- added & updated datasets

- Added [Urban Heat Island Intensity (UHII)](https://gee-community-catalog.org/projects/uhii/) - Added [High Res Extended Spring Indices database](https://gee-community-catalog.org/projects/spring_indices) - Added [Global Groundwater-Dependent Ecosystems (GDEs)](https://gee-community-catalog.org/projects/gde) - Added [Canada 2023 Wildfires](https://gee-community-catalog.org/projects/ca_fires) - Added new dataset for Insiders Program [National Structures Inventory](https://gee-community-catalog.org/projects/nsi/) - Updated [Canadian Satellite-Based Forest Inventory (SBFI)](https://gee-community-catalog.org/projects/ca_sbfi) - Updated [High-resolution annual forest land cover maps for Canada's forested ecosystems (1984-2022)](https://gee-community-catalog.org/projects/ca_lc) to include 2019-2022 datasets - Updated dataset for insiders program [gNATSGO (gridded National Soil Survey Geographic Database)](https://gee-community-catalog.org/projects/gnatsgo) - Updated Weekly updates to [USDM drought monitor](https://gee-community-catalog.org/projects/usdm/)
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diff --git a/community_datasets.json b/community_datasets.json
@@ -9196,7 +9196,7 @@
     "thematic_group": "Geophysical, Biological & Biogeochemical"
   },
   {
-    "title": "High-resolution annual forest land cover maps for Canada's forested ecosystems (1984-2019)",
+    "title": "High-resolution annual forest land cover maps for Canada's forested ecosystems (1984-2022)",
     "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-FORESTED-ECOSYSTEM-LC",
     "type": "image_collection",
     "id": "projects/sat-io/open-datasets/CA_FOREST_LC_VLCE2",
@@ -9206,6 +9206,171 @@
     "docs": "https://gee-community-catalog.org/projects/ca_lc/",
     "thematic_group": "Agriculture, Vegetation and Forestry"
   },
+  {
+    "title": "Enhanced Spring Indices: BloomDaymetv4",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/EXTENDED-SPRING-INDICES",
+    "type": "image_collection",
+    "id": "projects/sat-io/open-datasets/SIx_products/BloomDaymetv4",
+    "provider": "Izquierdo-Verdiguier. 2024",
+    "tags": "spring onset, phenology, climate change",
+    "license": "Creative Commons Attribution Non Commercial 4.0 International",
+    "docs": "https://gee-community-catalog.org/projects/spring_indices/",
+    "thematic_group": "Analysis Ready Data"
+  },
+  {
+    "title": "Enhanced Spring Indices: BloomEuropev3",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/EXTENDED-SPRING-INDICES",
+    "type": "image_collection",
+    "id": "projects/sat-io/open-datasets/SIx_products/BloomEuropev3",
+    "provider": "Izquierdo-Verdiguier. 2024",
+    "tags": "spring onset, phenology, climate change, Europe",
+    "license": "Creative Commons Attribution Non Commercial 4.0 International",
+    "docs": "https://gee-community-catalog.org/projects/spring_indices/",
+    "thematic_group": "Analysis Ready Data"
+  },
+  {
+    "title": "Enhanced Spring Indices: DI_Daymetv4",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/EXTENDED-SPRING-INDICES",
+    "type": "image_collection",
+    "id": "projects/sat-io/open-datasets/SIx_products/DI_Daymetv4",
+    "provider": "Izquierdo-Verdiguier. 2024",
+    "tags": "spring onset, phenology, climate change",
+    "license": "Creative Commons Attribution Non Commercial 4.0 International",
+    "docs": "https://gee-community-catalog.org/projects/spring_indices/",
+    "thematic_group": "Analysis Ready Data"
+  },
+  {
+    "title": "Enhanced Spring Indices: DI_Europev3",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/EXTENDED-SPRING-INDICES",
+    "type": "image_collection",
+    "id": "projects/sat-io/open-datasets/SIx_products/DI_Europev3",
+    "provider": "Izquierdo-Verdiguier. 2024",
+    "tags": "spring onset, phenology, climate change, Europe",
+    "license": "Creative Commons Attribution Non Commercial 4.0 International",
+    "docs": "https://gee-community-catalog.org/projects/spring_indices/",
+    "thematic_group": "Analysis Ready Data"
+  },
+  {
+    "title": "Enhanced Spring Indices: LeafDaymetv4",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/EXTENDED-SPRING-INDICES",
+    "type": "image_collection",
+    "id": "projects/sat-io/open-datasets/SIx_products/LeafDaymetv4",
+    "provider": "Izquierdo-Verdiguier. 2024",
+    "tags": "spring onset, phenology, climate change",
+    "license": "Creative Commons Attribution Non Commercial 4.0 International",
+    "docs": "https://gee-community-catalog.org/projects/spring_indices/",
+    "thematic_group": "Analysis Ready Data"
+  },
+  {
+    "title": "Enhanced Spring Indices: LeafEuropev3",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:analysis-ready-data/EXTENDED-SPRING-INDICES",
+    "type": "image_collection",
+    "id": "projects/sat-io/open-datasets/SIx_products/LeafEuropev3",
+    "provider": "Izquierdo-Verdiguier. 2024",
+    "tags": "spring onset, phenology, climate change, Europe",
+    "license": "Creative Commons Attribution Non Commercial 4.0 International",
+    "docs": "https://gee-community-catalog.org/projects/spring_indices/",
+    "thematic_group": "Analysis Ready Data"
+  },
+  {
+    "title": "Urban Heat Island Intensity: AMOD2",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/URBAN-HEAT-ISLAND-INTENSITY",
+    "type": "image_collection",
+    "id": "projects/sat-io/open-datasets/UHII/AMOD2",
+    "provider": "Yang et al 2024",
+    "tags": "urban, heat, climate, city",
+    "license": "Creative Commons Attribution 4.0 International",
+    "docs": "https://gee-community-catalog.org/projects/uhii/",
+    "thematic_group": "Weather and Climate Layers"
+  },
+  {
+    "title": "Urban Heat Island Intensity: MOD1",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/URBAN-HEAT-ISLAND-INTENSITY",
+    "type": "image_collection",
+    "id": "projects/sat-io/open-datasets/UHII/MOD1",
+    "provider": "Yang et al 2024",
+    "tags": "urban, heat, climate, city",
+    "license": "Creative Commons Attribution 4.0 International",
+    "docs": "https://gee-community-catalog.org/projects/uhii/",
+    "thematic_group": "Weather and Climate Layers"
+  },
+  {
+    "title": "Urban Heat Island Intensity: MOD2",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/URBAN-HEAT-ISLAND-INTENSITY",
+    "type": "image_collection",
+    "id": "projects/sat-io/open-datasets/UHII/MOD2",
+    "provider": "Yang et al 2024",
+    "tags": "urban, heat, climate, city",
+    "license": "Creative Commons Attribution 4.0 International",
+    "docs": "https://gee-community-catalog.org/projects/uhii/",
+    "thematic_group": "Weather and Climate Layers"
+  },
+  {
+    "title": "Urban Heat Island Intensity: MYD1",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/URBAN-HEAT-ISLAND-INTENSITY",
+    "type": "image_collection",
+    "id": "projects/sat-io/open-datasets/UHII/MYD1",
+    "provider": "Yang et al 2024",
+    "tags": "urban, heat, climate, city",
+    "license": "Creative Commons Attribution 4.0 International",
+    "docs": "https://gee-community-catalog.org/projects/uhii/",
+    "thematic_group": "Weather and Climate Layers"
+  },
+  {
+    "title": "Urban Heat Island Intensity: MYD2",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/URBAN-HEAT-ISLAND-INTENSITY",
+    "type": "image_collection",
+    "id": "projects/sat-io/open-datasets/UHII/MYD2",
+    "provider": "Yang et al 2024",
+    "tags": "urban, heat, climate, city",
+    "license": "Creative Commons Attribution 4.0 International",
+    "docs": "https://gee-community-catalog.org/projects/uhii/",
+    "thematic_group": "Weather and Climate Layers"
+  },
+  {
+    "title": "Urban Heat Island Intensity: SAT",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/URBAN-HEAT-ISLAND-INTENSITY",
+    "type": "image_collection",
+    "id": "projects/sat-io/open-datasets/UHII/SAT",
+    "provider": "Yang et al 2024",
+    "tags": "urban, heat, climate, city",
+    "license": "Creative Commons Attribution 4.0 International",
+    "docs": "https://gee-community-catalog.org/projects/uhii/",
+    "thematic_group": "Weather and Climate Layers"
+  },
+  {
+    "title": "Urban Heat Island Intensity: SMOD2",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/URBAN-HEAT-ISLAND-INTENSITY",
+    "type": "image_collection",
+    "id": "projects/sat-io/open-datasets/UHII/SMOD2",
+    "provider": "Yang et al 2024",
+    "tags": "urban, heat, climate, city",
+    "license": "Creative Commons Attribution 4.0 International",
+    "docs": "https://gee-community-catalog.org/projects/uhii/",
+    "thematic_group": "Weather and Climate Layers"
+  },
+  {
+    "title": "Urban Heat Island Intensity: SMYD1",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/weather-climate/URBAN-HEAT-ISLAND-INTENSITY",
+    "type": "image_collection",
+    "id": "projects/sat-io/open-datasets/UHII/SMYD1",
+    "provider": "Yang et al 2024",
+    "tags": "urban, heat, climate, city",
+    "license": "Creative Commons Attribution 4.0 International",
+    "docs": "https://gee-community-catalog.org/projects/uhii/",
+    "thematic_group": "Weather and Climate Layers"
+  },
+  {
+    "title": "Global Groundwater-Dependent Ecosystems (GDEs)",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:biodiversity-ecosystems-habitat/GROUNDWATER-DEP-ECOSYSTEMS",
+    "type": "image_collection",
+    "id": "projects/sat-io/open-datasets/GlobalGDEMap_v6_TNC",
+    "provider": "TNC, Rohde, M.M., Albano, C.M., Huggins, X. et al",
+    "tags": "Global Groundwater-Dependent Ecosystems, GDE Mapping, Conflict Hotspots, Climate Change, Food Security",
+    "license": "Creative Commons Attribution 4.0 International",
+    "docs": "https://gee-community-catalog.org/projects/gde/",
+    "thematic_group": "Biodiversity, Ecosystems & Habitat Layers"
+  },
   {
     "title": "Distance-to-second class for the leading tree species map 2019",
     "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-DISTANCE-2-SECOND-CLASS",
@@ -27432,6 +27597,28 @@
     "docs": "https://gee-community-catalog.org/projects/ca_sbfi/",
     "thematic_group": "Agriculture, Vegetation and Forestry"
   },
+  {
+    "title": "Canadian Satellite-Based Forest Inventory (SBFI): Gridded Labels",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-SBFI",
+    "type": "table",
+    "id": "projects/sat-io/open-datasets/CA_FOREST/CA_SBFI/Grid_Labels",
+    "provider": "Wulder et al. 2024",
+    "tags": "Landsat, land cover, change detection, forest structure, biomass, NFI",
+    "license": "Open Government Licence - Canada",
+    "docs": "https://gee-community-catalog.org/projects/ca_sbfi/",
+    "thematic_group": "Agriculture, Vegetation and Forestry"
+  },
+  {
+    "title": "Canada 2023 Wildfires",
+    "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/CA-FOREST-FIRE-2023",
+    "type": "image",
+    "id": "projects/sat-io/open-datasets/CA_FOREST/CA_Forest_Wildfire_2023_Summer_Fall",
+    "provider": "Pelletier et al. 2024",
+    "tags": "Wildfire, Tracking Intra- Inter-year Change (TIIC), Landsat, Sentinel, Burned Area, Fire Occurrence, Canada",
+    "license": "Open Government Licence - Canada",
+    "docs": "https://gee-community-catalog.org/projects/ca_fires/",
+    "thematic_group": "Fire Monitoring and Analysis"
+  },
   {
     "title": "GLC_FCS30D Global 30-meter Land Cover Change Dataset (1985-2022) Annual",
     "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-landuse-landcover/GLC-FCS30D",

diff --git a/docs/changelog.md b/docs/changelog.md
@@ -3,6 +3,17 @@
 ![GEE Community Datasets](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/samapriya/34bc0c1280d475d3a69e3b60a706226e/raw/community.json)
 ![GitHub Release](https://img.shields.io/github/v/release/samapriya/awesome-gee-community-datasets)
 
+#### Updated 2024-08-30
+- Added [Urban Heat Island Intensity (UHII)](https://gee-community-catalog.org/projects/uhii/)
+- Added [High Res Extended Spring Indices database](https://gee-community-catalog.org/projects/spring_indices)
+- Added [Global Groundwater-Dependent Ecosystems (GDEs)](https://gee-community-catalog.org/projects/gde)
+- Added [Canada 2023 Wildfires](https://gee-community-catalog.org/projects/ca_fires)
+- Added new dataset for Insiders Program [National Structures Inventory](https://gee-community-catalog.org/projects/nsi/)
+- Updated [Canadian Satellite-Based Forest Inventory (SBFI)](https://gee-community-catalog.org/projects/ca_sbfi)
+- Updated [High-resolution annual forest land cover maps for Canada's forested ecosystems (1984-2022)](https://gee-community-catalog.org/projects/ca_lc) to include 2019-2022 datasets
+- Updated dataset for insiders program [gNATSGO (gridded National Soil Survey Geographic Database)](https://gee-community-catalog.org/projects/gnatsgo)
+- Updated Weekly updates to [USDM drought monitor](https://gee-community-catalog.org/projects/usdm/)
+
 #### Updated 2024-08-01
 - Added [Global Mangrove Canopy Height Maps Derived from TanDEM-X](https://gee-community-catalog.org/projects/mangrove_ht_tandemx/)
 - Added [Cyanobacteria Aggregated Manual Labels (CAML)](https://gee-community-catalog.org/projects/caml/)

diff --git a/docs/projects/ca_fires.md b/docs/projects/ca_fires.md
@@ -0,0 +1,41 @@
+# Canada 2023 Wildfires
+
+Canada's 2023 wildfire season represented the largest area burned in a single fire season in Canada’s modern history. Using the Tracking Intra- and
+Inter-year Change (TIIC) algorithm, wildfires occurring within Canada’s forested ecosystems during the 2023 fire season were detected at a 30-m
+resolution. Time series data used to identify wildfires originated from Sentinel-2A and -2B, and Landsat-8 and -9. Fires have been grouped into two
+classes based on detection period: summer fires and fall fires. Summer fires were detected between May 30 and September 17, and fall fires were
+detected between September 17 and October 25. For summer fires, burned pixels were identified by TIIC as changed and typed as fire.
+
+For the fall period, TIIC only detected changes within a 4-km buffer of NRCan fire perimeters (https://cwfis.cfs.nrcan.gc.ca/datamart). This
+approach was used to limit commission errors that can occur due to known limitations of mapping with optical data in the fall due to phenology, snow
+cover, or low sun angles. For the 2023 fire season, the TIIC algorithm detected 12.74 Mha of burned area in Canada’s forested ecozones, representing
+1.8% of the total forest-dominated ecozone area. Of the 12.74 Mha, 11.57 Mha (90.9%) was burned by summer fires and 1.16 Mha (9.1%) by fall fires
+(Pelletier et al., 2024). You can download the [dataset here](https://opendata.nfis.org/downloads/forest_change/CA_Forest_Fires_2023.zip)
+
+
+#### Citation
+
+```
+Pelletier, F., Cardille, J.A., Wulder, M.A., White, J.C., Hermosilla, T., 2024. Revisiting the 2023 wildfire season in Canada. Science of Remote Sensing. 10, 100145. https://doi.org/10.1016/j.srs.2024.100145
+```
+
+![ca_forest23](https://github.com/user-attachments/assets/8856f1f0-752b-4718-8609-9495bbd66fb1)
+
+### Earth Engine Snippet
+
+```js
+var image = ee.Image("projects/sat-io/open-datasets/CA_FOREST/CA_Forest_Wildfire_2023_Summer_Fall");
+```
+
+Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:fire-monitoring-analysis/CA-FOREST-FIRE-2023
+
+#### License
+This work is licensed under and freely available to the public Open Government Licence - Canada.
+
+Created by: Pelletier et al. 2024
+
+Curated in GEE by : Spencer Bronson and Samapriya Roy
+
+keywords: Wildfire, Tracking Intra- Inter-year Change (TIIC), Landsat, Sentinel, Burned Area, Fire Occurrence, Canada
+
+Last updated on GEE: 2024-08-29
diff --git a/docs/projects/ca_lc.md b/docs/projects/ca_lc.md
@@ -1,10 +1,9 @@
-# High-resolution annual forest land cover maps for Canada's forested ecosystems (1984-2019)
-
-The annual time series of forest land cover maps are national in scope (entire 650 million hectare forested ecosystem) and represent a wall-to-wall land cover characterization yearly from 1984 to 2019. These time-series land cover maps were produced from annual time-series of Landsat image composites, forest change information, and ancillary topographic and hydrologic data following the framework described in Hermosilla et al. (2022), which builds upon the approach introduced in Hermosilla et al. (2018). The methodological innovations included (i) a refined training pool derived from existing land cover products using airborne and spaceborne measures of forest structure; (ii) selection of training samples proportionally to the land cover distribution using a distance=weighted approach; and (iii) generation of regional classification models using a 150x150 km tiling system. Maps are post-processed using disturbance information to ensure logical class transitions over time using a Hidden Markov Model. Hidden Markov Models assess individual year class likelihoods to reduce variability and possible noise in year-on-year class assignments (for instances when class likelihoods are similar).
+# High-resolution annual forest land cover maps for Canada's forested ecosystems (1984-2022)
 
+The annual time series of forest land cover maps are national in scope (entire 650 million hectare forested ecosystem) and represent a wall-to-wall land cover characterization yearly from 1984 to 2022. These time-series land cover maps were produced from annual time-series of Landsat image composites, forest change information, and ancillary topographic and hydrologic data following the framework described in Hermosilla et al. (2022), which builds upon the approach introduced in Hermosilla et al. (2018). The methodological innovations included (i) a refined training pool derived from existing land cover products using airborne and spaceborne measures of forest structure; (ii) selection of training samples proportionally to the land cover distribution using a distance=weighted approach; and (iii) generation of regional classification models using a 150x150 km tiling system. Maps are post-processed using disturbance information to ensure logical class transitions over time using a Hidden Markov Model. Hidden Markov Models assess individual year class likelihoods to reduce variability and possible noise in year-on-year class assignments (for instances when class likelihoods are similar).
 For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2022) No. 112780. DOI: https://doi.org/10.1016/j.rse.2021.112780 and Hermosilla et al. (2018) https://www.tandfonline.com/doi/full/10.1080/07038992.2018.1437719
 
-The data represents annual forest land cover of Canada's forested ecosystems for 1984-2019. An image compositing window of August 1 -30 days was used to generate the best-available-pixel (BAP) image composites used as the source data for land cover classification. The science and methods developed to generate the information outcomes shown here, that track and characterize the history of Canada's forests, were led by Canadian Forest Service of Natural Resources Canada, partnered with the University of British Columbia, with support from the Canadian Space Agency, augmented by processing capacity from WestGrid of Compute Canada.
+The data represents annual forest land cover of Canada's forested ecosystems for 1984-2022. An image compositing window of August 1 -30 days was used to generate the best-available-pixel (BAP) image composites used as the source data for land cover classification. The science and methods developed to generate the information outcomes shown here, that track and characterize the history of Canada's forests, were led by Canadian Forest Service of Natural Resources Canada, partnered with the University of British Columbia, with support from the Canadian Space Agency, augmented by processing capacity from WestGrid of Compute Canada.
 
 
 #### Citation
@@ -133,4 +132,4 @@ Curated in GEE by : Samapriya Roy
 
 keywords: Land cover; Classification; Machine learning; Land cover change; Landsat; Lidar; ICESat-2
 
-Last updated on GEE: 2021-11-14
+Last updated on GEE: 2024-08-29