SWORD_S2_timeSeries.ipynb

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        "<a href=\"https://colab.research.google.com/github/ericslevenson/arctic-surface-water/blob/main/SWORD_S2_timeSeries.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "'''"
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      "source": [
        "# Authenticate private account (only required for exporting to drive/gee/gcp)\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "\n",
        "# Earth Engine setup\n",
        "import ee # Trigger the authentication flow.\n",
        "ee.Authenticate()\n",
        "ee.Initialize(project='ee-eric-levenson') # Initialize the library.\n",
        "\n",
        "# Google Drive setup (if needed)\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Some common imports\n",
        "from IPython.display import Image\n",
        "import folium"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "3iQQHssaS_OV",
        "outputId": "0f72c288-82ca-41c5-cbf2-54eb8c09e8c4"
      },
      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
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      "source": [
        "# Import Sword Reaches\n",
        "reaches = ee.FeatureCollection(\"projects/sat-io/open-datasets/SWORD/reaches_merged\")\n",
        "\n",
        "## ***INPUTS***\n",
        "\n",
        "## Regularly Adjusted inputs ##\n",
        "\n",
        "# Time Period\n",
        "start = '2023-04-01'\n",
        "finish = '2024-09-30'\n",
        "\n",
        "## Rarely Adjusted inputs ##\n",
        "# Image scale\n",
        "pixScale = 10\n",
        "\n",
        "# ***EXPORTS***\n",
        "\n",
        "# Export Properties\n",
        "exportSelectors = ['date','id', 'waterArea', 'cloudArea', 'coverage'] # These will likely need to be changed to reflect your input shapefile. Specifically, 'count', 'centroid', and 'ratio' are all attributes from the imported EE asset. The others can stay the same.\n",
        "# ROI Description\n",
        "#roiLabel = str('05_'+str(tile))\n",
        "# Export Folder\n",
        "exportFolder = 'SWOT_RiverTimeSeries'\n",
        "\n",
        "CLD_PRB_THRESH = 50\n",
        "\n",
        "# ***EARTH ENGINE-IFY***\n",
        "eestart = ee.Date(start)\n",
        "eefinish = ee.Date(finish)\n",
        "startDoy = ee.Date(start).getRelative('day', 'year')\n",
        "endDoy = ee.Date(finish).getRelative('day', 'year')\n",
        "\n",
        "# reach of interest\n",
        "reach_id = 81260300131\n",
        "reach_id_str = str(reach_id)\n",
        "reach = ee.Feature(reaches.filter(ee.Filter.eq('reach_id', reach_id)).first()) # filter to that multiline\n",
        "reach = ee.Geometry.MultiLineString(ee.List(reach.geometry().coordinates())) # multilinestring for buffer\n"
      ],
      "metadata": {
        "id": "HZ2KHYpMWzKP"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "#### Step 1: Manually inspect for buffer distance\n",
        "bufferDistance = 2000 # customize buffer distance\n",
        "# buffer and some cleanup for plotting\n",
        "reachBuffer = ee.Feature(reach.buffer(distance=bufferDistance)) # buffer to custom distance\n",
        "reachBufferFC = ee.FeatureCollection(reachBuffer) # feature collection for plotting\n",
        "roi = reachBuffer.geometry() # geometry for clipping and such"
      ],
      "metadata": {
        "id": "5EPTUVbeJgy4"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "## Plot to check buffer level\n",
        "mapReach = folium.Map(zoom_start=10)\n",
        "folium.GeoJson(reachBufferFC.geometry().getInfo()).add_to(mapReach)\n",
        "display(mapReach)"
      ],
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              "        geo_json_edc3ac7aa5eb1cb27f4e367bde337ccf.setStyle(function(feature) {return feature.properties.style;});\n",
              "\n",
              "        \n",
              "    \n",
              "            geo_json_edc3ac7aa5eb1cb27f4e367bde337ccf.addTo(map_b88e787537b62e86e6a5dec05daabce2);\n",
              "        \n",
              "&lt;/script&gt;\n",
              "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Functions"
      ],
      "metadata": {
        "id": "8BGsumbOL097"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Image pre-processing and water classification"
      ],
      "metadata": {
        "id": "unqbinIZMEvr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## ***IMAGE PRE-PROCESSING METHODS***\n",
        "\n",
        "# Mask clouds in Sentinel-2\n",
        "def maskS2clouds(image):\n",
        "  '''Takes an input and adds two bands: cloud mask and clear mask'''\n",
        "  qa = image.select('QA60')\n",
        "  cloudBitMask = 1 << 10\n",
        "  cirrusBitMask = 1 << 11\n",
        "  clear_mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(qa.bitwiseAnd(cirrusBitMask).eq(0)).rename('clear_mask')\n",
        "  cloud_mask = qa.bitwiseAnd(cloudBitMask).eq(1).And(qa.bitwiseAnd(cirrusBitMask).eq(1)).rename('cloud_mask')\n",
        "  return image.addBands([cloud_mask,clear_mask])\n",
        "def add_cloud_bands(img):\n",
        "    # Get s2cloudless image, subset the probability band.\n",
        "    cld_prb = ee.Image(img.get('s2cloudless')).select('probability')\n",
        "\n",
        "    # Condition s2cloudless by the probability threshold value.\n",
        "    clouds = cld_prb.gte(CLD_PRB_THRESH).rename('cloud_mask')\n",
        "    clear = cld_prb.lt(CLD_PRB_THRESH).rename('clear_mask')\n",
        "    # Add the cloud probability layer and cloud mask as image bands.\n",
        "    return img.addBands(ee.Image([cld_prb, clouds, clear]))\n",
        "\n",
        "# Clip image\n",
        "def clip_image(image):\n",
        "  '''Clips to the roi defined at the beginning of the script'''\n",
        "  return image.clip(roi)\n",
        "\n",
        "def clip2rivers(image):\n",
        "  '''Clips an image based on the lake boundaries'''\n",
        "  return image.clip(rivers)\n",
        "\n",
        "# Get percentile cover\n",
        "def getCover(image):\n",
        "  '''calculates percentage of the roi covered by the clear mask. NOTE: this function\n",
        "  calls the global totPixels variable that needs to be calculated in the main script.'''\n",
        "  actPixels = ee.Number(image.updateMask(image.select('clear_mask')).reduceRegion(\n",
        "      reducer = ee.Reducer.count(),\n",
        "      scale = 1000, # keep same as totPixels\n",
        "      geometry = image.geometry(),\n",
        "      maxPixels=1e12,\n",
        "      ).values().get(0))\n",
        "  # calculate the perc of cover OF CLEAR PIXELS\n",
        "  percCover = actPixels.divide(totPixels).multiply(100).round()\n",
        "  # number as output\n",
        "  return image.set('percCover', percCover,'actPixels',actPixels)\n",
        "\n",
        "# Mosaic images by date, orbit, - basically combines images together that were taken on the same day\n",
        "def mosaicBy(imcol):\n",
        "  '''Takes an image collection (imcol) and creates a mosaic for each day\n",
        "  Returns: An image collection of daily mosaics'''\n",
        "  #return the collection as a list of images (not an image collection)\n",
        "  imlist = imcol.toList(imcol.size())\n",
        "  # Get all the dates as list\n",
        "  def imdate(im):\n",
        "    date = ee.Image(im).date().format(\"YYYY-MM-dd\")\n",
        "    return date\n",
        "  all_dates = imlist.map(imdate)\n",
        "  # get all orbits as list\n",
        "  def orbitId(im):\n",
        "    orb = ee.Image(im).get('SENSING_ORBIT_NUMBER')\n",
        "    return orb\n",
        "  all_orbits = imlist.map(orbitId)\n",
        "  # get all spacecraft names as list\n",
        "  def spacecraft(im):\n",
        "    return ee.Image(im).get('SPACECRAFT_NAME')\n",
        "  all_spNames = imlist.map(spacecraft)\n",
        "  # this puts dates, orbits and names into a nested list\n",
        "  concat_all = all_dates.zip(all_orbits).zip(all_spNames);\n",
        "  # here we unnest the list with flatten, and then concatenate the list elements with \" \"\n",
        "  def concat(el):\n",
        "    return ee.List(el).flatten().join(\" \")\n",
        "  concat_all = concat_all.map(concat)\n",
        "  # here, just get distinct combintations of date, orbit and name\n",
        "  concat_unique = concat_all.distinct()\n",
        "  # mosaic\n",
        "  def mosaicIms(d):\n",
        "    d1 = ee.String(d).split(\" \")\n",
        "    date1 = ee.Date(d1.get(0))\n",
        "    orbit = ee.Number.parse(d1.get(1)).toInt()\n",
        "    spName = ee.String(d1.get(2))\n",
        "    im = imcol.filterDate(date1, date1.advance(1, \"day\")).filterMetadata('SPACECRAFT_NAME', 'equals', spName).filterMetadata('SENSING_ORBIT_NUMBER','equals', orbit).mosaic()\n",
        "    return im.set(\n",
        "        \"system:time_start\", date1.millis(),\n",
        "        \"system:date\", date1.format(\"YYYY-MM-dd\"),\n",
        "        \"system:id\", d1)\n",
        "  mosaic_imlist = concat_unique.map(mosaicIms)\n",
        "  return ee.ImageCollection(mosaic_imlist)\n",
        "\n",
        "###########################################################################\n",
        "## ***WATER CLASSIFICATION METHODS***\n",
        "\n",
        "# Define NDWI image\n",
        "def ndwi(image):\n",
        "  '''Adds an NDWI band to the input image'''\n",
        "  return image.normalizedDifference(['B3', 'B8']).rename('NDWI').multiply(1000)\n",
        "\n",
        "# Basic ndwi classification\n",
        "def ndwi_classify(image):\n",
        "  '''Creates a binary image based on an NDWI threshold of 0'''\n",
        "  ndwimask = image.select('NDWI')\n",
        "  water = ndwimask.gte(0)\n",
        "  land = ndwimask.lt(0)\n",
        "  return(water)\n",
        "\n",
        "# OTSU thresholding from histogram\n",
        "def otsu(histogram):\n",
        "  '''Returns the NDWI threshold for binary water classification'''\n",
        "  counts = ee.Array(ee.Dictionary(histogram).get('histogram'))\n",
        "  means = ee.Array(ee.Dictionary(histogram).get('bucketMeans'))\n",
        "  size = means.length().get([0])\n",
        "  total = counts.reduce(ee.Reducer.sum(), [0]).get([0])\n",
        "  sum = means.multiply(counts).reduce(ee.Reducer.sum(), [0]).get([0])\n",
        "  mean = sum.divide(total)\n",
        "  indices = ee.List.sequence(1, size)\n",
        "  def func_xxx(i):\n",
        "    '''Compute between sum of squares, where each mean partitions the data.'''\n",
        "    aCounts = counts.slice(0, 0, i)\n",
        "    aCount = aCounts.reduce(ee.Reducer.sum(), [0]).get([0])\n",
        "    aMeans = means.slice(0, 0, i)\n",
        "    aMean = aMeans.multiply(aCounts) \\\n",
        "        .reduce(ee.Reducer.sum(), [0]).get([0]) \\\n",
        "        .divide(aCount)\n",
        "    bCount = total.subtract(aCount)\n",
        "    bMean = sum.subtract(aCount.multiply(aMean)).divide(bCount)\n",
        "    return aCount.multiply(aMean.subtract(mean).pow(2)).add(\n",
        "           bCount.multiply(bMean.subtract(mean).pow(2)))\n",
        "  bss = indices.map(func_xxx)\n",
        "  # Return the mean value corresponding to the maximum BSS.\n",
        "  return means.sort(bss).get([-1])\n",
        "\n",
        "# OTSU thresholding for an image\n",
        "def otsu_thresh(water_image):\n",
        "  '''Calculate NDWI and create histogram. Return the OTSU threshold.'''\n",
        "  NDWI = ndwi(water_image).select('NDWI').updateMask(water_image.select('clear_mask'))\n",
        "  histogram = ee.Dictionary(NDWI.reduceRegion(\n",
        "    geometry = roi,\n",
        "    reducer = ee.Reducer.histogram(255, 2).combine('mean', None, True).combine('variance', None, True),\n",
        "    scale = pixScale,\n",
        "    maxPixels = 1e12\n",
        "  ))\n",
        "  return otsu(histogram.get('NDWI_histogram'))\n",
        "\n",
        "# Classify an image using OTSU threshold.\n",
        "def otsu_classify(water_image):\n",
        "  '''(1) Calculate NDWI and create histogram. (2) Calculate NDWI threshold for\n",
        "  binary classification using OTSU method. (3) Classify image and add layer to input image.\n",
        "  '''\n",
        "  NDWI = ndwi(water_image).select('NDWI')\n",
        "  histogram = ee.Dictionary(NDWI.reduceRegion(\n",
        "    geometry = roi,\n",
        "    reducer = ee.Reducer.histogram(255, 2).combine('mean', None, True).combine('variance', None, True),\n",
        "    scale = pixScale,\n",
        "    maxPixels = 1e12\n",
        "  ))\n",
        "  threshold = otsu(histogram.get('NDWI_histogram'))\n",
        "  otsu_classed = NDWI.gt(ee.Number(threshold)).And(water_image.select('B8').lt(2000)).rename('otsu_classed')\n",
        "  return water_image.addBands([otsu_classed])\n",
        "\n",
        "def adaptive_thresholding(water_image):\n",
        "  '''Takes an image clipped to lakes and returns the water mask'''\n",
        "  NDWI = ndwi(water_image).select('NDWI')#.updateMask(water_image.select('clear_mask')) # get NDWI **TURNED OFF CLOUD MASK, SHOULD THIS STAY OFF?**\n",
        "  threshold = ee.Number(otsu_thresh(water_image))\n",
        "  threshold = threshold.divide(10).round().multiply(10)\n",
        "  # get fixed histogram\n",
        "  histo = NDWI.reduceRegion(\n",
        "      geometry = roi,\n",
        "      reducer = ee.Reducer.fixedHistogram(-1000, 1000, 200),\n",
        "      scale = pixScale, # This was 30, keep at 10!?!?\n",
        "      maxPixels = 1e12\n",
        "  )\n",
        "  hist = ee.Array(histo.get('NDWI'))\n",
        "  counts = hist.cut([-1,1])\n",
        "  buckets = hist.cut([-1,0])\n",
        "  #find split points from otsu threshold\n",
        "  threshold = ee.Array([threshold]).toList()\n",
        "  buckets_list = buckets.toList()\n",
        "  split = buckets_list.indexOf(threshold)\n",
        "  # split into land and water slices\n",
        "  land_slice = counts.slice(0,0,split)\n",
        "  water_slice = counts.slice(0,split.add(1),-1)\n",
        "  # find max of land and water slices\n",
        "  land_max = land_slice.reduce(ee.Reducer.max(),[0])\n",
        "  water_max = water_slice.reduce(ee.Reducer.max(),[0])\n",
        "  land_max = land_max.toList().get(0)\n",
        "  water_max = water_max.toList().get(0)\n",
        "  land_max = ee.List(land_max).getNumber(0)\n",
        "  water_max = ee.List(water_max).getNumber(0)\n",
        "  #find difference between land, water and otsu val\n",
        "  counts_list = counts.toList()\n",
        "  otsu_val = ee.Number(counts_list.get(split))\n",
        "  otsu_val = ee.List(otsu_val).getNumber(0)\n",
        "  land_prom = ee.Number(land_max).subtract(otsu_val)\n",
        "  water_prom = ee.Number(water_max).subtract(otsu_val)\n",
        "  #find land and water buckets corresponding to 0.9 times the prominence\n",
        "  land_thresh = ee.Number(land_max).subtract((land_prom).multiply(ee.Number(0.9)))\n",
        "  water_thresh = ee.Number(water_max).subtract((water_prom).multiply(ee.Number(0.9)))\n",
        "  land_max_ind = land_slice.argmax().get(0)\n",
        "  water_max_ind = water_slice.argmax().get(0)\n",
        "  li = ee.Number(land_max_ind).subtract(1)\n",
        "  li = li.max(ee.Number(1))\n",
        "  wi = ee.Number(water_max_ind).add(1)\n",
        "  wi = wi.min(ee.Number(199))\n",
        "  land_slice2 = land_slice.slice(0,li,-1).subtract(land_thresh)\n",
        "  water_slice2 = water_slice.slice(0,0,wi).subtract(water_thresh)\n",
        "  land_slice2  = land_slice2.abs().multiply(-1)\n",
        "  water_slice2 = water_slice2.abs().multiply(-1)\n",
        "  land_index = ee.Number(land_slice2.argmax().get(0)).add(land_max_ind)\n",
        "  water_index = ee.Number(water_slice2.argmax().get(0)).add(split)\n",
        "  land_level = ee.Number(buckets_list.get(land_index))\n",
        "  water_level = ee.Number(buckets_list.get(water_index))\n",
        "  land_level = ee.Number(ee.List(land_level).get(0)).add(5)\n",
        "  water_level = ee.Number(ee.List(water_level).get(0)).add(5)\n",
        "  #calculate water fraction and classify\n",
        "  water_fraction = (NDWI.subtract(land_level)).divide(water_level.subtract(land_level)).multiply(100).rename('water_fraction')\n",
        "  #water_fraction = conditional(water_fraction) #sets values less than 0 to 0 and greater than 100 to 100\n",
        "  water_75 = water_fraction.gte(75).rename('water_75'); #note, this is a non-binary classification, so we use 75% water as \"water\"\n",
        "  all_mask = water_image.select('B2').gt(5).rename('all_mask')\n",
        "  cloud_mask_ed = water_image.select('clear_mask').neq(1).rename('cloud_mask_ed')\n",
        "  return water_image.addBands([water_fraction,water_75,NDWI,cloud_mask_ed])\n",
        "# Apply cloud mask to other bands\n",
        "def applyMask(image):\n",
        "  img = image.updateMask(image.select('clear_mask'))\n",
        "  return img\n",
        "def binaryImage(image):\n",
        "  '''takes a multiband image and returns just the binary water_75 band'''\n",
        "  img = image.select('water_75')\n",
        "  return img\n",
        "def waterImage(image):\n",
        "  '''takes a multiband image and returns just the water fraction band'''\n",
        "  img = image.select('water_fraction')\n",
        "  return img"
      ],
      "metadata": {
        "id": "CvpW1MxuL6-I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Property Extraction"
      ],
      "metadata": {
        "id": "6G25Rz5_MJfz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def riverProps(image):\n",
        "  # Area images masked for everything\n",
        "  areaIm = image.pixelArea() # get a pixel area image\n",
        "  waterAreaIm = areaIm.updateMask(image.select('water_75')) # mask area image based on water\n",
        "  cloudAreaIm = areaIm.updateMask(image.select('cloud_mask')) # mask area image based on clouds\n",
        "  clearAreaIm = areaIm.updateMask(image.select('clear_mask')) # mask area image based on clearness\n",
        "  ## Sum Water\n",
        "  watersum = waterAreaIm.select('area').reduceRegion(\n",
        "      reducer=ee.Reducer.sum(),\n",
        "      geometry = roi,\n",
        "      scale = 10,\n",
        "      maxPixels=1e9\n",
        "  ).get('area')\n",
        "\n",
        "  ## Sum Clouds\n",
        "  cloudArea = cloudAreaIm.select('area').reduceRegion(\n",
        "      reducer=ee.Reducer.sum(),\n",
        "      geometry = roi,\n",
        "      scale = 10,\n",
        "      maxPixels=1e9\n",
        "  ).get('area')\n",
        "\n",
        "  # Get Date\n",
        "  date = image.date().format('yyyy-MM-dd')\n",
        "  # Get Coverage\n",
        "  coverage = image.get('percCover')\n",
        "\n",
        "  return ee.Feature(None, {'id': reach_id, 'date': date, 'waterArea': watersum, 'cloudArea': cloudArea, 'coverage':coverage})\n",
        "###############################################################################\n",
        "## ***EXPORT METHODS***\n",
        "def export_rivers(collection, description, fileNamePrefix, fileFormat, folder, selectors):\n",
        "  '''Export a feature collection of lake properties to google drive for a given day.'''\n",
        "  task = ee.batch.Export.table.toDrive(**{\n",
        "    'collection': collection,\n",
        "    'description': description,\n",
        "    'fileNamePrefix': fileNamePrefix,\n",
        "    'fileFormat': fileFormat,\n",
        "    'folder': folder,\n",
        "    'selectors': selectors\n",
        "  })\n",
        "  task.start()"
      ],
      "metadata": {
        "id": "XMiZudVU1aXw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Main"
      ],
      "metadata": {
        "id": "WUMzNVYHL4nt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## UPDATED MAIN...TRYING TO REMOVE THE LOOP\n",
        "\n",
        "images = ee.ImageCollection('COPERNICUS/S2_HARMONIZED').filterBounds(roi).filterDate(start,finish).filter(ee.Filter.calendarRange(startDoy, endDoy, 'day_of_year')).filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',60)) # Get Images\n",
        "  # Get cloud probability image collection\n",
        "s2Cloudless = ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY').filterBounds(roi).filterDate(start,finish).filter(ee.Filter.calendarRange(startDoy, endDoy, 'day_of_year'))\n",
        "  # Merge surface reflectance and cloud probability collections\n",
        "images = ee.ImageCollection(ee.Join.saveFirst('s2cloudless').apply(**{\n",
        "        'primary': images,\n",
        "        'secondary': s2Cloudless,\n",
        "        'condition': ee.Filter.equals(**{\n",
        "            'leftField': 'system:index',\n",
        "            'rightField': 'system:index'\n",
        "        })\n",
        "    }))\n",
        "images = images.map(add_cloud_bands)\n",
        "  # Mosaic images and add cloud/clear masks\n",
        "images_all = mosaicBy(images)\n",
        "  # Clip mosaics to roi\n",
        "riverimages = images_all.map(clip_image)\n",
        "#riverimages = images_all.map(clip2rivers) # Clip images to buffered lake mask\n",
        "# Filter by percentile cover\n",
        "# Get percent cover for each mosaic\n",
        "image_mask = riverimages.select('B2').mean().gte(0) #\n",
        "totPixels = ee.Number(image_mask.reduceRegion(\n",
        "    reducer = ee.Reducer.count(),\n",
        "    scale = 1000,\n",
        "    geometry = roi,\n",
        "    maxPixels = 1e12\n",
        "    ).values().get(0))\n",
        "riverimages = riverimages.map(getCover) # Add percent cover as an image property\n",
        "riverimages = riverimages.filterMetadata('percCover','greater_than',70) # remove images covering less than 50% of the ROI)\n",
        "\n",
        "###############################################################################\n",
        "## ***WATER CLASSIFICATION***\n",
        "riverimages = riverimages.map(adaptive_thresholding)\n",
        "\n",
        "rivers = riverimages.map(riverProps)\n",
        "\n",
        "export_rivers(rivers, reach_id_str, reach_id_str, 'CSV', exportFolder, exportSelectors)"
      ],
      "metadata": {
        "id": "4BHx1RnmMmPj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}