We assess and compare several algorithms to compensate for temporal decorrelation observed in repeat-pass L-band polarimetric interferometric synthetic aperture radar (PolInSAR) measurements of forest canopy height. The analysis is performed on data acquired with an approximately 45-min temporal baseline using the uninhabited aerial vehicle synthetic aperture radar collected in August 2009 over temperate and boreal forests of the U.S. state of Maine and the Canadian province of Québec. This investigation presents several compensation methods based on the classical random volume over ground model, which include fixing the value of the extinction parameter, fixing the temporal decorrelation magnitude, or varying temporal decorrelation estimates with height. We also compare results with the random motion over ground model. While these methods have been presented in the literature previously, a comparison of the different methods and an assessment of their height estimation accuracy applied to the same datasets have not yet been performed. In addition, we introduce the use of ancillary reference forest height data from airborne large footprint lidar to estimate model parameters and to mitigate solution ambiguities. We finally demonstrate that this mitigation strategy is robust and suitable for use with future spaceborne lidar missions such as the Global Ecosystems Dynamics Investigation. The resulting PolInSAR canopy height estimates correspond well with those obtained from coincident field and airborne lidar data. Height estimation differences of 3.4 m (RMSE) were observed between the PolInSAR- and lidar-derived canopy height maps when using the fixed extinction method. These can be partially attributed to inherent differences in the sensor spatial resolutions and geolocation accuracy. The RMS error between the PolInSAR height estimates and the field collected Lorey's heights was 2.4 m.

We demonstrate a method using lidar data fusion to improve the forest height estimation accuracy of multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR). Compared to single-baseline PolInSAR, multibaseline PolInSAR allows forest canopy height to be estimated more accurately across a wider range of height values. However, to arrive at a single forest height estimate, the estimates from the multiple baselines must be selected or weighted. A number of approaches to selecting between baselines have been proposed in the literature, but they are generally based on simple metrics of the PolInSAR data and do not necessarily capture the full range of characteristics that make one baseline produce more accurate forest height estimates than another. We solve this problem by treating baseline selection as a supervised classification problem that can be trained using a small amount of sparse lidar data located within the PolInSAR coverage area. We train a support vector machine classifier using a variety of coarse lidar sample spacings of 250 m and greater, to demonstrate that data from future spaceborne lidar missions will be sufficient for this purpose. We demonstrate results for multiple study areas in the country of Gabon using data collected by NASA's uninhabited aerial vehicle synthetic aperture radar and land, vegetation, and ice sensor lidar. The use of lidar fusion for PolInSAR baseline selection yields improved results compared to standard baseline selection methods, and further demonstrates the strong potential of PolInSAR and lidar fusion for remote sensing of forests.

This study evaluates global radar‐derived digital elevation models (DEMs), namely the Shuttle Radar Topography Mission (SRTM), NASADEM and GLO‐30 DEMs. We evaluate their accuracy over bare‐earth terrain and characterize elevation biases induced by forests using global Lidar measurements from the Ice, Cloud, and Land Elevation Satellite (ICESat)'s Geoscience Laser Altimeter System (GLAS), the Global Ecosystem Dynamics Investigation (GEDI) and the ICESat‐2 Advanced Topographic Laser Altimeter System (ATLAS) instruments collected on locally flat terrain. Our analysis is based on error statistics calculated for each DEM tile, which are then summarized as global error percentiles, providing a regional characterization of DEM quality. We find NASADEM to be a significant improvement upon the SRTM V3. Over bare ground areas, the mean elevation bias and root mean square error (RMSE) improved from 0.68 to 2.50 m respectively to 0.00 and 1.5 m as compared to ICESat/GLAS. GLO‐30 is more accurate with bare ground elevation bias and RMSE were below 0.05 and 0.55 m. Similar improvements were observed when compared to GEDI and ICESat‐2 measurements. The DEM biases associated with the presence of vegetation vary linearly with canopy height, and more closely follow the percentile of Lidar Relative Height (RH50). Other factors such as canopy density, radar frequency and Lidar technology also contribute to observed elevation biases. This global analysis highlights the potential of various technologies for mapping of Earth's topography, and the need for more advanced remote sensing observations that can resolve vegetation structure and sub‐canopy ground elevation.

Coastal wetlands are productive ecosystems driven by highly dynamic hydrological processes such as tides and river discharge, which operate at daily to seasonal timescales, respectively. The scientific community has been calling for landscape-scale measurements of hydrological variables that could help understand the flow of water and transport of sediment across coastal wetlands. While in situ water level gauge data have enabled significant advances, they are limited in coverage and largely unavailable in many parts of the world. In preparation for the NISAR mission, we investigate the use of spaceborne Interferometric Synthetic Aperture Radar (InSAR) observations of phase and coherence at L-band for landscape-scale monitoring of water level change and vegetation cover in coastal wetlands across seasons. We use L-band SAR images acquired by ALOS/PALSAR from 2007 to 2011 to study the impact of seasonal changes in vegetation cover on InSAR sensitivity to water level change in the wetlands of the Atchafalaya basin located in coastal Louisiana, USA. Seasonal variations are observed in the interferometric coherence ( $\gamma$ ) time-series over wetlands, with higher coherence during the winter and lower coherence during the summer. We show with InSAR time-series that coherence is inversely correlated with Normalized Difference Vegetation Index (NDVI). Our analysis of polarimetric scattering mechanisms demonstrates that double-bounce is the dominant mechanism in swamps while its weakness in marshes hinders estimation of water level changes. In swamps, water level change maps derived from InSAR are highly correlated (r² = 0.83) with in situ data from the Coastwide Reference Monitoring System (CRMS). From October to December, we observed that the water level may be below wetland elevation and thus not inundating wetlands significantly. Our analysis shows that water level can only be retrieved when both images used for InSAR are acquired when wetlands are inundated. The L-band derived-maps of water level change show large scale gradients originating from the Gulf Intracoastal Waterway rather than the main delta trunk channel, confirming its significant role as a source of hydrologic connectivity across these coastal wetlands. These results indicate that NISAR, with its InSAR observations every 12 days, will provide the measurements necessary to reveal large scale hydrodynamic processes that occur in swamps across seasons.

AirSWOT is an airborne Ka-band synthetic aperture radar, capable of mapping water surface elevation (WSE) and water surface slope (WSS) using single-pass interferometry. AirSWOT was designed as a calibration and validation instrument for the forthcoming Surface Water and Ocean Topography (SWOT) mission, an international spaceborne synthetic aperture radar mission planned for launch in 2022 which will enable global mapping of WSE and WSS. As an airborne instrument, capable of quickly repeating overflights, AirSWOT enables measurement of high frequency and fine scale hydrological processes encountered in coastal regions. In this paper, we use data collected by AirSWOT in the Mississippi River Delta and surrounding wetlands of coastal Louisiana, USA, to investigate the capabilities of Ka-band interferometry for mapping WSE and WSS in coastal marsh environments. We introduce a data-driven method to estimate the time-varying interferometric phase drift resulting from radar hardware response to environmental conditions. A system of linear equations based on AirSWOT measurements is solved for elevation bias and time-varying phase calibration parameters using weighted least squares. We observed AirSWOT WSE uncertainty of 12 cm RMS compared to in situ water level measurements when averaged over an area of 0.5 km ${}^2$ at incidence angles below ${15}^{\circ }$ . At higher incidence angles, the observed AirSWOT elevation bias is possibly due to residual phase calibration errors or radar backscatter from vegetation. Elevation profiles along the Wax Lake Outlet river channel indicate AirSWOT can measure WSS over a 24 km distance with uncertainty below 0.3 cm/km, 8% of the true water surface slope as measured by in situ data. The data analysis and results presented in this paper demonstrate the potential of AirSWOT to measure water surface elevation and slope within highly dynamic and spatially complex coastal environments.

AirSWOT is an experimental airborne Ka-band radar interferometer developed by NASA-JPL as a validation instrument for the forthcoming NASA Surface Water and Ocean Topography (SWOT) satellite mission. In 2017, AirSWOT was deployed as part of the NASA Arctic Boreal Vulnerability Experiment (ABoVE) to map surface water elevations across Alaska and western Canada. The result is the most extensive known collection of near-nadir airborne Ka-band interferometric synthetic aperture radar (InSAR) data and derivative high-resolution (3.6 m pixel) digital elevation models to produce water surface elevation (WSE) maps. This research provides a synoptic assessment of the 2017 AirSWOT ABoVE dataset to quantify regional WSE errors relative to coincident in situ field surveys and LiDAR data acquired from the NASA Land, Vegetation, and Ice Sensor (LVIS) airborne platform. Results show that AirSWOT WSE data can penetrate cloud cover and have nearly twice the swath-width of LVIS as flown for ABoVE (3.2 km vs. 1.8 km nominal swath-width). Despite noise and biases, spatially averaged AirSWOT WSEs can be used to estimate sub-seasonal hydrologic variability, as confirmed with field GPS surveys and in situ pressure transducers. This analysis informs AirSWOT ABoVE data users of known sources of measurement error in the WSEs as influenced by radar parameters including incidence angle, magnitude, coherence, and elevation uncertainty. The analysis also provides recommended best practices for extracting information from the dataset by using filters for these four parameters. Improvements to data handing would significantly increase the accuracy and spatial coverage of future AirSWOT WSE data collections, aiding scientific surface water studies, and improving the platform’s capability as an airborne validation instrument for SWOT.