Add LST Backward Average script (#333)

* Initial setup LST Backwar Average scripts and docs

* Create and update color ramp in one command

---------

Co-authored-by: Amber Mulder <amber.mulder@planet.com>

planetary-variables/land-surface-temperature/index.md

@@ -13,4 +13,5 @@ Planet's LST product provides near real-time measurements twice a day at 1:30 an
 
 -   [Land Surface Temperature Visualization]({% link planetary-variables/land-surface-temperature/land-surface-temperature-visualization/index.md %})
 -   [Land Surface Temperature Anomaly]({% link planetary-variables/land-surface-temperature/land-surface-temperature-anomaly/index.md %})
+-   [Land Surface Temperature Backward Average]({% link planetary-variables/land-surface-temperature/land-surface-temperature-backward-average/index.md %})
 -   [Land Surface Temperature Quality Flags]({% link planetary-variables/land-surface-temperature/land-surface-temperature-quality-flags/index.md %})

planetary-variables/land-surface-temperature/land-surface-temperature-backward-average/index.md

@@ -0,0 +1,33 @@
+---
+title: Land Surface Temperature Backward Average
+grand_parent: Planetary Variables
+parent: Land Surface Temperature
+layout: script
+nav_exclude: false
+scripts:
+    - [Visualization, script.js]
+    - [Raw Values, raw.js]
+examples:
+- zoom: '11'
+  lat: '44.8398'
+  lng: '-0.5294'
+  datasetId: '8d977093-cf9e-4351-8159-90f2522c29c1'
+  fromTime: '2022-12-01T00:00:00.000Z'
+  toTime: '2022-12-30T23:59:59.999Z'
+  platform:
+  - EOB
+  evalscripturl: https://custom-scripts.sentinel-hub.com/custom-scripts/planetary-variables/land-surface-temperature/land-surface-temperature-backward-average/script.js
+  additionalQueryParams: 
+  - - themeId
+    - PLANET_SANDBOX
+---
+## 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](https://planet.widen.net/s/tltwk6hnps)
+-   [Data sheet](https://planet.widen.net/s/ttvp2rvwzd)
+-   [Sentinel Hub documentation about Land Surface Temperature](https://docs.sentinel-hub.com/api/latest/data/planetary-variables/land-surface-temp/)

planetary-variables/land-surface-temperature/land-surface-temperature-backward-average/raw.js

@@ -0,0 +1,60 @@
+//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];
+}

planetary-variables/land-surface-temperature/land-surface-temperature-backward-average/script.js

@@ -0,0 +1,99 @@
+//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];
+}