About course
Soon, scientists will have numerous SAR observations (Sentinel-1A and NISAR combined) at high spatiotemporal resolution (~10 m with 6-12 days revisit interval), along with increased accessibility of reliable, high-speed internet connectivity, and computational resources.
The training program will introduce students to big data to develop SAR-based agricultural applications. The training program focuses on introducing SAR technology to the participants and handling fine resolution SAR -based remote sensing dataset for agricultural applications. The course work will have experimental learning that includes lectures, hands-on, fieldwork, evaluation, and certification.
The agro-industries and agro-insurance companies greatly recognize the value of using SAR data that provides an edge in their operational needs. The global SAR market size was valued at USD 2.21 billion in 2019 and is expected to grow at a compound annual growth rate of 10.7% from 2020 to 2027 [Reference: www.grandviewresearch.com]. The commercial market for SAR data is increasing and more applications are being developed for hydrology and agriculture. Especially, for agronomists, agricultural scientists, agro-economists, agro-insurance companies, agro-food and agricultural-related industries, crop insurance companies, and federal institutions. Based on the statistics presented in Fig. 1, there will be a consistent demand for highly skilled workers with knowledge and understanding of SAR data to run or develop applications for agriculture-related industries. Thus, undergraduate students possessing knowledge of SAR observations definitely have an edge when entering the job market. Overall, this cutting-edge knowledge will also put them in an upper pay bracket as compared with their peers.
Classroom lectures
The classroom lectures will be 40% of the overall course duration of 6 weeks (12 hours each week). The resource persons will teach topics covering - introduction to remote sensing, microwave remote sensing by SAR, and application of SAR observations to agriculture
Outline of the coursework, classroom lectures
| Sr. No. | Description of the course topic | Duration | Instructor |
| 1. | Introduction to Remote Sensing and Agricultural Applications | 4 hours | Project Director (PD) Das and C0-PD Dong |
| 2. | Introduction to Microwave Remote Sensing | 4 hours | PD Das |
| 3. | Current Practices of Satellite Data at Visible and Thermal frequencies for Agricultural Applications | 4 hours | Co-PD Ray |
| 4. | Active Microwave Remote Sensing and SAR | 4 hours | PD Das |
| 5. | Past, Current, and Future SAR Airborne and Satellite Missions | 4 hours | PD Das |
| 6. | SAR Observations and Soil Moisture Retrievals | 4 hours | PD Das |
| 7 | Software Tools for SAR Data Processing and Analysis. | 3 hours | PD Das |
| 8. | Introduction to Various Data Format used For SAR data | 3 hours | Co-PD Ray |
| 9. | Demonstration of Various SAR Data (Sentinel-1A/B, ALOS PALSAR, and Upcoming NISAR Mission). | 3 Hours | PD Das |
| 10. | Demonstration of Various SAR Data (Sentinel-1A/B, ALOS PALSAR, and Upcoming NISAR Mission). | 3 Hours | PD Das |
| 11. | SAR Observations for Agricultural Applications-I | 3 hours | PD Das |
| 12. | SAR Observations for Agricultural Applications-II | 3 hours | PD Das |
| 13. | Calibration and Validation of SAR-based Products | 3 hours | Co-PD Ray |
| 14. | Fusion of SAR data with Other Satellite Products for Enhanced Agricultural Applications | 3 hours | Co-PD Ray |
| 15. | Class Project Discussion and Team Formation | 4 hours | PD Das and Co-PD Ray |
| 16. | Leadership, Teamwork, Professional Dev. lectures | 4 hours | Co-PDs Dong and Reese |
| 17. | Project presentations | 2 hours | PD Das |
| 18. | Guest lectures (Special Topics) | 3 hours | PD Das |
| Total | 61 hours |
Hands-on and Lab work
Along with the classroom lectures, the most important aspect of this training is the hands-on work. Training program will have at least 2 hours of hands-on every day of the program.
Outline of the hands-on exercise in the BAE computer lab
| Sr. No. | Description of the topic covered in computing lab | Duration |
| 1. | Introduction to Python GUI tools for SAR Processing, | 4 hours |
| 2. | Download of current Satellite Data (Sentinel-1, PALSAR, and SMAP), know the data format and rendering | 4 hours |
| 3. | Download of current Satellite Data (LandSat, MODIS), computing NDVI for agricultural application | 4 hours |
| 4. | Work on downloaded SAR data from Sentinel, PALSAR, and NISAR (if available) to understand the data attributes | 4 hours |
| 5. | Work on downloaded SAR data from Sentinel, PALSAR, and NISAR (if available) to understand the data attributes, and influence of soil roughness, vegetation | 4 hours |
| 6. | Understanding SAR-based soil moisture retrievals and applying the soil moisture disaggregation algorithm to obtain high-resolution (100 m) over agricultural farms | 4 hours |
| 7. | Using SAR-based vegetation attributes retrievals and over agricultural farms | 4 hours |
| 8. | Using SAR-based vegetation attributes retrievals to detect crop water stress | 3 hours |
| 9. | Using SAR-based vegetation attributes retrievals to detect water-demand and crop types | 3 hours |
| 10. | Calibration and Validation of SAR-based Products using the in-situ soil moisture and vegetation data | 3 hours |
| 11. | Comparing/fusing the SAR-based retrievals against/with the visible and thermal band vegetation retrievals to enhance the agricultural applications | 3 hours |
| 12. | Python GUI introduction and data analysis | 3 hours |
| 13. | Accessing free SAR data from NASA DAACs and Public Websites | 2 hours |
| 14. | Project Presentation | 4 hours |
| Total Lab Hours | 49 hours | |
| Hours for Project preparation and development | 24 hours |
Fieldwork and Extension Activities
Looking at digitized images of agricultural fields or georeferenced maps on the computer does provide a broad perspective of an agricultural landscape. For a better understanding of the current on-ground status, for geophysical/biophysical studies, physical visits to the agricultural cropland are planned for the UG students. This will include, measurement of soil moisture, biomass, leaf-area-index (LAI), soil roughness, cropping density, etc.
Field hands-on exercises at MSU extension farms
| 1. | Use the Delta-T Theta or Hydra Probe to measure top layer (~5 cm) soil moisture from various locations/sites within the field in a regular pattern to capture the field average and variability. |
| 2. | Collect soil gravimetric sample, topsoil layer (0-5 cm depth) from at least one location in the field, collocating Theta Probe observation site. |
| 3. | Measure LAI in various locations within the field using a device such as LAI-2200C Plant Canopy Analyzer. |
| 4. | Collect crop biomass samples in each visit to determine crop vegetation-water-content (VWC). |
| 5. | One-time measurement of crop planting density, row width, row directions. |
| 6. | Weekly measurement of surface roughness through IPad Pro device. |
| 7. | Take pictures during each visit to monitor the progression of crop development. |
| 8. | Measure GPS locations of all the sites where Theta Probe and Canopy Imager are used, and corners of the field. |
