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   "source": [
    "# Footprint representativeness after Chu et al. (2021)\n",
    "\n",
    "A footprint climatology tells you *where the flux came from*. This notebook\n",
    "answers the complementary question: **how well does that source area stand in\n",
    "for the target area the site is being used to represent?** — the model grid\n",
    "cell, the satellite pixel window, or the fixed-radius disc a network product\n",
    "assigns to the tower.\n",
    "\n",
    "It walks through the whole method of\n",
    "\n",
    "> Chu, H., Luo, X., Ouyang, Z., et al. (2021). Representativeness of\n",
    "> Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites.\n",
    "> *Agricultural and Forest Meteorology* **301–302**, 108350.\n",
    "> [doi:10.1016/j.agrformet.2021.108350](https://doi.org/10.1016/j.agrformet.2021.108350)\n",
    "\n",
    "in four steps:\n",
    "\n",
    "1. **Build a monthly day/night climatology** from half-hourly tower data.\n",
    "2. **Load a land-cover raster and an EVI stack** onto the footprint grid.\n",
    "3. **Run `assess_representativeness`** — the geometry metrics of Eqs. 1–3 and\n",
    "   the categorical and continuous evaluations of Sect. 2.4.\n",
    "4. **Produce the four figures** of `representativeness_plotting`.\n",
    "\n",
    "The theory, all seven equations, and the classification tables are on the\n",
    "[Footprint representativeness](../representativeness.rst) page. The\n",
    "[validation](../validation.md) page compares these metrics against the\n",
    "authors' own published values.\n",
    "\n",
    "The tower record is the bundled AmeriFlux **US-CRT** BASE file (Curtice\n",
    "Walter-Berger cropland, north-west Ohio), May–October 2011."
   ]
  },
  {
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     "text": [
      "reading tower data from data/\n"
     ]
    }
   ],
   "source": [
    "import tempfile\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import rasterio\n",
    "import xarray as xr\n",
    "from matplotlib.colors import BoundaryNorm, ListedColormap\n",
    "from pyproj import Transformer\n",
    "from rasterio.transform import from_origin\n",
    "\n",
    "from fluxfootprints import (\n",
    "    ASYMMETRY_THRESHOLD,\n",
    "    BIAS_THRESHOLD,\n",
    "    assess_representativeness,\n",
    "    build_climatology,\n",
    "    compute_aerodynamic_params,\n",
    "    evaluate_landcover,\n",
    "    load_amf_df,\n",
    "    load_config,\n",
    "    monthly_climatologies,\n",
    "    plot_bias_density,\n",
    "    plot_footprint_target_scatter,\n",
    "    plot_landcover_composition,\n",
    "    plot_level_bars,\n",
    "    representativeness_table,\n",
    "    sample_raster_on_grid,\n",
    ")\n",
    "\n",
    "pd.set_option(\"display.width\", 160)\n",
    "pd.set_option(\"display.max_columns\", 40)\n",
    "\n",
    "DATA = Path(\"..\") / \"..\" / \"data\"        # repository data/ directory\n",
    "WORK = Path(tempfile.mkdtemp(prefix=\"representativeness-\"))   # scratch GeoTIFFs\n",
    "\n",
    "SITE = \"US-CRT\"\n",
    "TZ = -5                                   # US-CRT records local standard time\n",
    "START, END = \"2011-05-01\", \"2011-10-31\"   # one growing season\n",
    "STEP, HALF = 10.0, 1000.0                 # 10 m cells on a +/- 1 km domain\n",
    "RADII = (100.0, 250.0, 500.0, 1000.0)     # target-area radii [m]\n",
    "\n",
    "print(f\"reading tower data from {DATA.resolve().name}/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "293ea7b5",
   "metadata": {},
   "source": [
    "> **Note on the target radii.** Chu et al. (2021) evaluate 250 m to 3000 m\n",
    "> (`fluxfootprints.TARGET_RADII`). US-CRT is a short-canopy cropland site with a\n",
    "> 2.5 m tower, so its footprint is only a couple of hundred metres across; a\n",
    "> 1 km domain resolves it well and a 3 km disc would tell us nothing the 1 km\n",
    "> one does not. The radii above are chosen to bracket *this* footprint. Pass\n",
    "> `radii=TARGET_RADII` to reproduce the paper's series.\n",
    "\n",
    "## 1. Build a monthly day/night climatology\n",
    "\n",
    "Chu et al. aggregate every half-hourly footprint of a calendar month into a\n",
    "**daytime** and a **nighttime** climatology, truncate each at the contour\n",
    "enclosing 80 % of the source weight, and rescale the retained cells to sum to\n",
    "one. That truncated climatology is the unit of analysis for everything\n",
    "downstream.\n",
    "\n",
    "First the tower record. `load_config` reads the site's `.ini`, `load_amf_df`\n",
    "reads the AmeriFlux BASE file, and the columns are renamed to the solver's\n",
    "names."
   ]
  },
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   "id": "0763052f",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "US-CRT: 41.6285 N, -83.3471 E\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5158 half-hours from 2011-05-02 20:00:00 to 2011-10-31 17:30:00\n"
     ]
    },
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wind_dir</th>\n",
       "      <th>umean</th>\n",
       "      <th>ustar</th>\n",
       "      <th>ol</th>\n",
       "      <th>sigmav</th>\n",
       "      <th>zm</th>\n",
       "      <th>z0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMESTAMP_START</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-05-02 20:00:00</th>\n",
       "      <td>267.2680</td>\n",
       "      <td>2.18254</td>\n",
       "      <td>0.16236</td>\n",
       "      <td>27.346434</td>\n",
       "      <td>0.48027</td>\n",
       "      <td>2.232</td>\n",
       "      <td>0.0492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-03 05:00:00</th>\n",
       "      <td>37.9146</td>\n",
       "      <td>2.49536</td>\n",
       "      <td>0.15085</td>\n",
       "      <td>58.736718</td>\n",
       "      <td>0.66084</td>\n",
       "      <td>2.232</td>\n",
       "      <td>0.0492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-03 05:30:00</th>\n",
       "      <td>59.4592</td>\n",
       "      <td>4.49470</td>\n",
       "      <td>0.27739</td>\n",
       "      <td>686.206897</td>\n",
       "      <td>0.73097</td>\n",
       "      <td>2.232</td>\n",
       "      <td>0.0492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-03 06:00:00</th>\n",
       "      <td>64.3045</td>\n",
       "      <td>3.98159</td>\n",
       "      <td>0.26283</td>\n",
       "      <td>401.209677</td>\n",
       "      <td>0.83509</td>\n",
       "      <td>2.232</td>\n",
       "      <td>0.0492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-03 06:30:00</th>\n",
       "      <td>56.1142</td>\n",
       "      <td>3.40681</td>\n",
       "      <td>0.22406</td>\n",
       "      <td>354.092527</td>\n",
       "      <td>0.86838</td>\n",
       "      <td>2.232</td>\n",
       "      <td>0.0492</td>\n",
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      "text/plain": [
       "                     wind_dir    umean    ustar          ol   sigmav     zm      z0\n",
       "TIMESTAMP_START                                                                    \n",
       "2011-05-02 20:00:00  267.2680  2.18254  0.16236   27.346434  0.48027  2.232  0.0492\n",
       "2011-05-03 05:00:00   37.9146  2.49536  0.15085   58.736718  0.66084  2.232  0.0492\n",
       "2011-05-03 05:30:00   59.4592  4.49470  0.27739  686.206897  0.73097  2.232  0.0492\n",
       "2011-05-03 06:00:00   64.3045  3.98159  0.26283  401.209677  0.83509  2.232  0.0492\n",
       "2011-05-03 06:30:00   56.1142  3.40681  0.22406  354.092527  0.86838  2.232  0.0492"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config = load_config(DATA / \"US-CRT_config.ini\")\n",
    "lat = float(config[\"station_latitude\"])\n",
    "lon = float(config[\"station_longitude\"])\n",
    "print(f\"{SITE}: {lat:.4f} N, {lon:.4f} E\")\n",
    "\n",
    "frame = load_amf_df(DATA / \"AMF_US-CRT_BASE_HH_3-5.csv\", config).rename(\n",
    "    columns={\n",
    "        \"WD\": \"wind_dir\",\n",
    "        \"WS\": \"umean\",\n",
    "        \"USTAR\": \"ustar\",\n",
    "        \"MO_LENGTH\": \"ol\",\n",
    "        \"V_SIGMA\": \"sigmav\",\n",
    "    }\n",
    ")\n",
    "\n",
    "season = frame.loc[START:END, [\"wind_dir\", \"umean\", \"ustar\", \"ol\", \"sigmav\"]].dropna()\n",
    "# Kljun et al. (2015) do not apply in very weak turbulence; the paper filters\n",
    "# those half-hours out before aggregating (its Table S2).\n",
    "season = season[season[\"ustar\"] >= 0.15]\n",
    "season = compute_aerodynamic_params(\n",
    "    season, inst_height=2.5, crop_height=0.4, veg_type=\"alfalfa\"\n",
    ")\n",
    "print(f\"{len(season)} half-hours from {season.index.min()} to {season.index.max()}\")\n",
    "season.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e613c26f",
   "metadata": {},
   "source": [
    "`build_climatology` runs the Kljun et al. (2015) parameterisation over every\n",
    "half-hour and keeps the per-timestep footprints, which is what the monthly\n",
    "aggregation needs. This takes about a minute at 10 m resolution."
   ]
  },
  {
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   "execution_count": 3,
   "id": "351c7619",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frozen({'x': 201, 'y': 201})\n",
      "source weight inside the domain: 94.3%\n"
     ]
    }
   ],
   "source": [
    "model = build_climatology(\n",
    "    season,\n",
    "    model_type=\"ffp\",\n",
    "    ustar=\"ustar\",\n",
    "    ol=\"ol\",\n",
    "    umean=\"umean\",\n",
    "    sigmav=\"sigmav\",\n",
    "    wind_dir=\"wind_dir\",\n",
    "    zm=\"zm\",\n",
    "    z0=\"z0\",\n",
    "    h=2000.0,\n",
    "    dx=STEP,\n",
    "    dy=STEP,\n",
    "    domain=(-HALF, HALF, -HALF, HALF),\n",
    "    verbosity=0,\n",
    ")\n",
    "\n",
    "fclim = model.get_footprint_climatology()\n",
    "captured = float(fclim.sum()) * STEP * STEP\n",
    "print(fclim.sizes)\n",
    "print(f\"source weight inside the domain: {captured:.1%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b15c684",
   "metadata": {},
   "source": [
    "The captured fraction matters here more than usual: the truncation fraction is\n",
    "a fraction of the **full** footprint, not of the captured mass, so a domain\n",
    "holding much less than 80 % of the weight cannot produce a meaningful 80 %\n",
    "contour at all. At 94 % we have room to spare. Note that this is set by\n",
    "*resolution* as much as by extent — the footprint peaks within tens of metres\n",
    "of a 2.5 m tower, so a coarse grid loses the peak no matter how wide the\n",
    "domain.\n",
    "\n",
    "Now the aggregation. Day and night are separated at\n",
    "$SW_{IN,POT} > 0$; `monthly_climatologies` prefers a precomputed `SW_IN_POT`\n",
    "column when the record carries one and falls back to solar geometry otherwise,\n",
    "which is why it wants the tower position and the time zone."
   ]
  },
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       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-obj-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-group-box-contents > label {\n",
       "  color: var(--xr-font-color2);\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "  margin-block-start: 0;\n",
       "  margin-block-end: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item > input,\n",
       ".xr-group-box-contents > input,\n",
       ".xr-array-wrap > input {\n",
       "  display: block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item > input + label,\n",
       ".xr-var-item > input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item > input:enabled + label,\n",
       ".xr-var-item > input:enabled + label,\n",
       ".xr-array-wrap > input:enabled + label,\n",
       ".xr-group-box-contents > input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item > input:focus-visible + label,\n",
       ".xr-var-item > input:focus-visible + label,\n",
       ".xr-array-wrap > input:focus-visible + label,\n",
       ".xr-group-box-contents > input:focus-visible + label {\n",
       "  outline: auto;\n",
       "}\n",
       "\n",
       ".xr-section-item > input:enabled + label:hover,\n",
       ".xr-var-item > input:enabled + label:hover,\n",
       ".xr-array-wrap > input:enabled + label:hover,\n",
       ".xr-group-box-contents > input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "  white-space: nowrap;\n",
       "}\n",
       "\n",
       ".xr-section-summary > em {\n",
       "  font-weight: normal;\n",
       "}\n",
       "\n",
       ".xr-span-grid {\n",
       "  grid-column-end: -1;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.3em;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.6em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details,\n",
       ".xr-group-box-contents > label {\n",
       "  padding-top: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  grid-column: 1 / -1;\n",
       "  margin-top: 4px;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in ~ .xr-section-details {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-children {\n",
       "  display: inline-grid;\n",
       "  grid-template-columns: 100%;\n",
       "  grid-column: 1 / -1;\n",
       "  padding-top: 4px;\n",
       "}\n",
       "\n",
       ".xr-group-box {\n",
       "  display: inline-grid;\n",
       "  grid-template-columns: 0px 30px auto;\n",
       "}\n",
       "\n",
       ".xr-group-box-vline {\n",
       "  grid-column-start: 1;\n",
       "  border-right: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "  width: 0px;\n",
       "}\n",
       "\n",
       ".xr-group-box-hline {\n",
       "  grid-column-start: 2;\n",
       "  grid-row-start: 1;\n",
       "  height: 1em;\n",
       "  width: 26px;\n",
       "  border-bottom: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-group-box-contents {\n",
       "  grid-column-start: 3;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > label::before {\n",
       "  content: \"📂\";\n",
       "  padding-right: 0.3em;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label::before {\n",
       "  content: \"📁\";\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label {\n",
       "  padding-bottom: 0px;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-group-box-ellipsis {\n",
       "  font-size: 1.4em;\n",
       "  font-weight: 900;\n",
       "  color: var(--xr-font-color2);\n",
       "  letter-spacing: 0.15em;\n",
       "  cursor: default;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  border-color: var(--xr-background-color-row-odd);\n",
       "  margin-bottom: 0;\n",
       "  padding-top: 2px;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "  border-color: var(--xr-background-color-row-even);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 4MB\n",
       "Dimensions:                (month: 6, period: 2, x: 201, y: 201)\n",
       "Coordinates:\n",
       "  * month                  (month) datetime64[ns] 48B 2011-05-01 ... 2011-10-01\n",
       "  * period                 (period) &lt;U9 72B &#x27;daytime&#x27; &#x27;nighttime&#x27;\n",
       "  * x                      (x) float64 2kB -1e+03 -990.0 -980.0 ... 990.0 1e+03\n",
       "  * y                      (y) float64 2kB -1e+03 -990.0 -980.0 ... 990.0 1e+03\n",
       "Data variables:\n",
       "    footprint_climatology  (month, period, x, y) float64 4MB 0.0 0.0 ... 0.0 0.0\n",
       "    n_times                (month, period) int64 96B 591 214 727 ... 347 509 297\n",
       "    contour_level          (month, period) float64 96B 4.949e-06 ... 4.877e-06\n",
       "    contour_n_cells        (month, period) int64 96B 154 130 158 ... 157 214 160\n",
       "Attributes:\n",
       "    partition:         month+daynight\n",
       "    contour_fraction:  0.8\n",
       "    min_times:         1\n",
       "    n_timesteps:       5158\n",
       "    description:       Monthly footprint climatologies truncated at the 80% s...\n",
       "    reference:         Chu, H., et al. (2021). Agric. For. Meteorol., 301-302...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6afc732c-ede2-440e-b800-5ad21adbb337' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-6afc732c-ede2-440e-b800-5ad21adbb337' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 6</li><li><span class='xr-has-index'>period</span>: 2</li><li><span class='xr-has-index'>x</span>: 201</li><li><span class='xr-has-index'>y</span>: 201</li></ul></div></li><li class='xr-section-item'><input id='section-6cd4704d-dc6f-46b8-a7ec-3bf752959bcc' class='xr-section-summary-in' type='checkbox' checked /><label for='section-6cd4704d-dc6f-46b8-a7ec-3bf752959bcc' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2011-05-01 ... 2011-10-01</div><input id='attrs-7078d4e0-9356-44cb-853a-79c0264b271a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7078d4e0-9356-44cb-853a-79c0264b271a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb88629a-3a90-4ad4-909c-001072896028' class='xr-var-data-in' type='checkbox'><label for='data-fb88629a-3a90-4ad4-909c-001072896028' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2011-05-01T00:00:00.000000000&#x27;, &#x27;2011-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-07-01T00:00:00.000000000&#x27;, &#x27;2011-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-09-01T00:00:00.000000000&#x27;, &#x27;2011-10-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>period</span></div><div class='xr-var-dims'>(period)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;daytime&#x27; &#x27;nighttime&#x27;</div><input id='attrs-09bd5e52-761b-4569-954c-a8e110d6ff46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09bd5e52-761b-4569-954c-a8e110d6ff46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73147af0-1fae-4883-8925-b0276638a51b' class='xr-var-data-in' type='checkbox'><label for='data-73147af0-1fae-4883-8925-b0276638a51b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;daytime&#x27;, &#x27;nighttime&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1e+03 -990.0 ... 990.0 1e+03</div><input id='attrs-de006e06-f112-4a9f-9e4a-874d1ae43b58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de006e06-f112-4a9f-9e4a-874d1ae43b58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a40939c-4469-4cd4-8fb8-a81cb543e067' class='xr-var-data-in' type='checkbox'><label for='data-6a40939c-4469-4cd4-8fb8-a81cb543e067' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1000.,  -990.,  -980., ...,   980.,   990.,  1000.], shape=(201,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1e+03 -990.0 ... 990.0 1e+03</div><input id='attrs-79aee169-7602-4d19-a70c-ab99a50bad3f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79aee169-7602-4d19-a70c-ab99a50bad3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ee291e3-0dc4-4c2d-82f3-b71dc465c9eb' class='xr-var-data-in' type='checkbox'><label for='data-3ee291e3-0dc4-4c2d-82f3-b71dc465c9eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1000.,  -990.,  -980., ...,   980.,   990.,  1000.], shape=(201,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-241c926e-6c04-4506-88e6-bf4d45f3949b' class='xr-section-summary-in' type='checkbox' checked /><label for='section-241c926e-6c04-4506-88e6-bf4d45f3949b' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>footprint_climatology</span></div><div class='xr-var-dims'>(month, period, x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-f87915b9-04d3-4c40-a21f-0b1d17c54ca0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f87915b9-04d3-4c40-a21f-0b1d17c54ca0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbe40cf5-16c7-4a66-a4f6-3b9b1f4e5a4d' class='xr-var-data-in' type='checkbox'><label for='data-dbe40cf5-16c7-4a66-a4f6-3b9b1f4e5a4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>renormalised footprint climatology</dd><dt><span>contour_fraction :</span></dt><dd>0.8</dd></dl></div><div class='xr-var-data'><pre>array([[[[0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.]]],\n",
       "\n",
       "\n",
       "       [[[0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "...\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.]]],\n",
       "\n",
       "\n",
       "       [[[0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.]]]], shape=(6, 2, 201, 201))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_times</span></div><div class='xr-var-dims'>(month, period)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>591 214 727 162 ... 608 347 509 297</div><input id='attrs-cb74f544-8f26-40ba-8e1a-650c8623b7d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb74f544-8f26-40ba-8e1a-650c8623b7d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-935abe84-ecd1-4b1c-ba7d-5ba53b10322c' class='xr-var-data-in' type='checkbox'><label for='data-935abe84-ecd1-4b1c-ba7d-5ba53b10322c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>contributing timesteps</dd></dl></div><div class='xr-var-data'><pre>array([[591, 214],\n",
       "       [727, 162],\n",
       "       [713, 157],\n",
       "       [646, 187],\n",
       "       [608, 347],\n",
       "       [509, 297]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>contour_level</span></div><div class='xr-var-dims'>(month, period)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.949e-06 7.234e-06 ... 4.877e-06</div><input id='attrs-94821724-5990-4d7f-8f1c-d08335c8240c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94821724-5990-4d7f-8f1c-d08335c8240c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43fc4fb5-f17d-4b54-b8c6-4645d1c5802a' class='xr-var-data-in' type='checkbox'><label for='data-43fc4fb5-f17d-4b54-b8c6-4645d1c5802a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m-2</dd><dt><span>long_name :</span></dt><dd>source weight at the contour</dd></dl></div><div class='xr-var-data'><pre>array([[4.94931543e-06, 7.23372297e-06],\n",
       "       [4.81165151e-06, 6.63963404e-06],\n",
       "       [4.27392540e-06, 5.58384255e-06],\n",
       "       [4.03031790e-06, 5.72514281e-06],\n",
       "       [2.96156894e-06, 5.28603576e-06],\n",
       "       [2.99356499e-06, 4.87718694e-06]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>contour_n_cells</span></div><div class='xr-var-dims'>(month, period)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>154 130 158 128 ... 218 157 214 160</div><input id='attrs-1a427841-d7b1-4ef1-bbc3-31b894f1b166' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a427841-d7b1-4ef1-bbc3-31b894f1b166' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc13f2f7-b41a-4771-9c8c-33fedb7547d4' class='xr-var-data-in' type='checkbox'><label for='data-bc13f2f7-b41a-4771-9c8c-33fedb7547d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>cells inside the contour</dd></dl></div><div class='xr-var-data'><pre>array([[154, 130],\n",
       "       [158, 128],\n",
       "       [172, 151],\n",
       "       [180, 150],\n",
       "       [218, 157],\n",
       "       [214, 160]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c5abb471-afb1-4793-aa8a-02e9d745f79d' class='xr-section-summary-in' type='checkbox' checked /><label for='section-c5abb471-afb1-4793-aa8a-02e9d745f79d' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>partition :</span></dt><dd>month+daynight</dd><dt><span>contour_fraction :</span></dt><dd>0.8</dd><dt><span>min_times :</span></dt><dd>1</dd><dt><span>n_timesteps :</span></dt><dd>5158</dd><dt><span>description :</span></dt><dd>Monthly footprint climatologies truncated at the 80% source-weight contour and renormalised to unit sum, after Chu et al. (2021), Sect. 2.2.</dd><dt><span>reference :</span></dt><dd>Chu, H., et al. (2021). Agric. For. Meteorol., 301-302, 108350.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 4MB\n",
       "Dimensions:                (month: 6, period: 2, x: 201, y: 201)\n",
       "Coordinates:\n",
       "  * month                  (month) datetime64[ns] 48B 2011-05-01 ... 2011-10-01\n",
       "  * period                 (period) <U9 72B 'daytime' 'nighttime'\n",
       "  * x                      (x) float64 2kB -1e+03 -990.0 -980.0 ... 990.0 1e+03\n",
       "  * y                      (y) float64 2kB -1e+03 -990.0 -980.0 ... 990.0 1e+03\n",
       "Data variables:\n",
       "    footprint_climatology  (month, period, x, y) float64 4MB 0.0 0.0 ... 0.0 0.0\n",
       "    n_times                (month, period) int64 96B 591 214 727 ... 347 509 297\n",
       "    contour_level          (month, period) float64 96B 4.949e-06 ... 4.877e-06\n",
       "    contour_n_cells        (month, period) int64 96B 154 130 158 ... 157 214 160\n",
       "Attributes:\n",
       "    partition:         month+daynight\n",
       "    contour_fraction:  0.8\n",
       "    min_times:         1\n",
       "    n_timesteps:       5158\n",
       "    description:       Monthly footprint climatologies truncated at the 80% s...\n",
       "    reference:         Chu, H., et al. (2021). Agric. For. Meteorol., 301-302..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clim = monthly_climatologies(\n",
    "    model,\n",
    "    partition=\"month+daynight\",\n",
    "    latitude=lat,\n",
    "    longitude=lon,\n",
    "    tz=TZ,\n",
    ")\n",
    "clim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cc2bf1e5",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2026-08-30T16:14:56.865385Z",
     "shell.execute_reply": "2026-08-30T16:14:56.864678Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>period</th>\n",
       "      <th>daytime</th>\n",
       "      <th>nighttime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-05-01</th>\n",
       "      <td>591</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-01</th>\n",
       "      <td>727</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>713</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01</th>\n",
       "      <td>646</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-01</th>\n",
       "      <td>608</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-01</th>\n",
       "      <td>509</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "period      daytime  nighttime\n",
       "month                         \n",
       "2011-05-01      591        214\n",
       "2011-06-01      727        162\n",
       "2011-07-01      713        157\n",
       "2011-08-01      646        187\n",
       "2011-09-01      608        347\n",
       "2011-10-01      509        297"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Half-hours contributing to each monthly climatology\n",
    "clim.n_times.to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34884f19",
   "metadata": {},
   "source": [
    "Six months, split day and night: twelve climatologies, each already truncated\n",
    "at its own 80 % contour and renormalised. `contour_n_cells` shows how compact\n",
    "they are — 130 to 220 cells of 10 m, so a source area of one to two hectares."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc09d7a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-30T16:14:56.868249Z",
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     "shell.execute_reply": "2026-08-30T16:14:56.874559Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>period</th>\n",
       "      <th>daytime</th>\n",
       "      <th>nighttime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-05-01</th>\n",
       "      <td>154</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-01</th>\n",
       "      <td>158</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>172</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01</th>\n",
       "      <td>180</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-01</th>\n",
       "      <td>218</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-01</th>\n",
       "      <td>214</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "period      daytime  nighttime\n",
       "month                         \n",
       "2011-05-01      154        130\n",
       "2011-06-01      158        128\n",
       "2011-07-01      172        151\n",
       "2011-08-01      180        150\n",
       "2011-09-01      218        157\n",
       "2011-10-01      214        160"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clim.contour_n_cells.to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06bc5c8f",
   "metadata": {},
   "source": [
    "## 2. Load a land-cover raster and an EVI stack\n",
    "\n",
    "The evaluations of Sect. 2.4 compare the climatology against two external\n",
    "products: a categorical land-cover map, and a time series of a continuous\n",
    "vegetation index.\n",
    "\n",
    "**In production**, `fluxfootprints.representativeness_data` fetches both\n",
    "straight from Google Earth Engine onto a tower-centred grid — NLCD for the\n",
    "categorical field, and Landsat 5/8 Collection 2 EVI (the paper's Eq. 4,\n",
    "cloud-screened to < 1 % obscured) for the continuous one:\n",
    "\n",
    "```python\n",
    "from fluxfootprints import fetch_landsat_evi, fetch_nlcd, footprint_grid_geometry\n",
    "\n",
    "geom = footprint_grid_geometry(model.x, model.y, lat, lon)\n",
    "\n",
    "nlcd = fetch_nlcd(geom.x_origin, geom.y_origin, geom.crs, year=2016)\n",
    "scenes = fetch_landsat_evi(\n",
    "    geom.x_origin, geom.y_origin, geom.crs,\n",
    "    start=\"2011-05-01\", end=\"2011-11-01\",\n",
    ")\n",
    "```\n",
    "\n",
    "That needs the `gee` extra (`pip install 'fluxfootprints[gee]'`) and an\n",
    "authenticated Earth Engine project, so this notebook stays offline and\n",
    "**synthesises two stand-in products instead**: a 30 m land-cover map and six\n",
    "monthly EVI scenes, written to GeoTIFF exactly as a downloaded product would\n",
    "be. Everything from here on is the real code path.\n",
    "\n",
    "The landscape is a plausible one for north-west Ohio: the tower stands in a\n",
    "soybean field, with a wooded strip to the south-west, pasture to the\n",
    "north-east, a developed edge to the south-east, and open water to the north."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b342fc12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-30T16:14:56.877331Z",
     "iopub.status.busy": "2026-08-30T16:14:56.877082Z",
     "iopub.status.idle": "2026-08-30T16:14:57.110493Z",
     "shell.execute_reply": "2026-08-30T16:14:57.109719Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cultivated crops         0.251\n",
       "Deciduous forest         0.226\n",
       "Hay/pasture              0.212\n",
       "Open water               0.157\n",
       "Developed, open space    0.154\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EPSG = 32617  # WGS 84 / UTM zone 17N, the local zone for US-CRT\n",
    "east, north = Transformer.from_crs(\n",
    "    \"EPSG:4326\", f\"EPSG:{EPSG}\", always_xy=True\n",
    ").transform(lon, lat)\n",
    "\n",
    "RES, N = 30.0, 141                       # 30 m cells, 4.2 km across\n",
    "half_px = (N // 2) * RES\n",
    "transform = from_origin(east - half_px - RES / 2, north + half_px + RES / 2, RES, RES)\n",
    "\n",
    "eastings = (np.arange(N) - N // 2) * RES\n",
    "northings = (N // 2 - np.arange(N)) * RES\n",
    "XX, YY = np.meshgrid(eastings, northings)\n",
    "distance = np.hypot(XX, YY)\n",
    "bearing = np.degrees(np.arctan2(XX, YY)) % 360.0\n",
    "\n",
    "\n",
    "def write_geotiff(path, array, dtype, nodata):\n",
    "    # Write one band to a georeferenced GeoTIFF.\n",
    "    with rasterio.open(\n",
    "        path, \"w\", driver=\"GTiff\", height=N, width=N, count=1,\n",
    "        dtype=dtype, crs=f\"EPSG:{EPSG}\", transform=transform, nodata=nodata,\n",
    "    ) as dst:\n",
    "        dst.write(array.astype(dtype), 1)\n",
    "\n",
    "\n",
    "CROPS, FOREST, PASTURE, DEVELOPED, WATER = 82, 41, 81, 21, 11\n",
    "CLASS_NAMES = {\n",
    "    82: \"Cultivated crops\",\n",
    "    41: \"Deciduous forest\",\n",
    "    81: \"Hay/pasture\",\n",
    "    21: \"Developed, open space\",\n",
    "    11: \"Open water\",\n",
    "}\n",
    "\n",
    "land = np.full((N, N), CROPS, dtype=\"int16\")\n",
    "land[(distance > 220) & (bearing >= 185) & (bearing < 265)] = FOREST\n",
    "land[(distance > 320) & (bearing >= 20) & (bearing < 95)] = PASTURE\n",
    "land[(distance > 700) & (bearing >= 95) & (bearing < 150)] = DEVELOPED\n",
    "land[(distance > 1300) & ((bearing >= 300) | (bearing < 20))] = WATER\n",
    "\n",
    "landcover_path = WORK / \"US-CRT_landcover_2011.tif\"\n",
    "write_geotiff(landcover_path, land, \"int16\", 0)\n",
    "\n",
    "pd.Series(land.ravel()).map(CLASS_NAMES).value_counts(normalize=True).round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35517dbb",
   "metadata": {},
   "source": [
    "The EVI stack gives each class its own seasonal curve. The soybean field swings\n",
    "from bare soil in May to a closed canopy in August and back; the forest and\n",
    "pasture are flatter, and the developed and water classes barely move. That\n",
    "contrast is the whole point: it is what makes a footprint-weighted EVI differ\n",
    "from a disc-averaged one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4782c97",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-30T16:14:57.112725Z",
     "iopub.status.busy": "2026-08-30T16:14:57.112487Z",
     "iopub.status.idle": "2026-08-30T16:14:57.146104Z",
     "shell.execute_reply": "2026-08-30T16:14:57.145064Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('2011-05-15 00:00:00'),\n",
       " Timestamp('2011-06-15 00:00:00'),\n",
       " Timestamp('2011-07-15 00:00:00'),\n",
       " Timestamp('2011-08-15 00:00:00'),\n",
       " Timestamp('2011-09-15 00:00:00'),\n",
       " Timestamp('2011-10-15 00:00:00')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PHENOLOGY = {              # EVI by class and month\n",
    "    CROPS:     {5: 0.16, 6: 0.34, 7: 0.71, 8: 0.78, 9: 0.44, 10: 0.19},\n",
    "    FOREST:    {5: 0.42, 6: 0.55, 7: 0.58, 8: 0.57, 9: 0.48, 10: 0.32},\n",
    "    PASTURE:   {5: 0.38, 6: 0.45, 7: 0.41, 8: 0.39, 9: 0.40, 10: 0.33},\n",
    "    DEVELOPED: {5: 0.15, 6: 0.19, 7: 0.20, 8: 0.20, 9: 0.18, 10: 0.14},\n",
    "    WATER:     {5: 0.03, 6: 0.03, 7: 0.04, 8: 0.04, 9: 0.03, 10: 0.02},\n",
    "}\n",
    "\n",
    "rng = np.random.default_rng(11)\n",
    "evi_paths = {}\n",
    "for month in range(5, 11):\n",
    "    stamp = pd.Timestamp(f\"2011-{month:02d}-15\")   # one clear scene per month\n",
    "    field = np.zeros((N, N))\n",
    "    for code_, curve in PHENOLOGY.items():\n",
    "        field = np.where(land == code_, curve[month], field)\n",
    "    field = field + 0.03 * rng.standard_normal((N, N))   # within-class texture\n",
    "    path = WORK / f\"US-CRT_evi_{stamp:%Y%m%d}.tif\"\n",
    "    write_geotiff(path, field, \"float32\", np.nan)\n",
    "    evi_paths[stamp] = path\n",
    "\n",
    "list(evi_paths)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "928bafa1",
   "metadata": {},
   "source": [
    "`sample_raster_on_grid` georeferences the tower-centred footprint grid and warps\n",
    "each raster onto it, so the products line up with the climatology cell for cell.\n",
    "Land-cover codes must go through **nearest neighbour** — bilinear resampling of\n",
    "class codes invents classes that are not in the legend — which is what\n",
    "`categorical=True` selects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c62ad9e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-30T16:14:57.148616Z",
     "iopub.status.busy": "2026-08-30T16:14:57.148402Z",
     "iopub.status.idle": "2026-08-30T16:14:57.926261Z",
     "shell.execute_reply": "2026-08-30T16:14:57.925221Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('x', 'y') (201, 201)\n",
      "classes on the grid: [np.float64(11.0), np.float64(21.0), np.float64(41.0), np.float64(81.0), np.float64(82.0)]\n"
     ]
    }
   ],
   "source": [
    "landcover = sample_raster_on_grid(\n",
    "    landcover_path, model.x, model.y, lat, lon, categorical=True\n",
    ")\n",
    "scenes = {\n",
    "    stamp: sample_raster_on_grid(path, model.x, model.y, lat, lon)\n",
    "    for stamp, path in evi_paths.items()\n",
    "}\n",
    "\n",
    "print(landcover.dims, landcover.shape)\n",
    "print(\"classes on the grid:\", sorted(np.unique(landcover.values[np.isfinite(landcover.values)])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a23ef6f",
   "metadata": {},
   "source": [
    "Before running the analysis, it is worth seeing what the tower actually sees\n",
    "against what the target discs contain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e4ba4f2f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-30T16:14:57.928636Z",
     "iopub.status.busy": "2026-08-30T16:14:57.928419Z",
     "iopub.status.idle": "2026-08-30T16:14:58.653676Z",
     "shell.execute_reply": "2026-08-30T16:14:58.652590Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "july_day = clim.footprint_climatology.sel(period=\"daytime\", month=\"2011-07-01\")\n",
    "\n",
    "codes = sorted(CLASS_NAMES)\n",
    "cmap = ListedColormap([\"#3b6ea5\", \"#c8b0a0\", \"#3f7f4f\", \"#c9d48a\", \"#d9b45b\"])\n",
    "norm = BoundaryNorm([c - 0.5 for c in codes] + [codes[-1] + 0.5], cmap.N)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 5), sharex=True, sharey=True)\n",
    "\n",
    "axes[0].pcolormesh(model.x, model.y, landcover.T, cmap=cmap, norm=norm, shading=\"auto\")\n",
    "axes[0].set_title(\"Land cover\")\n",
    "\n",
    "weights = july_day.where(july_day > 0)\n",
    "mesh = axes[1].pcolormesh(model.x, model.y, weights.T, cmap=\"magma_r\", shading=\"auto\")\n",
    "fig.colorbar(mesh, ax=axes[1], label=\"source weight [-]\", shrink=0.85)\n",
    "axes[1].set_title(\"July 2011 daytime climatology, truncated at 80 %\")\n",
    "\n",
    "for ax in axes:\n",
    "    for radius in RADII:\n",
    "        ax.add_patch(plt.Circle((0, 0), radius, fill=False, ec=\"0.15\", lw=0.9, ls=\"--\"))\n",
    "        ax.annotate(f\"{radius:.0f} m\", (0, radius), textcoords=\"offset points\",\n",
    "                    xytext=(3, 3), fontsize=8, color=\"0.15\")\n",
    "    ax.plot(0, 0, marker=\"^\", ms=9, color=\"k\")\n",
    "    ax.set_aspect(\"equal\")\n",
    "    ax.set_xlim(-HALF, HALF)\n",
    "    ax.set_ylim(-HALF, HALF)\n",
    "    ax.set_xlabel(\"east of tower [m]\")\n",
    "axes[0].set_ylabel(\"north of tower [m]\")\n",
    "\n",
    "handles = [plt.Rectangle((0, 0), 1, 1, fc=cmap(norm(c))) for c in codes]\n",
    "axes[0].legend(handles, [CLASS_NAMES[c] for c in codes], loc=\"lower left\", fontsize=7.5)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dd98be5",
   "metadata": {},
   "source": [
    "That is the whole problem in one picture. The source area is a compact lobe a\n",
    "couple of hundred metres across, entirely inside the soybean field; the 500 m\n",
    "and 1000 m discs take in forest, pasture, developed ground, and water that the\n",
    "tower never sees.\n",
    "\n",
    "## 3. Run `assess_representativeness`\n",
    "\n",
    "One call drives everything: the geometry metrics of Eqs. 1–3, the categorical\n",
    "comparison and the continuous one at every radius, and the three-level\n",
    "classification of each. Passing the already-aggregated `clim` Dataset rather\n",
    "than the model reuses the aggregation instead of repeating it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ffcb11a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-30T16:14:58.655703Z",
     "iopub.status.busy": "2026-08-30T16:14:58.655469Z",
     "iopub.status.idle": "2026-08-30T16:14:58.959723Z",
     "shell.execute_reply": "2026-08-30T16:14:58.958693Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(126, 34)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "scope      kind       \n",
       "period     categorical    48\n",
       "           continuous     48\n",
       "           climatology    12\n",
       "site       categorical     8\n",
       "           continuous      8\n",
       "site_year  climatology     2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = assess_representativeness(\n",
    "    clim,\n",
    "    station_lat=lat,\n",
    "    station_lon=lon,\n",
    "    site_id=SITE,\n",
    "    landcover=landcover,\n",
    "    continuous=scenes,\n",
    "    radii=RADII,\n",
    "    tz=TZ,\n",
    ")\n",
    "print(results.shape)\n",
    "results.reset_index()[[\"scope\", \"kind\"]].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e4a22d",
   "metadata": {},
   "source": [
    "The frame is tidy and sparse: rows of three scopes and three kinds, indexed by\n",
    "`(site, year, month, period, radius, variable)`, with each row carrying only\n",
    "the columns its own analysis produces. Select a slice before reading values off\n",
    "it.\n",
    "\n",
    "### Climatology metrics — Eqs. 1–3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "48e3430a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-30T16:14:58.961817Z",
     "iopub.status.busy": "2026-08-30T16:14:58.961660Z",
     "iopub.status.idle": "2026-08-30T16:14:58.983721Z",
     "shell.execute_reply": "2026-08-30T16:14:58.982192Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period</th>\n",
       "      <th>fetch</th>\n",
       "      <th>area</th>\n",
       "      <th>symmetry</th>\n",
       "      <th>seasonal_overlap</th>\n",
       "      <th>daynight_overlap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>daytime</td>\n",
       "      <td>98.873</td>\n",
       "      <td>18266.667</td>\n",
       "      <td>0.597</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nighttime</td>\n",
       "      <td>102.545</td>\n",
       "      <td>14600.000</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.842</td>\n",
       "      <td>0.959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      period    fetch       area  symmetry  seasonal_overlap  daynight_overlap\n",
       "0    daytime   98.873  18266.667     0.597             0.918             0.959\n",
       "1  nighttime  102.545  14600.000     0.469             0.842             0.959"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.query(\"scope == 'site_year' and kind == 'climatology'\").reset_index()[\n",
    "    [\"period\", \"fetch\", \"area\", \"symmetry\", \"seasonal_overlap\", \"daynight_overlap\"]\n",
    "].round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af64951",
   "metadata": {},
   "source": [
    "Reading these:\n",
    "\n",
    "- **X80 ≈ 100 m, A80 ≈ 1.5–1.8 ha.** A short canopy on a low tower gives a\n",
    "  small source area. The nighttime footprint *reaches* farther than the\n",
    "  daytime one — as the paper finds for > 95 % of its site-years — but here it\n",
    "  encloses less area, so nights are the more elongated of the two.\n",
    "- **S80 ≈ 0.60 day, 0.47 night**, both comfortably above\n",
    "  `ASYMMETRY_THRESHOLD` (0.30). The wind rose is broad enough that the source\n",
    "  area stays roughly disc-like rather than collapsing into a single lobe,\n",
    "  though the lower nighttime value reflects exactly the elongation above.\n",
    "- **O80,season ≈ 0.92 day / 0.84 night** (Eq. 2). The daytime source area is\n",
    "  stable through the season; at night it wanders more, which is the usual\n",
    "  signature of light, variable winds under a stable boundary layer.\n",
    "- **O80,daynight ≈ 0.96** (Eq. 3). Day and night sample nearly the same ground\n",
    "  here — this site has no drainage flow to send nights somewhere else.\n",
    "\n",
    "### Land-cover representativeness — Sect. 2.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "14d859c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-30T16:14:58.986388Z",
     "iopub.status.busy": "2026-08-30T16:14:58.986180Z",
     "iopub.status.idle": "2026-08-30T16:14:59.002577Z",
     "shell.execute_reply": "2026-08-30T16:14:59.001889Z"
    }
   },
   "outputs": [
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period</th>\n",
       "      <th>radius</th>\n",
       "      <th>dominant_class</th>\n",
       "      <th>value_footprint</th>\n",
       "      <th>value_target</th>\n",
       "      <th>chi2</th>\n",
       "      <th>p_value</th>\n",
       "      <th>level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>daytime</td>\n",
       "      <td>100.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>daytime</td>\n",
       "      <td>250.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.957</td>\n",
       "      <td>88.851</td>\n",
       "      <td>0.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>daytime</td>\n",
       "      <td>500.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.699</td>\n",
       "      <td>3375.424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>daytime</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>28581.048</td>\n",
       "      <td>0.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nighttime</td>\n",
       "      <td>100.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>nighttime</td>\n",
       "      <td>250.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.957</td>\n",
       "      <td>88.851</td>\n",
       "      <td>0.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nighttime</td>\n",
       "      <td>500.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.699</td>\n",
       "      <td>3375.424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>nighttime</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>28581.048</td>\n",
       "      <td>0.0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      period  radius dominant_class  value_footprint  value_target       chi2  p_value level\n",
       "0    daytime   100.0             82              1.0         1.000      0.000      1.0  high\n",
       "1    daytime   250.0             82              1.0         0.957     88.851      0.0   low\n",
       "2    daytime   500.0             82              1.0         0.699   3375.424      0.0   low\n",
       "3    daytime  1000.0             82              1.0         0.524  28581.048      0.0   low\n",
       "4  nighttime   100.0             82              1.0         1.000      0.000      1.0  high\n",
       "5  nighttime   250.0             82              1.0         0.957     88.851      0.0   low\n",
       "6  nighttime   500.0             82              1.0         0.699   3375.424      0.0   low\n",
       "7  nighttime  1000.0             82              1.0         0.524  28581.048      0.0   low"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical = results.query(\"scope == 'site' and kind == 'categorical'\").reset_index()\n",
    "categorical[\n",
    "    [\"period\", \"radius\", \"dominant_class\", \"value_footprint\", \"value_target\",\n",
    "     \"chi2\", \"p_value\", \"level\"]\n",
    "].round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3d82fa0",
   "metadata": {},
   "source": [
    "The dominant class is cultivated crops (NLCD 82) everywhere, and it holds\n",
    "**100 %** of the footprint weight at every radius — the footprint never leaves\n",
    "the field. What changes is the disc: 100 % crop at 100 m, 96 % at 250 m, 70 %\n",
    "at 500 m, and 52 % at 1000 m.\n",
    "\n",
    "The `level` column falls to `low` from 250 m outwards, and it is worth being\n",
    "precise about *why*. Both shares are still above 80 % at 250 m, so the\n",
    "percentage criteria are met; it is the chi-square test that fails. That test\n",
    "scales both compositions to pseudo-counts by the number of classified cells in\n",
    "the target area — and on a 10 m grid a 250 m disc holds nearly 2000 of them, so\n",
    "even a 4 % difference in composition is resolved as significant. **Read the\n",
    "p-value as a comparison against the paper's fixed 30 m Landsat grid, not as an\n",
    "absolute significance**; a finer grid makes the same test stricter. The\n",
    "`_composition_chi_square` docstring spells this out.\n",
    "\n",
    "### Continuous-field representativeness — Eqs. 5–7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "df2db431",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-30T16:14:59.004823Z",
     "iopub.status.busy": "2026-08-30T16:14:59.004586Z",
     "iopub.status.idle": "2026-08-30T16:14:59.026381Z",
     "shell.execute_reply": "2026-08-30T16:14:59.025637Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_footprint_evi</th>\n",
       "      <th>mean_target_evi</th>\n",
       "      <th>mean_bias</th>\n",
       "      <th>within_10pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100.0</th>\n",
       "      <td>0.439</td>\n",
       "      <td>0.437</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250.0</th>\n",
       "      <td>0.439</td>\n",
       "      <td>0.438</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500.0</th>\n",
       "      <td>0.439</td>\n",
       "      <td>0.440</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000.0</th>\n",
       "      <td>0.439</td>\n",
       "      <td>0.419</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>0.333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean_footprint_evi  mean_target_evi  mean_bias  within_10pct\n",
       "radius                                                              \n",
       "100.0                0.439            0.437      0.010           1.0\n",
       "250.0                0.439            0.438     -0.003           1.0\n",
       "500.0                0.439            0.440     -0.057           0.5\n",
       "1000.0               0.439            0.419     -0.023         0.333"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "periods = results.query(\"scope == 'period' and kind == 'continuous'\").reset_index()\n",
    "periods.groupby(\"radius\").agg(\n",
    "    mean_footprint_evi=(\"value_footprint\", \"mean\"),\n",
    "    mean_target_evi=(\"value_target\", \"mean\"),\n",
    "    mean_bias=(\"bias\", \"mean\"),\n",
    "    within_10pct=(\"within_threshold\", \"mean\"),\n",
    ").round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53ca32c5",
   "metadata": {},
   "source": [
    "`within_10pct` is the share of matched scenes whose sensor location bias\n",
    "(Eq. 6) sits inside `BIAS_THRESHOLD` (±10 %). It falls from **1.00** at 100 m\n",
    "and 250 m to **0.50** at 500 m and **0.33** at 1000 m: the wider the disc, the\n",
    "less often the tower's own view of it is within 10 % of the truth. This is the\n",
    "paper's Sect. 3.3 result, and it comes straight off these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "37cb73d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-30T16:14:59.028163Z",
     "iopub.status.busy": "2026-08-30T16:14:59.027944Z",
     "iopub.status.idle": "2026-08-30T16:14:59.046036Z",
     "shell.execute_reply": "2026-08-30T16:14:59.045330Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period</th>\n",
       "      <th>radius</th>\n",
       "      <th>slope</th>\n",
       "      <th>intercept</th>\n",
       "      <th>r_squared</th>\n",
       "      <th>p_value</th>\n",
       "      <th>n</th>\n",
       "      <th>sufficient</th>\n",
       "      <th>level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>daytime</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.014</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>daytime</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.989</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>daytime</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>daytime</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nighttime</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.017</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>nighttime</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nighttime</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.787</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>nighttime</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      period  radius  slope  intercept  r_squared  p_value  n  sufficient   level\n",
       "0    daytime   100.0  1.014     -0.007      1.000      0.0  6        True    high\n",
       "1    daytime   250.0  0.989      0.005      0.999      0.0  6        True    high\n",
       "2    daytime   500.0  0.785      0.096      0.995      0.0  6        True  medium\n",
       "3    daytime  1000.0  0.628      0.144      0.986      0.0  6        True  medium\n",
       "4  nighttime   100.0  1.017     -0.009      1.000      0.0  6        True    high\n",
       "5  nighttime   250.0  0.991      0.003      0.999      0.0  6        True    high\n",
       "6  nighttime   500.0  0.787      0.095      0.995      0.0  6        True  medium\n",
       "7  nighttime  1000.0  0.629      0.143      0.986      0.0  6        True  medium"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continuous = results.query(\"scope == 'site' and kind == 'continuous'\").reset_index()\n",
    "continuous[\n",
    "    [\"period\", \"radius\", \"slope\", \"intercept\", \"r_squared\", \"p_value\", \"n\",\n",
    "     \"sufficient\", \"level\"]\n",
    "].round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b717974f",
   "metadata": {},
   "source": [
    "The Eq. 7 model II slope $\\beta_1$ falls from **1.01** at 100 m to **0.63** at\n",
    "1000 m. A slope below one means the footprint-weighted EVI swings further than\n",
    "the disc average does — the tower tracks its own field's phenology, while the\n",
    "disc dilutes that swing with forest, pasture, and water that barely change\n",
    "through the season. The classification drops from `high` to `medium` as the\n",
    "disc widens, even though $R^2$ stays above 0.98 throughout: it is the *slope*\n",
    "leaving the 0.9–1.1 band that costs the site its `high`.\n",
    "\n",
    "## 4. The four figures\n",
    "\n",
    "`fluxfootprints.representativeness_plotting` draws the paper's four diagnostics.\n",
    "Each takes what the analysis functions already return and hands back\n",
    "`(fig, ax)` without showing or saving anything, so titles, layout, and output\n",
    "stay with the caller.\n",
    "\n",
    "### Fig. 1e — land-cover composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8d64eab3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-30T16:14:59.047836Z",
     "iopub.status.busy": "2026-08-30T16:14:59.047601Z",
     "iopub.status.idle": "2026-08-30T16:14:59.282762Z",
     "shell.execute_reply": "2026-08-30T16:14:59.281940Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "july_categorical = evaluate_landcover(july_day, landcover, radii=RADII)\n",
    "\n",
    "fig, ax = plot_landcover_composition(july_categorical, class_labels=CLASS_NAMES)\n",
    "ax.set_title(\"US-CRT, July 2011 daytime: footprint vs. target-area composition\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45f87b4f",
   "metadata": {},
   "source": [
    "The filled circle is the footprint-weighted share, the triangles the target-area\n",
    "shares on a dark-to-light radius ramp. Crops sit at 100 % under the footprint\n",
    "and trail leftwards as the disc widens; every other class trails the other way,\n",
    "from 0 % under the footprint out to its share of the 1 km disc.\n",
    "\n",
    "### Figs. 1f and 6 — footprint against target-area values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9d719690",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-30T16:14:59.285628Z",
     "iopub.status.busy": "2026-08-30T16:14:59.285314Z",
     "iopub.status.idle": "2026-08-30T16:14:59.481294Z",
     "shell.execute_reply": "2026-08-30T16:14:59.480524Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daytime_pairs = periods.query(\"period == 'daytime'\")\n",
    "\n",
    "fig, ax = plot_footprint_target_scatter(daytime_pairs, variable=\"EVI\")\n",
    "ax.set_title(\"US-CRT daytime: matched monthly EVI, with the Eq. 7 RMA fits\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "445fbf2f",
   "metadata": {},
   "source": [
    "Six matched scenes per radius, under the reduced major axis fit of Eq. 7 and the\n",
    "1:1 line. The 100 m series lies on 1:1; the wider discs pivot away from it,\n",
    "flattening exactly as their slopes in the table above say they should. The line\n",
    "is the RMA rather than ordinary least squares because both axes are spatial\n",
    "averages of one noisy raster and both carry error — an OLS slope would sit\n",
    "systematically shallower than the values the paper's Table 1 reports.\n",
    "\n",
    "### Fig. 7 — density of the sensor location bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "911b76c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-30T16:14:59.483843Z",
     "iopub.status.busy": "2026-08-30T16:14:59.483453Z",
     "iopub.status.idle": "2026-08-30T16:14:59.694198Z",
     "shell.execute_reply": "2026-08-30T16:14:59.693363Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_bias_density(daytime_pairs, threshold=BIAS_THRESHOLD)\n",
    "ax.set_title(\"US-CRT daytime: sensor location bias (Eq. 6) by target area\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c91d80c1",
   "metadata": {},
   "source": [
    "One Gaussian kernel density per radius over the per-scene biases. The peak is\n",
    "tall and narrow at 100 m and collapses into a broad, flat spread by 1000 m —\n",
    "the paper's Fig. 7 result, that the wider the disc, the further a given month's\n",
    "footprint-weighted value strays from it. The dashed guides are the ±10 % of\n",
    "`BIAS_THRESHOLD`; the published figure draws no guides, so they are opt-in.\n",
    "\n",
    "### Figs. 5 and 8 — the three-level index across target areas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ba875cd6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-30T16:14:59.696094Z",
     "iopub.status.busy": "2026-08-30T16:14:59.695847Z",
     "iopub.status.idle": "2026-08-30T16:14:59.849284Z",
     "shell.execute_reply": "2026-08-30T16:14:59.848398Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "site_levels = results.query(\"scope == 'site'\").reset_index()\n",
    "\n",
    "fig, ax = plot_level_bars(site_levels, level_column=\"level\")\n",
    "ax.set_ylabel(\"Percentage of evaluations (%)\")\n",
    "ax.set_title(\"US-CRT: representativeness level by target area\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79d3ca5d",
   "metadata": {},
   "source": [
    "In the paper this panel pools hundreds of sites, and each bar is the share of\n",
    "*sites* still classified `high` at that radius. Here there is one site, so the\n",
    "bar pools its eight site-level verdicts instead — land cover and vegetation\n",
    "index, daytime and nighttime. The shape is the paper's all the same: wholly\n",
    "`high` at 100 m, and progressively less so as the disc widens.\n",
    "\n",
    "## 5. Exporting in the published layouts\n",
    "\n",
    "`representativeness_table` reshapes the tidy frame into the column schema of\n",
    "the authors' Datasets S4–S6, so a local analysis can be read next to, or\n",
    "concatenated with, the published tables.\n",
    "`export_representativeness_tables` writes all three to CSV in one call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "028aa018",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-30T16:14:59.851659Z",
     "iopub.status.busy": "2026-08-30T16:14:59.851295Z",
     "iopub.status.idle": "2026-08-30T16:14:59.877976Z",
     "shell.execute_reply": "2026-08-30T16:14:59.877299Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SITE_ID</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>TARGET_AREA</th>\n",
       "      <th>VARIABLE</th>\n",
       "      <th>N</th>\n",
       "      <th>INTERCEPT</th>\n",
       "      <th>INTERCEPT_LCL</th>\n",
       "      <th>INTERCEPT_UCL</th>\n",
       "      <th>SLOPE</th>\n",
       "      <th>SLOPE_LCL</th>\n",
       "      <th>SLOPE_UCL</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>REP_EVI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US-CRT</td>\n",
       "      <td>daytime</td>\n",
       "      <td>100.0</td>\n",
       "      <td>EVI</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1.014</td>\n",
       "      <td>0.984</td>\n",
       "      <td>1.046</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US-CRT</td>\n",
       "      <td>daytime</td>\n",
       "      <td>250.0</td>\n",
       "      <td>EVI</td>\n",
       "      <td>6</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.989</td>\n",
       "      <td>0.955</td>\n",
       "      <td>1.024</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US-CRT</td>\n",
       "      <td>daytime</td>\n",
       "      <td>500.0</td>\n",
       "      <td>EVI</td>\n",
       "      <td>6</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.045</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US-CRT</td>\n",
       "      <td>daytime</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>EVI</td>\n",
       "      <td>6</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.534</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.077</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US-CRT</td>\n",
       "      <td>nighttime</td>\n",
       "      <td>100.0</td>\n",
       "      <td>EVI</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1.017</td>\n",
       "      <td>0.987</td>\n",
       "      <td>1.047</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>US-CRT</td>\n",
       "      <td>nighttime</td>\n",
       "      <td>250.0</td>\n",
       "      <td>EVI</td>\n",
       "      <td>6</td>\n",
       "      <td>0.003</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.957</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.004</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>US-CRT</td>\n",
       "      <td>nighttime</td>\n",
       "      <td>500.0</td>\n",
       "      <td>EVI</td>\n",
       "      <td>6</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.787</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.044</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>US-CRT</td>\n",
       "      <td>nighttime</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>EVI</td>\n",
       "      <td>6</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.077</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SITE_ID     PERIOD  TARGET_AREA VARIABLE  N  INTERCEPT  INTERCEPT_LCL  INTERCEPT_UCL  SLOPE  SLOPE_LCL  SLOPE_UCL     R2   RMSE    MAE REP_EVI\n",
       "0  US-CRT    daytime        100.0      EVI  6     -0.007         -0.021          0.006  1.014      0.984      1.046  1.000  0.006  0.005    high\n",
       "1  US-CRT    daytime        250.0      EVI  6      0.005         -0.010          0.020  0.989      0.955      1.024  0.999  0.006  0.005    high\n",
       "2  US-CRT    daytime        500.0      EVI  6      0.096          0.060          0.129  0.785      0.710      0.868  0.995  0.052  0.045  medium\n",
       "3  US-CRT    daytime       1000.0      EVI  6      0.144          0.095          0.185  0.628      0.534      0.739  0.986  0.091  0.077  medium\n",
       "4  US-CRT  nighttime        100.0      EVI  6     -0.009         -0.023          0.004  1.017      0.987      1.047  1.000  0.007  0.006    high\n",
       "5  US-CRT  nighttime        250.0      EVI  6      0.003         -0.012          0.018  0.991      0.957      1.026  0.999  0.006  0.004    high\n",
       "6  US-CRT  nighttime        500.0      EVI  6      0.095          0.058          0.128  0.787      0.712      0.870  0.995  0.051  0.044  medium\n",
       "7  US-CRT  nighttime       1000.0      EVI  6      0.143          0.094          0.184  0.629      0.535      0.741  0.986  0.091  0.077  medium"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "representativeness_table(results, \"S6\").round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c968fa",
   "metadata": {},
   "source": [
    "## What to take away\n",
    "\n",
    "- The footprint is **not** the target area, and the gap is a function of\n",
    "  radius. At US-CRT the tower represents its own 100 m surroundings almost\n",
    "  perfectly and a 1 km cell only loosely — worth knowing before the site is\n",
    "  used to validate a kilometre-scale product.\n",
    "- **Both evaluations agree on the direction** and disagree on the pace. The\n",
    "  categorical index falls off a cliff at 250 m because the chi-square test is\n",
    "  sharpened by the fine grid; the continuous index degrades gradually. Read\n",
    "  them together, and read the p-value with the grid in mind.\n",
    "- The **slope of Eq. 7** is the most informative single number here. A slope\n",
    "  below one says the tower's field swings more than the surrounding cell does,\n",
    "  which is what upscaling from this site would have to correct for.\n",
    "- The land-cover and EVI products here are **synthetic**. Swap in\n",
    "  `fetch_nlcd` and `fetch_landsat_evi`, or any pair of rasters on disc, and\n",
    "  nothing else in the notebook changes.\n",
    "\n",
    "### Next\n",
    "\n",
    "- [Footprint representativeness](../representativeness.rst) — the theory, all\n",
    "  seven equations, and the classification tables.\n",
    "- [Validation against Chu et al. (2021)](../validation.md) — these metrics\n",
    "  recomputed from the authors' own published climatology rasters."
   ]
  }
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