The objective of this course is to provide students with a basic understanding of Google Earth Engine (GEE) for accessing, processing and visualizing remote sensed images for water resources monitoring and management.

The students on completion of this course would be able to:

• Aware of various satellite data products available for water resources applications

• Develop image processing skills for water resources mapping and management

• Experience working with open-source GEE API platforms, JavaScript and Python

None.

1. Google Earth Engine API basics

1.1.  Basics of GEE & API Syntax

1.2.  Image and Image collection temporal filtering 

1.3.  Spatial clipping 

1.4.  Compositing and mosaicking images

1.5.  Calculating indices from images

1.6.  Image processing

1.7.  Visualizing and exporting the images

2. Remote sensing data products for water resources applications

2.1.  Landsat data for surface water monitoring

2.2.  CHIRPS for precipitation monitoring

2.3.  MODIS for vegetation monitoring

2.4.  SMAP for soil moisture monitoring

2.5.  GRACE for groundwater monitoring

3. Flood monitoring and mapping 

3.1. Sentinel-1 SAR data preprocessing

3.2. Change detection for flood extent mapping

3.3. Flood impact assessment

3.4. Flood damage assessment

4. Drought monitoring and mapping

4.1. Palmer drought index

4.2. SPI and SPEI indices

4.3. Drought impact assessment

4.4. Drought damage assessment

5. Groundwater recharge estimation

5.1.  Soil texture and hydraulic properties

5.2.  Importing precipitation and potential evapotranspiration

5.3.  Thornthwaite-Mather procedure for recharge estimation

5.4.  Groundwater recharge mapping

6. Water quality mapping

6.1. Sentinel-2 applications for water quality monitoring

6.2. Monitoring and mapping Chlorophyll concentrations

6.3. Monitoring and mapping Total Suspended Solids

6.4. Monitoring and mapping Dissolved Oxygen

6.5. Water quality predictions

1. Hands on session -1: Image collection filtering, clipping and merging

2. Hands on session -2: Image indices calculation end exporting images

3. Hands on session -3: Land use and landcover mapping with machine learning

4. Hands on session -4: Flood mapping

5. Hands on session -5: Flood damage assessment

6. Hands on session -6: Drought mapping

7. Hands on session -7: Drought damage assessment 

8. Hands on session -8: Soil properties retrieval

9. Hands on session -9: Groundwater recharge estimation

10. Hands on session -10 : Water quality monitoring

No Designated textbook, but class notes and handouts will be provided

(1) Google Earth Engine Applications; Kumar, L., Mutanga, O., Eds.; Mdpi AG, 2019.Google Earth Engine Applications

(2) Google Earth Engine Applications; MDPI, 2019. https://doi.org/10.3390/books978-3-03897-885-5.

Classroom lectures   30h

Lab   45h

Self-study (incl. assignments) 135h

Teaching and learning methods include classroom lectures, hands on session and assignments.

The final grade is computed according to the following weight distribution: Mid-semester exam (30%), Final exam (40%), Group project (10%) and, Assignments (20%). Open-book examination is given in both mid-semester and final exams.

An “A” will be awarded if a student is able to show profound understanding of the principles of irrigation and drainage systems, and is adequately able to apply the knowledge for suggesting management for real problems. A student who is participating and contributing actively in class discussions and assignments would be placed in this category. A “B” will be awarded if a student is able to show satisfactory command over the subject matter and show an overall understanding of all given topics. A “C” will be given if a student is able to show satisfactory command over the subject matter. A “D” will be given if a student displays very limited knowledge of the subject matter, and does not appear motivated to learn new things.