Draft: Add swc backward average script

planetary-variables/soil-water-content/index.md

@@ -14,3 +14,4 @@ Planet's SWC product provides near-daily measurements at spatial resolutions of
 -   [Soil Water Content Visualization]({% link planetary-variables/soil-water-content/soil-water-content-visualization/index.md %})
 -   [Soil Water Content Anomaly]({% link planetary-variables/soil-water-content/soil-water-content-anomaly/index.md %})
 -   [Derived Root-Zone Soil Water Content]({% link planetary-variables/soil-water-content/derived-root-zone-soil-water-content/index.md %})
+-   [Soil Water Content Backward Average]({% link planetary-variables/soil-water-content/soil-water-content-backward-average/index.md %})

planetary-variables/soil-water-content/soil-water-content-backward-average/index.md

@@ -0,0 +1,34 @@
+---
+title: Soil Water Content Backward Average 
+grand_parent: Planetary Variables
+parent: Soil Water Content
+layout: script
+nav_exclude: false
+scripts:
+    - [Visualization, script.js]
+    - [Raw Values, raw.js]
+examples:
+- zoom: '11'
+  lat: '41.1921'
+  lng: '-93.845'
+  datasetId: '65f7e4fb-a27a-4fae-8d79-06a59d7e6ede'
+  fromTime: '2022-05-01T00:00:00.000Z'
+  toTime: '2022-05-26T23:59:59.999Z'
+  platform:
+  - EOB
+  evalscripturl: https://custom-scripts.sentinel-hub.com/custom-scripts/planetary-variables/soil-water-content/soil-water-content-backward-average/script.js
+  additionalQueryParams: 
+  - - themeId
+    - PLANET_SANDBOX
+---
+## General description
+The Soil Water Content Backward Average is a method to reduce data gaps and measurement noise in the Soil Water Content (SWC) data. Depending on the requirements, we can choose a lookback period, for example 20 days. The 20-day backward average of SWC for day n is the average of SWC 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
+Our Soil Water Content provides a great reflection of the immediate soil conditions. However, daily measurements often exhibit short-term fluctuations due to rainfall events, evaporation, or changes in soil properties. The Soil Water Content Backward Average is suitable for applications where long-term soil moisture conditions 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
+-   [SWC Technical specifications](https://developers.planet.com/docs/planetary-variables/soil-water-content-technical-specification/)
+-   [SWC Data sheet](https://planet.widen.net/s/cv7bfjhhd5)
+-   [Sentinel Hub documentation about Soil Water Content](https://docs.sentinel-hub.com/api/latest/data/planetary-variables/soil-water-content/)

planetary-variables/soil-water-content/soil-water-content-backward-average/raw.js

@@ -0,0 +1,57 @@
+//VERSION=3
+
+const nDays = 20;           // The number of days to load data for
+const scaleFactor = 1000;   // The scale factor for the SWC values
+
+function setup() {
+    return {
+      input: ["SWC", "dataMask"],
+      output: { bands: 1, sampleType: "FLOAT32" },
+      mosaicking: "ORBIT"
+    };
+  }
+
+  function preProcessScenes(collections) {
+    collections.scenes.orbits = collections.scenes.orbits.filter(function (orbit) {
+      var orbitDateFrom = new Date(orbit.dateFrom)
+      // Select all images within the last nDays
+      return orbitDateFrom.getTime() >= (collections.to.getTime() - (nDays * 24 * 3600 * 1000));
+    })
+    return collections
+  }
+
+  function get_mean_swc_value(swc, dataMask) {
+    // Get the sum of all SWC values
+    let n_valid_dates = 0;
+    let sum = 0;
+    for (let i = 0; i < swc.length; i++) {
+        if (dataMask[i]) {
+            sum += swc[i];
+            n_valid_dates += 1;
+        }
+    }
+
+    // Calculate the mean SWC value
+    let mean_swc_value = NaN
+    if (n_valid_dates > 0) {
+      mean_swc_value = sum / n_valid_dates;
+    }
+
+    return mean_swc_value;
+  }
+
+function evaluatePixel(samples) {
+    // When there are no dates, return no data
+    if (samples.length == 0) return [NaN, NaN, NaN, 0];
+
+    // Extract SWC values and dataMask
+    var swc = samples.map(sample => sample.SWC / scaleFactor);
+    var dataMask = samples.map(sample => sample.dataMask);
+
+    // Calculate mean SWC value
+    const mean_swc_val = get_mean_swc_value(swc, dataMask);
+
+    return [mean_swc_val];
+  }
+
+

planetary-variables/soil-water-content/soil-water-content-backward-average/script.js

@@ -0,0 +1,96 @@
+//VERSION=3
+
+const nDays = 20;           // The number of days to load data for
+const scaleFactor = 1000;   // The scale factor for the SWC values
+const vmin = 0.0;           // The minimum value of the colormap
+const vmax = 0.4;           // The maximum value of the colormap
+
+function setup() {
+    return {
+      input: ["SWC", "dataMask"],
+      output: { id: "default", bands: 4 },
+      mosaicking: "ORBIT"
+    };
+  }
+
+  function preProcessScenes(collections) {
+    collections.scenes.orbits = collections.scenes.orbits.filter(function (orbit) {
+      var orbitDateFrom = new Date(orbit.dateFrom)
+      // Select all images within the last nDays
+      return orbitDateFrom.getTime() >= (collections.to.getTime() - (nDays * 24 * 3600 * 1000));
+    })
+    return collections
+  }
+
+  function get_mean_swc_value(swc, dataMask) {
+    // Get the sum of all SWC values
+    let n_valid_dates = 0;
+    let sum = 0;
+    for (let i = 0; i < swc.length; i++) {
+        if (dataMask[i]) {
+            sum += swc[i];
+            n_valid_dates += 1;
+        }
+    }
+
+    // Calculate the mean SWC value
+    let mean_swc_value = NaN
+    if (n_valid_dates > 0) {
+      mean_swc_value = sum / n_valid_dates;
+    }
+
+    return mean_swc_value;
+  }
+
+
+  const cmap = [
+    [0.0, 0xfff7ea],
+    [0.05, 0xfaedda],
+    [0.1, 0xede4cb],
+    [0.15, 0xdedcbd],
+    [0.2, 0xced3af],
+    [0.25, 0xbdcba3],
+    [0.3, 0xaac398],
+    [0.35, 0x96bc90],
+    [0.4, 0x80b48a],
+    [0.45, 0x68ac86],
+    [0.5, 0x4da484],
+    [0.55, 0x269c83],
+    [0.6, 0x009383],
+    [0.65, 0x008a85],
+    [0.7, 0x008186],
+    [0.75, 0x007788],
+    [0.8, 0x006d8a],
+    [0.85, 0x00618c],
+    [0.9, 0x00558d],
+    [0.95, 0x00478f],
+    [1.0, 0x003492],
+];
+
+  // Prepare colormap based on provided min and max values
+  const visualizer = new ColorRampVisualizer(cmap, vmin, vmax);
+
+
+function evaluatePixel(samples) {
+    // When there are no dates, return no data
+    if (samples.length == 0) return [NaN, NaN, NaN, 0];
+
+    // Extract SWC values and dataMask
+    var swc  = samples.map(sample => sample.SWC / scaleFactor);
+    var dataMask = samples.map(sample => sample.dataMask);
+
+    // Calculate mean SWC value
+    const mean_swc_val = get_mean_swc_value(swc, dataMask);
+
+    // Set opacity to 0 if there is no valid data
+    let opacity = 1;
+    if (isNaN(mean_swc_val)) {
+        opacity = 0
+    }
+
+    // Apply colormap
+    imgVals = visualizer.process(mean_swc_val);
+
+    return [...imgVals, opacity];
+  }
+