=s, eps=eps)\n", "C = OneLayer(ke_h=ke, ke_v=ke, d=d, ks_v=ks, ks_h=ks, canopy=models['canopy'])\n", "RT = RTModel(theta=theta, models=models, surface=S, canopy=C, freq=f)\n", "RT.sigma0()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3 Visualization" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot backscatter changes for short and high alfalfa based on the incidence angle\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "ax.plot(theta_deg, 10.*np.log10(back_short), label='short alfalfa', color='b')\n", "ax.plot(theta_deg, 10.*np.log10(RT.stot[pol]), label='tall alfalfa', color='r')\n", "\n", "ax.legend()\n", "\n", "ax.grid()\n", "ax.set_xlabel('Incidence angle [°]')\n", "ax.set_ylabel('Sigma vv [dB]')\n", "ax.set_xlim(0.,70.)\n", "ax.set_ylim(-16.,6.)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Oh04+WaterCloud for different incidence angles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.1 Input parameters for the RT model combination" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Parameters for both models\n", "theta = np.deg2rad(40) # incidence angle [radians]\n", "f = 5.3 # Frequency [GHz]\n", "\n", "# soil parameter\n", "s = 0.3/100. # surface roughness [m]\n", "mv = np.linspace(0.01,0.4) # soil moisture [m³/m³]\n", "# calculations\n", "lam = f2lam(f) # wavelength [m]\n", "k = 2.*np.pi/lam # radar wave number\n", "ks = k * s\n", "\n", "# canopy parameters\n", "A_vv = 0.0950 # empirical parameter - need to be optimized for each test site\n", "B_vv = 0.5513 # empirical parameter - need to be optimized for each test site\n", "A_hh = 0 # empirical parameter - need to be optimized for each test site\n", "B_hh = 0 # empirical parameter - need to be optimized for each test site\n", "A_hv = 0 # empirical parameter - need to be optimized for each test site\n", "B_hv = 0 # empirical parameter - need to be optimized for each test site\n", "\n", "V1 = 0.02 # parameter describing the canopy (todo: find realistic values)\n", "V2 = np.linspace(0.5,5) # # parameter describing the canopy (todo: find realistic values)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.2 Forward modeling of RT model combination Oh04 and WaterCloud" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING: Permittivity cannot be calculated due to missing soil texture!\n" ] } ], "source": [ "# Define models and polarization to be used\n", "models = {'surface' : 'Oh04', 'canopy' : 'water_cloud'}\n", "pol='vv' # target polarization\n", "\n", "# Soil model initialization based on previously defined input parameters\n", "S = Soil(f=f, s=s, mv=mv)\n", "\n", "# Canopy model initialization based on previously defined input parameters\n", "C = OneLayer(A_vv=A_vv, B_vv=B_vv, A_hh=A_hh, B_hh=B_hh, A_hv=A_hv, B_hv=B_hv, V1=V1, V2=V2, d=1, ks_v=1, ke_v=1, ke_h=1, canopy=models['canopy'])\n", "\n", "# Combined Model initialization\n", "RT = RTModel(theta=theta, models=models, surface=S, canopy=C, freq=f)\n", "\n", "# Run RT model\n", "RT.sigma0()\n", "backscatter_vv = RT.stot[pol] # total modeled backscatter\n", "backscatter_vv_g = RT.s0g[pol] # modeled ground contribution\n", "backscatter_vv_c = RT.s0c[pol] # modeled canopy contribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.3 Visualization" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "ax.plot(mv, 10.*np.log10(backscatter_vv), label='total backscatter', color='b')\n", "ax.plot(mv, 10.*np.log10(backscatter_vv_g), label='ground contribution', color='r')\n", "ax.plot(mv, 10.*np.log10(backscatter_vv_c), label='canopy contribution', color='g')\n", "\n", "ax.legend()\n", "\n", "ax.grid()\n", "ax.set_xlabel('Soil moisture [m³/m³]')\n", "ax.set_ylabel('Sigma nought [dB]')\n", "\n", "ax.set_ylim(-40.,-10.)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.12" } }, "nbformat": 4, "nbformat_minor": 2 }