---
# System prepended metadata

title: Examples with Google Earth Engine

---

LIDAR = Light Detection and Radar

#### EXAMPLES 

- Find your Watershed (Hydrosphere)
    - Calculate the boundaries of 
- Examine your Soils (Geosphere)
    - Clay content of soil texture (SSC)
- Species Population change (Biosphere)
    - Calculate tree population numbers.
    - Calculate carbon capture rates. 


#### RESOURCES : 

earth DNA  https://earthdna.org/
CITAB UTAD : https://www.citab.utad.pt/
Luciana Fina : Soils video instalations    
SoilGrids: https://soilgrids.org/ (Global Soil Content Map)
CEIIA : https://www.ceiia.com/space

www.generic-mapping-tools.org

#### RELATED PROJECTS

[STFC Food Network](https://www.stfcfoodnetwork.org/)
[ESLA - EUCLID](https://elsa-euclid.github.io/)

#### DATASETS 

- Anthropogenic biomes
- ESDAC : https://esdac.jrc.ec.europa.eu/
    - LUCAS : https://esdac.jrc.ec.europa.eu/projects/lucas
    - SENSING : https://esdac.jrc.ec.europa.eu/search/node/sensing
- CLC : https://land.copernicus.eu/en/products/corine-land-cover
- SQAPP : https://www.isqaper-is.eu/sqapp-the-soil-quality-app
- ICAERUS DDML : 
- [Index Database :](https://www.indexdatabase.de/)   

## QUESTIONS : 

## What do we use satellite remote sensing for?
Satellite remote sensing, specifically Earth Observation (EO), is a useful tool for observing and analysing large-scale ecosystem changes. 

Satellites have been crucial to analyse regional and global changes to our climate and habitats, due to their ability to capture large areas of the Earth.

## Why satellites?
Using satellites for remote sensing, as opposed to modes of sensing closer to the ground like drone imaging, has large advantages and disadvantages.

#### Pros of satellite remote sensing:
- Consistency
- Global reach
- Can observe large areas of land
- A lot of open-source datasets from the European Space Agency (ESA) and NASA
- Historical datasets, starting from the 1980's


#### Cons of satellite remote sensing:
- Affected by cloud cover (see next section for ways to get around this)
- Relatively large pixels for open source datasets (10m x 10m minimum size)


The European Space Agency (ESA) launched the Copernicus Programme in 2014 to offer Earth Observation datasets and processing tools that can be used worldwide. As part of the programme, multiple satellites, called "Sentinels" have been launched and the data is available, along with softwares to process the data to draw conclusions from it.



## What are the different modes of observation and what advantages do they offer?
Plenty of observation tools can be used on satellites, each with clear pros and cons. Analysing the main modes of observation:

### 1. Multi-Spectral Imaging (MSI) - ESA Sentinel 2
- Multispectral Images break down the light spectrum into specific wavelengths that are of particular importance. For example, plant health can often be inferred from the ratio between the amount of Near Infrared Light. A Multispectral Camera interested in measuring plant health would measure only these wavelengths, rather than all wavelengths in a range of the light spectrum.
    
  ![Multispectral_fig1](https://hackmd.io/_uploads/H11uyFPt0.jpg)
  Image 1 - Multispectral Imaging ([Photonics.com](https://www.photonics.com/Articles/Multispectral_Lighting_A_Practical_Option_for/a66251))
    
- As opposed to a normal camera, which receives light from most wavelengths in the visual part of the spectrum, Multispectral Imaging focuses only on pre-determined wavelengths.

- The ESA Sentinel-2 mission has a MultiSpectral imager with a revisit frequency of 5 days, and images at 10m, 20m and 60m pixel resolution. More information on the Sentinel 2 mission [here](https://sentiwiki.copernicus.eu/web/s2-mission)

#### Common Uses
- Vegetation Health through NDVI, EVI, etc. For more information on NDVI read [here](https://www.streambatch.io/knowledge/ndvi-from-first-principles)
- Habitat Mapping
- Presence of Water and Moisture through NDWI


**Pros of Sentinel 2 MSI**
- 10m resolution for 4 spectral bands, comparatively very precise (other spectral bands have 20m and 60m resolution)
- 5 day revisit frequency accurate for long-term observation

**Cons of Sentinel 2 MSI**
- Cloud cover makes images unuseable


### 2. **Synthetic Aperture Radar (SAR) - ESA Sentinel 1**
- Synthetic Aperture Radar (SAR) images function with the radar principle of sending pulses of light from a satellite, and based on how the pulse was reflected and received again by the satellite, getting an idea of the terrain observed. An example of a SAR image is shown below
 
![3 - SAR Image](https://hackmd.io/_uploads/HJWzetPYC.jpg)
Image 2 - Example SAR Image ([Reference](https://www.openpr.com/news/709782/synthetic-aperture-radar-sar-market.html))

- Information is inferred from the image based on how much of the pulse returns to the receiver on the satellite (known as "backscatter"), and if the polarization of the pulse stayed the same or changed.

![3 - SAR Backscatter](https://hackmd.io/_uploads/r1WGxKvK0.png)
Image 3 - SAR Backscatter [Reference](https://www.researchgate.net/figure/Radar-backscattering-mechanisms-for-forest-wetland-and-soil-surfaces-a-b-g-h_fig4_342872604)


#### Common Uses
- Deforestation tracking
- Soil moisture analysis
- Wildfire damage measurement

#### Pros of Sentinel 1 SAR
- Unaffected by clouds
- Data can be complemented with ESA Sentinel 2 to cross reference

#### Cons of Sentinel 1 SAR 
- A lot of "noise" that must be pre-processed
- Steep learning curve to understand how to use the data
    
    
    
## What open-source resources exist?
#### European Space Agency Sentinel Applications Platform (ESA SNAP)
#### Pros:
- No need to write code
- A lot of online tutorials  
#### Cons:
- Processing happens on user's computer, takes longer to compute and analyse results
- Images stored on user's computer, and are often more than 1 GB in size
#### Google Earth Engine (GEE)
#### Pros:
- Image processing happens on Google servers, on the cloud
- No need to download images onto user's computer
-  Historical satellite data from non-ESA missions (e.g. Landsat)
- Fast feedback and results
#### Cons:
- Need to write code in JavaScript
- Becomes very technical very quickly
- Free for non-commercial single-user, cost for anything commercial or academic

## What commercial resources exist?
- Earth Blox
## How can that data be processed, and what conclusions can we draw from that processing?
- Many free, online tutorials exist online to use open source resources such as Google Earth Engine and ESA SNAP to not only visualize, but obtain insights from satellite remote sensing data
- See **Example Code with Google Earth Engine** below for a few examples to understand your region's water and soil
## How can the data be combined with other forms of sensing?
- Satellite remote sensing can be combined with higher resolution, under and above ground measurements to create a more comprehensive understanding of an ecosystem. Combining these datasets with those of drones, on-the-ground sensors, and more can help . The main advantages of satellite remote sensing (large scale, and long-term habitat change tracking) can be well complemented with other modes of sensing.
- World maps, (such as soilgrids.org) often use sensors on the ground to verify and compare data to what is obtained from satellite imagery, and then extrapolate that data with Machine-Learning algorithms to create entire world maps approximating conditions in these ecosystems.


# Example Code with Google Earth Engine
- Example Code 1: Water.
    - Understand how water has changed in your region, comparing the period between 1984 - 1999, and 2000 - 2015.
    - Visualize your watershed to understand the region and community that you share water with https://code.earthengine.google.com/122784d414fa2bcc4841f8fac96b1896 
- Example Code 2: Soil 
    - Understand the  Soil Organic Carbon content in your region, as well as clay, silt, ph, and more.
    - https://code.earthengine.google.com/61b3833a1a02ad5802cab629bf57092f

## Google Earth Engine Datasets and Resources

- Future trends and possibilities


### REFERENCES

- https://www.cisuc.uc.pt/en/people/penousal-machado Informatics Visualisation scientist
- From Regional to parcel scale : a high resolution map of cover crops across europe combining satalite data with statistical surveys : https://www.sciencedirect.com/science/article/pii/S0048969723009166
- 