The NASA ISRO Synthetic Aperture Radar (NISAR) is scheduled for launch early in 2024 from the Satish Dhawan Space Centre (SDSC), at Sriharikota, near Chennai, India. This mission is the result of a collaboration between NASA and Indian Space Research Organization (ISRO), where NASA has contributed elements of the mission such as an L-band SAR, and ISRO has contributed other elements, such as an S-band SAR. After successful launch, the NISAR mission will collect left-looking L-band SAR data over most of the Earth’s land areas twice during every 12-day exact repeat orbit. (once while in an ascending orbit direction and once while in a descending orbit direction). NASA and ISRO have individual and joint requirements on the mission that include the performance of the imaging radars onboard the spacecraft. For example, NASA must demonstrate that this L-band SAR will achieve a set of identified science measurement accuracy requirements that span Ecosystem science, Solid Earth science, and Cryosphere science disciplines. Likewise, ISRO has several applications objectives on both the L-band and S-band data from NISAR that the ISRO science team and project will be developing and testing. Pre-launch and post-launch activities have been planned to validate that these requirements are met. Here, we will discuss how the NASA plans are being executed and will present any initial results at the conference.

Interferometric Synthetic Aperture Radar (InSAR) time series analysis is a powerful technique to estimate long-term water level changes in wetlands ecosystems. However, few studies have applied InSAR on wetlands that are highly segmented by canals and levees due in part to the challenge of selecting qualified reference points to minimize unwrapping errors, which, by contrast, is a relatively easy task for unsegmented wetlands. Here we developed a new method to automatically select the optimal reference point for InSAR time series analysis. The method selects reference points by considering temporal behaviors of coherence and InSAR phase connectivity from each reference point to its wetland of interest. We tested the method on six managed and highly segmented wetland units within the Sacramento National Wildlife Refuge in the Central Valley, California. We validated the InSAR measurement against water depth gauge measurements during a low water depth (<10 cm) period in 2017. The overall accuracy of the estimated water depth changes achieved an RMSE of 1.49 cm. Compared with three existing methods, our method showed significantly lower RMSE values overall. This new automatic method enables us to maximize the performance of InSAR to predict water depth and could be applied to other types of InSAR applications as well.

Coastal wetlands play a crucial role in supporting diverse ecosystems and providing numerous ecosystem services. The monitoring of wetland hydrodynamics is essential for understanding and assessing their vulnerability to environmental stressors. In recent years, InSAR (Interferometric Synthetic Aperture Radar) time series analysis has emerged as a valuable tool for studying wetland hydrodynamics. However, accurate wetland water level change monitoring is occasionally hindered by the presence of high amounts of atmospheric water vapor over coastal areas, which mislead the interpretation of InSAR retrievals.In this paper, we present a methodological approach based on Independent Component Analysis (ICA) combined with image segmentation as a blind source separation technique to discriminate between Water Level Change (WLC) related features and wet tropospheric delay features here referred to as 'cloud-induced features' in a UAVSAR WLC time series. Our findings provide a specific methodological case study towards addressing the challenges associated with wet tropospheric delay in Airborne InSAR, and a potential alternative solution for improved and more accurate water level change monitoring in coastal wetlands.

Here, we present an enhanced algorithm to correct interferometric synthetic aperture radar (InSAR) phase unwrapping errors by incorporating iterative spatial bridging between islands and phase closure among interferograms. We use rapid repeat airborne synthetic aperture radar acquisitions from NASA’s airborne uninhabited aerial vehicle synthetic aperture radar (UAVSAR) instrument to estimate short-term changes in water level within coastal wetlands from a stack of consecutive interferograms acquired with very short temporal separation (~30 min). The algorithm is applied to six consecutive UAVSAR images collected in tidal wetlands of the Wax Lake Delta, Louisiana, USA. Validation of our water level change retrievals within situfield observations was conclusive with high correlation and an RMSE generally smaller than 3 cm. Comparison of our algorithm with other phase unwrapping error correction methods shows significant improvement (30%–35% increase in the number of correctly unwrapped pixels) when applied to rapid changes in water level. The set of corrections presented in this work enables measurement of water level change in deltas and other areas where tides drive highly dynamic flooding of inland vegetated areas. Although demonstrated for water level change, the method is applicable to other InSAR datasets with large spatial gradients or observed discontinuities between coherent but spatially isolated areas.

In the above article[1], Table I(b) cited an incorrect reference number. Reference [12] should have been given as [13], provided here as[2].