{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 3: Inversion of RT-models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Narrative (Inversion of RT-models, soil moisture retrieval from radar backscatter)\n", "\n", "This notebook demonstrates a workflow for retrieving soil moisture (sm) from radar backscatter (**synthetic data set**).\n", "\n", "The main process is:\n", "1. **Parametrization**: Defining the physical properties of the soil and vegetation.\n", "2. **Forward Modeling** (creating a synthetic dataset): Simulating what a microwave satellite (e.g., Sentinel-1) would \"see\" (backscatter) given the parametrization of the soil and vegetation.\n", "3. **Inversion**: Using an optimization solver to find the soil moisture value that best matches the \n", " observed backscatter.\n", "4. **Visualization**: Visualize the difference between the synthetic created truth of soil moisture (sm_truth) and the modeled soil moisture\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Requirements\n", "\n", "- Installation of SenSE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Retrieval of soil moisture with RT-model combination **Oh92+SSRT**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1 Parametrization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First of all, we need to import several packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "#from sense.surface import Dubois95\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\n", "import random\n", "from sense.surface import Oh92, Oh04\n", "from scipy.optimize import minimize" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next step is to define which RT models should be used (example combination of surface: Oh92 and canopy: SSRT)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Choose models (surface: Oh92; canopy: SSRT (turbid_isotropic))\n", "#------------------------------------------------------------------\n", "canopy = 'turbid_isotropic' # other options are 'turbid_rayleigh' or 'water_cloud' \n", "surface = 'Oh92' # other options are 'Oh04\", 'Dubois95', 'I2EM', 'WaterCloud'\n", "models = {'surface' : surface, 'canopy' : canopy}\n", "pol='vv' # use of Vertical-Vertical polarization due to its sensitivity to soil moisture" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After the definition of the model the model model parameters need to be defined. Furthermore, for the parameter soil moisture (sm) a synthetic time series is created." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# model parameter Oh92 (surface part)\n", "#----------------------\n", "freq = 5.405 # Frequency in GHz (C-band, typical for Sentinel-1)\n", "s = 0.013 # Surface roughness (m)\n", "clay = 0.0738 # Clay fraction (0-1)\n", "sand = 0.2408 # Sand fraction (0-1)\n", "bulk = 1.45 # Soil bulk density (g/cm3)\n", "theta = np.deg2rad(35) # Incidence angle in radians\n", "\n", "# Generating a synthetic time series of soil moisture\n", "sm_truth = np.random.uniform(low=0.05, high=0.35, size=(50,))\n", "\n", "# model parameter SSRT (canopy part)\n", "#---------------------\n", "d = 0.55 # Canopy height (m)\n", "tau = 0.45 # Optical depth (unitless) - characterizes attenuation\n", "ke = tau/d # Extinction coefficient\n", "omega = 0.175 # Single scattering albedo\n", "ks = omega * ke # Scattering coefficient" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2 Forward modeling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Forward simulation. Using the previously defined model parameter to create a \"synthetic truth\" dataset of sigma nought radar backscatter values (simuulated Sentinel-1 backscatter values for vv polarization)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# run model to produce backscatter\n", "#----------------------------------\n", "S = Soil(f=freq, s=s, mv=sm_truth, sand=sand, clay=clay, bulk=bulk)\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=freq)\n", "RT.sigma0()\n", "back_vv = RT.stot['vv']\n", "back_hv = RT.stot['hv']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's put some uncertainty (15%) on the synthetic (truth) backscatter time series" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# put uncertainty on backscatter\n", "unc = np.random.uniform(low=-np.mean(back_vv)*0.15, high=np.mean(back_vv)*0.15, size=(len(back_vv),))\n", "back_vv = back_vv - unc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.3 Inversion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define some helper function so that we can run the RT model in an inversion process. The aim is to provide backscatter to the RT model thus we can retrieve soil moisture values " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Helper Functions for Soil Moisture Retrieval (Inversion)\n", "# ---------------------------------------------------------\n", "\n", "def run_model(dic, models):\n", " \"\"\"\n", " Executes the Forward Radiative Transfer Model.\n", " This function takes physical parameters (soil moisture, roughness, vegetation)\n", " and simulates what the radar backscatter (sigma0) should look like.\n", " \"\"\"\n", " \n", " # 1. Define Soil Properties: \n", " # mv = volumetric soil moisture, s = surface roughness, f = frequency\n", " soil = Soil(mv=dic['mv'], s=dic['s'], clay=dic['clay'], \n", " sand=dic['sand'], f=dic['f'], bulk=dic['bulk'])\n", "\n", " # 2. Define Canopy (Vegetation) Properties:\n", " # d = height, ke = extinction coefficient, omega = single scattering albedo\n", " # We calculate scattering coefficients (ks) based on the extinction and albedo.\n", " can = OneLayer(canopy=dic['canopy'], ke_h=dic['ke'], ke_v=dic['ke'], d=dic['d'], \n", " ks_h = dic['omega']*dic['ke'],\n", " ks_v = dic['omega']*dic['ke'])\n", "\n", " # 3. Combine into the Radiative Transfer Model (RTModel):\n", " # theta = incidence angle of the satellite sensor\n", " S = RTModel(surface=soil, canopy=can, models=models, theta=dic['theta'], freq=dic['f'])\n", " \n", " # Run the simulation\n", " S.sigma0()\n", " \n", " # Return the total backscatter for VV and VH polarizations\n", " # Note: [::-1] is used here to ensure the dictionary key matches the internal naming\n", " return S.__dict__['stot']['vv'[::-1]], S.__dict__['stot']['vh'[::-1]]\n", "\n", "\n", "def solve_fun(VALS, var_opt, dic, models):\n", " \"\"\"\n", " Bridge function that updates the parameter dictionary with the \n", " current 'guess' from the optimizer and runs the model.\n", " \"\"\"\n", " for i in range(len(var_opt)):\n", " dic[var_opt[i]] = VALS[i]\n", "\n", " vv, vh = run_model(dic, models)\n", " return vv, vh\n", "\n", "\n", "def fun_opt(VALS, var_opt, dic, models, pol):\n", " \"\"\"\n", " The Objective (Cost) Function.\n", " This calculates the 'error' between the satellite observation and our model.\n", " The optimizer tries to minimize this value to find the true soil moisture.\n", " \"\"\"\n", " \n", " # We use Least Squares (np.square) to penalize larger differences between \n", " # the observed data (dic['vv']) and the simulated data (solve_fun(...)).\n", " \n", " if pol == 'vv':\n", " # Minimize error for VV polarization only\n", " return(np.nansum(np.square(solve_fun(VALS, var_opt, dic, models)[0]-dic['vv'])))\n", " \n", " elif pol == 'vh':\n", " # Minimize error for VH polarization only (often used for vegetation)\n", " return(np.nansum(np.square(solve_fun(VALS, var_opt, dic, models)[1]-dic['vh'])))\n", " \n", " elif pol == 'vv_vh':\n", " # Dual-polarization retrieval: seeks a balance that fits both VV and VH signals\n", " return(np.nansum(np.square((solve_fun(VALS, var_opt, dic, models)[0]-dic['vv'])/2 + \n", " (solve_fun(VALS, var_opt, dic, models)[1]-dic['vh'])/2)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can run the inversion process to estimate soil moisture based on a synthetic radar backscatter time series" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Execution of the Soil Moisture Retrieval (Inversion Loop)\n", "# ---------------------------------------------------------\n", "\n", "# Initial Dictionary: Stores all constant physical and sensor parameters.\n", "# These values (roughness, clay content, frequency, etc.) are held constant \n", "# while we solve for the unknown variable (soil moisture).\n", "dic = {\n", " \"mv\": 0.2, # Initial placeholder for soil moisture\n", " \"s\": s, # Soil roughness (m)\n", " \"clay\": clay, # Clay fraction\n", " \"sand\": sand, # Sand fraction\n", " \"f\": freq, # Radar frequency (GHz)\n", " \"bulk\": bulk, # Soil bulk density\n", " \"canopy\": canopy, \n", " \"d\": d, # Vegetation height (m)\n", " \"ke\": ke, # Extinction coefficient\n", " \"vv\": back_vv, # VV Polarization backscatter (Target)\n", " \"vh\": back_hv, # VH Polarization backscatter\n", " \"theta\": theta, # Incidence angle\n", " \"omega\": omega # Single scattering albedo\n", "}\n", "\n", "# Optimization Configuration\n", "var_opt = ['mv'] # The variable we want to retrieve (Soil Moisture)\n", "guess = [0.2] # The starting point for the solver (20% moisture)\n", "bounds = [(0.05, 0.35)] # Physical limits: prevents the solver from picking impossible values\n", "\n", "# 'L-BFGS-B' is a common algorithm for \"constrained\" optimization.\n", "# It is efficient for finding the minimum of a function within specific bounds.\n", "method = 'L-BFGS-B'\n", "\n", "# List to store the results for each time-step or pixel\n", "sm_retrieved = []\n", "\n", "# Loop through each radar observation (Backscatter time-series or array)\n", "for i, ii in enumerate(back_vv):\n", " \n", " # Update the dictionary with the specific observation for this iteration.\n", " # We feed the solver the exact backscatter (VV and VH) measured by the satellite.\n", " dic = {\n", " \"mv\": 0.2, \"s\": s, \"clay\": clay, \"sand\": sand, \"f\": freq, \"bulk\": bulk, \n", " \"canopy\": canopy, \"d\": d, \"ke\": ke, \"vv\": back_vv[i], \"vh\": back_hv[i], \n", " \"theta\": theta, \"omega\": omega\n", " }\n", " \n", " # The Minimize function:\n", " # It repeatedly calls 'fun_opt', adjusting the 'mv' value until the \n", " # simulated backscatter matches the observed backscatter (back_vv[i]).\n", " res = minimize(fun_opt, guess, args=(var_opt, dic, models, pol), \n", " bounds=bounds, method=method)\n", "\n", " # Optional: Execute the function one last time with the optimized result \n", " # to verify the final cost/error.\n", " fun_opt(res.x, var_opt, dic, models, 'vv')\n", " \n", " # Append the successful retrieval (res.x[0]) to our results list\n", " sm_retrieved.append(res.x[0])\n", "\n", "# Result: 'sm_retrieved' now contains the estimated soil moisture values\n", "# corresponding to your radar backscatter inputs.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.4 Visualization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see how the original soil moisture compares to the model retrieved soil moisture (remember we put some uncertainty on the synthetic radar backscatter time series)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualization\n", "# --------------\n", "plt.plot(sm_truth, label='original sm')\n", "plt.plot(sm_retrieved, label='RT model retrieved sm')\n", "plt.xlabel('Time steps')\n", "plt.ylabel('Soil moisture [m³/m³]')\n", "plt.grid()\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.07096531 -0.05807146 0.01251829 -0.00632529 0.00620911 0.04418123\n", " 0.01134357 -0.00903754 -0.02017916 -0.02151024 0.05653444 0.05320612\n", " 0.03115364 0.0514544 0.01884815 0.05680743 -0.02811593 -0.0691892\n", " 0.00311033 0.04973507 -0.05383806 0.05722382 0.00780491 -0.06037814\n", " -0.04537121 0.06090895 -0.01498087 0.02355465 -0.0140295 0.04369223\n", " 0.0104189 -0.04397722 0.00366088 -0.04648176 -0.02879221 -0.03507189\n", " -0.03865148 0.06035384 0.03567255 0.07813208 -0.06770152 0.04486928\n", " -0.00846511 -0.01962522 -0.06494518 0.01139348 -0.02261724 0.07030648\n", " -0.05304622 0.01062841]\n", "0.03630178393595633\n" ] } ], "source": [ "# Difference between retrieved and truth (synthetic) soil moisture\n", "# -----------------------------------------------------------------\n", "diff = sm_truth - sm_retrieved\n", "diff_average = np.sum(abs(diff))/len(diff)\n", "print(diff)\n", "print(diff_average)" ] } ], "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 }