├── LICENSE
├── README.md
└── javascript_codes
    └── LST-downscaling_GEE_APP.js


/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2022 Daniel Paluba
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/README.md:
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 1 | # Landsat-8 Land Surface Temperature downscaling using Sentinel-2 in Google Earth Engine
 2 | A Google Earth Engne tool to downscale Landsat Land Surface Temperature data to 10 m resolution using Sentinel-2 data. 
 3 | This code repository is an attachment for the article in Remote Sensing: Onačillová, K.; Gallay, M.; Paluba, D.; Péliová, A.; Tokarčík, O.; Laubertová, D. Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment. Remote Sens. 2022, 14, 4076. [https://doi.org/10.3390/rs14164076](https://www.mdpi.com/2072-4292/14/16/4076/htm). 
 4 | 
 5 | </b> This repository contains a folder "javascript_codes" where you can find:
 6 |   - A JavaScript Google Earth Engine (GEE) code "LST_downscaling_GEE_APP.js" used in the [LST-downscaling GEE Application](https://danielp.users.earthengine.app/view/lst-downscaling) to downscale (sharpen) the Land Surface Temperature (LST) derived from Landsat thermal sensing using the spectral bands of Sentinel-2 
 7 |   
 8 |    ## How to use the [LST-downscaling GEE Application](https://danielp.users.earthengine.app/view/lst-downscaling)
 9 | 1. Define the 5 input parameters: 
10 |     - start and end date (to select the desired time frame), 
11 |     - Landsat collection (to select whether to use the Landsat 8 or Landsat 9 image collection), 
12 |     - maximum cloud cover allowed for the image tiles and 
13 |     - the region of interest (ROI). 
14 |       - <sub>The default ROI set-ting is focused on Košice City. However, the user can change the location where the analysis will be performed. The ROI is created using the Geometry Tools in the GEE, which allow users to move or delete and then delineate their own new geometric fea-tures, such as polygons, to be applied in the analyses. </sub>
15 | 
16 | 2. Click on **“Generate Landsat 8/9 and Sentinel-2 Image Collections”** button to generate available image IDs. 
17 |     - Based on this information, the list of Landsat 8/9 and Sentinel-2 imagery IDs that meet the entry criteria will be displayed under the button in the right panel. 
18 |     - The user will recieve a list of Landsat 8/9 and Sentinel-2 Image IDs that meet the selected criteria.
19 | 3. Enter Image IDs for the Landsat and for the Sentinel-2 Collection. 
20 |     - The user can select two image IDs from the resulting list – one ID for the Landsat 8/9 collection and one for the Sentinel-2 collection and enter their exact ID to the newly displayed text fields. Several Landsat 8/9 and Sentinel-2 imagery may be available in a given time window, therefore we recommend selecting data sets that were acquired on the same day. If images for Landsat 8/9 and Sentinel-2 collections are not available on the same acquisition day, we recommend choosing the datasets by the closest acquisition time to account for similar spectral characteristics of the derived spectral indices from both satellites. 
21 | 4. Click on **"Generate Downscaled LST"** button to perform the Landsat 8/9 LST Downscaling to 10 m spatial resolution.
22 | **Note:** All input parameters, including image IDs, are pre-filled with parameters required to perform analysis showed in this paper. Without modifying the input parameters, the user gets the (slightly different) results produced in this article.
23 |     - Tthe following 5 images: Landsat 8/9 and Sentinel-2 natural color images (RGB), original Landsat 8/9 LST in 30 m, downscaled LST to 10 m spatial resolution with and without assuming residuals are added to the Map.
24 | 5. (Optional) Click on **"Generate charts of spectral indices vs Landsat LST"** to generate scatterplots of correlation between Landsat 8/9 NDVI, NDWI and NDBI spectral indices and Landsat 8/9 LST bands.
25 | 
26 | ### Final LST product download option
27 | To download the downscaled LST images use the [GEE Code Editor version](https://code.earthengine.google.com/005680c8acf54715c9b10e946400d842) or directly the code provided in the "LST_downscaling_GEE_APP.js" code in the "javascript_codes" folder.
28 | 
29 | ## Outputs of the algorithm
30 | There are three different output types of the algorithm: (1) the main output is the downscaled LST 10 m with residuals, (2) bivariate scatter plots of LSTL8 vs. NDVIL8, NDBIL8, NDWIL8, (3) downscaling regression model. In the GEE code, the user will obtain the following outputs:
31 |   - LST 10 m with residuals - final downscaled LST with regression residuals in 10 m spatial resolution
32 |   - LST 10 m (no residuals) - downscaled LST in 10 m spatial resolution (without added regression residuals)
33 |   - Landsat 8/9 LST – Landsat 8 Land Surface Temperature in 30 m spatial resolution
34 |   - Landsat 8/9 LST regression residuals – regression residuals of LST computed as the difference between model estimation and the corresponding observation by Landsat 8/9 at 30 m resolution 
35 |   - Sentinel-2 RGB – true color composite for Sentinel-2
36 |   - Landsat 8/9 RGB –  true color composite for Landsat 8/9
37 | 
38 | 
39 |   ## About the Landsat-8 Land Surface Temperature downscaling using Sentinel-2 in GEE
40 |   This algorithm aims to downscale the coarse spatial resolution (100/30 m) Landsat 8/9 LST to finer spatial resolution (10 m) for more accurate mapping of LST. Three different indices, namely the normalized difference vegetation index (NDVI), built-up index (NDBI), and water index (NDWI) were used for disaggregation of Landsat LST (100/30 m) to 10 m Sentinel-2 spatial resolution using linear regression. The algorithm was developed in the cloud-based platform Google Earth Engine (GEE) using Landsat-8 and Sentinel-2 open access data. We conclude that the proposed downscaling model, by addressing the linear relationship of LST at coarse and fine spatial resolutions, can be successfully applied to produce high-resolution LST maps suitable for studies of the urban thermal environment at local scales. The performance was validated by the adjusted R2 returned at the stage of model development as well as by the validation of downscaled LST results using data logger measurements at 6 sites in the Košice city, Slovakia, to represent different types of land cover.
41 |   
42 |   ## A brief description of the methodology:
43 |   Based on the input parameters, the respective images of Landsat Surface Reflectance (SR) Collection 2 and Sentinel-2 Level 2A (L2A) collections are used to NDVI, NDBI, and NDWI spectral indices for Landsat 8/9 (NDVIL8, NDBIL8, NDWIL8) in 30 m resolution and for Sentinel-2 in a 10 m resolution, respectively. 
44 | </b>The LST is then calculated at a 30 m resolution using the Landsat 8/9 thermal band (B10) converted to brightness temperature in degrees Celsius (LSTL8). Then, the linear regression model between LSTL8 and the spectral indices NDVIL8, NDBIL8, NDWIL8 is used to calculate regression coefficients. Finally, these regression coefficients are used to calculate the downscaled LST using Sentinel-2 NDVI, NDBI, and NDWI spectral indices in a 10 m resolution. Regression residuals are resampled using bicubic interpolation, filtered using Gaussian convolution and added back to the downscaled LST. Also, Landsat 8/9 and Sentinel-2 natural color images (RGB) are generated to compare with the final LST layers.
45 | </b>For more detailed information see [the published article in Remote Sensing journal](https://www.mdpi.com/journal/remotesensing).
46 | 


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/javascript_codes/LST-downscaling_GEE_APP.js:
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  1 | /*
  2 | Authors: Onačillová, K., Gallay, M., Paluba, D., Péliová, A., Tokarčík, O., Laubertová, D. 
  3 | (For more info contact: katarina.onacillova@upjs.sk)
  4 | 
  5 | This code is free and open. 
  6 | By using this code and any data derived with it, 
  7 | you agree to cite the following reference 
  8 | in any publications derived from them:
  9 |  
 10 |     Onačillová, K., Gallay, M., Paluba, D., Péliová, A., Tokarčík, O., Laubertová, D. 2022: 
 11 |     Combining Landsat 8 and Sentinel 2 data in Google Earth Engine 
 12 |     to derive higher resolution land surface temperature maps in urban environment
 13 | 
 14 | This function selects the Landsat 8 and Sentinel 2 data based on user inputs
 15 | and performes the downscaling of LST
 16 | 
 17 | ###########################################################################################################################################################################
 18 | */
 19 | 
 20 | // Set up panels and widgets for display 
 21 | // Set up title and summary widgets
 22 | 
 23 | // // drawing tools
 24 | // var drawingTools = Map.drawingTools();
 25 | 
 26 | // // Use the addLayer method on the drawing tools directly.
 27 | // var geometries = [ee.Geometry.Polygon(
 28 | //         [[[21.134189623651263, 48.82947658234015],
 29 | //           [21.134189623651263, 48.57888560664585],
 30 | //           [21.433567065057513, 48.57888560664585],
 31 | //           [21.433567065057513, 48.82947658234015]]], null, false)];
 32 | 
 33 | // drawingTools.addLayer(geometries, 'my_geometry1', 'red');
 34 | // This sets the available draw modes. point and line would also be available
 35 | 
 36 | var map = ui.Map();
 37 | // Prints true since drawingTools() adds drawing tools to the map.
 38 | map.drawingTools().setDrawModes(["polygon", "rectangle"])
 39 | // Replace the default Map with the newly created map.
 40 | ui.root.widgets().reset([map]);
 41 | 
 42 | // App title
 43 | var header = ui.Label('Landsat Land Surface Temperature downscaling using Sentinel-2', {fontSize: '23px', fontWeight: 'bold', color: '77797e'});
 44 | 
 45 | // App summary
 46 | var text = ui.Label(
 47 |   'Landsat land surface temperature downscaled at 10 m by Sentinel-2 data. ' +
 48 |   'Developed for urban management.',
 49 |     {fontSize: '15px'});
 50 | 
 51 | // App title2
 52 | var header2 = ui.Label('How to cite:', {fontSize: '16px', fontWeight: 'bold', margin:'0px 0px -5px 8px'});
 53 | 
 54 | // App summary2
 55 | var text2 = ui.Label(
 56 |   'Onačillová, K.; Gallay, M.; Paluba, D.; Péliová, A.; Tokarčík, O.; Laubertová, D. Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment. Remote Sens. 2022, 14, 4076.',
 57 |     {fontSize: '15px', margin:'5px 0px 0px 8px'});
 58 | 
 59 | var textLink = ui.Label(' https://doi.org/10.3390/rs14164076',
 60 |   {fontSize: '15px', margin:'0px 0px 0px 8px',color:'blue'})
 61 |   .setUrl('https://doi.org/10.3390/rs14164076');
 62 |   
 63 | // Create a panel to hold text
 64 | var panel = ui.Panel({
 65 |   widgets:[header, text, header2],//Adds header and text
 66 |   style:{width: '500px',position:'middle-right', margin: '10px'}});
 67 | 
 68 | panel.add(text2)
 69 | panel.add(textLink)  
 70 | 
 71 | // Create variable for additional text and separators
 72 | 
 73 | // This creates another panel to house a line separator and instructions for the user
 74 | var intro = ui.Panel([
 75 |   ui.Label({
 76 |     value: '__________________________________________________________',
 77 |     style: {fontWeight: 'bold',  color: '77797e'},
 78 |   })]);
 79 | 
 80 | // Add panel to the larger panel 
 81 | panel.add(intro)
 82 | 
 83 | // Add main panel to the root of GUI
 84 | ui.root.insert(1,panel)
 85 | 
 86 | 
 87 | 
 88 | // Defining startDate and endDate in the UI
 89 | var dateLabel = ui.Label({
 90 |     value:'Select start and end dates to find a proper L8/9 and S2 image pair',
 91 |     style: {fontWeight: 'bold', fontSize: '14px', margin: '10px 5px'}
 92 |   })
 93 | 
 94 | var startLabel = ui.Label({
 95 |     value:'Start date',
 96 |     style: {margin: '0 55px 0 10px',fontSize: '12px',color: 'gray'}
 97 |   })
 98 | var endLabel = ui.Label({
 99 |     value:'End date',
100 |     style: {margin: '0 0px 0 10px',fontSize: '12px',color: 'gray'}
101 |   })
102 | var startDate_selected = ui.Textbox({placeholder: 'Start Date',  value: '2018-08-21',
103 |   style: {width: '100px'}});
104 | var endDate_selected = ui.Textbox({placeholder: 'End Date',  value: '2018-08-27',
105 |   style: {width: '100px'}});
106 |   
107 | var Landsat_collection_label = ui.Label('Choose a Landsat Collection',
108 |   {fontWeight: 'bold', fontSize: '14px', margin:'5px 0px 0px 8px'});
109 | 
110 | var cloudSliderLabel = ui.Label('Set the maximum cloud coverage (in %)',
111 |   {fontWeight: 'bold', fontSize: '14px', margin:'5px 0px 0px 8px'});
112 | 
113 | // Define labels for Landsat 8 and 9 Collections
114 | var L8 = 'Landsat 8 Image Collection',
115 |     L9 = 'Landsat 9 Image Collection';
116 | 
117 | // Define the select button for the AOI
118 | var selectCollection = ui.Select({
119 |   items:[L8,L9],
120 |   placeholder:'Select Landsat Collection',value: 'Landsat 8 Image Collection'
121 |   });
122 | 
123 | function setLandsatCollection(){
124 |   LCOll = selectCollection.getValue();
125 |   if (LCOll == L8){
126 |       collection = "L8";
127 |   }
128 |   else if(LCOll == L9){
129 |       collection = "L9";
130 |   }
131 | };
132 | 
133 | // Add a slider bar widget
134 | var cloudSlider = ui.Slider({min:0,max:100, style:{width:'200px'}}).setValue(5);
135 | 
136 | var GenerateImagesButton = ui.Button('Generate Landsat 8/9 and Sentinel-2 Image Collections', generateImsButton);
137 | 
138 | var appUseText1 = ui.Label('A brief description of how to work with the application can found at the',
139 |   {fontWeight: 'italic', fontSize: '14px', margin:'5px 0px 0px 8px'});
140 | var appUseLink1 = ui.Label(' GitHub repository ',
141 |   {fontWeight: 'italic', fontSize: '14px', margin:'0px 0px 0px 3px',color:'blue'})
142 |   .setUrl('https://github.com/palubad/LST-downscaling-to-10m-GEE#how-to-use-the-lst-downscaling-gee-application');
143 | var appUseText3 = ui.Label(' or in the ',
144 |   {fontWeight: 'italic', fontSize: '14px', margin:'0px 0px 0px 3px'});
145 | var appUseLink2 = ui.Label(' article',
146 |   {fontWeight: 'italic', fontSize: '14px', margin:'0px 0px 0px 3px',color:'blue'})
147 |   .setUrl('https://www.mdpi.com/2072-4292/14/16/4076/htm');
148 | var appUseText4 = ui.Label('.',
149 |   {fontWeight: 'italic', fontSize: '14px', margin:'0px 0px 0px 0px'});
150 | 
151 | var appUseText5 = ui.Label('To download the downscaled LST images use the ',
152 |   {fontWeight: 'italic', fontSize: '14px', margin:'5px 0px 0px 8px'});
153 | var appUseLink5 = ui.Label(' GEE Code Editor version',
154 |   {fontWeight: 'italic', fontSize: '14px', margin:'5px 0px 0px 3px',color:'blue'})
155 |   .setUrl('https://code.earthengine.google.com/005680c8acf54715c9b10e946400d842');
156 | var appUseText6 = ui.Label('.',
157 |   {fontWeight: 'italic', fontSize: '14px', margin:'5px 0px 0px 0px'});
158 | 
159 | panel.add(appUseText1)
160 |   .add((ui.Panel([appUseLink1, appUseText3, appUseLink2, appUseText4],ui.Panel.Layout.flow('horizontal'),{margin:'0 0 10px 0px'})))
161 |   .add((ui.Panel([appUseText5, appUseLink5, appUseText6],ui.Panel.Layout.flow('horizontal'),{margin:'0 0 10px 0px'})))
162 |   .add(dateLabel)
163 |   .add((ui.Panel([startLabel, endLabel],ui.Panel.Layout.flow('horizontal'))))
164 |   .add((ui.Panel([startDate_selected, endDate_selected],ui.Panel.Layout.flow('horizontal'))))
165 |   .add(Landsat_collection_label)
166 |   .add(selectCollection)
167 |   .add(cloudSliderLabel)
168 |   .add(cloudSlider)
169 |   .add(GenerateImagesButton);
170 | 
171 | // Define input parameters
172 | var startDate = startDate_selected.getValue(),
173 |     endDate = endDate_selected.getValue(),
174 |     collection = selectCollection.getValue(), // select "L8" or "L9"
175 |     // selected_geometry = map.drawingTools().layers().get(0).toGeometry(),
176 |     cloud_cover = ee.Number(cloudSlider.getValue());
177 | 
178 | var ROI = /* color: #98ff00 */ee.Geometry.Polygon(
179 |         [[[21.134189623651263, 48.82947658234015],
180 |           [21.134189623651263, 48.57888560664585],
181 |           [21.433567065057513, 48.57888560664585],
182 |           [21.433567065057513, 48.82947658234015]]], null, false);
183 | 
184 | map.centerObject(ROI,12);
185 | map.drawingTools().addLayer([ROI], 'ROI', 'green')
186 | 
187 | // Create a panel to hold the the next part.
188 | var panel2 = ui.Panel();
189 | panel2.style().set({
190 |   width: '500px',
191 |   position: 'bottom-right'
192 | });
193 | panel.add(panel2);
194 | 
195 | function generateImsButton () {
196 |   panel2.clear();
197 | var selected_geometry = map.drawingTools().layers().get(0).toGeometry();
198 | // var selected_geometry = ROI;
199 | 
200 |   // Set image collection conditions
201 | if (selectCollection.getValue() == "Landsat 8 Image Collection") {
202 |   var collection_id = "LANDSAT/LC08/C02/T1_L2";
203 | } 
204 | if (selectCollection.getValue() == "Landsat 9 Image Collection") {
205 |   var collection_id = "LANDSAT/LC09/C02/T1_L2";
206 | } 
207 | //Select Landsat 8 Surface Reflectance dataset coverage 
208 | var Landsat_collection = ee.ImageCollection(collection_id)      //Select the ImageCollection
209 |                     .filterBounds(selected_geometry)                         //Filter the ImageCollection by your study area
210 |                     .filterDate(startDate_selected.getValue(), endDate_selected.getValue())            //Filter the ImageCollection by the date interval
211 |                     .filterMetadata("CLOUD_COVER", "less_than", ee.Number(cloudSlider.getValue()));   //Filter the ImageCollection by the % of the cloud cover
212 | print(Landsat_collection, "- available Landsat images","that meet the input criteria"); /*Print available features (datasets) of Landsat_collection 
213 |                                         that meet the input criteria - see the results in the Console window*/
214 | 
215 | //Select Sentinel-2 Level-2A collection dataset coverage 
216 | var S2_collection = ee.ImageCollection("COPERNICUS/S2_SR")
217 |                 .filterBounds(selected_geometry)
218 |                 .filterDate(startDate_selected.getValue(), endDate_selected.getValue())
219 |                 .filterMetadata("CLOUDY_PIXEL_PERCENTAGE", "less_than", ee.Number(cloudSlider.getValue()));
220 | print(S2_collection, "- available Sentinel-2 images","that meet the input criteria");
221 | 
222 | var landsatList = Landsat_collection.toList(Landsat_collection.size())
223 | var landsatImagesText = "";
224 | 
225 | if ((Landsat_collection.size().getInfo() > 0) && (S2_collection.size().getInfo()>0)) {
226 |     for (var i=0; i<Landsat_collection.size().getInfo();i++) {
227 |       landsatImagesText = ee.String(landsatImagesText).cat(ee.Image(landsatList.get(i)).id()).cat('; ')
228 |     }
229 |     
230 |     var available_imagesLabel = ui.Label({
231 |         value:'Available Landsat image IDs:',
232 |         style: {margin: '0 0 0 10px',fontSize: '14px'}
233 |       })
234 |     var available_images = ui.Label({
235 |         value: landsatImagesText.getInfo(),
236 |         style: {margin: '0 0 0 10px',fontSize: '11px',color: 'gray'}
237 |       })
238 |     
239 |     var S2List = S2_collection.toList(S2_collection.size())
240 |     var S2ImagesText = "";
241 |     
242 |     for (var i=0; i<S2_collection.size().getInfo();i++) {
243 |       S2ImagesText = ee.String(S2ImagesText).cat(ee.Image(S2List.get(i)).id()).cat('; ')
244 |     }
245 |     
246 |     var available_imagesLabel_S2 = ui.Label({
247 |         value:'Available Sentinel-2 image IDs:',
248 |         style: {margin: '5px 0 0 10px',fontSize: '14px'}
249 |       })
250 |     var available_images_S2 = ui.Label({
251 |         value: S2ImagesText.getInfo(),
252 |         style: {margin: '0 0 0 10px',fontSize: '11px',color: 'gray'}
253 |       })
254 |     
255 |     panel2.add(available_imagesLabel)
256 |           .add(available_images)
257 |           .add(available_imagesLabel_S2)
258 |           .add(available_images_S2)
259 |   
260 |   var IDLabels = ui.Label({
261 |       value:'Enter the selected image IDs',
262 |       style: {fontWeight: 'bold', fontSize: '14px', margin: '10px 0 0 10px'}
263 |     })
264 |     
265 |   var LandsatLabel = ui.Label({
266 |       value:'Enter the selected Landsat ID',
267 |       style: {margin: '5px 0 0 10px',fontSize: '12px',color: 'gray'}
268 |     })
269 |     
270 |   var S2Label = ui.Label({
271 |       value:'Enter the selected Sentinel-2 ID',
272 |       style: {margin: '5px 0 0 10px',fontSize: '12px',color: 'gray'}
273 |     })
274 |   
275 |   var LandsatID = ui.Textbox({placeholder: 'Start Date',  value: 'LC08_186026_20180823',
276 |     style: {width: '350px'}});
277 |   
278 |   var S2ID = ui.Textbox({placeholder: 'End Date',  value: '20180823T094029_20180823T094320_T34UEU',
279 |     style: {width: '350px'}});
280 |   
281 |   panel2.add(IDLabels)
282 |     .add(LandsatLabel)
283 |     .add(LandsatID)
284 |     .add(S2Label)
285 |     .add(S2ID)
286 | 
287 | 
288 |   var otherPartsButton = ui.Button('Generate Downscaled LST', GenerateOtherParts);
289 |   
290 |   panel2.add(otherPartsButton);
291 |   
292 |   // Create a panel to hold the the next part.
293 |   var panel3 = ui.Panel();
294 |   panel3.style().set({
295 |     width: '500px',
296 |     position: 'bottom-right'
297 |   });
298 |   panel2.add(panel3);
299 |   
300 | 
301 |   function GenerateOtherParts () {
302 |     // reset the map
303 |     panel3.clear();
304 |     map.layers().reset();
305 |     
306 |     map.drawingTools().layers().get(0).setShown(false)
307 |     var selected_geometry = map.drawingTools().layers().get(0).toGeometry();
308 |   // var selected_geometry = ROI;
309 |   
310 |     var sentinel_dataset_ID = ee.String('COPERNICUS/S2_SR/');
311 |     
312 |     // To be filled after the first RUN
313 |     var S2_selected_dataset = ee.Image(ee.String(ee.String(sentinel_dataset_ID).cat(ee.String(S2ID.getValue()))).getInfo()), //Select one feature (dataset) of the printed S2_collection
314 |         Landsat_selected_dataset = ee.Image(ee.String(ee.String(collection_id).cat('/').cat(ee.String(LandsatID.getValue()))).getInfo()); //Select one feature (dataset) of the printed Landsat_collection
315 |     
316 |     var S2_date = S2_selected_dataset.get('system:time_start');
317 |     var S2_StartDate = ee.Date(S2_date).format('YYYY-MM-dd');
318 |     var S2_EndDate = ee.Date(ee.Number.parse(S2_date).add(86400000)).format('YYYY-MM-dd');
319 |     
320 |     var L_date = Landsat_selected_dataset.get('system:time_start');
321 |     var L_StartDate = ee.Date(L_date).format('YYYY-MM-dd');
322 |     var L_EndDate = ee.Date(ee.Number.parse(L_date).add(86400000)).format('YYYY-MM-dd');
323 |     
324 |     //Select Landsat 8 Surface Reflectance dataset coverage 
325 |     var selected_Landsat_collection = ee.ImageCollection(collection_id)      //Select the ImageCollection
326 |                         .filterBounds(selected_geometry)                         //Filter the ImageCollection by your study area
327 |                         .filterDate(L_StartDate, L_EndDate)            //Filter the ImageCollection by the date interval
328 |                         .filterMetadata("CLOUD_COVER", "less_than", ee.Number(cloudSlider.getValue()))   //Filter the ImageCollection by the % of the cloud cover
329 |                         .first();
330 |     
331 |     Landsat_selected_dataset = selected_Landsat_collection
332 |     
333 |     //Select Sentinel-2 Level-2A collection dataset coverage 
334 |     var selected_S2_collection = ee.ImageCollection("COPERNICUS/S2_SR")
335 |                     .filterBounds(selected_geometry)
336 |                     .filterDate(S2_StartDate, S2_EndDate)
337 |                     .filterMetadata("CLOUDY_PIXEL_PERCENTAGE", "less_than", ee.Number(cloudSlider.getValue()))
338 |                     .median();
339 |     
340 |     // Apply Scaling factors to Landsat 8/9 Collection 2 images
341 |     function applyScaleFactors(image) {
342 |       var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2).multiply(10000);
343 |       var thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0).subtract(273.15);
344 |       return image.addBands(opticalBands, null, true)
345 |                   .addBands(thermalBands, null, true);
346 |     }
347 |   
348 |   // Landsat_collection = Landsat_collection.map(applyScaleFactors);
349 |   Landsat_selected_dataset = applyScaleFactors(Landsat_selected_dataset);
350 |   
351 |   //Visualization parameters for color compositions RGB, CIR
352 |   var vizParams2 = {bands: ['SR_B4', 'SR_B3', 'SR_B2'],
353 |                     min: 0,
354 |                     max: 3000,
355 |                     gamma: 1.4,};
356 |   
357 |   // var vizParams3 = {bands: ['SR_B5', 'SR_B4', 'SR_B3'],
358 |   //                   min: 0,
359 |   //                   max: 3000,
360 |   //                   gamma: 1.4,};
361 |   
362 |   // ####################################################################################################################
363 |   
364 |   //Visualise the Landsat 8 Surface Reflectance image as a natural colour image (30m spatial resolution)
365 |   
366 |   var L8_image = Landsat_selected_dataset.clip(selected_geometry);
367 |   print(L8_image, 'Landsat-8 clipped Landsat_selected_dataset');
368 |   
369 |   // Map.addLayer(L8_image, vizParams2, "L8_image 30m", 0);
370 |   
371 |   //Select and visualise the Landsat 8 Surface Reflectance image as a colour infrared image (30m spatial resolution).
372 |   // var image2 = ee.Image("LANDSAT/LC08/C01/T1_SR/LC08_186026_20180823").clip(selected_geometry);
373 |   
374 |   // Map.addLayer(image2, vizParams3, "L8 CIR 30m", 0);
375 |   
376 |   // ####################################################################################################################
377 |   
378 |   //Calculate Landsat 8 spectral indices NDVI, NDWI and NDBI
379 |   
380 |   //Landsat 8 NDVI (30m spatial resolution)
381 |   var ndvi = Landsat_selected_dataset.normalizedDifference(['SR_B5', 'SR_B4']).rename('ndvi');
382 |   var ndviParams = {min: -1, max: 1, palette: ['purple', 'pink', 'green']};
383 |   var ndviclipped = ndvi.clip(selected_geometry);
384 |   // print(ndviclipped,'ndvi');
385 |   // Map.addLayer(ndviclipped, ndviParams, 'L8_ndvi', 0);
386 |   
387 |   
388 |   //Landsat 8 NDWI (30m spatial resolution).
389 |   var ndwi = Landsat_selected_dataset.normalizedDifference(['SR_B3', 'SR_B5']).rename('ndwi');
390 |   var ndwiParams = {min: -1, max: 1, palette: ['green', 'yellow', 'red', 'blue', 'navy']};
391 |   var ndwiclipped = ndwi.clip(selected_geometry);
392 |   //print(ndwiclipped,'ndwi');
393 |   //Map.addLayer(ndwiclipped, ndwiParams, 'L8_ndwi');
394 |   
395 |   //Landsat 8 NDBI (30m spatial resolution).
396 |   var ndbi = Landsat_selected_dataset.normalizedDifference(['SR_B6', 'SR_B5']).rename('ndbi');
397 |   var ndbiParams = {min: -1, max: 1, palette: ['blue', 'yellow', 'red']};
398 |   var ndbiclipped = ndbi.clip(selected_geometry);
399 |   //print(ndbiclipped,'ndbi');
400 |   //Map.addLayer(ndbiclipped, ndbiParams, 'L8_ndbi');
401 |   
402 |   
403 |   // ####################################################################################################################
404 |   
405 |   //Calculate Landsat 8 LST in Celsius Degrees (30m spatial resolution))
406 |   var L8_LST_30m = L8_image.select('ST_B10').rename('L8_LST_30m');
407 |   
408 |   //min max L8_LST_30m
409 |   var min = ee.Number(L8_LST_30m.reduceRegion({
410 |             reducer: ee.Reducer.min(),
411 |             scale: 30,
412 |             maxPixels: 1e9
413 |             }).values().get(0));
414 |   
415 |   var max = ee.Number(L8_LST_30m.reduceRegion({
416 |             reducer: ee.Reducer.max(),
417 |             scale: 30,
418 |             maxPixels: 1e9
419 |             }).values().get(0));
420 |   
421 |   print("Landsat 8 LST range (°C)", 
422 |         "- 30m spatial resolution:", "", min, 'min L8 LST (30m)', "", max, 'max L8 LST (30m)');
423 |   
424 |   // Map.addLayer(L8_LST_30m, {min: 21.157310795087767, max:41.58105080520278, //set LST min max
425 |   //             palette: ['040274', '040281', '0502a3', '0502b8', '0502ce', '0502e6',
426 |   //                       '0602ff', '235cb1', '307ef3', '269db1', '30c8e2', '32d3ef',
427 |   //                       //'3be285', '3ff38f', '86e26f', '3ae237', 'b5e22e', 
428 |   //                       'd6e21f', 'fff705', 'ffd611', 'ffb613', 'ff8b13', 'ff6e08', 'ff500d',
429 |   //                       'ff0000', 'de0101', 'c21301', 'a71001', '911003']},'L8-LST 30m');
430 |    
431 |    Export.image.toDrive({
432 |     image: L8_LST_30m,
433 |     description: 'L8_LST_30m',
434 |     folder: "image EE",
435 |     scale: 30,
436 |     region: selected_geometry,
437 |     fileFormat: 'GeoTIFF',
438 |     formatOptions: {
439 |       cloudOptimized: true
440 |     }
441 |   });
442 |   
443 |   // ####################################################################################################################
444 |   
445 |   var medianpixels = selected_S2_collection
446 |   var S2_image = medianpixels.clip(selected_geometry).divide(10000)   
447 |   
448 |   // ####################################################################################################################
449 |   
450 |   // //Select and visualise the Sentinel 2 image as a natural colour image (10m spatial resolution).
451 |   // Map.addLayer(S2_image, {bands: ['B4', 'B3', 'B2'], min: 0, max: 0.23}, 'S2 RGB 10m', 0);
452 |   
453 |   // //Select and visualise the Sentinel 2 image as a colour infrared image (10m spatial resolution).
454 |   // Map.addLayer(S2_image, {bands: ['B8', 'B4', 'B3'], min: 0, max: 0.23}, 'S2 CIR 10m', 0);
455 |   
456 |   // ####################################################################################################################
457 |   
458 |   //Calculate Sentinel 2 spectral indices NDVI, NDWI and NDBI
459 |   
460 |   //Sentinel 2 NDVI (10m spatial resolution).
461 |   var S2_ndvi = S2_image.normalizedDifference(['B8', 'B4']).rename('S2_NDVI');
462 |   var S2_ndviParams4 = {min: -1, max: 1, palette: ['purple', 'pink', 'green']};
463 |   var S2_ndviclipped = S2_ndvi.clip(selected_geometry);
464 |   //print(S2_ndviclipped,'S2_ndvi');
465 |   //Map.addLayer(S2_ndviclipped, S2_ndviParams4, 'S2_ndvi');
466 |   
467 |   //Sentinel 2 NDWI (10m spatial resolution).
468 |   var S2_ndwi = S2_image.normalizedDifference(['B3', 'B11']).rename('S2_NDWI');
469 |   var S2_ndwiParams4 = {min: -1, max: 1, palette: ['green', 'yellow', 'red', 'blue', 'navy']};
470 |   var S2_ndwiclipped = S2_ndwi.clip(selected_geometry);
471 |   //print(S2_ndwiclipped,'S2_ndwi');
472 |   //Map.addLayer(S2_ndwiclipped, S2_ndwiParams4, 'S2_ndwi');
473 |   
474 |   //Sentinel 2 NDBI (10m spatial resolution).
475 |   var S2_ndbi = S2_image.normalizedDifference(['B11', 'B8']).rename('S2_NDBI');
476 |   var S2_ndbiParams4 = {min: -1, max: 1, palette: ['blue', 'yellow', 'purple']};
477 |   var S2_ndbiclipped = S2_ndbi.clip(selected_geometry);
478 |   //print(S2_ndbiclipped,'S2_ndbi');
479 |   //Map.addLayer(S2_ndbiclipped, S2_ndbiParams4, 'S2_ndbi');
480 |   
481 |   
482 |   // ####################################################################################################################
483 |   //Create CHART for linearRegression
484 |   
485 |   var imgForChart = L8_LST_30m.addBands([ndvi,ndwi,ndbi])
486 |   var randomPoints = ee.FeatureCollection.randomPoints(selected_geometry, 500, 40);
487 | 
488 |   // Add values of image bands to the "points"
489 |   var pointsWithValue = imgForChart.reduceRegions({
490 |       collection: randomPoints,
491 |       reducer: ee.Reducer.mean(),
492 |       scale: 30,
493 |   });
494 |   
495 |   var getValues = ee.FeatureCollection(pointsWithValue.filter(ee.Filter.notNull(['L8_LST_30m', 'ndvi', 'ndwi', 'ndbi'])));
496 | 
497 |   
498 |   // Functions to create arrays containing all the values (LIA, VH and VV) of selected points
499 |   var LST_points = getValues.aggregate_array('L8_LST_30m');
500 |   var NDVI_points = getValues.aggregate_array('ndvi');
501 |   var NDWI_points = getValues.aggregate_array('ndwi');
502 |   var NDBI_points = getValues.aggregate_array('ndbi');
503 |   
504 |     
505 | 
506 | 
507 |   // ##########################################################################
508 |   // Regression Calculation 
509 |   
510 |   // preparing bands
511 |   var bands = ee.Image(1).addBands(ndvi).addBands(ndbi).addBands(ndwi).addBands(L8_LST_30m).rename(["constant", "ndvi", "ndbi", "ndwi", "L8"]);
512 |   
513 |   // run the multiple regression analysis
514 |   var imageRegression = bands.reduceRegion({
515 |                         reducer: ee.Reducer.linearRegression({numX:4, numY:1}),
516 |                         geometry: selected_geometry,
517 |                         scale: 30,
518 |                         });
519 |   
520 |   // create images from coefficients
521 |   print ("", "* Multipe linear regression model" ,"coefficients for Landsat 8 LST downscaling")
522 |   var coefList2 = ee.Array(imageRegression.get("coefficients")).toList();
523 |   var intercept2 = ee.Image(ee.Number(ee.List(coefList2.get(0)).get(0)));
524 |   var intercept2_list = ee.List(coefList2.get(0)).get(0);
525 |   var slopeNDVI2 = ee.Image(ee.Number(ee.List(coefList2.get(1)).get(0)));
526 |   var slopeNDVI2_list =  ee.List(coefList2.get(1)).get(0);
527 |   var slopeNDBI2 = ee.Image(ee.Number(ee.List(coefList2.get(2)).get(0)));
528 |   var slopeNDBI2_list =  ee.List(coefList2.get(2)).get(0);
529 |   var slopeNDWI2 = ee.Image(ee.Number(ee.List(coefList2.get(3)).get(0)));
530 |   var slopeNDWI2_list =  ee.List(coefList2.get(3)).get(0);
531 |   
532 |   print(intercept2_list, "intercept", "",
533 |         slopeNDVI2_list, "slope NDVI", "", 
534 |         slopeNDBI2_list, "slope NDBI", "",
535 |         slopeNDWI2_list, "slope NDWI");
536 |   
537 |   // calculate the final downscaled image
538 |   var downscaled_LST_10m = ee.Image(intercept2).add(slopeNDVI2.multiply(S2_ndvi))
539 |               .add(slopeNDBI2.multiply(S2_ndbi)).add(slopeNDWI2.multiply(S2_ndwi));
540 |   
541 |   // add to map
542 |   //Map.addLayer(downscaled_LST_10m, lstParams2, 'S2-LST 10m');
543 |   
544 |   // #######################################################################
545 |   // L8-LST 30 m model calculation
546 |   
547 |   var L8_LST_MODEL = intercept2.add(slopeNDVI2.multiply(ndvi))
548 |               .add(slopeNDBI2.multiply(ndbi))
549 |               .add(slopeNDWI2.multiply(ndwi)).clip(selected_geometry);
550 |   
551 |   var L8_RESIDUALS = L8_LST_30m.subtract(L8_LST_MODEL);
552 |   
553 |   var palette = ['040274', '040281', '0502a3', '0502b8', '0502ce', '0502e6',
554 |                         '0602ff', '235cb1', '307ef3', '269db1', '30c8e2', '32d3ef',
555 |                         //'3be285', '3ff38f', '86e26f', '3ae237', 'b5e22e', 
556 |                         'd6e21f', 'fff705', 'ffd611', 'ffb613', 'ff8b13', 'ff6e08', 'ff500d',
557 |                         'ff0000', 'de0101', 'c21301', 'a71001', '911003'];
558 |   
559 |   // Map.addLayer(L8_LST_MODEL, {min: 21.157310795087767, max:41.58105080520278, //set LST min max
560 |   //             palette: palette}, 'L8_LST_MODEL');
561 |   
562 |   
563 |   // ####################################################################### 
564 |   // Gaussian convolution
565 |   
566 |   // Define a gaussian kernel
567 |   var gaussian = ee.Kernel.gaussian({
568 |     radius: 1.5, units: 'pixels'
569 |   });
570 |   
571 |   // Smooth the image by convolving with the gaussian kernel.
572 |   var L8_RESIDUALS_gaussian = L8_RESIDUALS.resample("bicubic").convolve(gaussian);
573 |   
574 |   var visParam_residuals = {
575 |           min: -10,
576 |           max: 9,
577 |           palette: ['blue', 'yellow', 'red']
578 |           };
579 |   
580 |   // Map.addLayer(L8_RESIDUALS, visParam_residuals, "L8_RESIDUALS_original",false);
581 |   // Map.addLayer(L8_RESIDUALS_gaussian, visParam_residuals, "L8_RESIDUALS_gaussian",false);
582 |   
583 |   // 
584 |   // Calculate the final downscaled LSTs
585 |   var downscaled_LST_10m2 = ee.Image(intercept2).add(slopeNDVI2.multiply(S2_ndvi))
586 |                 .add(slopeNDBI2.multiply(S2_ndbi)).add(slopeNDWI2.multiply(S2_ndwi));
587 |   
588 |   // Map.addLayer(downscaled_LST_10m2, lstParams2, 'S2-LST 10m (no residuals)');
589 |   
590 |   var S2_LST_10_w_Residuals = downscaled_LST_10m2.add(L8_RESIDUALS_gaussian)
591 | 
592 |   var min_legend = ee.Number(S2_LST_10_w_Residuals.reduceRegion({
593 |           reducer: ee.Reducer.min(),
594 |           scale: 10,
595 |           maxPixels: 1e12,
596 |           crs: 'EPSG:4326'
597 |           }).values().get(0));
598 | 
599 |   var max_legend = ee.Number(S2_LST_10_w_Residuals.reduceRegion({
600 |             reducer: ee.Reducer.max(),
601 |             scale: 10,
602 |             maxPixels: 1e12,
603 |             crs: 'EPSG:4326'
604 |             }).values().get(0));
605 |   
606 |   
607 |   var lstParams2 = {min: min_legend.getInfo(), max:max_legend.getInfo(), 
608 |                   palette: ['040274', '040281', '0502a3', '0502b8', '0502ce', '0502e6',
609 |                             '0602ff', '307ef3', '30c8e2', '32d3ef',
610 |                             'fff705', 'ffd611', 'ffb613', 'ff8b13', 'ff6e08', 'ff500d',
611 |                             'ff0000', 'de0101', 'c21301', 'a71001', '911003']};
612 | 
613 |   // Export the final image to Drive
614 |   Export.image.toDrive({
615 |     image: S2_LST_10_w_Residuals,
616 |     description: 'Downscaled_LST_usingS2_10m',
617 |     folder: "image EE",
618 |     scale: 10,
619 |     region: selected_geometry,
620 |     crs: 'EPSG:4326',
621 |     maxPixels: 1e12,
622 |     fileFormat: 'GeoTIFF',
623 |     formatOptions: {
624 |       cloudOptimized: true
625 |     }
626 |   });
627 |   
628 |   
629 |   
630 |   // ##################################################################### //
631 |   // #################### User interface preparation ##################### //
632 |   // ##################################################################### //
633 |   
634 |   // Inspiration was taken from Abigail Barenblitt & Temilola Fatoyinbo presentation and code available at
635 |   // https://appliedsciences.nasa.gov/sites/default/files/2020-10/Part3App_Final.pdf
636 |   // https://code.earthengine.google.com/705e6c7974f42373f1b9535e2bd40243
637 |   
638 |   // Setting up map appearance and app layers 
639 |   // Center the map to the selected_geometry
640 |   Map.centerObject(selected_geometry,12); 
641 |   
642 |   // Create variables for GUI layers for each layer 
643 |   var L8_image_viz = ui.Map.Layer(L8_image, vizParams2, "Landsat 8/9 RGB",false);
644 |   var S2_image_viz = ui.Map.Layer(S2_image, {bands: ['B4', 'B3', 'B2'], min: 0, max: 0.23}, 'Sentinel-2 RGB',false);
645 |   var L8_LST_viz = ui.Map.Layer(L8_LST_30m, lstParams2,'L8-LST 30m', false);
646 |   var residuals_layer = ui.Map.Layer(L8_RESIDUALS_gaussian, visParam_residuals, "Landsat 8/9 LST regression residuals",false);
647 |   var L8_LST_10_noResiduals = ui.Map.Layer(downscaled_LST_10m2, lstParams2, 'S2-LST 10m (no residuals)', false);
648 |   var L8_LST_10_with_Residuals = ui.Map.Layer(S2_LST_10_w_Residuals, lstParams2, "S2-LST 10m with residuals", true);
649 |   
650 | 
651 |   // Add layers to the map
652 |   map.add(L8_image_viz);
653 |   map.add(S2_image_viz);
654 |   map.add(residuals_layer);
655 |   map.add(L8_LST_viz);
656 |   map.add(L8_LST_10_noResiduals);
657 |   map.add(L8_LST_10_with_Residuals);
658 |   
659 |   ///////////////////////////////////////////////////////////////
660 |   //            Add checkbox widgets and legends               //
661 |   ///////////////////////////////////////////////////////////////
662 |   
663 |   var selectLayerLabel = ui.Label({
664 |       value:'Select layers to display.',
665 |       style: {fontWeight: 'bold', fontSize: '16px', margin: '10px 0 0 10px'}
666 |     });
667 |   
668 |   // Add checkboxes to our display
669 |   var additionalCheck = ui.Checkbox('Landsat 8/9 RGB').setValue(false); 
670 |   var additionalCheck2 = ui.Checkbox('Sentinel-2 RGB').setValue(false);
671 |   var additionalCheck3 = ui.Checkbox('Landsat 8/9 LST').setValue(false);
672 |   var additionalCheck4 = ui.Checkbox('Landsat 8/9 LST regression residuals').setValue(false); 
673 |   var additionalCheck5 = ui.Checkbox('LST 10 m (no residuals)').setValue(false); 
674 |   var additionalCheck6 = ui.Checkbox('LST 10 m with residuals').setValue(true); 
675 |   var additionalCheck7 = ui.Checkbox('ROI').setValue(false); 
676 | 
677 |   // Create legend
678 |   // Set position of panel
679 |   var legend = ui.Panel({
680 |     style: {
681 |       position: 'bottom-left',
682 |       padding: '8px 15px'
683 |     }
684 |   });
685 |   
686 |   // The following creates and styles 1 row of the legend.
687 |   var makeRowa = function(color, name) {
688 |    
689 |         // Create the label that is actually the colored box.
690 |         var colorBox = ui.Label({
691 |           style: {
692 |             backgroundColor: '#' + color,
693 |             // Use padding to give the box height and width.
694 |             padding: '8px',
695 |             margin: '0 0 4px 0'
696 |           }
697 |         });
698 |    
699 |         // Create a label with the description text.
700 |         var description = ui.Label({
701 |           value: name,
702 |           style: {margin: '0 0 4px 6px'}
703 |         });
704 |    
705 |         // Return the panel
706 |         return ui.Panel({
707 |           widgets: [colorBox, description],
708 |           layout: ui.Panel.Layout.Flow('horizontal')
709 |         });
710 |   };
711 |   
712 |   
713 |   // Legend
714 |   // Legend Label
715 |   var legendLabel = ui.Label({value:'Land Surface Temperature (°C)',
716 |   style: {fontWeight: 'bold', fontSize: '16px', margin: '10px 0 0 10px'}
717 |   });
718 |   
719 |   // This uses function to construct a legend for the given single-band vis
720 |   // parameters.  Requires that the vis parameters specify 'min' and 
721 |   // 'max' but not 'bands'.
722 |   function makeLegend (params) {
723 |     var lon = ee.Image.pixelLonLat().select('longitude');
724 |     var gradient = lon.multiply((params.max-params.min)/100.0).add(params.min);
725 |     var legendImage = gradient.visualize(params);
726 |     
727 |     var thumb = ui.Thumbnail({
728 |       image: legendImage, 
729 |       params: {bbox:'0,0,100,8', dimensions:'256x20'},  
730 |       style: {position: 'bottom-center'}
731 |     });
732 |     
733 |     
734 |     var panel4 = ui.Panel({
735 |       widgets: [
736 |         ui.Label(ee.Number(min_legend).round().getInfo()), 
737 |         ui.Label({style: {stretch: 'horizontal'}}), 
738 |         ui.Label(ee.Number(max_legend).round().getInfo())
739 |       ],
740 |       layout: ui.Panel.Layout.flow('horizontal'),
741 |       style: {stretch: 'horizontal', maxWidth: '270px', padding: '0px 0px 0px 0px',
742 |       margin: '0 0 0 0'}
743 |     });
744 |     return ui.Panel().add(panel4).add(thumb);
745 |   }
746 |   
747 |   var ChartButton = ui.Button('Generate charts of spectral indices vs Landsat 8/9 LST', GenerateCharts);
748 |   
749 |   
750 |   // Add widgets to the panel in an order
751 |   panel3.add(selectLayerLabel)
752 |         .add(additionalCheck6)
753 |         .add(additionalCheck5)
754 |         .add(additionalCheck3)
755 |         .add(additionalCheck4)
756 |         .add(additionalCheck2)
757 |         .add(additionalCheck)
758 |         .add(additionalCheck7)
759 |         .add(legendLabel)
760 |         .add(makeLegend(lstParams2))
761 |         .add(ChartButton)
762 |   
763 |     // Create a panel to hold the the next part.
764 |   var panel5 = ui.Panel();
765 |   panel5.style().set({
766 |     width: '500px',
767 |     position: 'bottom-right'
768 |   });
769 |   panel3.add(panel5);
770 |   
771 |   
772 |   function GenerateCharts() {
773 |     panel5.clear();
774 |     var chartNDVI = ui.Chart.array.values(LST_points, 0, NDVI_points)
775 |     .setOptions({
776 |       title: 'Correlation LST - NDVI  based on Landsat 8/9 image',
777 |       legend: true,
778 |       hAxis: {title: 'NDVI'},
779 |       vAxis: {title: 'LST (°C)'},
780 |       series: {
781 |           0: {pointSize: 1, color: 'blue',visibleInLegend: false}, // observations
782 |         },
783 |       trendlines: {
784 |           0: {
785 |               type: 'linear',
786 |               color: 'CC0000',
787 |               showR2: true,
788 |               visibleInLegend: true
789 |           }
790 |       }
791 |     });
792 |     
793 |     var chartNDWI = ui.Chart.array.values(LST_points, 0, NDWI_points)
794 |     .setOptions({
795 |       title: 'Correlation LST - NDWI  based on Landsat 8/9 image',
796 |       legend: true,
797 |       hAxis: {title: 'NDWI'},
798 |       vAxis: {title: 'LST (°C)'},
799 |       series: {
800 |           0: {pointSize: 1, color: 'blue',visibleInLegend: false}, // observations
801 |         },
802 |       trendlines: {
803 |           0: {
804 |               type: 'linear',
805 |               color: 'CC0000',
806 |               showR2: true,
807 |               visibleInLegend: true
808 |           }
809 |       }
810 |     });
811 |     
812 |       var chartNDBI = ui.Chart.array.values(LST_points, 0, NDBI_points)
813 |     .setOptions({
814 |       title: 'Correlation LST - NDBI  based on Landsat 8/9 image',
815 |       legend: true,
816 |       hAxis: {title: 'NDBI'},
817 |       vAxis: {title: 'LST (°C)'},
818 |       series: {
819 |           0: {pointSize: 1, color: 'blue',visibleInLegend: false}, // observations
820 |         },
821 |       trendlines: {
822 |           0: {
823 |               type: 'linear',
824 |               color: 'CC0000',
825 |               showR2: true,
826 |               visibleInLegend: true
827 |           }
828 |       }
829 |     });
830 |     panel5.add(chartNDVI)
831 |         .add(chartNDWI)
832 |         .add(chartNDBI)
833 |   } 
834 |   
835 |   ///////////////////////////////////////////////////////////////
836 |   //             Add functionality to widgets                  //
837 |   ///////////////////////////////////////////////////////////////
838 |   
839 |   //For each checkbox we create function so that clicking the checkbox
840 |   //Turns on layers of interest
841 |   
842 |   // L8_image
843 |   var doCheckbox = function() {
844 |     
845 |     additionalCheck.onChange(function(checked){
846 |     L8_image_viz.setShown(checked)
847 |     })
848 |   }
849 |   doCheckbox();
850 |   
851 |   // S2_RGB
852 |   var doCheckbox2 = function() {
853 |     
854 |     additionalCheck2.onChange(function(checked){
855 |     S2_image_viz.setShown(checked)
856 |     })
857 |     
858 |   
859 |   }
860 |   doCheckbox2();
861 |   
862 |   // L8_LST
863 |   var doCheckbox3 = function() {
864 |     
865 |     additionalCheck3.onChange(function(checked){
866 |     L8_LST_viz.setShown(checked)
867 |     })
868 |     
869 |   
870 |   }
871 |   doCheckbox3();
872 |   
873 |   // Residuals
874 |   var doCheckbox4 = function() {
875 |     
876 |     additionalCheck4.onChange(function(checked){
877 |     residuals_layer.setShown(checked)
878 |     })
879 |   }
880 |   doCheckbox4();
881 |   
882 |   // LST 10 m without residuals
883 |   var doCheckbox5 = function() {
884 |     
885 |     additionalCheck5.onChange(function(checked){
886 |     L8_LST_10_noResiduals.setShown(checked)
887 |     })
888 |     
889 |   
890 |   }
891 |   doCheckbox5();
892 |   
893 |   // FINAL downscaled image with residuals
894 |   var doCheckbox6 = function() {
895 |     
896 |     additionalCheck6.onChange(function(checked){
897 |     L8_LST_10_with_Residuals.setShown(checked)
898 |     })
899 |     
900 |   
901 |   }
902 |   doCheckbox6();
903 |   
904 |   var doCheckbox7 = function() {
905 |   
906 |   additionalCheck7.onChange(function(checked){
907 |   map.drawingTools().layers().get(0).setShown(checked)
908 |   })
909 |   
910 | 
911 |   }
912 |   doCheckbox7();
913 |     
914 |   }
915 |   
916 | }
917 |   else {
918 |     var Error_message = ui.Label({
919 |       value:'There are 0 images either in Sentinel-2 or Landsat 8/9 Image Collections. Please try to change some parameters and generate images again.',
920 |       style: {margin: '0 0 0 10px',fontSize: '14px', color: 'red'}
921 |     })
922 |     panel2.add(Error_message)
923 |   }
924 |   }
925 | 


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