Land Surface Temperature Backward Average

//VERSION=3

// Set defaultVis to false to scale and set color_min and color_max values.
// LST has two observations per day: 1h30 and 13h30 solar local time

const date = "2022-12-31"; // The date for which the backward average is calculated
const nDays = 20; // The number of days to load data for
const scaleFactor = 100; // The scale factor for the SWC values
const color_min = 260; // The minimum value of the colormap.
const color_max = 280; // The maximum value of the colormap.
const sensing_time = "0130"; // Observation time: "0130" or "1330" or ""
const variable = "LST"; // Variable of interest: "LST" or "LST_MaskedPixels"

function setup() {
  return {
    input: [variable, "dataMask"],
    output: { id: "default", bands: 4 },
    mosaicking: "TILE",
  };
}

// Select files based on sensing time (0130 or 1330) and within the last nDays
function preProcessScenes(collections) {
  var calculationDate = new Date(date);
  collections.scenes.tiles = collections.scenes.tiles.filter(function (tile) {
    var tileDate = new Date(tile.date);
    return (
      tile.dataPath.includes("T" + sensing_time) &&
      tileDate.getTime() >= calculationDate.getTime() - nDays * 24 * 3600 * 1000
    );
  });
  return collections;
}

function get_mean_lst_value(samples) {
  // Get the sum of all LST values
  let n_valid_dates = 0;
  let sum = 0;
  for (let i = 0; i < samples.length; i++) {
    if (samples[i].dataMask) {
      sum += samples[i].LST / scaleFactor;
      n_valid_dates += 1;
    }
  }

  // Calculate the mean LST value
  let mean_lst_value = NaN;
  if (n_valid_dates > 0) {
    mean_lst_value = sum / n_valid_dates;
  }

  return mean_lst_value;
}

// Create color ramp 250 - 340 (full range)
const cmap = [
  [263, 0x000004],
  [266, 0x06051a],
  [270, 0x140e36],
  [274, 0x251255],
  [278, 0x3b0f70],
  [282, 0x51127c],
  [286, 0x641a80],
  [289, 0x782281],
  [293, 0x8c2981],
  [297, 0xa1307e],
  [301, 0xb73779],
  [305, 0xca3e72],
  [309, 0xde4968],
  [313, 0xed5a5f],
  [316, 0xf7705c],
  [320, 0xfc8961],
  [324, 0xfe9f6d],
  [328, 0xfeb77e],
  [332, 0xfecf92],
  [336, 0xfde7a9],
  [340, 0xfcfdbf],
];

// Initialize the ColorRamp
const visualizer = new ColorRampVisualizer(cmap, color_min, color_max);

function evaluatePixel(samples) {
  // When there are no dates, return no data
  if (samples.length == 0) return [NaN, NaN, NaN, 0];

  // Calculate mean LST value
  const mean_lst_val = get_mean_lst_value(samples);

  // Set opacity to 0 if there is no valid data
  let opacity = 1;
  if (isNaN(mean_lst_val)) {
    opacity = 0;
  }

  // Apply colormap
  imgVals = visualizer.process(mean_lst_val);
  return [...imgVals, opacity];
}

//VERSION=3

// LST has two observations per day: 1h30 and 13h30 solar local time

const date = "2022-12-31"; // The date for which the backward average is calculated
const nDays = 20; // The number of days to load data for
const scaleFactor = 100; // The scale factor for the SWC values
const sensing_time = "0130"; // Observation time: "0130" or "1330" or ""
const variable = "LST"; // Variable of interest: "LST" or "LST_MaskedPixels"

function setup() {
  return {
    input: [variable, "dataMask"],
    output: { bands: 1, sampleType: "FLOAT32" },
    mosaicking: "TILE",
  };
}

// Select files based on sensing time (0130 or 1330) and within the last nDays
function preProcessScenes(collections) {
  var calculationDate = new Date(date);
  collections.scenes.tiles = collections.scenes.tiles.filter(function (tile) {
    var tileDate = new Date(tile.date);
    return (
      tile.dataPath.includes("T" + sensing_time) &&
      tileDate.getTime() >= calculationDate.getTime() - nDays * 24 * 3600 * 1000
    );
  });
  return collections;
}

function get_mean_lst_value(samples) {
  // Get the sum of all LST values
  let n_valid_dates = 0;
  let sum = 0;
  for (let i = 0; i < samples.length; i++) {
    if (samples[i].dataMask) {
      sum += samples[i].LST / scaleFactor;
      n_valid_dates += 1;
    }
  }

  // Calculate the mean LST value
  let mean_lst_value = NaN;
  if (n_valid_dates > 0) {
    mean_lst_value = sum / n_valid_dates;
  }

  return mean_lst_value;
}

function evaluatePixel(samples) {
  // When there are no dates, return no data
  if (samples.length == 0) return [NaN];

  // Calculate mean LST value
  const mean_lst_val = get_mean_lst_value(samples);

  return [mean_lst_val];
}

Evaluate and Visualize

• EO Browser

General description

The Land Surface Temperature Backward Average is a method to reduce data gaps and measurement noise in the Land Surface Temperature (LST) data. Depending on the requirements, we can choose a lookback period, for example 20 days. The 20-day backward average of LST for day n is the average of LST over the 20 days preceding day n. We compute the backward average using all available measurements within this 20-day period, and therefore, we do have a valid value for every day, except in case of prolonged data unavailability, such as during long frost and snow periods.

Why it is useful

The Land Surface Temperature Backward Average is suitable for applications where long-term temperatures are more relevant than daily fluctuations. The moving average operation reduces day-to-day variations and in the resulting time series, seasonal and longer-term changes can be easily detected. It can be used for monitoring drought risk, yield forecasting and analysis of climate change.

Useful links

• Product specifications
• Data sheet
• Sentinel Hub documentation about Land Surface Temperature