Advancing Ecosystem Monitoring and Management through
Synthetic Aperture Radar (SAR) Remote Sensing

Jingqiu Huang

doi:10.23880/jenr-16000392

Journal of Ecology & Natural Resources Research Article 5 min read

Advancing Ecosystem Monitoring and Management through Synthetic Aperture Radar (SAR) Remote Sensing

Jingqiu Huang^*

^* Corresponding author

ISSN: 2578-4994 10.23880/jenr-16000392 Received: August 15, 2024 Published: September 23, 2024

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14 references

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Commentary

As a new member of the Editorial Board for the Journal of Ecology & Natural Resources (JENR), I am delighted to be invited to share my views on how Synthetic Aperture Radar (SAR) Remote Sensing could advance ecosystem monitoring and management. SAR remote sensing has emerged as a powerful tool in ecological research, providing critical insights into ecosystem dynamics. SAR operates effectively under all weather conditions and during both day and night, making it an excellent complementary tool to optical satellites images like the Landsat series and Sentinel-2 [1, 2]. SAR offers various types of time-series information for earth observation, including SAR backscatter energy intensity (amplitude) time-series, InSAR coherence time-series, and InSAR elevation change time-series. In this commentary, I will focus on the application of SAR amplitude time-series change detection in ecosystem monitoring. Finally, I will introduce the application of SAR phase for mapping floodplain subsidence and river sediment aggradation rates, enhancing flood risk modelling and ecosystem impact assessment.

Flood Inundation Detection

Flooding poses significant risks to ecosystems, affecting both terrestrial and aquatic habitats. SAR amplitude time- series can provide timely information on flood extent and duration, essential for flood risk response. SAR’s capability to detect surface water extent through SAR amplitude changes is well-documented in studies like [3]. A recent development of HydroSAR is designed to monitor flooding events using Sentinel-1 SAR data in the Hindu Kush Himalaya region [4]. The algorithms used in HydroSAR are assessed for accuracy, with flood extent mapping showing over 90% accuracy and Commentary water depth measurement accuracy within 1 meter.

Soil Moisture Variation

Soil moisture is key to understanding hydrological processes and vegetation health. SAR’s sensitivity to the dielectric properties of soil moisture has been explored in numerous studies, including [5, 6]. Utilizing SAR amplitude time-series, researchers can monitor soil moisture dynamics across large spatial scales, informing agricultural practices, drought management, and climate models. The high temporal resolution of SAR satellite revisit times allows for continuous monitoring, capturing both seasonal and year-to-year soil moisture variations.

Crop Monitoring

SAR amplitude is sensitive to crop moisture and the geometry of canopy roughness. These characteristics are leveraged in agricultural monitoring to determine crop types, locations, and growth cycles globally [7]. This is crucial for optimizing resource use, improving global food security. However, the interactions of SAR with crop structure and moisture are complex and often difficult to understand. Modelling may be required for better interpretation of SAR amplitude time-series for crop growth monitoring.

Deforestation Monitoring

Deforestation and reforestation are critical processes influencing biodiversity, carbon cycles [8], and climate regulation. High-impact studies, such as [9, 10] have demonstrated SAR’s effectiveness in tracking forest biomass changes. By analysing SAR time-series data, researchers can accurately assess deforestation rates and reforestation efforts, aiding in the development of conservation strategies and policy-making [11].

Floodplain subsidence and river sediment aggradation rate mapping

Based on the SAR phase, we can map surface elevation change time-series [12]. Subsidence is a widespread process where the Earth’s surface sinks due to natural and human- induced factors [13, 14]. I am currently working on mapping river sediment aggradation rates to understand the impact of increased flood risk on ecosystems, including sediment/ soil nutrient cycling, floodplain irrigation, the effects of flood inundation on crops, and sustainable agriculture.

Open source toolbox on SAR amplitude time- series analysis with Sentinel-1 SAR data

The Sentinel constellation offers a great resource of free remote sensing data. Open source tools are also essential for fully exploring the potential of SAR products and conducting reproducible research data analysis. Here a few open-source toolboxes that perform SAR time-series analysis.

SAR intensity and coherence time-series plot notebook from University of Edinburgh https://zenodo.org/doi/10.5281/zenodo.13222093
HydroSAR flood extent and depth mapping notebook from ASF Open SAR Lab https://github.com/ASFOpenSARlab/opensarlab- notebooks/tree/master/SAR_Training/English/HydroSAR

Conclusion

At the end, I want to express my personal view on the interdisciplinary nature of SAR remote sensing. Yes, SAR is indeed a powerful tool to detect changes in flood dynamics, soil monitoring, crops, forests, even sinking earth and aggraded riverbeds. However, each subject cannot be fully understood solely through SAR remote sensing due to the complexity of natural and anthropogenic effects on the Earth. This means we need expertise in fields such as hydrology, geomorphology, phenology, ecology, software engineering, and so on, in addition to being SAR experts. This challenge could be addressed through effective interdisciplinary collaboration with respect and understanding.

References

Flores-Anderson AI, Herndon KE, Thapa RB, Cherrington E (2019) The SAR handbook: Comprehensive methodologies for forest monitoring and biomass estimation. ICIMOD.
Woodhouse IH (2017). Introduction to microwave remote sensing. 1st (Edn.), CRC press, pp: 1-400.
Schumann GP, Baldassarre D, Alsdorf G, Bates PD (2010) Near real-time flood wave approximation on large rivers from space: application to the River Po, Northern Italy. Water Resources Research 46(5): 1-8.
Meyer FJ, Schultz LA, Osmanoglu B, Kennedy JH, Jo M, et al. (2024) HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya.
Wagner W, Blöschl G, Pampaloni P, Calvet JC, Bizzarri B, et al. (2007). Operational readiness of microwave remote sensing of soil moisture for hydrologic applications. Hydrology Research 38(1): 1-20.
El Hajj M, Baghdadi N, Zribi M, Belaud G, Cheviron B, et al. (2016) Soil moisture retrieval over irrigated grassland using X-band SAR data. Remote Sensing of Environment 176: 202-218.
McNairn H, Champagne C, Shang J, Holmstrom D, Reichert G (2009) Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories. ISPRS Journal of Photogrammetry and Remote Sensing 64(5): 434-449.
Mitchard ET (2018) The tropical forest carbon cycle and climate change. Nature 559(7715): 527-534.
Lucas R, Armston J, Fairfax R, Fensham R, Accad A, et al. (2010) An evaluation of the ALOS PALSAR L-band backscatter—Above ground biomass relationship Queensland, Australia: Impacts of surface moisture condition and vegetation structure. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 3(4): 576-593.
Mitchard ET, Saatchi SS, Woodhouse IH, Nangendo G, Ribeiro N, et al. (2009) Using satellite radar backscatter to predict above‐ground woody biomass: A consistent relationship across four different African landscapes. Geophysical Research Letters 36(23): 1-6.
Reiche J, Verbesselt J, Hoekman D, Herold M (2015) Fusing Landsat and SAR time series to detect deforestation in the tropics. Remote Sensing of Environment 156: 276- 293.
Berardino P, Fornaro G, Lanari R, Sansosti E (2003) A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing 40(11): 2375-2383.
Huang J, Sinclair HD, Pokhrel P, Watson CS (2024) Rapid subsidence in the Kathmandu Valley recorded using Sentinel-1 InSAR. International Journal of Remote Sensing 45(1): 1-20.
Nicholls RJ, Shirzaei M (2024) Earth’s sinking surface. Science 384(6693): 268-269.

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